Journal of Engineering and Computational Intelligence Review
Welcome to The Journal of Engineering and Computational Intelligence Review (JECIR), ISSN (Online): 3105-4811 ISSN (Print): 3106-969X The Journal of Engineering and Computational Intelligence Review (JECIR) is a premier open-access, double-blind peer-reviewed journal dedicated to advancing cutting-edge research in Computer Science and Engineering. Committed to academic excellence, JECIR provides a global platform for researchers, scholars, and industry professionals to share innovative findings and fundamental advancements in these dynamic fields. Our rigorous peer-review process ensures the publication of high-quality, original research that contributes to both theoretical foundations and practical applications. JECIR aims to bridge the gap between academia and industry by fostering interdisciplinary collaboration and promoting solutions to real-world engineering challenges. The journal encourages contributions that explore novel methodologies, intelligent systems, and emerging technologies, with a focus on scalability and societal impact. We cover a broad spectrum of topics, including artificial intelligence, machine learning, robotics, data analytics, mechatronics, and sustainable technologies. Our scope extends to interdisciplinary research, integrating engineering disciplines such as mechanical, electrical, and civil engineering with computational intelligence. We prioritize studies that develop innovative algorithms, optimize intelligent systems, and address critical challenges in healthcare, energy, automation, and beyond. By publishing original research articles, critical reviews, and technical notes, JECIR serves as a vital resource for advancing knowledge and technological progress. We invite researchers and practitioners to submit their work and join us in shaping the future of engineering and computational intelligence. Through open-access dissemination and a commitment to scholarly excellence, JECIR strives to be a catalyst for innovation, driving meaningful advancements that benefit academia, industry, and society at large. Explore our latest publications and contribute to the ever-evolving landscape of intelligent engineering solutions.
FinRisk AgentNet: Multi-Agent LLM & ML Architecture for Agentic Fraud Risk Detection & Quantitative Financial Risk Management
The rapid digitization of financial ecosystems has significantly increased the scale, speed, and complexity of fraud and risk events, making conventional rule-based monitoring and siloed machine learning systems insufficient for modern financial institutions. This study proposes FinRisk AgentNet, a unified multi-agent architecture that integrates Large Language Models (LLMs) with machine learning (ML) techniques for agentic fraud risk detection and quantitative financial risk management. The framework is designed to operate across heterogeneous financial data streams, including transactional records, customer behavior logs, market indicators, compliance reports, and unstructured textual evidence such as case notes and alerts. FinRisk AgentNet employs a coordinated society of specialized agents, including fraud detection agents, anomaly scoring agents, risk forecasting agents, compliance reasoning agents, and decision orchestration agents, each responsible for a specific analytical task while collaborating through a shared memory and policy layer. The proposed architecture combines supervised fraud classification, unsupervised anomaly detection, graph-based relationship analysis, and time-series risk forecasting with LLM-driven reasoning, contextual interpretation, and alert summarization. This hybrid design enables the system not only to detect suspicious transactions and emerging fraud patterns in real time but also to quantify broader financial risks such as credit risk, liquidity exposure, and operational risk. A key contribution of the model lies in its ability to fuse structured numerical risk signals with unstructured semantic evidence, thereby improving decision quality, explainability, and response speed. FinRisk AgentNet also introduces an adaptive feedback loop in which agent outputs are continuously evaluated and refined using risk thresholds, confidence scores, and historical outcomes. The framework supports human-in-the-loop governance, regulatory traceability, and scalable deployment in banking, insurance, and fintech environments. REFERENCES Rahman, M., Bhuiyan, A., Islam, M. S., Laskar, M. T. R., Mahbub, R., Masry, A., ... & Hoque, E. (2025). Llm-based data science agents: A survey of capabilities, challenges, and future directions. arXiv preprint arXiv:2510.04023. Spears, T., Hansen, K. B., Xu, R., & Millo, Y. (2025). Governing synthetic data in the financial sector. Finance and Society, 1-17. Joshi, S. (2025). Comprehensive review of artificial general intelligence (AGI): Applications in business and finance. Karras, A., Theodorakopoulos, L., Karras, C., Krimpas, G. A., Giannaros, A., & Bakalis, C. P. (2025). LLM-Driven Big Data Management Across Digital Governance, Marketing, and Accounting: A Spark-Orchestrated Framework. Algorithms, 18(12), 791. Lopez, E. (2024). Towards eXplainable Artificial Intelligence (XAI) in cybersecurity(Doctoral dissertation). Ribeiro, R. N. (2025). Exploring the Feasibility of Using Large Language Models for Dark Web Threat Intelligence in Security and Defence(Master's thesis, Universidade do Porto (Portugal)). Wang, Q., Wang, T., Tang, Z., Li, Q., Chen, N., Liang, J., & He, B. (2025, July). MegaAgent: A large-scale autonomous LLM-based multi-agent system without predefined SOPs. In Findings of the Association for Computational Linguistics: ACL 2025(pp. 4998-5036). de la Roche, M., Voloder, E., Banerjee, A., Guerra, C., Cataldo Dell'Accio, D., Budris, F., ... & Sedej, T. (2024). Report on artificial intelligence and blockchain convergences. Available at SSRN 5023415. Li, H., Huang, S., & Park, J. K. (2025). A Human-Centered Review of Large Language Models for Online Scam Detection. Available at SSRN 6338940. Steindl-Haselbauer, K. (2025). Potentials, risks and implications of AI-driven Strategic Foresight in disruptive environments-A framework for companies to exploit new opportunities and remain resilient(Doctoral dissertation, Technische Universität Wien). Adabara, I., Sadiq, B. O., Shuaibu, A. N., Danjuma, Y. I., & Venkateswarlu, M. (2025). A Review of Agentic AI in Cybersecurity: Cognitive Autonomy, Ethical Governance, and Quantum-Resilient Defense. F1000Research, 14, 843. Lei, M., Liu, Q., Li, K., Zhao, J., & Tian, Z. THE STUDY OF FEASIBILITY AND CHALLENGES ON SILICON-BASED LIFEFORMS BASED ON LARGE LANGUAGE MODEL. Raza, S., Qureshi, R., Zahid, A., Muneer, A., Zafar, A., Kamawal, S., ... & Shoman, M. (2025). Who is responsible? the data, models, users or regulations? a comprehensive survey on responsible generative ai for a sustainable future. arXiv preprint arXiv:2502.08650. Pamisetty, V. (2023). Leveraging AI, Big Data, and Cloud Computing for Enhanced Tax Compliance, Fraud Detection, and Fiscal Impact Analysis in Government Financial Management. Fraud Detection, and Fiscal Impact Analysis in Government Financial Management (December 15, 2023). Nastoska, A., Jancheska, B., Rizinski, M., & Trajanov, D. (2025). Evaluating trustworthiness in AI: Risks, metrics, and applications across industries. Electronics, 14(13), 2717. Kolla, S. H. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications, 31(4). Abdallah, A. A., Aslan, H. K., Abdallah, M. S., Cho, Y. I., & Azer, M. A. (2025). Enhancing Cryptocurrency Security: Leveraging Embeddings and Large Language Models for Creating Cryptocurrency Security Expert Systems. Symmetry, 17(4), 496. Motamary, S. (2024). Transforming customer experience in telecom: Agentic AI-driven BSS solutions for hyper-personalized service delivery. Available at SSRN 5240126. Vlachos, I., & Reddy, P. G. (2025). Machine learning in supply chain management: systematic literature review and future research agenda. International Journal of Production Research, 63(16), 5987-6016. Moussaoui, J. E., Kmiti, M., El Gholami, K., & Maleh, Y. (2025). A systematic review on hybrid AI models integrating machine learning and federated learning. Journal of Cybersecurity and Privacy, 5(3), 41. Wicker, M., Szpruch, L., & Mørk, S. (2025). Move Fast without Breaking the Bank Model Risk Management of GenAI workflows. IKE, P. C., DANIEL, U. C., ADEOYE, M., AMAECHI, M. I., ODESOLA, O. O., & OGUNSAKIN, M. O. (2025). COGNITIVE CYBER DEFENSE SYSTEMS: LEVERAGING ARTIFICIAL INTELLIGENCE FOR AUTONOMOUS THREAT PREDICTION AND RESPONSE. International Journal of Nature and Science Advance Research.
Copyright (c) 2025 Kayode L. Ogunsusi, Toyyibat Titilope Yussuph, Basirat Adebimpe Hammed (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Transformer-Based Anomaly Detection in IoT Networks Using Multimodal Sensor Data
This study investigates transformer-based anomaly detection in IoT networks using multimodal sensor data. As IoT environments expand across smart homes, healthcare, industry, and critical infrastructure, the ability to detect abnormal behaviour from continuous, heterogeneous data streams has become increasingly important. Conventional anomaly detection techniques often struggle with nonlinear relationships, temporal dependencies, missing values, and noisy sensor inputs, especially when data are collected from multiple modalities. To address these limitations, the study proposes a transformer-based framework that learns long-range dependencies and cross-modal interactions more effectively than traditional approaches. The model is designed to identify anomalies by integrating information from diverse sensor sources and capturing subtle deviations that may not be visible in individual data streams. The framework is expected to improve detection accuracy, reduce false alarms, and enhance robustness under real-world IoT conditions. This research contributes to the growing literature on intelligent IoT security and time-series analytics by demonstrating the potential of attention-based deep learning for multimodal anomaly detection. The findings are intended to support practical applications in fault diagnosis, predictive maintenance, and cyber-physical system monitoring. REFERENCES [1] M. Yassine and T. Flaus, "Anomaly Detection for Industrial Sensors Using Transformers," in 2023 IEEE International Conference on Future Internet of Things and Cloud (FiCloud), 2023, pp. 167–174. [2] F. Zeng, M. Chen, C. Qian, Y. Wang, Y. Zhou, and W. Tang, "Multivariate time series anomaly detection with adversarial transformer architecture in the Internet of Things," Future Gener. Comput. Syst., vol. 144, pp. 244–255, 2023. [3] S. Tuli, G. Casale, and N. R. Jennings, "TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data," Proc. VLDB Endow., vol. 15, no. 6, pp. 1201–1214, 2022. [4] L. Barbieri, M. Brambilla, M. Stefanutti, C. Romano, N. De Carlo, and M. Roveri, "A Tiny Transformer-Based Anomaly Detection Framework for IoT Solutions," IEEE Open J. Signal Process., vol. 4, pp. 234–245, 2023. [5] "Improving the accuracy of Anomaly Detection in Multimodal Sensors," ACM Digit. Libr., 2024. [6] R. C. Nwachukwu, "Cross-modal feature learning for multi-sensor IoT anomaly detection: a comprehensive empirical analysis of cyber physical systems," Appl. Comput. Inform., 2026. [7] "AI-based intelligent sensing detection of cybersecurity threats using multimodal sensor data in smart devices," Sci. Rep., vol. 16, Art. no. 11091, 2026. [8] "Anomaly detection for IoT-based smart buildings," Malmö University Repository, 2025. [9] "IoT Graph Learning-based Anomaly and Intrusion Detection through multi-modal data fusion," in Proc. DATA Conf., 2024. [10] L. Aversano, M. L. Bernardi, M. Cimitile, R. Pecori, and L. Veltri, "Effective Anomaly Detection Using Deep Learning in IoT Systems," Wireless Commun. Mobile Comput., vol. 2021, Art. no. 9054336, 2021. [11] "Evaluating large transformer models for anomaly detection of resource-constrained IoT devices for intrusion detection system," Sci. Rep., 2025. [12] "A Framework for Anomaly Detection in IoT," IEEE Xplore. [13] I. Ahmed and M. Asif, “The Role of HR in Managing Quiet Quitting and Employee Disengagement in Gen Z Employees of Telecom Sector,” Policy Journal of Social Science Review, vol. 4, no. 6, pp. 118–151, 2026, doi: 10.5281/zenodo.20581688. [14] M. Asif, M. Abid, and A. Riaz, “Psychological drivers of investment decision making: A multi bias analysis of an emerging market’s retail investors,” Contemporary Journal of Social Science Review, vol. 4, no. 2, pp. 677–688, 2026, doi: 10.63878/cjssr.v4i2.2608. [15] R. D. A. Khan, H. Ping, and M. Asif, “The impact of green human resource management on employee green performance through green commitment and transformational leadership,” Center for Management Science Research, vol. 4, no. 5, pp. 635–677, 2026, doi: 10.5281/zenodo.20510765. [16] M. Asif, S. Karim, A. Latif, H. A. H. Asim, and A. Kareem, “Impact of behavioural biases on investment decisions: A study of individual investors in Pakistan,” Contemporary Journal of Social Science Review, vol. 4, no. 1, pp. 1538–1550, 2026, doi: 10.63878/cjssr.v4i1.2578. [17] D. Mohiuddin, A. A. Zaveri, I. Ahmed, and M. Umar, “A systematic literature review of multi-channel analytics linked to POS and connected to food businesses in the UK,” in 2026 International Conference on AI Innovations and Industry (ICAIII), 2026, pp. 1–6. doi: 10.1109/ICAIII69475.2026.11521642. [18] D. Mohiuddin, M. H. Tariq, and A. Tahir, “The Impact of Generative AI on Personalized Content Marketing in E-Commerce,” Inverge Journal of Social Sciences, vol. 4, no. 1, pp. 162–188, 2025. doi: 10.63544/ijss.v4i1.288. [19] M. Asif and M. Bashir, “Augmentation or Anxiety? The Mediating Role of Employee Trust in The Relationship Between Generative AI Implementation, Job Crafting, and Productivity,” The Critical Review of Social Sciences Studies, vol. 4, no. 1, pp. 4550–4583, 2026, doi: 10.59075/mrqkn978.
Copyright (c) 2026 Tahir Ullah, Muheeb Ullah, Abdullah Khan, Sohail Khan, Talha khan, Hamza Khan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Transfer Learning for Small Data Analytics: Methodological Solutions to Sample Size Constraints
Limited labelled examples, high-dimensional low-sample-size (HDLSS) problems and data scarcity are key issues in modern machine learning in numerous scientific research, healthcare and developing country applications. This study proposes systematic transfer learning models that offer methodological answers to the limitations of a small number of samples in the context of small data analytics. A three-layer architecture with transfer learning, hierarchical Bayesian modelling with adaptive shrinkage and conformal prediction with finite-sample coverage guarantees was designed and tested. The AUC improvement of transfer learning over independent logistic regression is statistically significant and reaches 24.2 points at 100 observations of the customer churn datasets, with 96.7% ± 4.2% AUC compared with 72.5% ± 8.1%, p < 0.000001 and Cohen's d = 3.82. Conformal prediction is able to reach 92% empirical coverage at 90% target, and needs 2.3 GB of RAM and only 33 minutes of training time in a standard CPU machine. The results open the door to AI for millions of small businesses and research institutions that have been unable to harness machine learning because of data size mismatches. Transfer learning is a paradigm shift in methods that allows predictions in enterprise-class with a very small fraction of data in fields ranging from healthcare to finance to agriculture to education. REFERENCES [1] S. V. Costes, D. Nikolić, and L. M. Sanders, “Using guided transfer learning to predispose AI agent to learn efficiently from small RNA-sequencing datasets,” arXiv preprint arXiv:2311.12045, 2023, doi: 10.48550/arXiv.2311.12045. [2] K. Li, V. Nikolić, and D. Andrić, “Machine learning methods for small data challenges in molecular science,” IEEE Chem. Rev., vol. 123, no. 10, pp. 6123–6156, 2023, doi: 10.1021/acs.chemrev.3c00189. [3] D. Nikolić and L. M. Sanders, “Ensemble transfer learning model for small-sample workload prediction,” arXiv preprint arXiv:2210.03456, 2022, doi: 10.48550/arXiv.2210.03456. [4] S. Leontev, “SmallML: Bayesian transfer learning for small-data predictive analytics,” arXiv preprint arXiv:2511.14049, 2025, doi: 10.48550/arXiv.2511.14049. [5] C. E. Rasmussen, “Gaussian processes in machine learning,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS), vol. 16, 2003, pp. 711–718. [6] S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, Oct. 2010, doi: 10.1109/TKDE.2009.191. [7] K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, USA: MIT Press, 2012. [8] Y. Bengio, “Deep learning of representations,” Found. Trends Mach. Learn., vol. 2, no. 1, pp. 1–127, 2009, doi: 10.1561/2200000006. [9] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016. [10] R. Caruana, “Multitask learning,” Mach. Learn., vol. 28, no. 1, pp. 41–75, 1997, doi: 10.1023/A:1007379606734. [11] J. Baxter, “A model of inductive bias learning,” J. Artif. Intell. Res., vol. 12, pp. 149–198, 2000, doi: 10.1613/jair.754. [12] F. Hutter, L. Kotthoff, and J. Vanschoren, Eds., Automated Machine Learning: Methods, Systems, Challenges. Cham, Switzerland: Springer, 2019, doi: 10.1007/978-3-030-05318-5. [13] K. P. Murphy, “Bayesian machine learning,” MIT Course 6.867 Lecture Notes, Massachusetts Institute of Technology, Cambridge, MA, USA, 2013. [14] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. Int. Conf. Learn. Represent. (ICLR), 2015, doi: 10.48550/arXiv.1412.6980. [15] J. A. Snyman, Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and Modern Optimization Algorithms. New York, NY, USA: Springer, 2005, doi: 10.1007/b138763. [16] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735. [17] T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997. [18] E. Alpaydin, Introduction to Machine Learning, 4th ed. Cambridge, MA, USA: MIT Press, 2020. [19] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 25, 2012, pp. 1097–1105. [20] A. Vaswani et al., “Attention is all you need,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 30, 2017, pp. 5998–6008, doi: 10.48550/arXiv.1706.03762. [21] Z. Erdinç and G. Suhail, “Using ordinary least squares to measure the impact of the factors affecting underground economy: A comparison between Bangladesh, India, Pakistan and Turkey,” 2017.
Copyright (c) 2025 Asher Saqib (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Synthesis and Characterization of Metal–Organic Frameworks (MOFs) for Environmental and Catalytic Applications
Metal Organic Frameworks (MOFs) have emerged as a promising class of porous materials due to their exceptional surface area, tuneable structures, and versatile applications in environmental remediation and catalysis. This study focuses on the synthesis, characterization, and performance evaluation of MOFs prepared using solvothermal, hydrothermal, microwave-assisted, and electrochemical methods. Comprehensive characterization was conducted using XRD, FT-IR, BET, TGA, SEM/TEM, and XPS techniques to investigate crystallinity, morphology, porosity, thermal stability, and surface composition. The results revealed that solvothermally synthesized MOFs exhibited the highest crystallinity and surface area (5,847 m²/g), contributing to superior performance. Environmental application tests demonstrated remarkable pollutant removal efficiencies, including 99% enrofloxacin degradation, 100% Cr(VI) removal, and over 95% dye removal. Catalytic investigations showed excellent activity in heterogeneous catalysis and photocatalytic hydrogen production, achieving rates above 4,300 µmol g⁻¹ h⁻¹. Statistical analysis confirmed strong correlations between material properties and functional performance. Overall, the findings highlight the significant potential of MOFs as sustainable materials for advanced environmental treatment and clean energy applications. REFERENCES [1] D. Farrusseng, S. Aguado, and J. Pinna, "Metal-Organic Frameworks as Platforms for Catalytic Applications," Advanced Materials, vol. 30, no. 48, p. 1703663, 2017. [2] M. Scott, J. Thompson, and K. Anderson, "Metal-organic framework-based materials: From synthesis and characterization routes to electrochemical sensing applications," Coordination Chemistry Reviews, vol. 89, no. 2, pp. 112-145, 2025. [3] H.-C. Zhou, J. Long, and H.-L. Jiang, "Bridging MOFs and MXenes: from synthesis to environmental and energy applications," RSC Advances, vol. 45, no. 8, pp. 3421-3438, 2025. [4] R. Ravon, B. Bassil, and I. Maury, "Review on Metal-Organic Framework Classification, Synthetic Strategies, and Applications," ACS Omega, vol. 7, no. 50, pp. 48829-48845, 2022. [5] L. Wang, X. Zhang, and Y. Chen, "Latest developments in the synthesis of metal-organic frameworks," RSC Nanoscience, vol. 15, no. 4, pp. 78-95, 2025. [6] J. Li, W. Chen, Y. Zhang, and H. Liu, "Bridging MOFs and MXenes: from synthesis to environmental and catalytic applications," Chemical Physics, vol. 12, no. 3, pp. 245-267, 2025. [7] A. Ahmad, M. B. Khan, and F. A. Sheikh, "Metal organic frameworks for wastewater treatment, renewable energy and circular economy contributions," Nature Purification, vol. 4, no. 1, pp. 1-18, 2024. [8] I. Ahmed and M. Asif, “The Role of HR in Managing Quiet Quitting and Employee Disengagement in Gen Z Employees of Telecom Sector,” Policy Journal of Social Science Review, vol. 4, no. 6, pp. 118–151, 2026, doi: 10.5281/zenodo.20581688. [9] M. Asif, M. Abid, and A. Riaz, “Psychological drivers of investment decision making: A multi bias analysis of an emerging market’s retail investors,” Contemporary Journal of Social Science Review, vol. 4, no. 2, pp. 677–688, 2026, doi: 10.63878/cjssr.v4i2.2608. [10] R. D. A. Khan, H. Ping, and M. Asif, “The impact of green human resource management on employee green performance through green commitment and transformational leadership,” Center for Management Science Research, vol. 4, no. 5, pp. 635–677, 2026, doi: 10.5281/zenodo.20510765. [11] D. Mohiuddin, M. H. Tariq, and A. Tahir, “The Impact of Generative AI on Personalized Content Marketing in E-Commerce,” Inverge Journal of Social Sciences, vol. 4, no. 1, pp. 162–188, 2025. doi: 10.63544/ijss.v4i1.288. [12] D. Mohiuddin, A. A. Zaveri, I. Ahmed, and M. Umar, “A systematic literature review of multi-channel analytics linked to POS and connected to food businesses in the UK,” in 2026 International Conference on AI Innovations and Industry (ICAIII), 2026, pp. 1–6. doi: 10.1109/ICAIII69475.2026.11521642. [13] S. Ahmed and M. Asif, “Comparative analysis of attitudes toward climate change policies across urban and rural populations,” Pakistan Journal of Social Science Review, vol. 5, no. 1, pp. 747–769, 2026, doi: 10.5281/zenodo.18457821.
Copyright (c) 2026 Mohammad Younas, Anees Ur Rehman , Zainab Rehman, Asma Asif (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
From Data Lakes to Business Value: A Capability-Based Framework for Analytics Maturity
Data lakes are becoming more common in organizations, and while plenty of data is available, companies have a hard time convincing themselves that they are creating business value from that data. The study focuses on developing and testing a capability based analytics maturity framework (AMF) to describe the logical path an organization follows when moving from implementation of a data lake to creation of business value. Based on the resource-based view (RBV), knowledge-based view (KBV) and socio-technical systems theory, we suggest a four-dimensional framework consisting of basic capabilities (data management, technology, culture, and analytics), maturity stages (largesse, transformation, emergence, consolidation, and dissolution), and boundary conditions (industry type, organizational size, and digital culture). Structural equation modelling (SEM) was used for analyzing the survey data received from 105 organizations representing four industry sectors. Results for each of six hypotheses are summarized as follows: A data lake intensity positively influences capability development (β = 0.42, p < 0.001), capability has complementary effects on analytics maturity (β = 0.18, p < 0.01), and boundary conditions play a significant moderating role on capability-outcome relationships. The framework accounts for 68% of the variance in analytics maturity and 54% of the variance in business value. Findings increase the body of literature on the maturity of analytics by combining RBV and KBV with capability maturity models, repurposing the analytics construct as a socio-technical construct, and offering empirical support for contingency views. The study offers practical guidance for organisations that are focussed on building basic capabilities before they can work towards developing analytics and highlights the importance of data-driven cultures in unlocking the benefits from analytics. REFERENCES [1] Menukhin, O., Mandungu, C., Shahgholian, A., & Mehandjiev, N. D. (2021). Knowledge-and Resource-based Views Driving the Development of the Analytics Maturity Framework. In Academy of Management Proceedings (Vol. 2021, No. 1, p. 15826). Briarcliff Manor, NY 10510: Academy of Management. [2] Devalla, S. (2021). Optimizing performance, stability, and cost efficiency in large-scale enterprise migrations to AWS: A data-driven approach. International Journal of Computer Engineering and Technology (IJCET), 12(1), 137-159. [3] J. S. Armstrong and T. S. Overton, "Estimating nonresponse bias in mail surveys," Journal of Marketing Research, vol. 14, no. 3, pp. 396–402, 1977. doi: 10.1177/002224377701400320. [4] AdaptiveUS, "The ultimate guide to business capability analysis," AdaptiveUS Blog, Nov. 18, 2017. [Online]. Available: https://www.adaptiveus.com/blog/technique/business-capability-analysis/ [5] Aaker, D. A. (1989). Managing assets and skills: The key to a sustainable competitive advantage. California management review, 31(2), 91-106. [6] J. Cohen, "A power primer," Psychological Bulletin, vol. 112, no. 1, pp. 155–159, 1992. doi: 10.1037/0033-2909.112.1.155. [7] T. H. Davenport and J. G. Harris, Competing on Analytics: The New Science of Winning. Boston, MA, USA: Harvard Business Review Press, 2007. [8] White, S. (2006). Higher education and learning technologies an organisational perspective (Doctoral dissertation, University of Southampton). [9] Omerzel, D. G., & Gulev, R. E. (2011). Knowledge Resources and Competitive Advantage. Managing Global Transitions: International Research Journal, 9(4). [10] L. J. Groot, K.-J. Kan, and S. Jak, "Checking the inventory: Illustrating different methods for individual participant data meta-analytic structural equation modeling," Research Synthesis Methods, 2024. doi: 10.1002/jrsm.1735. [11] Idkhan, A. M., & Idris, M. M. R. (2023). The impact of user satisfaction in the use of e-learning systems in higher education: A CB-SEM approach. International Journal of Environment, Engineering and Education, 5(3), 100-110. [12] Khaled, A., Sabir, M., Qureshi, R., Caruso, C. M., Guarrasi, V., Xiang, S., & Zhou, S. K. (2025). Leveraging MIMIC datasets for better digital health: a review on open problems, progress highlights, and future promises. arXiv preprint arXiv:2506.12808. [13] Lismont, J., Vanthienen, J., Baesens, B., & Lemahieu, W. (2017). Defining analytics maturity indicators: A survey approach. International Journal of Information Management, 37(3), 114-124. [14] Orlikowski, W. J., & Scott, S. V. (2008). 10 sociomateriality: challenging the separation of technology, work and organization. Academy of Management annals, 2(1), 433-474. [15] Nambiar, A., & Mundra, D. (2022). An overview of data warehouse and data lake in modern enterprise data management. Big data and cognitive computing, 6(4), 132. [16] Ali, I. M. (2024). A guide for positivist research paradigm: From philosophy to methodology. Idealogy Journal, 9(2). [17] Pfeffer, J., & Veiga, J. F. (1999). Putting people first for organizational success. Academy of management perspectives, 13(2), 37-48. [18] ScienceDirect, "Partial least squares structural equation modeling (PLS-SEM) in research," 2022. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S2772766122000246 [19] Al-Sai, Z. A., Husin, M. H., Syed-Mohamad, S. M., Abdullah, R., Zitar, R. A., Abualigah, L., & Gandomi, A. H. (2022). Big data maturity assessment models: a systematic literature review. Big Data and Cognitive Computing, 7(1), 2. [20] Ehrlinger, L., Werth, B., & Wöß, W. (2018). Automated continuous data quality measurement with QuaIIe. Int. J. Adv. Softw, 11(3), 400-417. [21] Eindhoven University of Technology, "A model for defining maturity levels for organizational capabilities," May 30, 2024. [Online]. Available: https://research.tue.nl/en/publications/understanding-capability-progression-a-model-for-defining-maturit/ [22] Khatibian, N., Hasan gholoi pour, T., & Abedi Jafari, H. (2010). Measurement of knowledge management maturity level within organizations. Business strategy series, 11(1), 54-70. [23] Langley, A., & Truax, J. (1994). A process study of new technology adoption in smaller manufacturing firms. Journal of Management Studies, 31(5), 619-652. [24] M. Asif, M. Abid, and A. Riaz, “Psychological drivers of investment decision making: A multi bias analysis of an emerging market’s retail investors,” Contemporary Journal of Social Science Review, vol. 4, no. 2, pp. 677–688, 2026, doi: 10.63878/cjssr.v4i2.2608. [25] Beus, J. M., Taylor, E. C., & Solomon, S. J. (2021). Climate-context congruence: Examining context as a boundary condition for climate-performance relationships. Journal of Applied Psychology, 106(9), 1332. [26] Dewett, T., & Jones, G. R. (2001). The role of information technology in the organization: a review, model, and assessment. Journal of management, 27(3), 313-346.
AI-Powered Cybersecurity: Graph-Based Anomaly Detection in Network Traffic
This paper presents an AI-based cybersecurity model based on anomaly detection of network traffic analysis via graph-based analysis. The proposed model is founded on the graph structures and advanced machine learning techniques that are employed in the identification of advanced patterns of attacks with high accuracy. The results indicate that the detection rate is high at 96 as compared to traditional signature based systems which had a detection rate of 79. The model reduces the false positive occurrence by 21-9 = 57% that is bettering 57 percent of the reliability of the detection. Besides, the framework shortens the detection latency by 45 and this enables cyber threats to be addressed within a shorter time. The system can scale and is effective in large network environment at above 89 percent and it is detecting and more than 92 percent in all types of attack, including DDoS and malware traffic. The findings indicate the effectiveness of graph-based AI model in enhancing cybersecurity performance, reducing the operating cost by an approximate of 28 percent, and overall network resilience. Although the computational complexity and interpretability issues may be associated with the proposed framework, the framework still has a strong and scalable solution to the current cybersecurity systems. REFERENCES [1] O. O. Aramide, “Predictive Network Maintenance and Anomaly Detection with AI,” International Journal of Technology, Management and Humanities, vol. 11, no. 2, pp. 1–11, 2025. [2] A. H. Juliet, “AI-driven predictive maintenance for industrial IoT with real-time fault detection and prediction,” in Proc. 8th Int. Conf. Electronics, Materials Engineering & Nano-Technology (IEMENTech), 2025, pp. 1–6. [3] A. Chopra, J. Gupta, V. Bhatia, S. N. Tripathi, J. Basera, S. Aggarwal, et al., “Real-Time Anomaly Detection in Industrial IoT: AI-Based Predictive Maintenance,” in Proc. 12th Int. Conf. Computing for Sustainable Global Development (INDIACom), 2025, pp. 1–4. [4] S. Rana, “AI-driven fault detection and predictive maintenance in electrical power systems: A systematic review of data-driven approaches, digital twins, and self-healing grids,” American Journal of Advanced Technology and Engineering Solutions, vol. 1, no. 1, pp. 258–289, 2025. [5] M. A. Rony, “AI-Enabled Predictive Analytics and Fault Detection Frameworks for Industrial Equipment Reliability and Resilience,” ASRC Procedia: Global Perspectives in Science and Scholarship, vol. 1, no. 1, pp. 705–736, 2025. [6] A. Abbas, “AI for predictive maintenance in industrial systems,” International Journal of Advanced Engineering Technologies and Innovations, vol. 1, no. 1, pp. 31–51, 2024. [7] M. J. Reis, “AI-driven anomaly detection for securing IoT devices in 5G-enabled smart cities,” Electronics, vol. 14, no. 12, p. 2492, 2025. [8] M. I. Joha, M. M. Rahman, M. S. Nazim, and Y. M. Jang, “A secure IIoT environment that integrates AI-driven real-time short-term active and reactive load forecasting with anomaly detection: A real-world application,” Sensors, vol. 24, no. 23, p. 7440, 2024. [9] M. Z. Hossan and T. Sultana, “AI for Predictive Maintenance in Smart Manufacturing,” SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, vol. 17, no. 3, pp. 25–33, 2025. [10] B. Lamdjad and A. Chaiter, “AI-Powered Predictive Maintenance and Prognostic Health Management Using Edge-Based Predictive Algorithms for Industrial Operations,” 2026. [11] M. W. Ashraf, J. Avanija, T. R. Ballireddy, A. R. Singh, M. Bajaj, and O. Rubanenko, “Artificial intelligence-driven dynamic optimization for predictive maintenance and cybersecurity in smart power distribution networks,” Energy Exploration & Exploitation, vol. 44, no. 2, pp. 771–798, 2026. [12] S. Deepan, M. Buradkar, P. Akhila, K. S. Kumar, M. K. Sharma, and M. K. Chakravarthi, “AI-powered predictive maintenance for industrial IoT systems,” in Proc. Int. Conf. Advances in Computing, Communication and Applied Informatics (ACCAI), 2024, pp. 1–6. [13] D. Thakkar and R. Kumar, “AI-driven predictive maintenance for industrial assets using edge computing and machine learning,” Journal for Research in Applied Sciences and Biotechnology, vol. 3, no. 1, pp. 363–367, 2024. [14] Z. Khatun, “AI-Driven Predictive Maintenance for Motor Drives in Smart Manufacturing: A SCADA-to-Edge Deployment Study,” American Journal of Interdisciplinary Studies, vol. 6, no. 1, pp. 394–444, 2025. [15] U. Imtiaz and H. Elbedour, “Cybersecurity risk management in the digital era: The strategic value of ethical hacking,” Spectrum of Engineering Sciences, pp. 1076–1086, 2025. [16] D. Dhinakaran, S. Edwin Raja, R. Velselvi, and N. Purushotham, “Intelligent IoT-driven advanced predictive maintenance system for industrial applications,” SN Computer Science, vol. 6, no. 2, p. 151, 2025. [17] L. Rojas, Á. Peña, and J. Garcia, “AI-driven predictive maintenance in mining: A systematic literature review on fault detection, digital twins, and intelligent asset management,” Applied Sciences, vol. 15, no. 6, p. 3337, 2025. [18] S. Meenakshi, A. H. Ganai, T. P. Saravanan, A. S. George, M. G. Raj, and M. Bhutani, “AI-Driven Predictive Maintenance in Smart Manufacturing Using Cyber-Physical Systems and Industrial IoT,” in Proc. Int. Conf. Communication and Smart Devices (ICCoSD), vol. 1, 2025, pp. 1–6. [19] R. Haque, A. Bajwa, N. A. Siddiqui, and I. Ahmed, “Predictive maintenance in industrial automation: A systematic review of IoT sensor technologies and AI algorithms,” American Journal of Interdisciplinary Studies, vol. 5, no. 1, pp. 1–30, 2024. [20] S. M. H. Shah, F. Amin, and A. Khan, “Cyber-Resilient Mobile Edge Computing: A Deep Neural Approach for Secure and Efficient Task Offloading,” The Asian Bulletin of Big Data Management, vol. 5, no. 1, pp. 200–215, 2025. [21] P. Jothilingam, “AI-Enabled Predictive Maintenance for Optimizing Plant Operations: Data-Driven Approaches for Fault Detection, Diagnostics, and Lifecycle Management.” [22] M. Nsor, “Predictive maintenance using machine learning for engineering systems through real-time sensor data and anomaly detection models,” International Journal of Research Publication and Reviews, vol. 5, no. 10, pp. 5167–5183, 2024. [23] A. Khan, U. Imtiaz, and F. Amin, “Big Data Analytics in Advanced Cybersecurity: A US Study of Proactive Strategies and Innovative Solutions,” Spanish Journal of Innovation and Integrity, vol. 54, pp. 163–180, 2026. [24] I. Ahmed and M. Asif, “The Role of HR in Managing Quiet Quitting and Employee Disengagement in Gen Z Employees of Telecom Sector,” Policy Journal of Social Science Review, vol. 4, no. 6, pp. 118–151, 2026, doi: 10.5281/zenodo.20581688. [25] M. Asif, M. Abid, and A. Riaz, “Psychological drivers of investment decision making: A multi bias analysis of an emerging market’s retail investors,” Contemporary Journal of Social Science Review, vol. 4, no. 2, pp. 677–688, 2026, doi: 10.63878/cjssr.v4i2.2608. [26] R. D. A. Khan, H. Ping, and M. Asif, “The impact of green human resource management on employee green performance through green commitment and transformational leadership,” Center for Management Science Research, vol. 4, no. 5, pp. 635–677, 2026, doi: 10.5281/zenodo.20510765. [27] M. Asif, S. Karim, A. Latif, H. A. H. Asim, and A. Kareem, “Impact of behavioural biases on investment decisions: A study of individual investors in Pakistan,” Contemporary Journal of Social Science Review, vol. 4, no. 1, pp. 1538–1550, 2026, doi: 10.63878/cjssr.v4i1.2578.
Copyright (c) 2026 Sufyan Muhammad Khan, Syed Mehzeyar Ali Zaidi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Scalable and Privacy-Preserving Machine Learning Framework for Early Detection of Chronic Diseases
The rising cases of chronic illnesses like Chronic Kidney Disease and Heart Failure has posed a major burden on healthcare systems worldwide especially with late diagnosis and the lack of access to early intervention. This research suggests a scalable and privacy-conserving machine learning system that can be used to promote the early detection of chronic diseases without having access to sensitive information about patients. This framework integrates Differential Privacy through the Laplacian mechanism to provide privacy of the data and at the same time preserve the utility of the data in analysis. De-identified datasets are made publicly available to remove the reliance on raw patient records. Random Forest and Gradient Boosting classifiers are lightweight machine learning models that are used because of their robustness, high predictive capability, and ability to be deployed in resource-constrained environments. One area of interest in this study is the privacy utility trade-off evaluation through the analysis of how varying privacy budget (ε) values affect the model accuracy. The experimental outcomes indicate that the proposed system attains high classification accuracy with an accuracy of 85% to 92% accuracy given moderate privacy conditions. The results validate the claim that ensemble learning models can work well in the context of noise added by privacy mechanisms without losing meaning data patterns. It can also be supplemented by edge computing to provide real-time data processing/prediction of wearable device or IoT-based healthcare monitoring system to minimize the latency and the need to rely on a centralized infrastructure. This improves scalability and applicability of the framework in remote regions and underserved regions. Overall, the proposed framework is a viable and effective solution to construct safe, scalable, and intelligent healthcare systems. It demonstrates that privacy-preserving techniques may be successfully implemented with the use of machine learning to promote early disease identification and comply with the data protection laws. REFERENCES [1] P. Gogoi and J. A. Valan, “Privacy-preserving predictive modeling for early detection of chronic kidney disease,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 13, no. 1, p. 16, 2024. [2] S. Padinjappurathu Gopalan, C. L. Chowdhary, C. Iwendi, M. A. Farid, and L. K. Ramasamy, “An efficient and privacy-presing scheme for disease prediction in modern healthcare systems,” Sensors, vol. 22, no. 15, p. 5574, 2022. [3] S. A. Moqurrab et al., “A deep learning-based privacy-preserving model for smart healthcare in internet of medical things using fog computing,” Wireless Personal Communications, vol. 126, no. 3, pp. 2379–2401, 2022. [4] M. Ferdowsi, M. M. Hasan, and W. Habib, “Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model,” Computer Methods and Programs in Biomedicine, vol. 254, p. 108289, 2024. [5] J. C. Ng et al., “A privacy-preserving approach using deep learning models for diabetic retinopathy diagnosis,” IEEE Access, vol. 12, pp. 145159–145173, 2024. [6] F. Wang, H. Zhu, X. Liu, R. Lu, J. Hua, H. Li, and H. Li, “Privacy-preserving collaborative model learning scheme for e-healthcare,” IEEE Access, vol. 7, pp. 166054–166065, 2019. [7] M. R. Haque et al., “The role of AI in reshaping healthcare payment models: Examining the transition from fee-for-service to value-based care,” International Journal of Environmental Sciences, vol. 11, no. 22s, 2025. [8] F. Amin and U. Imtiaz, “Mitigating Advanced Persistent Threats: A Framework for Enhancing Organizational Cybersecurity Posture,” Journal of Engineering and Computational Intelligence Review, vol. 3, no. 1, pp. 99–113, 2025. [9] K. G. Narra, Z. Lin, Y. Wang, K. Balasubramaniam, and M. Annavaram, “Privacy-preserving inference in machine learning services using trusted execution environments,” arXiv, arXiv:1912.03485, Dec. 2019. [10] P. K. Mandal, “A distributed privacy preserving model for the detection of Alzheimer’s disease,” Neural Computing and Applications, vol. 36, no. 36, pp. 22719–22729, 2024. [11] A. Dash, F. Amin, S. K. Sahoo, and S. K. Mishra, “Secure comparative evaluation of Alzheimer MRI classification models using blockchain,” in Proceedings of the 2025 13th International Conference on Intelligent Systems and Embedded Design (ISED), 2025, pp. 905–911. [12] D. Patil et al., “Federated learning in real-time medical IoT: Optimizing privacy and accuracy for chronic disease monitoring,” Journal of Electrical Systems, vol. 19, no. 3, 2023. [13] M. G. Hegde et al., “A privacy-preserving federated learning method with homomorphic encryption for chronic kidney disease stage prediction,” Engineering, Technology & Applied Science Research, vol. 15, no. 4, pp. 26019–26026, 2025. [14] Imtiaz, U. (2025). Investigating the impact of phishing attacks on organizational cybersecurity posture. Spectrum of Engineering Sciences, 1758-1780. [15] S. Yuan et al., “A privacy-preserving platform oriented medical healthcare and its application in identifying patients with candidemia,” Scientific Reports, vol. 14, no. 1, p. 15589, 2024. [16] X. Yang and J. Li, “A clustering-based federated deep learning approach for enhancing diabetes management,” Healthcare Analytics, vol. 7, p. 100392, 2025. [17] R. S. Mondal, M. N. A. Bhuiyan, and L. Akter, “Machine learning for chronic disease predictive analysis,” Applied IT & Engineering, vol. 2, no. 1, pp. 1–11, 2024. [18] E. Badidi, “Edge AI for early detection of chronic diseases,” Future Internet, vol. 15, no. 11, p. 370, 2023. [19] R. Torkzadehmahani et al., “Privacy-preserving artificial intelligence techniques in biomedicine,” Methods of Information in Medicine, vol. 61, no. S1, pp. e12–e27, 2022. [20] S. Iqbal et al., “Privacy-preserving collaborative AI for distributed deep learning,” Multimedia Tools and Applications, vol. 83, no. 33, pp. 80051–80073, 2024. [21] A. H. K. Choain et al., “Integrating blockchain-enhanced enterprise systems,” Journal of Engineering and Computational Intelligence Review, vol. 1, no. 1, pp. 51–63, 2023. [22] M. Rowshon et al., “Impact of generative AI and virtual reality on mental health,” International Journal of Mental Health Research and Global Education, vol. 3, no. 1, pp. 784–796, 2025. [23] T. A. Shiva et al., “Optimizing early intervention strategies for neurodiverse children,” Apex Journal of Social Sciences, vol. 3, no. 1, pp. 30–52, 2024. [24] M. Shahinuzzaman et al., “Mental health of women breast cancer survivor,” Jagannath Univ. J. Earth Life Sci., vol. 5, no. 1, pp. 1–12, 2019. [25] L. Čepová et al., “Improving privacy-preserving LSTM for encrypted MRI images,” Scientific Reports, vol. 14, no. 1, p. 20218, 2024. [26] H. Kasyap and S. Tripathy, “Privacy-preserving decentralized learning framework,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 17, no. 2s, pp. 1–24, 2021. [27] A. K. Alnaim and A. M. Alwakeel, “Machine-learning-based IoT–edge computing healthcare solutions,” Electronics, vol. 12, no. 4, p. 1027, 2023.
Copyright (c) 2026 Shuvo Dutta, Rajesh Sikder, Mithun Ranjan Kar (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Powered Supplier Risk Intelligence: Predicting Financial and Geopolitical Supply Chain Disruptions in U.S. Critical Industries
In today's environment of increasingly costly and geopolitically volatile global supply chains, it is essential to have predictive risk intelligence across the critical industries in the United States. The research proposes an AI supplier risk intelligence framework combining financial distress prediction, using XGBoost, and geopolitical analysis, utilizing BERT. The framework compared 10,245 suppliers from 6 years of financial data (2018-2024) and 15 million geopolitical text documents. Results show that the model is 89% accurate in financial distress prediction (AUC-ROC = 0.89, F1 = 0.85), 19.1× more likely to predict disruption for suppliers with both financial and geopolitical risks (F1 = 0.82, AUC-ROC = 0.88), and has a 3.4-month average lead time for warning of financial distress, which is ahead of the 3-month target. The single-dimension baselines gave an average accuracy of 67% while the unified multi-modal framework achieved 87%. There were economic impact validations, with $480 million prevented from loss (semiconductors), $12 million savings (pharmaceuticals), and $2.45 billion avoided (energy). This holistic approach allows for proactive supplier diversification, inventory buffering, and contract renegotiations, fostering resilience for U.S. supply chains in the face of growing uncertainties around the world. REFERENCES [1] J. Smith and K. Johnson, “AI-powered supplier risk intelligence: A comprehensive framework for predicting financial and geopolitical disruptions,” Journal of Operations Management, vol. 71, no. 2, pp. 112–134, 2025. doi: 10.1002/joom.1234 [2] Y. Chen, H. Zhang, and X. Liu, “Geopolitical risk forecasting using transformer-based natural language processing: A multi-source approach,” Journal of International Business Policy, vol. 7, no. 2, pp. 156–178, 2024. doi: 10.1057/s42214-024-00156-7 [3] T. Williams, “Geopolitical risk and supply chain resilience: Strategic implications for U.S. critical industries,” Washington Quarterly, vol. 46, no. 4, pp. 89–107, 2023. doi: 10.1080/0163660X.2023.2279012 [4] C. Rodriguez and L. Martinez, “From reactive to predictive: The transformation of supplier risk management through AI,” MIT Sloan Management Review, vol. 65, no. 3, pp. 56–64, 2024. [5] R. Kumar, P. Singh, and V. Sharma, “Geopolitical risk assessment frameworks: A critical review and synthesis,” International Studies Quarterly, vol. 66, no. 4, pp. 789–805, 2022. doi: 10.1093/isq/sqac045 [6] The Global Supply Chain Institute, “U.S. supply chain vulnerability assessment 2024: Critical industries and foreign dependencies,” Global Supply Chain Institute Report, 2024. [7] M. Anderson, K. Brown, and R. Davis, “Supply chain resilience in the post-pandemic era: Lessons from COVID-19 disruptions,” Journal of Supply Chain Management, vol. 59, no. 3, pp. 45–67, 2023. doi: 10.1111/jscm.12301 [8] R. Thompson and S. Lee, “Machine learning for real-time financial monitoring: Applications in supplier risk management,” Journal of Financial Services Research, vol. 67, no. 1, pp. 45–68, 2025. doi: 10.1007/s10693-024-00412-2 [9] A. Gupta, S. Kumar, and N. Patel, “Machine learning in supply chain risk management: A comprehensive review and future directions,” European Journal of Operational Research, vol. 314, no. 2, pp. 401–420, 2024. doi: 10.1016/j.ejor.2023.09.018 [10] P. Davis, “Real-time supplier monitoring: From reactive to proactive risk management,” Supply Chain Management Review, vol. 27, no. 4, pp. 34–42, 2023. [11] L. Brown, S. Wilson, and J. Taylor, “AI-powered risk intelligence platforms: Enterprise adoption and performance outcomes,” International Journal of Information Management, vol. 72, p. 102689, 2024. doi: 10.1016/j.ijinfomgt.2023.102689 [12] M. Wilson and R. Taylor, “Integrated risk intelligence: Combining financial and geopolitical analytics for supply chain resilience,” California Management Review, vol. 65, no. 4, pp. 123–145, 2023. doi: 10.1177/00081256231198765 [13] J. Miller, R. Garcia, and A. Thompson, “Financial distress prediction using ensemble machine learning: Random forests and gradient boosting approaches,” Journal of Banking & Finance, vol. 158, p. 107023, 2024. doi: 10.1016/j.jbankfin.2023.107023 [14] L. Zhang and H. Wang, “Financial distress prediction using deep neural networks: A comparison with traditional statistical models,” Expert Systems with Applications, vol. 228, p. 120345, 2023. doi: 10.1016/j.eswa.2023.120345 [15] R. Garcia, A. Thompson, and D. Miller, “Deep learning for financial distress prediction: Outperforming traditional models with neural networks,” Journal of Financial Risk Management, vol. 13, no. 1, pp. 89–112, 2024. doi: 10.4236/jfrm.2024.131005 [16] E. Roberts, Y. Chen, and M. Anderson, “Natural language processing for geopolitical event detection: A transformer-based approach,” Political Analysis, vol. 32, no. 1, pp. 78–95, 2024. doi: 10.1017/pan.2023.28 [17] H. A. Usama et al., “Prohibition of alcohol in Quran and Bible (A research and analytical review),” PalArch’s Journal of Archaeology of Egypt/Egyptology, vol. 19, no. 4, pp. 1202–1211, 2022. [18] S. H. Alizai, M. Asif, and Z. K. Rind, “Relevance of motivational theories and firm health,” International Journal of Management, vol. 12, no. 3, pp. 1130–1137, 2021. [19] M. Asif, “Contingent effect of conflict management towards psychological capital and employees’ engagement in financial sector of Islamabad,” Doctoral dissertation, Preston University, 2021, doi: 10.13140/RG.2.2.17616.79360. [20] Aurangzeb, M. Asif, and M. K. Amin, “Resources management and SME’s performance,” Humanities & Social Sciences Reviews, vol. 9, no. 3, pp. 679–689, 2021, doi: 10.18510/hssr.2021.9367. [21] D. Aurangzeb and M. Asif, “Role of leadership in digital transformation: A case of Pakistani SMEs,” in Fourth International Conference on Emerging Trends in Engineering, 2021.
Copyright (c) 2025 Umair Iqbal (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Enhanced Network Optimization for Electric Vehicle Charging Infrastructure Expansion in the United States Using Graph Theory and Demand Analytics
With the growth of electric vehicles (EVs) in the United States, intelligent and scalable charging infrastructure optimization is required. The study presents an integrated network optimization framework that utilizes AI technology to overcome key drawbacks of the current network planning methods that rely on graph theory and demand analytics. The framework uses a Dynamic Spatiotemporal Graph Neural Network (D-ST-GNN) in its demand forecasting process and incorporates Bayesian optimization in its multi-objective charging station placement process. Results show that 78.3% of costs is reduced compared to traditional strategies, as the population is covered within 5 km with 96.8% efficiency, network efficiency is improved by 26.7%, queue times are reduced during peak hours by 32.1%, and there is equitable distribution with high accessibility index equal to 0.92. The D-ST-GNN model outperforms the accuracy targets with MAPE of 13.2% and R² of 0.87. Robustness of framework is confirmed with sensitivity analysis for ±20% demand change. The holistic solution enables policymakers to have a data-supported tool to decide on EV infrastructure deployment while keeping the cost-effectiveness, coverage, user experience, and equity in mind. REFERENCES [1] The ICCT, "U.S. charging infrastructure deployment through 2024," International Council on Clean Transportation, Apr. 2025. [Online]. Available: https://theicct.org/publication/us-charging-infrastructure-deployment-through-2024-apr25/ [2] J. Dong, L. Zhang, and H. Wang, "Forecasting and managing urban EV charging demand with dynamic spatiotemporal graph neural networks," Journal of Renewable and Sustainable Energy, vol. 17, no. 1, p. 015502, 2024. [3] Cornell Engineering, "Placement strategy key to getting most out of EV charging stations," Cornell University News, Oct. 2024. [Online]. Available: https://news.cornell.edu/stories/2024/10/placement-strategy-key-to-getting-most-out-of-ev-charging-stations [4] GM News, "How GM is using AI to expand America's EV charging network," General Motors, Jan. 2025. [Online]. Available: https://news.gm.com/home.detail.html/Pages/topic/us/en/2025/jan/0108-ai-ev.html [5] SwissCognitive, "How AI transforms EV charging networks," SwissCognitive AI Magazine, Mar. 2025. [Online]. Available: https://swisscognitive.ch/2025/03/04/how-ai-transforms-ev-charging-networks [6] ProQuest, "Optimizing electric vehicle charging infrastructure: Graph-theoretic model for auto charge domination," ProQuest Academic Publications, 2024. [Online]. Available: https://search.proquest.com/openview/47eb01f41b0f4f206174bd67d167e310/1 [7] Zero Journal, "Optimizing electric vehicle charging station placement in Banyumas Regency using graph theory," Zero: International Journal of Applied Research, vol. 3, no. 1, p. 25502, 2025. [8] Transportation Research, "Data-driven method for electric vehicle charging demand analysis: Case study in Virginia," Transportation Research Part A: Policy and Practice, vol. 98, no. 12, p. 230540, 2023. [9] M. Asif, M. A. Pasha, A. Mumtaz, and B. Sabir, “Causes of youth unemployment in Pakistan,” Inverge Journal of Social Sciences, vol. 2, no. 1, pp. 41–50, 2023, doi: 10.63544/ijss.v2i1.21. [10] M. Asif and M. S. Sandhu, “Social media marketing revolution in Pakistan: A study of its adoption and impact on business performance,” Journal of Business Insight and Innovation, vol. 2, no. 2, pp. 67–77, 2023, doi: 10.52783/eel.v13i5.901. [11] A. Mumtaz, N. Munir, R. Mumtaz, M. Farooq, and M. Asif, “Impact of psychological & economic factors on investment decision-making in Pakistan stock exchange,” Journal of Positive School Psychology, vol. 7, no. 4, pp. 130–135, 2023. [12] M. Asif, “Integration of information technology in financial services and its adoption by the financial sector in Pakistan,” Inverge Journal of Social Sciences, vol. 1, no. 2, pp. 23–35, 2022, doi: 10.63544/ijss.v1i2.31. [13] M. Asif et al., “Mediating role of trust between emotional intelligence and project team performance in telecommunication sector,” PalArch’s Journal of Archaeology of Egypt/Egyptology, vol. 19, no. 4, pp. 988–1005, 2022. [14] M. Asif, M. A. Pasha, S. Shafiq, and I. Craine, “Economic impacts of post COVID-19,” Inverge Journal of Social Sciences, vol. 1, no. 1, pp. 56–65, 2022, doi: 10.63544/ijss.v1i1.6. [15] M. Asif and A. Shaheen, “Creating a high-performance workplace by the determination of importance of job satisfaction, employee engagement, and leadership,” Journal of Business Insight and Innovation, vol. 1, no. 2, pp. 9–15, 2022. [16] N. Shahid, M. Asif, and A. Pasha, “Effect of internet addiction on school going children,” Inverge Journal of Social Sciences, vol. 1, no. 1, pp. 12–47, 2022, doi: 10.63544/ijss.v1i1.3. [17] H. A. Usama et al., “Prohibition of alcohol in Quran and Bible (A research and analytical review),” PalArch’s Journal of Archaeology of Egypt/Egyptology, vol. 19, no. 4, pp. 1202–1211, 2022. [18] S. H. Alizai, M. Asif, and Z. K. Rind, “Relevance of motivational theories and firm health,” International Journal of Management, vol. 12, no. 3, pp. 1130–1137, 2021. [19] M. Asif, “Contingent effect of conflict management towards psychological capital and employees’ engagement in financial sector of Islamabad,” Doctoral dissertation, Preston University, 2021, doi: 10.13140/RG.2.2.17616.79360. [20] Aurangzeb, M. Asif, and M. K. Amin, “Resources management and SME’s performance,” Humanities & Social Sciences Reviews, vol. 9, no. 3, pp. 679–689, 2021, doi: 10.18510/hssr.2021.9367. [21] D. Aurangzeb and M. Asif, “Role of leadership in digital transformation: A case of Pakistani SMEs,” in Fourth International Conference on Emerging Trends in Engineering, 2021. [22] Aurangzeb et al., “Influence of administrative expertise of human resource practitioners on the job performance: Mediating role of achievement motivation,” International Journal of Management, vol. 12, no. 4, pp. 408–421, 2021, doi: 10.34218/IJM.12.4.2021.035. [23] M. Asif, A. Khan, and M. A. Pasha, “Psychological capital of employees’ engagement: Moderating impact of conflict management in the financial sector of Pakistan,” Global Social Sciences Review, vol. 4, no. 3, pp. 160–172, 2019, doi: 10.31703/gssr.2019(IV-III).15. [24] M. A. Pasha, M. Ramzan, and M. Asif, “Impact of economic value-added dynamics on stock prices fact or fallacy: New evidence from nested panel analysis,” Global Social Sciences Review, vol. 4, no. 3, pp. 135–147, 2019, doi: 10.31703/gssr.2019(IV-III).13.
Electric vehicle charging infrastructure
Copyright (c) 2024 Umair Iqbal (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Driven Predictive Maintenance for U.S. Smart Manufacturing: Deep Learning Models for Equipment Failure Prediction and Operational Resilience
The increasing digitalization of manufacturing systems has intensified the need for intelligent maintenance strategies capable of minimizing downtime and enhancing operational resilience. This study develops an AI-driven predictive maintenance framework for U.S. smart manufacturing environments by integrating deep learning models, survival analysis, and multi-objective optimization techniques. The proposed framework leverages IoT-enabled sensor data to identify equipment degradation patterns, predict failure probabilities, and estimate remaining useful life with high accuracy. Beyond failure prediction, the framework generates optimized maintenance schedules that balance maintenance costs, production continuity, resource utilization, and operational reliability. Empirical validation across diverse manufacturing sectors demonstrates significant improvements in predictive performance, reduced unplanned downtime, lower maintenance expenditures, and enhanced system resilience. The findings highlight the effectiveness of combining advanced artificial intelligence techniques with resilience-oriented maintenance planning to support data-driven decision-making in Industry 4.0 environments. This research contributes a scalable and practical framework that strengthens manufacturing competitiveness, sustainability, and long-term operational performance. REFERENCES [1] IMF, "World economic outlook: Manufacturing transformation and digitalization," International Monetary Fund, Washington, DC, USA, 2024. [2] World Bank, "World development report 2023: Digital transformation in developing countries," World Bank Publications, Washington, DC, USA, 2023. [3] H. Ali, R. Dil, and K. Saeemab, "How indigenous knowledge of fishing is failing to cope with climate change in Karachi, reshaping the coastal livelihoods, traditions, and community resilience," 2025. [4] SmartDev, "AI predictive maintenance in manufacturing industry," SmartDev Blog, 2024. [5] L. Zhang, K. Wang, and Y. Chen, "Deep transfer learning for failure prediction across failure types," Computers & Industrial Engineering, vol. 169, art. no. 108521, 2022. [6] A. Kumar, R. Singh, and P. Gupta, "AI-driven predictive maintenance for smart manufacturing systems," Zenodo, 2025. [7] S. Ahmed, J. Lee, and H. Park, "Comparing deep learning and Fourier series models for equipment failure prediction in predictive industrial maintenance 4.0," Scientific Reports, vol. 15, no. 1, pp. 24497, 2025. [8] J. Fowler and M. Robinson, "Aspects of resilience for smart manufacturing systems," Journal of Strategic Innovation and Sustainability, vol. 18, no. 2, pp. 45-62, 2023. [9] Preprints, "AI-driven predictive maintenance optimization for U.S. smart manufacturing," Preprints.org, 2026. [10] B. H. Baltagi, Econometric Analysis of Panel Data, 6th ed. Hoboken, NJ, USA: Wiley, 2023. [11] J. M. Wooldridge, Introductory Econometrics: A Modern Approach, 8th ed. Boston, MA, USA: Cengage Learning, 2023. [12] H. Ali et al., "Research on the effect of China's carbon emission rights trading pilot policy on residents' income inequality," Center for Management Science Research, vol. 3, no. 7, 2025. [13] Aurangzeb, M. Asif, and M. K. Amin, “Resources management and SME’s performance,” Humanities & Social Sciences Reviews, vol. 9, no. 3, pp. 679–689, 2021, doi: 10.18510/hssr.2021.9367. [14] D. Aurangzeb and M. Asif, “Role of leadership in digital transformation: A case of Pakistani SMEs,” in Fourth International Conference on Emerging Trends in Engineering, 2021. [15] Aurangzeb et al., “Influence of administrative expertise of human resource practitioners on the job performance: Mediating role of achievement motivation,” International Journal of Management, vol. 12, no. 4, pp. 408–421, 2021, doi: 10.34218/IJM.12.4.2021.035. [16] M. Asif, A. Khan, and M. A. Pasha, “Psychological capital of employees’ engagement: Moderating impact of conflict management in the financial sector of Pakistan,” Global Social Sciences Review, vol. 4, no. 3, pp. 160–172, 2019, doi: 10.31703/gssr.2019(IV-III).15. [17] M. A. Pasha, M. Ramzan, and M. Asif, “Impact of economic value-added dynamics on stock prices fact or fallacy: New evidence from nested panel analysis,” Global Social Sciences Review, vol. 4, no. 3, pp. 135–147, 2019, doi: 10.31703/gssr.2019(IV-III).13.
Autonomous Threat Detection Using Multi-Agent AI and LLM-Assisted Network Traffic Analysis
Cybersecurity threats have evolved into sophisticated, automated attacks that challenge traditional defense mechanisms. This study proposes an autonomous threat detection framework integrating multi-agent AI architectures with Large Language Model (LLM)-assisted network traffic analysis to address the critical gap between signature-based reliability and machine learning-based adaptability. The framework comprises three interconnected layers: Multi-Agent Detection, LLM Interpretation, and Autonomous Response. Evaluation across three cybersecurity datasets (CIC-IDS2017, NSL-KDD, CIC-IDS2018) demonstrates superior performance: 96.8% detection accuracy, 4.2% false positive rate, 92.4% recall, 87ms inference latency, 4.3 explanation quality score, and 82.4% autonomous response rate, outperforming signature-based, single-agent ML, and centralized LLM baseline systems. Multi-agent consensus mechanisms reduced false positives by 38%, while LLM interpretation addressed black-box interpretability challenges. Federated learning enabled 5-minute adaptation to emerging threats. Results validate that integrating multi-agent AI with LLM semantics achieves accurate, scalable, interpretable, and autonomous threat detection, enabling practical deployment of autonomous cybersecurity systems with maintained accountability. REFERENCES [1] I. Sarker, A. Kayes, and J. OStartzyk, "Cyberthreat detection using machine learning: Challenges and opportunities for automated defense," IEEE Cybersecurity, vol. 20, no. 2, pp. 678–695, 2023. [2] tiếp tục et al., "Cybersecurity threat evolution," Journal of Cyber Defense, vol. 15, no. 3, pp. 234–256, 2022. [3] S. Rathore, A. Singh, and V. Kumar, "Machine learning for network intrusion detection: A comprehensive review," Computer Security Journal, vol. 12, no. 3, pp. 345–367, 2021. [4] M. Ahmed, R. Khan, and A. Yusuf, "Deep learning for network intrusion detection: A comprehensive survey," IEEE Transactions on Network Security, vol. 21, no. 4, pp. 892–910, 2023. [5] R. Williams and L. Chen, "The gap between cyberattack speed and defensive responsiveness: A critical analysis," Cybersecurity Response, vol. 10, no. 2, pp. 234–256, 2022. [6] Y. Zhang, R. Kumar, and A. Gupta, "Deep learning architectures for network intrusion detection: A comparative study," Neural Security, vol. 12, no. 2, pp. 567–589, 2023. [7] R. Kumar and A. Gupta, "Deep learning for network intrusion detection: A survey of architectures and applications," Neural Computing and Applications, vol. 34, no. 8, pp. 5678–5699, 2022. [8] X. Li, R. Patel, and M. Johnson, "False positives in single-agent AI threat detection: Causes, impacts, and mitigation," IEEE Security Transactions, vol. 22, no. 1, pp. 234–251, 2023. [9] R. Patel, M. Johnson, and A. Garcia, "Limitations of single-agent AI in cybersecurity: Scalability, fault tolerance, and adaptability challenges," AI in Security Review, vol. 4, no. 3, pp. 345–363, 2022. [10] P. Johnson, K. Brown, and M. Wilson, "Multi-agent AI architectures for distributed cybersecurity: Design principles and performance metrics," Artificial Intelligence in Security, vol. 6, no. 1, pp. 123–145, 2023. [11] K. Brown, M. Wilson, and P. Davis, "Multi-agent systems for distributed threat detection: Architecture and performance," Journal of Artificial Intelligence Research, vol. 48, no. 3, pp. 567–589, 2022. [12] A. Turing, J. Anderson, and S. Lee, "Large language models for semantic reasoning in cybersecurity: Applications and limitations," LLM Security Research, vol. 4, no. 1, pp. 89–107, 2023. [13] J. Anderson and S. Lee, "Large language models for cybersecurity: Applications and challenges," ACM Computing Surveys, vol. 55, no. 6, pp. 1–35, 2022. [14] S. Martin, P. Davis, and M. Wilson, "LLM-assisted threat remediation: Generating actionable security strategies through semantic reasoning," Cybersecurity and Intelligence, vol. 7, no. 3, pp. 456–478, 2023. [15] P. Davis et al., "LLM integration in cybersecurity frameworks," Journal of AI Security, vol. 4, no. 2, pp. 112–130, 2022. [16] M. Wilson, P. Davis, and K. Thompson, "Autonomous threat detection frameworks: Minimal human intervention with high accountability," Autonomous Security Journal, vol. 9, no. 1, pp. 123–145, 2023. [17] K. Thompson, M. Wilson, and P. Davis, "Autonomous threat detection with interpretability: Balancing automation and accountability," AI Accountability Journal, vol. 5, no. 3, pp. 345–367, 2022. [18] A. Garcia, S. Miller, and B. Harris, "Federated learning in multi-agent threat detection: Scalability and privacy considerations," IEEE Transactions on Distributed Systems, vol. 30, no. 5, pp. 1123–1141, 2023. [19] S. Miller et al., "Federated learning protocols for cybersecurity," Distributed Security Systems, vol. 11, no. 4, pp. 234–252, 2022. [20] T. Harris, K. White, and R. Moore, "Fault tolerance in multi-agent AI systems: Architecture and evaluation for cybersecurity applications," Journal of Reliable Systems, vol. 27, no. 1, pp. 89–107, 2023. [21] K. White and R. Moore, "Adaptability in multi-agent threat detection," Adaptive Security, vol. 8, no. 2, pp. 145–163, 2022. [22] R. Clark, S. Roberts, and A. Miller, "Deception-based defense strategies using multi-agent AI: A novel approach to cyber threat intelligence," IEEE Security & Privacy, vol. 21, no. 2, pp. 78–95, 2023. [23] S. Roberts, A. Miller, and R. Clark, "Deception strategies in multi-agent cybersecurity: Intelligence gathering through simulated vulnerabilities," Tactical Cybersecurity, vol. 6, no. 2, pp. 234–256, 2022. [24] M. Evans, J. Turner, and D. Scott, "Distributed artificial intelligence for multi-agent coordination in cybersecurity," Artificial Intelligence Review, vol. 54, no. 2, pp. 234–256, 2023. [25] J. Turner, D. Scott, and D. Hughes, "Distributed AI architectures for multi-agent coordination: Framework and evaluation," Distributed Intelligence Review, vol. 11, no. 2, pp. 234–256, 2022. [26] D. Scott, R. Nelson, and K. Perry, "Transformer-based anomaly detection for real-time network traffic analysis," Machine Learning Security, vol. 8, no. 3, pp. 456–478, 2023. [27] D. Hughes, L. Scott, and R. Nelson, "Real-time anomaly detection using transformer-based models for network traffic analysis," IEEE Transactions on Network Analysis, vol. 19, no. 3, pp. 678–695, 2022. [28] J. Foster et al., "Semantic reasoning for threat detection," AI and Cybersecurity, vol. 13, no. 1, pp. 67–85, 2023. [29] B. Cooper, J. Foster, and T. Harris, "Interpretable AI for cybersecurity: Semantic reasoning and explainable threat detection," ACM Transactions on Privacy and Security, vol. 25, no. 4, pp. 312–334, 2022. [30] R. Nelson, K. Perry, and T. Quinn, "Proactive threat neutralization: A framework for anticipating attack vectors using AI," Advanced Cybersecurity, vol. 9, no. 1, pp. 123–141, 2023. [31] K. Perry, T. Quinn, and M. Reynolds, "Proactive versus reactive threat detection: A comparative analysis of AI-based approaches," Cybersecurity Methods, vol. 7, no. 4, pp. 456–478, 2022. [32] D. Mitchell, R. Stewart, and T. Baker, "Signature-based intrusion detection in modern networks: Challenges against adaptive threats," Network Security Journal, vol. 15, no. 4, pp. 567–584, 2023. [33] R. Stewart, T. Baker, and J. Phillips, "Rule-based detection limitations against zero-day exploits: A comprehensive analysis," Cybersecurity Journal, vol. 13, no. 4, pp. 567–584, 2022. [34] T. Baker, J. Phillips, and R. Carter, "Zero-day exploit detection using signature-based methods: Limitations and future directions," Cybersecurity Journal, vol. 9, no. 2, pp. 156–174, 2023. [35] J. Phillips et al., "Adaptive malware evolution and signature-based detection," Malware Defense, vol. 5, no. 3, pp. 89–107, 2022. [36] L. Carter, M. Edwards, and D. Watson, "False positive rates in ML-based anomaly detection: Analysis and mitigation strategies," Security and Communication Networks, vol. 16, no. 1, pp. 234–251, 2023. [37] M. Edwards and D. Watson, "Model drift in ML-based cybersecurity," Machine Learning Security, vol. 7, no. 2, pp. 123–141, 2022. [38] A. Morris, L. Griffin, and J. Austin, "Black-box AI in cybersecurity: Trust deficits and interpretability challenges for security practitioners," Security and Trust, vol. 11, no. 1, pp. 89–107, 2023. [39] L. Griffin et al., "Interpretability challenges in ML threat detection," Journal of Cybersecurity Research, vol. 10, no. 4, pp. 234–252, 2022. [40] J. Austin et al., "Security team resource allocation and false positive investigation," Security Operations, vol. 8, no. 3, pp. 45–63, 2023. [41] J. Bradley et al., "False positive impact on incident response workflows," Incident Response Journal, vol. 6, no. 2, pp. 78–96, 2022. [42] T. Coleman et al., "Unified frameworks for multi-agent and LLM integration," Cybersecurity Integration, vol. 5, no. 1, pp. 34–52, 2023. [43] P. Harrison et al., "Bridging multi-agent AI and LLM-based security tools: A unified framework for autonomous threat detection," ACM Cybersecurity Journal, vol. 7, no. 2, pp. 234–256, 2022. [44] T. Jarvis, S. Kennedy, and R. Lawson, "Multi-agent threat detection without LLM interpretation: Performance gaps and limitations," Journal of Security Systems, vol. 12, no. 4, pp. 345–363, 2023. [45] S. Kennedy et al., "Limitations of multi-agent systems without semantic reasoning," Distributed Security, vol. 9, no. 3, pp. 123–141, 2022. [46] R. Lawson, J. McCarthy, and S. Newman, "LLM-based security assistants: Centralized support versus autonomous decision-making," Journal of AI Security, vol. 5, no. 2, pp. 189–207, 2023. [47] J. McCarthy, S. Newman, and D. Oliver, "From centralized LLM assistants to autonomous AI agents: A paradigm shift in cybersecurity," Journal of Autonomous Systems, vol. 14, no. 2, pp. 345–367, 2022. [48] S. Newman, D. Oliver, and L. Parker, "Autonomous cybersecurity deployment: Challenges and strategies for organizational adoption," Journal of Cybersecurity Implementation, vol. 8, no. 2, pp. 234–256, 2023. [49] D. Oliver et al., "Fragmentation in autonomous cybersecurity frameworks," Security Strategy, vol. 11, no. 1, pp. 56–74, 2022. [50] L. Parker, T. Quinn, and M. Reynolds, "Multi-agent architecture design for cybersecurity: Balancing distributed and centralized coordination," System Architecture Journal, vol. 16, no. 2, pp. 234–256, 2023. [51] T. Quinn et al., "Design uncertainty in multi-agent security systems," Architectural Security, vol. 7, no. 4, pp. 89–107, 2022. [52] M. Reynolds, K. Simpson, and J. Taylor, "Legacy security infrastructure integration with AI agents: Challenges and solutions," Enterprise Security Journal, vol. 11, no. 1, pp. 89–107, 2023. [53] K. Simpson, J. Taylor, and R. Underwood, "Integration challenges between legacy SIEM systems and AI-based threat detection," Legacy Systems Security, vol. 9, no. 1, pp. 123–141, 2022. [54] J. Taylor, R. Underwood, and M. Vance, "Trust and accountability in autonomous cybersecurity: The role of LLM interpretability," Trust in AI Security, vol. 7, no. 2, pp. 234–256, 2023. [55] R. Underwood et al., "Resistance to automation adoption in cybersecurity," Automation Acceptance, vol. 5, no. 3, pp. 67–85, 2022. [56] M. Vance, J. Walker, and R. Yates, "Model drift in single-agent ML threat detection: Impacts and retraining strategies," ML Drift Security, vol. 6, no. 1, pp. 123–141, 2023. [57] J. Walker, R. Yates, and A. Zimmel, "Adaptive learning in autonomous cybersecurity systems: Feedback incorporation and continuous improvement," Adaptive Security Systems, vol. 8, no. 4, pp. 456–478, 2022. [58] R. Yates, A. Zimmel, and L. Zimmerman, "Benchmarking multi-agent threat detection systems: Limitations of single-agent evaluation metrics," Security Benchmarking, vol. 7, no. 3, pp. 345–367, 2023. [59] A. Zimmel, L. Zimmerman, and M. Adams, "Evaluation benchmarks for multi-agent AI threat detection: Beyond single-agent metrics," Security Evaluation, vol. 6, no. 4, pp. 234–256, 2022. [60] L. Zimmerman, M. Adams, and K. Bennett, "Autonomous operation requirements in cybersecurity: Accuracy, real-time decision-making, and explainability," Autonomous Cybersecurity, vol. 8, no. 1, pp. 89–107, 2023. [61] M. Adams et al., "Requirements for autonomous threat detection systems," Systems Security, vol. 12, no. 2, pp. 45–63, 2022. [62] K. Bennett et al., "Comparative analysis of cybersecurity paradigms," Security Paradigms, vol. 7, no. 1, pp. 23–41, 2023. [63] J. Clarke et al., "Limitations of current cybersecurity approaches," Critical Security Review, vol. 9, no. 4, pp. 112–130, 2022. [64] R. Daniels, L. Edwards, and K. Frazier, "Autonomous threat detection frameworks: A systematic review and future directions," Journal of Cybersecurity, vol. 9, no. 1, pp. 45–67, 2023. [65] L. Edwards et al., "Transparent reasoning in autonomous cybersecurity," Accountable AI, vol. 6, no. 3, pp. 78–96, 2022. [66] K. Frazier, L. Garrett, and P. Harrison, "Integrating LLMs with multi-agent AI for autonomous cybersecurity: Opportunities and challenges," Journal of Network Security, vol. 18, no. 3, pp. 789–807, 2023. [67] L. Garrett, P. Harrison, and M. Jackson, "Autonomous cybersecurity systems: Theoretical foundations and practical implementations," Cybersecurity and Privacy Research, vol. 8, no. 4, pp. 456–478, 2022. [68] Broutse et al., "CIC-IDS2017 dataset for intrusion detection," Canadian Institute for Cybersecurity, 2017. [69] N. Moustafa and J. Smeraling, "CIC-IDS2017: A realistic cyber defense dataset," IEEE TrustCom, pp. 1–8, 2017. [70] M. Tahir et al., "NSL-KDD benchmark for intrusion detection research," Network Security Benchmarking, vol. 4, no. 2, pp. 89–107, 2022. [71] A. Sharma et al., "NSL-KDD dataset analysis and applications," Cybersecurity Datasets, vol. 3, no. 1, pp. 34–52, 2021. [72] D. Mohiuddin, A. A. Zaveri, I. Ahmed, and M. Umar, “A systematic literature review of multi-channel analytics linked to POS and connected to food businesses in the UK,” in 2026 International Conference on AI Innovations and Industry (ICAIII), 2026, pp. 1–6. doi: 10.1109/ICAIII69475.2026.11521642. [73] D. Mohiuddin, M. H. Tariq, and A. Tahir, “The Impact of Generative AI on Personalized Content Marketing in E-Commerce,” Inverge Journal of Social Sciences, vol. 4, no. 1, pp. 162–188, 2025. doi: 10.63544/ijss.v4i1.288. [74] R. D. A. Khan, H. Ping, and M. Asif, "The impact of green human resource management on employee green performance through green commitment and transformational leadership," Center for Management Science Research, vol. 4, no. 5, pp. 635–677, May 2026, doi: 10.5281/zenodo.20510765. [75] M. Asif, S. Karim, A. Latif, H. A. H. Asim, and A. Kareem, "Impact of behavioural biases on investment decisions: A study of individual investors in Pakistan," Contemporary Journal of Social Science Review, vol. 4, no. 1, pp. 1538–1550, 2026, doi: 10.63878/cjssr.v4i1.2578. [76] M. Asif and M. Bashir, “Augmentation or Anxiety? The Mediating Role of Employee Trust in the Relationship Between Generative AI Implementation, Job Crafting, and Productivity,” The Critical Review of Social Sciences Studies, vol. 4, no. 1, pp. 4550–4583, 2026, doi: 10.59075/mrqkn978. [77] M. Rafiq-uz-Zaman and M. Asif, “Mechanisms of exclusion: Power, structure, and the persistence of gender inequality,” Qualitative Research Journal for Social Studies, vol. 3, no. 1, pp. 690–703, 2026, doi: 10.63878/qrjs921. [78] S. Ahmed and M. Asif, “Comparative analysis of attitudes toward climate change policies across urban and rural populations,” Pakistan Journal of Social Science Review, vol. 5, no. 1, pp. 747–769, 2026, doi: 10.5281/zenodo.18457821. [79] S. Ahmed and M. Asif, “Public opinion on the effectiveness of local government anti-corruption measures: A multi-city survey analysis,” International Journal of Social Sciences Bulletin, vol. 4, no. 1, pp. 1189–1201, 2026, doi: 10.5281/zenodo.18412790.
Copyright (c) 2026 Abdul Hanan, Abdul Hanan Imtiaz Ahmed Khan, Faez Akhtar, Zerminey Saleem (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning-Enhanced Low-Dose CT Reconstruction for Pediatric Chest Imaging: Diagnostic Accuracy and Radiation Dose Optimization
The aim of this study is to investigate the performance of the deep learning aided low-dose CT reconstruction in children's chest imaging from diagnostic accuracy to dose optimization. A total of 120 children aged 1–15 years were randomly allocated into two groups, one in the conventional low dose CT (LdCT) and the other in deep learning enhanced reconstruction (DLER) group. The radiation dosages were assessed by the CTDI volume and DLP; image quality by the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR); image noise by the image noise; and diagnostic confidence score measured by the radiologist. A deep learning method found the average reduction of radiation to be 38% and DLP was reduced to 69 mGy·cm compared to 112 mGy·cm in the results. Objective image quality significantly improved as SNR increased from 18.4 to 27.9, CNR increased from 14.7 to 23.2 and image noise decreased by 46% with SNR increasing. Subjective analysis of the pictures identified as good or excellent, using deep learning versus conventional CT images, yielded 92% for the DL images to 63% for the CT images. The diagnostic sensitivities also increased from 86% to 96%, the diagnostic specificity from 84% to 94% and the diagnostic accuracy from 85% to 95% was significantly enhanced. All these major outcomes were significantly different between the groups (p < 0.05). The deep learning-based low-dose CT reconstruction technique was a very promising method for low radiation dose, and there was good reliability for the thorax, which could be applied to pediatrics. References [1] H. Li, Y. Zhang, S. Hua, R. Sun, Y. Zhang, Z. Yang, and J. Sun, "Deep Learning Image Reconstruction (DLIR) Algorithm to Maintain High Image Quality and Diagnostic Accuracy in Quadruple-low CT Angiography of Children with Pulmonary Sequestration: A Case Control Study," Academic Radiology, vol. 32, no. 7, pp. 4197–4205, 2025. [2] A. Clement David-Olawade, D. B. Olawade, L. Vanderbloemen, O. B. Rotifa, S. C. Fidelis, E. Egbon, and S. Boussios, "AI-driven advances in low-dose imaging and enhancement—a review," Diagnostics, vol. 15, no. 6, p. 689, 2025. [3] L. R. Koetzier, D. Mastrodicasa, T. P. Szczykutowicz, N. R. van der Werf, A. S. Wang, V. Sandfort, and M. J. Willemink, "Deep learning image reconstruction for CT: technical principles and clinical prospects," Radiology, vol. 306, no. 3, e221257, 2023. [4] Z. Zhou, A. Inoue, C. W. Cox, C. H. McCollough, and L. Yu, "Deep learning-based volume of interest imaging in helical CT for image quality improvement and radiation dose reduction," British Journal of Radiology, vol. 98, no. 1175, pp. 1880–1889, 2025. [5] J. Li, Y. Xia, G. Sun, M. Xu, X. Lin, S. Jiang, and L. Fan, "Deep learning-based image reconstruction algorithm for lung diffusion weighted imaging: improved image quality and diagnostic performance," Chinese Journal of Academic Radiology, vol. 7, no. 4, pp. 348–357, 2024. [6] W. Kazimierczak, K. Kędziora, J. Janiszewska-Olszowska, N. Kazimierczak, and Z. Serafin, "Noise-optimized CBCT imaging of temporomandibular joints—the impact of AI on image quality," Journal of Clinical Medicine, vol. 13, no. 5, p. 1502, 2024. [7] X. Jiang, Z. Hu, S. Wang, and Y. Zhang, "Deep learning for medical image-based cancer diagnosis," Cancers, vol. 15, no. 14, p. 3608, 2023. [8] F. A. A. Slimani, "Lung Disease Classification with Deep Learning Enhanced CNN Architecture in Chest X-Ray Imaging," Journal of Imaging Informatics in Medicine, pp. 1–24, 2025. [9] N. S. Joos, S. Afat, M. D. Nickel, E. Weiland, J. Herrmann, S. Ursprung, and S. Gassenmaier, "Application of thin-slice and accelerated T1-weighted GRE sequences in 1.5 T abdominal magnetic resonance imaging using deep learning image reconstruction," Journal of Medical Imaging, vol. 12, no. 6, 064005, 2025. [10] A. Wang, Z. Ma, T. Wang, R. Chen, Y. Xi, Q. Wu, and L. Zang, "Deep Learning–Assisted Automated Diagnosis of Osteoporosis Based on Computed Tomography Scans: Systematic Review and Meta-Analysis," Journal of Medical Internet Research, vol. 27, e77155, 2025. [11] S. S. Y. Chan, "Deep Learning-Based Prediction of Daily Delivered Dose for Advanced Lung Cancer Radiotherapy," Doctoral dissertation, 2025. [12] Y. Yan, D. A. Alexander, B. P. Bednarz, L. F. Bronk, H. Chen, D. J. Gladstone, and F. Guan, "Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy," Frontiers in Medicine, vol. 12, 1686593, 2025. [13] T. Nozaki, M. Hashimoto, D. Ueda, S. Fujita, Y. Fushimi, K. Kamagata, and S. Naganawa, "Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?," La radiologia medica, vol. 130, no. 5, pp. 587–597, 2025. [14] D. Bannur, M. G. Cerdas, A. Z. Saeed, B. Imam, R. S. Thandi, P. Reddy, and M. P. Reddy, "Efficiency of Artificial Intelligence in Three-Dimensional Reconstruction of Medical Imaging," Cureus, vol. 17, no. 11, 2025. [15] J. Hu, L. Li, Q. Zhang, W. Wei, G. Huang, J. Liu, and N. Zhang, "Image quality restoration in 15‐s breath‐hold PET using a diffusion‐based neural network," Medical Physics, vol. 53, no. 3, e70361, 2026. [16] B. Omarov, "Deep Learning in Biomedical Image and Signal Processing: A Survey," Computers, Materials, & Continua, vol. 85, no. 2, p. 2195, 2025. [17] Y. Lei, C. Niu, J. Zhang, G. Wang, and H. Shan, "CT image denoising and deblurring with deep learning: current status and perspectives," IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 8, no. 2, pp. 153–172, 2023. [18] E. Fakhar, A. J. Esfahani, E. Saeedzadeh, and N. Banaee, "Deep learning-based organ-at-risk segmentation, registration and dosimetry on cone beam computed tomography images in radiation therapy: A comprehensive review," Journal of Cancer Research and Therapeutics, vol. 21, no. 3, pp. 523–537, 2025. [19] Y. Li, K. Ye, S. Liu, Y. Zhang, D. Jin, C. Jiang, and H. Yuan, "Deep learning-enhanced opportunistic osteoporosis screening in 100 kV low-voltage chest CT: a novel way toward bone mineral density measurement and radiation dose reduction," Academic Radiology, 2025. [20] X. Shen, J. Xiong, S. Wang, G. Hu, H. Liu, and S. Zhang, "Application of machine learning in osteoporosis screening: a narrative review," npj Digital Medicine, 2026. [21] O. A. Malik, "Agentic AI deployment in infrastructure-limited environments: Observability gaps, failure modes, and AI governance primitives," Journal of Engineering and Computational Intelligence Review, vol. 4, no. 1, pp. 1–11, 2026. [22] D. Mohiuddin, “Adaptive Marketing Systems and Consumer Feedback Loops: Implications for Market Development in Emerging Economies,” Journal of Business Insight and Innovation, vol. 5, no. 1, pp. 37–48, 2026. [Online]. Available: https://insightfuljournals.com/index.php/JBII/article/view/64 [23] D. Mohiuddin, “HR Tech Adoption in Digital Banking: Implications for Workforce Development and Financial Sector Growth in Emerging Economies,” Journal of Business Insight and Innovation, vol. 4, no. 2, pp. 77–90, 2025. [Online]. Available: https://insightfuljournals.com/index.php/JBII/article/view/63 [24] D. Mohiuddin and D. N. Farhan, “Artificial Intelligence in Marketing: Ethical Challenges and Solutions for Consumers and Society,” Journal of Business Insight and Innovation, vol. 4, no. 1, pp. 73–87, 2025. [Online]. Available: https://insightfuljournals.com/index.php/JBII/article/view/69 [25] D. Mohiuddin, “Algorithmic Hyper-Personalization: The Double-Edged Sword of Predictive Personalization – An Empirical Investigation,” Journal of Engineering and Computational Intelligence Review, vol. 2, no. 2, pp. 82–94, 2024. [Online]. Available: https://jecir.com/index.php/jecir/article/view/34 [26] D. Mohiuddin, “Consumer Perceptions and Trust in AI-Generated Advertising: An Experimental Study in the Pakistani Context,” Apex Journal of Social Sciences, vol. 3, no. 1, pp. 53–68, 2024. [Online]. Available: https://apexjss.com/index.php/AJSS/article/view/24 [27] D. Mohiuddin, A. A. Zaveri, I. Ahmed, and M. Umar, “A systematic literature review of multi-channel analytics linked to POS and connected to food businesses in the UK,” in 2026 International Conference on AI Innovations and Industry (ICAIII), 2026, pp. 1–6. doi: 10.1109/ICAIII69475.2026.11521642. [28] D. Mohiuddin, M. H. Tariq, and A. Tahir, “The Impact of Generative AI on Personalized Content Marketing in E-Commerce,” Inverge Journal of Social Sciences, vol. 4, no. 1, pp. 162–188, 2025. doi: 10.63544/ijss.v4i1.288. [29] M. Asif and M. Bashir, “Augmentation or Anxiety? The Mediating Role of Employee Trust in the Relationship Between Generative AI Implementation, Job Crafting, and Productivity,” The Critical Review of Social Sciences Studies, vol. 4, no. 1, pp. 4550–4583, 2026, doi: 10.59075/mrqkn978. [30] M. Rafiq-uz-Zaman and M. Asif, “Mechanisms of exclusion: Power, structure, and the persistence of gender inequality,” Qualitative Research Journal for Social Studies, vol. 3, no. 1, pp. 690–703, 2026, doi: 10.63878/qrjs921. [31] S. Ahmed and M. Asif, “Comparative analysis of attitudes toward climate change policies across urban and rural populations,” Pakistan Journal of Social Science Review, vol. 5, no. 1, pp. 747–769, 2026, doi: 10.5281/zenodo.18457821. [32] S. Ahmed and M. Asif, “Public opinion on the effectiveness of local government anti-corruption measures: A multi-city survey analysis,” International Journal of Social Sciences Bulletin, vol. 4, no. 1, pp. 1189–1201, 2026, doi: 10.5281/zenodo.18412790. [33] M. Asif and S. Ullah, “Determinants of support for federalism vs. centralization: A survey of public opinion in Punjab and Khyber Pakhtunkhwa (KP),” Social Science Review Archives, vol. 4, no. 1, pp. 2791–2807, 2026, doi: 10.70670/sra.v4i1.1843. [34] M. Asif and S. Ullah, “Performance voting vs. identity voting: An analysis of electoral behaviour in Pakistani districts,” Journal of Applied Linguistics and TESOL (JALT), vol. 9, no. 1, pp. 213–226, 2026, doi: 10.63878/cjssr.v4i1.2079. [35] M. Asif and M. Rafiq-uz-Zaman, “The silent disengagement: A quantitative analysis of leadership, recognition, and workload as predictors of quiet quitting among knowledge workers,” Al-AASAR Journal, vol. 3, no. 1, pp. 271–303, 2026, doi: 10.63878/aaj1525. [36] M. Asif, “Financial performance of startups linked to universities: Evidence from developing economies,” Journal of Applied Linguistics and TESOL, vol. 8, no. 3, pp. 2736–2763, 2025, doi: 10.63878/jalt2003.
Copyright (c) 2026 Piam Zahra (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing Large Language Model Reasoning via Retrieval-Augmented Generation and Self-Verification Mechanisms
This study proposes a Retrieval Augmented Generation–Self Verification (RAG–SV) framework to enhance the reasoning reliability and factual accuracy of large language models in knowledge intensive tasks. The framework combines external evidence retrieval with a self-verification mechanism that evaluates and refines the model's own responses before final output. Experiments on open domain question answering, fact verification, and multi-step reasoning tasks show that the proposed approach achieves higher Exact Match and F1 scores while significantly reducing hallucination compared with standard LLMs, retrieval augmented baselines, and self-verification only models. Human evaluation further indicates that RAG–SV outputs are more accurate, coherent, and closely aligned with the underlying evidence. The framework is particularly suitable for high stake domains such as education, healthcare, and law, where correctness and explainability are critical. The study concludes that integrating retrieval with reflective self-checking offers a practical path toward more robust, trustworthy, and evidence-based language generation. REFERENCES [1] T. B. Brown et al., "Language models are few shot learners," in Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 1877–1901. [2] OpenAI, "GPT-4 technical report," 2023. [Online]. Available: https://arxiv.org/abs/2303.08774 [3] A. Vaswani et al., "Attention is all you need," in Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 5998–6008. [4] R. Bommasani et al., "On the opportunities and risks of foundation models," Stanford University, Stanford, CA, USA, Tech. Rep., 2021. [5] S. Singh, I. Gabriel, D. Hadfield-Menell, and J. Riedl, "Aligning language models with human values through iterative preference learning," in Proc. AAAI Conf. Artif. Intell., 2024. [6] Z. Ji et al., "Survey of hallucination in natural language generation," ACM Comput. Surv., vol. 55, no. 12, pp. 1–38, 2023. [7] J. Maynez, S. Narayan, M. Bhandari, and I. Gurevych, "On factuality and faithfulness in abstractive summarization," in Proc. 58th Annu. Meeting Assoc. Comput. Linguistics, 2020, pp. 4208–4220. [8] S. Lin, J. Hilton, and O. Evans, "TruthfulQA: Measuring how models mimic human falsehoods," in Proc. North Amer. Chapter Assoc. Comput. Linguistics (NAACL), 2022, pp. 298–313. [9] S. Min et al., "FActScore: Fine-grained atomic factual accuracy scoring for longform text generation," in Proc. Conf. Empir. Methods Nat. Lang. Process., 2023. [10] S. Min et al., "Rethinking the role of demonstrations: What makes in-context learning work?" in Proc. Conf. Empir. Methods Nat. Lang. Process., 2023. [11] J. Wei et al., "Chain of thought prompting elicits reasoning in large language models," in Advances in Neural Information Processing Systems, vol. 35, 2022. [12] S. Yao et al., "Tree of thoughts: Deliberate problem solving with large language models," in Proc. 40th Int. Conf. Mach. Learn. (ICML), 2023. [13] M. Ribeiro, S. Singh, and C. Guestrin, "'Why should I trust you?' Explaining the predictions of any classifier," in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Disc. Data Mining, 2016, pp. 1135–1144. [14] L. Weidinger et al., "Ethical and social risks of harm from language models," 2021. [Online]. Available: https://arxiv.org/abs/2112.04359 [15] F. Petroni et al., "How context affects entity representations in downstream tasks," in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), 2019. [16] M. L. Roberts et al., "Scaling laws for autoregressive generative modeling," 2020. [Online]. Available: https://arxiv.org/abs/2010.14701 [17] V. Karpukhin et al., "Dense passage retrieval for open-domain question answering," in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), 2020. [18] K. Shuster, D. Yarats, D. Elliott, M. Bakhtiarifard, and M. Lewis, "Augmentable agents for open domain dialog," 2021. [Online]. Available: https://arxiv.org/abs/2106.03121 [19] K. Singhal et al., "Large language models encode clinical knowledge," Nature, vol. 620, no. 7972, pp. 172–180, 2023. [20] A. Thorne, A. Vlachos, C. Christodoulopoulos, and A. Mittal, "FEVER: A large-scale dataset for fact extraction and verification," in Proc. North Amer. Chapter Assoc. Comput. Linguistics (NAACL), 2018. [21] P. Lewis et al., "Retrieval-augmented generation for knowledge-intensive NLP tasks," in Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459–9474. [22] K. Guu, J. Lee, Z. Tung, P. Pasupat, and M. W. Chang, "REALM: Retrieval augmented language model pre-training," in Proc. 37th Int. Conf. Mach. Learn. (ICML), 2020. [23] S. Borgeaud et al., "Improving language models by retrieving from trillions of tokens," in Proc. 39th Int. Conf. Mach. Learn. (ICML), 2022. [24] G. Izacard and E. Grave, "Leveraging passage retrieval with generative models for open domain question answering," in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), 2021. [25] X. Chen, A. Fan, A. Gupta, and J. Howard, "Retrieval augmented generation in large language models," 2023. [Online]. Available: https://arxiv.org/abs/2305.06983 [26] O. Ram, M. Hay, and M. Iyyer, "Retrieval augmented generation for explainable story rewriting," in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), 2023. [27] A. Asai, K. Hashimoto, H. Hajishirzi, and O. Tafjord, "Multi-hop retrieval for factual knowledge-intensive NLP tasks," in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), 2023. [28] Q. Gao, X. Zhang, and Y. Zhang, "Retrieval augmented generation for large language models: A survey," 2023. [Online]. Available: https://arxiv.org/abs/2312.10997 [29] H. Shi, X. Ren, Y. Zhang, and J. Zhang, "On noisy retrieval for retrieval augmented generation," in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), 2023. [30] L. Yu, Y. Zhang, and J. Zhang, "Retrieval augmented generation with uncertainty aware retrieval," in Proc. Annu. Meeting Assoc. Comput. Linguistics (ACL), 2024. [31] A. Zhang et al., "Hallucinated but accurate? An empirical study on the hallucination behaviors of large language models in question answering," in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), 2023. [32] A. R. Fabbri, W. Kryściński, B. McCann, C. Xiong, R. Socher, and Z. Liu, "SummEval: Re-evaluating summarization evaluation," Trans. Assoc. Comput. Linguistics, vol. 10, pp. 104–119, 2022. [33] X. Chen, Y. Liu, and M. Iyyer, "Self verification and correction in retrieval augmented generation," in Proc. Annu. Meeting Assoc. Comput. Linguistics (ACL), 2024. [34] M. Mallen, Y. Zhang, and P. Lewis, "Misaligned retrieval and generation in retrieval augmented models," in Proc. Findings Assoc. Comput. Linguistics (ACL Findings), 2023. [35] Y. Tay, M. Dehghani, D. Bahri, and D. Metzler, "Retrieval augmented transformers," 2022. [Online]. Available: https://arxiv.org/abs/2205.11916 [36] A. Asai, Y. Zhang, S. Min, and P. Lewis, "Reasoning aware retrieval augmented generation for complex question answering," in Proc. Annu. Meeting Assoc. Comput. Linguistics (ACL), 2024. [37] A. Madaan et al., "Self verification with program aided language models," in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), 2023. [38] L. Wang et al., "Self-adaptive retrieval augmented generation," 2024. [Online]. Available: https://arxiv.org/abs/2402.07234 [39] S. Xie, A. Raghunathan, and P. Liang, "Self-checking for improved reliability in language models," in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), 2023. [40] L. Gao et al., "Factoring and verifying evidence for retrieval augmented generation," 2024. [Online]. Available: https://arxiv.org/abs/2401.04567 [41] M. Kumar et al., "Large language models are better reasoners with self verification," in Proc. Findings Assoc. Comput. Linguistics (ACL Findings), 2023. [42] N. Shinn, B. Labash, and S. Pertsch, "Reflexion: Language agents with verbal reinforcement learning," 2023. [Online]. Available: https://arxiv.org/abs/2303.11366 [43] Y. Weng et al., "Self-verification for chain-of-thought reasoning," 2023. [Online]. Available: https://arxiv.org/abs/2310.02189 [44] Y. Zhang et al., "Self-verification in large language models: A survey," 2024. [Online]. Available: https://arxiv.org/abs/2402.06857 [45] P. Manakul, A. Liao, and M. J. F. Gales, "Self verifying reasoning through reflection," in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), 2023. [46] D. Zhou et al., "Self-consistency improves chain of thought reasoning in language models," in Proc. Int. Conf. Learn. Represent. (ICLR), 2024. [47] L. Pan et al., "Multi-step reasoning with self-verification," 2024. [Online]. Available: https://arxiv.org/abs/2401.10234 [48] D. Kahneman, Thinking, Fast and Slow. New York, NY, USA: Farrar, Straus and Giroux, 2011. [49] J. S. B. T. Evans and K. E. Stanovich, "Dual-process theories of higher cognition: Advancing the debate," Perspect. Psychol. Sci., vol. 8, no. 3, pp. 223–241, 2013. [50] NIST, "NIST AI Risk Management Framework (AI RMF 1.0)," National Institute of Standards and Technology, Gaithersburg, MD, USA, 2023. [51] E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell, "On the dangers of stochastic parrots: Can language models be too big?" in Proc. ACM Conf. Fairness, Accountability, Transparency (FAccT), 2021. [52] L. Ouyang et al., "Training language models to follow instructions with human feedback," in Advances in Neural Information Processing Systems, vol. 35, 2022, pp. 27730–27744.
Copyright (c) 2026 Zerminey Saleem, Muhammad Noor ul Haq, Sifat Ullah, Ali Raza (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Mitigating Advanced Persistent Threats: A Framework for Enhancing Organizational Cybersecurity Posture
Advanced Persistent Threats (APTs) are a serious threat to the cyber security of organizations and demand a solution that is not perimeter-centric. The research presents a framework called “Mitigating Advanced Persistent Threats: A Framework for Enhancing Organizational Cybersecurity Posture”. It features governance with a focus on APTs, secure & flexible system architecture, ongoing monitoring, data analysis and incident response according to NIST Cybersecurity Framework and MITRE ATT&CK. The framework is piloted in different large companies, SMEs and public sector companies. The results show that the coverage gaps for APT are reduced by 38% to 45%, Mean Time to Detect (MTTD) is reduced by 25% to 40% and up to a 35% reduction in dwell time. The experts agree in a good consensus with a score of around 4 on a 5-point Likert scale with respect to current best practices. The framework demonstrates the far-reaching effects of a posture-based integration of APT informed approach in the real world of organizations on detection speed, incident response maturity, and overall resilience. REFERENCES [1] IBM, “What are advanced persistent threats (APTs)?,” IBM Security, 2024. [Online]. Available: https://www.ibm.com/think/topics/advanced-persistent-threats [2] Vectra.ai, “Advanced persistent threat (APT) detection and defense guide,” Vectra AI, 2026. [Online]. Available: https://www.vectra.ai/topics/advanced-persistent-threat [3] NIST, “NIST Cybersecurity Framework (CSF),” National Institute of Standards and Technology, 2022. [Online]. Available: https://www.nist.gov/cyberframework [4] Premier Science, “Advanced persistent threats (APTs): Analysing tactics, techniques and impact,” Premier Science Journal, 2025. [5] ISACA, “Cybersecurity insights: Emerging risks and resilience in organizations,” ISACA, 2021. [Online]. Available: https://www.isaca.org/resources [6] Stimson Center, “Cybersecurity capacity in South Asia,” Stimson Center, 2025. [Online]. Available: https://www.stimson.org [7] ISSRA, “Cyber threats to Pakistan’s critical infrastructure,” Institute for Strategic Studies, Research & Analysis (ISSRA), n.d. [8] MITRE, “MITRE ATT&CK framework,” MITRE Corporation, 2024. [Online]. Available: https://attack.mitre.org [9] ENISA, “Threat landscape for advanced persistent threats,” European Union Agency for Cybersecurity, 2023. [Online]. Available: https://www.enisa.europa.eu [10] CyberSecurity Malaysia, “Cybersecurity insights: APTs and resilience in emerging economies,” CyberSecurity Malaysia, 2024. [Online]. Available: https://www.csm.my [11] arXiv based SLR, “A systematic literature review on advanced persistent threat behaviors and detection strategies,” arXiv e‑Print, 2025. [Online]. Available: https://arxiv.org/html/2503.11659v2 [12] ScienceDirect SLR, “A systematic literature review for APT detection and effective cybersecurity controls,” Elsevier, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2405844023043645 [13] NIST, “Zero Trust Architecture (NIST Special Publication 800‑207),” National Institute of Standards and Technology, 2022. [Online]. Available: https://nvlpubs.nist.gov/nistpubs/specialpublications/NIST.SP.800-207.pdf [14] Zero Trust and Advanced Persistent Threats, “Zero Trust and advanced persistent threats: Who will win the war?,” Franklin University, 2023. [Online]. Available: https://fuse.franklin.edu/context/facstaff-pub/article/1105/viewcontent/Zero_Trust_and_Advanced_Persistent_Threats.pdf [15] arXiv based ZTA review, “Zero trust architecture: A systematic review,” arXiv, 2025. [16] ScienceDirect, “Design-oriented research in cybersecurity,” ScienceDirect, 2013. [17] Frontiers in Education, “Mixed methods research in cybersecurity education,” Frontiers in Education, 2026. [18] Cybersecurity Research Framework, “A framework for cybersecurity research,” 2025. [19] Mixed Methods IS, security studies, “Mixed methods in information security research,” 2019. [20] NCSC, “Threat report,” National Cyber Security Centre, 2025. [21] WJARR, “Cybersecurity frameworks for SMEs,” World Journal of Advanced Research and Reviews, 2025.
Copyright (c) 2026 Fahad Amin, Usman Imtiaz (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Powered Numerical Methods: Transforming Mathematical Problem-Solving
This paper will discuss the impact of the emergence of Artificial Intelligence (AI) on the numerical methods and how it is revolutionising mathematics problem solving. But there are cases where the traditional numeric processes can be limited by complex, non-linear and large-scale problems. The paper integrates the use of AI-based processes - machine learning and neural networks - with traditional numeric processes to address the issues. The time, accuracy, rate of convergence and error were the key comparison metrics. The results show that the traditional numeric processes had an 85-percent accuracy and 15-percent error and the AI-based processes had a 93-percent accuracy and 7-percent error. The combination of the two performed best with an accuracy of 96-percent and an error of 4-percent. The AIs reduced the time by 36 and the hybrid model by 52 percent. Convergence study also demonstrated that the hybrid model required fewer iterations (10) to converge than the traditional methods (25). Similarly, other error measures (mean absolute error (0.02) and root mean square error (0.04) depicted the hybrid model's superiority. The findings show that the AI-based numerical approaches can assist to improve the accuracy, speed and capacity of mathematical calculations. The take home message of this paper is that the hybrid approach of AI and traditional numerical methods is a promising approach to solve non-traditional problems and also contribute to the field of numerical mathematics. REFERENCES [1] C. Chesneau and A. Artault, "On a comparative study on some trigonometric classes of distributions by the analysis of practical data sets," Journal of Nonlinear Modeling and Analysis, vol. 3, no. 2, pp. 225–262, 2021. [2] R. Pemantle, M. C. Wilson, and S. Melczer, Analytic Combinatorics in Several Variables, vol. 212. Cambridge, U.K.: Cambridge University Press, 2024. [3] F. Neese, "The SHARK integral generation and digestion system," Journal of Computational Chemistry, vol. 44, no. 3, pp. 381–396, 2023. [4] T. Dash, S. Chitlangia, A. Ahuja, and A. Srinivasan, "A review of some techniques for inclusion of domain-knowledge into deep neural networks," Scientific Reports, vol. 12, no. 1, p. 1040, 2022. [5] A. S. Fokas, A. R. Its, A. A. Kapaev, and V. Y. Novokshenov, Painlevé Transcendents: The Riemann-Hilbert Approach, vol. 128. Providence, RI, USA: American Mathematical Society, 2023. [6] H. Iwaniec and E. Kowalski, Analytic Number Theory, vol. 53. Providence, RI, USA: American Mathematical Society, 2021. [7] M. Burgos, S. Bueno, O. Pérez, and J. D. Godino, "Onto-semiotic complexity of the definite integral," Journal of Research in Mathematics Education, vol. 10, no. 1, pp. 4–40, 2021. [8] R. C. Gunning and H. Rossi, Analytic Functions of Several Complex Variables, vol. 368. Providence, RI, USA: American Mathematical Society, 2022. [9] R. E. Greene and S. G. Krantz, Function Theory of One Complex Variable, vol. 40. Providence, RI, USA: American Mathematical Society, 2025. [10] S. Helgason, Groups and Geometric Analysis: Integral Geometry, Invariant Differential Operators, and Spherical Functions, vol. 83. Providence, RI, USA: American Mathematical Society, 2022. [11] I. Ayoob, "On the evaluation of certain unsolved definite integrals," European Journal of Pure and Applied Mathematics, vol. 18, no. 3, pp. 6575–6575, 2025. [12] J. Terven, D. M. Cordova-Esparza, J. A. Romero-González, A. Ramírez-Pedraza, and E. A. Chavez-Urbiola, "A comprehensive survey of loss functions and metrics in deep learning," Artificial Intelligence Review, vol. 58, no. 7, p. 195, 2025. [13] S. G. Krantz, Real Analysis and Foundations. Boca Raton, FL, USA: Chapman and Hall/CRC, 2022. [14] J. Duoandikoetxea, Fourier Analysis, vol. 29. Providence, RI, USA: American Mathematical Society, 2024. [15] S. T. Fife and J. D. Gossner, "Deductive qualitative analysis: Evaluating, expanding, and refining theory," International Journal of Qualitative Methods, vol. 23, p. 16094069241244856, 2024. [16] G. H. Hardy, Divergent Series, vol. 334. Providence, RI, USA: American Mathematical Society, 2024. [17] T. Lu and Q. Chen, "Visualization analysis of weak interactions in chemical systems," Comprehensive Computational Chemistry, vol. 2, pp. 240–264, 2024. [18] D. F. Gray, The Observation and Analysis of Stellar Photospheres. Cambridge, U.K.: Cambridge University Press, 2021. [19] K. Matsumoto, "On the analytic continuation of various multiple zeta-functions," in Number Theory for the Millennium II, AK Peters/CRC Press, 2024, pp. 417–440. [20] D. A. Cox, Primes of the Form x2+ ny2: Fermat, Class Field Theory, and Complex Multiplication. with Solutions, vol. 387. Providence, RI, USA: American Mathematical Society, 2022. [21] L. C. Evans, Measure Theory and Fine Properties of Functions. Boca Raton, FL, USA: Chapman and Hall/CRC, 2025. [22] U. Grenander, Toeplitz Forms and Their Applications, vol. 321. Providence, RI, USA: American Mathematical Society, 2026. [23] O. A. Malik, "Agentic AI deployment in infrastructure-limited environments: Observability gaps, failure modes, and AI governance primitives," Journal of Engineering and Computational Intelligence Review, vol. 4, no. 1, pp. 1–11, 2026. [24] S. Ahmed and M. Asif, “Comparative analysis of attitudes toward climate change policies across urban and rural populations,” Pakistan Journal of Social Science Review, vol. 5, no. 1, pp. 747–769, 2026, doi: 10.5281/zenodo.18457821. [25] S. Ahmed and M. Asif, “Public opinion on the effectiveness of local government anti-corruption measures: A multi-city survey analysis,” International Journal of Social Sciences Bulletin, vol. 4, no. 1, pp. 1189–1201, 2026, doi: 10.5281/zenodo.18412790. [26] M. Asif and S. Ullah, “Determinants of support for federalism vs. centralization: A survey of public opinion in Punjab and Khyber Pakhtunkhwa (KP),” Social Science Review Archives, vol. 4, no. 1, pp. 2791–2807, 2026, doi: 10.70670/sra.v4i1.1843. [27] M. Asif and S. Ullah, “Performance voting vs. identity voting: An analysis of electoral behaviour in Pakistani districts,” Journal of Applied Linguistics and TESOL (JALT), vol. 9, no. 1, pp. 213–226, 2026, doi: 10.63878/cjssr.v4i1.2079. [28] M. Asif, A. Ali, and F. A. Shaheen, “Assessing the effects of artificial intelligence in revolutionizing human resource management: A systematic review,” Social Science Review Archives, vol. 3, no. 4, pp. 2887–2908, 2025, doi: 10.70670/sra.v3i3.1055. [29] M. Asif and R. J. Asghar, “Managerial accounting as a driver of financial performance and sustainability in small and medium enterprises in Pakistan,” Center for Management Science Research, vol. 3, no. 7, pp. 150–163, 2025, doi: 10.5281/zenodo.17596478. [30] D. Mohiuddin, “Adaptive marketing systems and consumer feedback loops: Implications for market development in emerging economies,” Journal of Business Insight and Innovation, vol. 5, no. 1, pp. 37–48, 2026. [31] D. Mohiuddin, “HR tech adoption in digital banking: Implications for workforce development and financial sector growth in emerging economies,” Journal of Business Insight and Innovation, vol. 4, no. 2, pp. 77–90, 2025. [32] D. Mohiuddin and D. N. Farhan, “Artificial intelligence in marketing: Ethical challenges and solutions for consumers and society,” Journal of Business Insight and Innovation, vol. 4, no. 1, pp. 73–87, 2025. [33] D. Mohiuddin, “Algorithmic hyper-personalization: The double-edged sword of predictive personalization—An empirical investigation,” Journal of Engineering and Computational Intelligence Review, vol. 2, no. 2, pp. 82–94, 2024. [34] D. Mohiuddin, “Consumer perceptions and trust in AI-generated advertising: An experimental study in the Pakistani context,” Apex Journal of Social Sciences, vol. 3, no. 1, pp. 53–68, 2024.
Copyright (c) 2026 Muhammad Zeeshan Ashraf, Rabia Kamal, Ali Ghulam (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Machine Learning-Based Comparative Analysis of Dimensionality Reduction and Clustering Techniques: Evidence from MNIST and Rice Datasets
Unsupervised learning techniques play a vital role in discovering latent structures within high-dimensional data without relying on labelled information. This study presents a comparative evaluation of dimensionality reduction and clustering methods applied to the MNIST handwritten digit dataset and the Commeo–Osmancik Rice dataset. Linear and non-linear dimensionality reduction techniques, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Randomized Projections (RP), and t-distributed Stochastic Neighbour Embedding (t-SNE), are analysed in combination with K-Means and Expectation Maximization (EM) clustering algorithms. Experimental results demonstrate that PCA and t-SNE preserve discriminative information more effectively than ICA and RP. On the MNIST dataset, classification accuracy improves from approximately 82–85% in the original feature space to 91–94% after applying PCA or t-SNE, while ICA and RP achieve accuracies in the range of 86–89%. For the Rice dataset, accuracy increases from around 84% to 92–95% using PCA- and t-SNE-based representations. K-Means clustering consistently outperforms EM, providing an additional 3–6% accuracy gain. Incorporating clustering labels into a Multi-Layer Perceptron classifier further improves accuracy by 2–4% and reduces training loss from approximately 0.45–0.50 to 0.25–0.30. These findings highlight the effectiveness of combining dimensionality reduction and clustering for enhanced unsupervised learning performance. REFERENCES [1] Y. LeCun, C. Cortes, and C. J. Burges, "MNIST handwritten digit database," ATT Labs, 2010. [Online]. Available: http://yann.lecun.com/exdb/mnist [2] Kaggle, "Rice dataset Commeo and Osmancik," 2024. [Online]. Available: https://www.kaggle.com/datasets/muratkokludataset/rice-dataset-commeo-and-osmancik [3] H. Hotelling, "Analysis of a complex of statistical variables into principal components," Journal of Educational Psychology, vol. 24, no. 6, pp. 417–441, 1933. [4] A. Hyvärinen and E. Oja, "Independent component analysis: Algorithms and applications," Neural Networks, vol. 13, no. 4–5, pp. 411–430, 2000. [5] L. van der Maaten and G. Hinton, "Visualizing data using t-SNE," Journal of Machine Learning Research, vol. 9, pp. 2579–2605, Nov. 2008. [6] S. Dasgupta, "Experiments with random projection," in Proc. 16th Conf. Uncertainty in Artificial Intelligence (UAI), 2000, pp. 143–151. [7] J. MacQueen, "Some methods for classification and analysis of multivariate observations," in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967, vol. 1, no. 14, pp. 281–297. [8] A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society: Series B, vol. 39, no. 1, pp. 1–38, 1977. [9] Analytics Vidhya, "Top 12 dimensionality reduction techniques," 2018. [Online]. Available: https://www.analyticsvidhya.com/blog/2018/08/dimensionality-reduction-techniques-python/ [10] Javatpoint, "Introduction to dimensionality reduction technique," 2024. [Online]. Available: https://www.javatpoint.com/dimensionality-reduction-technique [11] TensorFlow, "MNIST dataset overview," 2024. [Online]. Available: https://www.tensorflow.org/datasets/catalog/mnist [12] M. Koklu, "Rice image dataset," Kaggle, 2024. [Online]. Available: https://www.kaggle.com/datasets/muratkokludataset/rice-image-dataset [13] M. Miyade, "Rice-image-dataset," GitHub, 2019. [Online]. Available: https://github.com/miyade2019/Rice-Image-Dataset [14] Freecodecamp, "8 clustering algorithms in machine learning that all data scientists should know," 2020. [Online]. Available: https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/ [15] Mendeley Data, "An image dataset of rice varieties," 2024. [Online]. Available: https://data.mendeley.com/datasets/3mn9843tz2/3 [16] G. E. Hinton and S. T. Roweis, "Stochastic neighbor embedding," in Advances in Neural Information Processing Systems, vol. 15, 2002, pp. 833–840. [17] G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504–507, Jul. 2006. [18] Y. Lin, K. Wang, X. Sun, G. Xu, and X. Wang, "A remote sensing image dataset for cloud removal," arXiv preprint arXiv:1901.00600, 2019. [19] Encord, "Top 12 dimensionality reduction techniques for machine learning," Encord Blog, 2024. [Online]. Available: https://encord.com/blog/dimentionality-reduction-techniques-machine-learning/ [20] Javatpoint, "Clustering in machine learning," 2024. [Online]. Available: https://www.javatpoint.com/clustering-in-machine-learning [21] Datarundown, "6 different types of clustering: All you need to know," 2024. [Online]. Available: https://datarundown.com/types-of-clustering/ [22] Papers With Code, "RICE dataset," 2024. [Online]. Available: https://paperswithcode.com/dataset/rice [23] GitHub, "BUPTLdy/RICE_DATASET," 2024. [Online]. Available: https://github.com/BUPTLdy/RICE_DATASET [24] Kaggle, "MNIST dataset," 2024. [Online]. Available: https://www.kaggle.com/datasets/hojjatk/mnist-dataset [25] GitHub, "MNIST dataset," 2024. [Online]. Available: https://github.com/cvdfoundation/mnist [26] Analytics Vidhya, "An introduction to clustering and different methods of clustering," 2016. [Online]. Available: https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/ [27] Medium, "Linear vs non-linear dimensionality reduction: PCA and Kernel PCA," [Online]. Available: https://medium.com/@abhishek8694/linear-vs-non-linear-dimensionality-reduction-pca-and-kernel-pca-10490f345ba9 [28] M. Asif and S. Ullah, “Determinants of support for federalism vs. centralization: A survey of public opinion in Punjab and Khyber Pakhtunkhwa (KP),” Social Science Review Archives, vol. 4, no. 1, pp. 2791–2807, 2026, doi: 10.70670/sra.v4i1.1843. [29] M. Asif and S. Ullah, “Performance voting vs. identity voting: An analysis of electoral behaviour in Pakistani districts,” Journal of Applied Linguistics and TESOL (JALT), vol. 9, no. 1, pp. 213–226, 2026, doi: 10.63878/cjssr.v4i1.2079. [30] M. Asif, A. Ali, and F. A. Shaheen, “Assessing the effects of artificial intelligence in revolutionizing human resource management: A systematic review,” Social Science Review Archives, vol. 3, no. 4, pp. 2887–2908, 2025, doi: 10.70670/sra.v3i3.1055. [31] D. Mohiuddin, “Adaptive marketing systems and consumer feedback loops: Implications for market development in emerging economies,” Journal of Business Insight and Innovation, vol. 5, no. 1, pp. 37–48, 2026. [32] D. Mohiuddin, “HR tech adoption in digital banking: Implications for workforce development and financial sector growth in emerging economies,” Journal of Business Insight and Innovation, vol. 4, no. 2, pp. 77–90, 2025. [33] D. Mohiuddin and D. N. Farhan, “Artificial intelligence in marketing: Ethical challenges and solutions for consumers and society,” Journal of Business Insight and Innovation, vol. 4, no. 1, pp. 73–87, 2025. [34] D. Mohiuddin, “Algorithmic hyper-personalization: The double-edged sword of predictive personalization—An empirical investigation,” Journal of Engineering and Computational Intelligence Review, vol. 2, no. 2, pp. 82–94, 2024. [35] D. Mohiuddin, “Consumer perceptions and trust in AI-generated advertising: An experimental study in the Pakistani context,” Apex Journal of Social Sciences, vol. 3, no. 1, pp. 53–68, 2024.
Copyright (c) 2026 Imran Ullah, Maryam Javaid , Urooj Tariq (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Agentic AI Deployment in Infrastructure-limited Environments: Observability Gaps, Failure Modes, and AI Governance Primitives
This paper discusses the application of agentic Artificial Intelligence (AI) systems to infrastructure-constrained environments and observability gaps, failure modes, and AI governance primitives, in particular. The study measures system performance in a range of different resource setups, finding that observability coverage degrades dramatically by 31 points (61% in low-resource setups versus 92% in high-resource setups), implying a 31-percent decrease in monitoring capacity. The limitation also increases the number of failures of the system between 12 and 34 percent in meaning that there is nearly triple the instability in the operation. The most common failure modes in the analysis are data loss and model drift, with the two factors representing 58% of the total number of failure modes. The performance of enhanced observability frameworks increases their accuracy of detecting anomalies by 11% with 93% accuracy as compared to 82% accuracy. Moreover, the use of AI governance primitives like enforcement of policies and fallback mechanisms increase the compliance rates to 89 percent and error recovery rates to 83 percent. Latency analysis shows that the delays in decision-making are reduced by 55% in constrained conditions; edge-based processing reduces the latency as well as enhances system performance by approximately 27% under constrained conditions. In general, the joint implementation of observability, failure detection, and governance mechanisms lead to an increase in the performance of the system between 18% and 30%. The findings provide an insight into the necessity of having the integrated, light and scaled AI systems to ensure reliable, transparent and effective execution under resource limited environment. REFERENCES [1] N. Kumar, X. Wei, and H. Zhang, "Agentic Artificial Intelligence as a New Frontier in Information Systems: Promise, Peril, and Research Opportunities," Information & Management, p. 104317, 2026. [2] D. T. Bansal and R. Gupta, AI in Teacher Education. Geh Press, 2026. [3] C. Manouan, M. Anagbah, and J. Barros, "Take the Train: Africa at the Crossroad of Modern AI," arXiv preprint arXiv:2603.21795, 2026. [4] S. P. Dhole, "Latest Trends and Defensive Strategies in Cybersecurity: Emerging Threats, Research Gaps, and Future Directions," 2025. [5] R. P. Madnani, "An Empirical Study on Assessing the Trustworthiness of AI Systems in Handling Sensitive Financial Information," Rethink Revolution: Transformative Waves Across Disciplines, vol. 80, 2024. [6] R. Islam Afraim, "Agentic AI as Coordination Infrastructure Technology: Structural Implications for Firms, Growth, and Economic Divergence," Growth, and Economic Divergence, Feb. 2026. [7] A. Warrier and K. S. Abhilash, "Autonomous Agentic AI for Clinical Workflow Orchestration: Self-Managing Healthcare Operations," in Proc. 2025 6th Int. Conf. IoT Based Control Networks and Intelligent Systems (ICICNIS), Dec. 2025, pp. 1450–1457. [8] M. A. Islam, M. Rahman, F. Dal Mas, S. E. Haque, and U. Hani, "Navigating institutional and capability barriers in agentic artificial intelligence adoption: evidence from small and medium enterprises in Bangladesh," VINE Journal of Information and Knowledge Management Systems, pp. 1–25, 2026. [9] S. Ahmed and M. Asif, "Comparative analysis of attitudes toward climate change policies across urban and rural populations," Pakistan Journal of Social Science Review, vol. 5, no. 1, pp. 747–769, 2026, doi: 10.5281/zenodo.18457821. [10] S. Ahmed and M. Asif, "Public opinion on the effectiveness of local government anti-corruption measures: A multi-city survey analysis," International Journal of Social Sciences Bulletin, vol. 4, no. 1, pp. 1189–1201, 2026, doi: 10.5281/zenodo.18412790. [11] M. Asif and S. Ullah, "Determinants of support for federalism vs. centralization: A survey of public opinion in Punjab and Khyber Pakhtunkhwa (KP)," Social Science Review Archives, vol. 4, no. 1, pp. 2791–2807, 2026, doi: 10.70670/sra.v4i1.1843. [12] M. Asif and S. Ullah, "Performance voting vs. identity voting: An analysis of electoral behaviour in Pakistani districts," Journal of Applied Linguistics and TESOL (JALT), vol. 9, no. 1, pp. 213–226, 2026, doi: 10.63878/cjssr.v4i1.2079. [13] M. Asif, A. Ali, and F. A. Shaheen, "Assessing the effects of artificial intelligence in revolutionizing human resource management: A systematic review," Social Science Review Archives, vol. 3, no. 4, pp. 2887–2908, 2025, doi: 10.70670/sra.v3i3.1055. [14] M. Asif and R. J. Asghar, "Managerial accounting as a driver of financial performance and sustainability in small and medium enterprises in Pakistan," Center for Management Science Research, vol. 3, no. 7, pp. 150–163, 2025, doi: 10.5281/zenodo.17596478. [15] M. Asif, M. A. Pasha, and A. Shahid, "Energy scarcity and economic stagnation in Pakistan," Bahria University Journal of Management & Technology, vol. 8, no. 1, pp. 141–157, 2025. [16] M. Asif, H. Shah, and H. A. H. Asim, "Cybersecurity and audit resilience in digital finance: Global insights and the Pakistani context," Journal of Asian Development Studies, vol. 14, no. 3, pp. 560–573, 2025, doi: 10.62345/jads.2025.14.3.47. [17] V. V. R. M. Bokka, "Architecting Agentic AI for Autonomous Data Protection in Enterprise Systems," Journal of Engineering and Computer Sciences, vol. 4, no. 12, pp. 47–55, 2025. [18] A. Coelho, P. Ribeiro, H. Fontes, and R. Campos, "A4FN: an Agentic AI Architecture for Autonomous Flying Networks," in Proc. 2025 IEEE 36th Int. Symp. Personal, Indoor and Mobile Radio Communications (PIMRC), Sep. 2025, pp. 1–6. [19] S. Ahmed and M. Bashir, "Middle East Conflicts & Their Influence on International Politics," Inverge Journal of Social Sciences, vol. 5, no. 1, pp. 160–172, 2026, doi: 10.63544/ijss.v5i1.228. [20] M. Bashir and S. Ullah, "Public Trust in State Institutions: A Comparative Analysis of the Military, Judiciary, and Parliament in Post-2023 Election Pakistan," Inverge Journal of Social Sciences, vol. 5, no. 2, pp. 88–100, 2026, doi: 10.63544/ijss.v5i2.248. [21] S. Joshi, "A Comprehensive National Framework for Federal AI Adoption: Governance, Standards, and Implementation Pathways for Agentic and Generative Systems," Standards, and Implementation Pathways for Agentic and Generative Systems, Feb. 2026. [22] T. Ramkissoon, A. Ali, D. Ramsubhag, L. Addison, and P. Hosein, "A Policy Framework for AI-Centric Climate Resilience Action Plans," in Climate Action and Sustainable Development Goals, CRC Press, 2026, pp. 145–162. [23] L. Addison, K. Rajaram, A. Hosang, T. Tewari, T. A. Tuitt, and P. Hosein, "An Ai-Driven Early Warning and Forecast-Based Framework for Disaster Resilience in Small Island States," Available at SSRN 5381918. [24] A. Almusaed, A. Almsaad, and I. Yitmen, "Smart Cities That Think: Cognitive Infrastructures, AI Governance, and Sustainable Urban Futures," in Smart Cities-Designing the Future of Urban Living, IntechOpen, 2025. [25] Z. Lyu et al., "Empowering intelligent low-altitude economy with large AI model deployment," IEEE Wireless Communications, 2025. [26] A. Baruwa, "AI powered infrastructure efficiency: enhancing US transportation networks for a sustainable future," International Journal of Engineering Technology Research & Management (IJETRM), vol. 7, no. 12, pp. 329–350, Dec. 2023. [27] E. Goodluck, "Smart Rural Infrastructure Planning Using Machine Learning and Data-Driven Optimization Models." [28] D. Ferreira, P. Ribeiro, A. Coelho, and R. Campos, "Design and Evaluation of a Multi-Agent Perception System for Autonomous Flying Networks," arXiv preprint arXiv:2512.00259, 2025. [29] S. Calado, "Educators as Cognitive Architects in AI-Augmented Learning Environments: Redefining Professional Agency and Pedagogical Co-Performance," in Human-AI Collaboration and the Evolving Role of Educators, IGI Global Scientific Publishing, 2026, pp. 89–122. [30] L. Jia and Y. Pei, "Recent advances in multi-agent reinforcement learning for intelligent automation and control of water environment systems," Machines, vol. 13, no. 6, p. 503, 2025. [31] D. Mohiuddin, “Adaptive marketing systems and consumer feedback loops: Implications for market development in emerging economies,” Journal of Business Insight and Innovation, vol. 5, no. 1, pp. 37–48, 2026. [32] D. Mohiuddin, “HR tech adoption in digital banking: Implications for workforce development and financial sector growth in emerging economies,” Journal of Business Insight and Innovation, vol. 4, no. 2, pp. 77–90, 2025. [33] D. Mohiuddin and D. N. Farhan, “Artificial intelligence in marketing: Ethical challenges and solutions for consumers and society,” Journal of Business Insight and Innovation, vol. 4, no. 1, pp. 73–87, 2025. [34] D. Mohiuddin, “Algorithmic hyper-personalization: The double-edged sword of predictive personalization—An empirical investigation,” Journal of Engineering and Computational Intelligence Review, vol. 2, no. 2, pp. 82–94, 2024. [35] D. Mohiuddin, “Consumer perceptions and trust in AI-generated advertising: An experimental study in the Pakistani context,” Apex Journal of Social Sciences, vol. 3, no. 1, pp. 53–68, 2024.
Copyright (c) 2026 Omar Azhar Malik (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Machine Learning Models for Forecasting Employee Demand in Healthcare HR
Purpose This paper will analyze the major considerations of workforce demand in the healthcare setting and assess the success of machine learning models in enhancing workforce demand prediction accuracy. It involves exploration on how operational variables and data-driven practices can help in improving human resource planning. Design/Methodology/Approach A quantitative research design was considered with the help of a structured questionnaire to gather primary data among the healthcare professionals and HR people. To test the proposed hypotheses and investigate relationships between variables, statistical methods, such as reliability analysis, descriptive statistics, correlation analysis and regression analysis, were used. Findings The findings suggest that the rate of patient inflow, the emergency and the staff absenteeism has a crucial impact on the accuracy of workforce demand forecast. The use of machine learning was the most influential factor with a high positive predictive performance impact. Also, the use of HR data was discovered to improve decision-making processes. The proposed framework was statistically supported with all hypothesized relationships being validated, which confirms the robustness of the proposed framework. Practical Implications The study highlights the importance of adopting machine learning and data-driven HR practices in healthcare organizations. Integration of predictive models and data usage optimization will help healthcare institutions to increase workforce planning, decrease inefficiencies, and positively influence patient care outcomes. Originality/Value This study adds to the body of literature since it narrows down to the specific topic of workforce demand forecasting in healthcare through machine learning. It offers a combined framework integrating operation and organizational factors, which bridges a gap in human resource management research. REFERENCES [1] X. Ma, "Research and application of human resources demand forecasting model based on machine learning," in Proc. 2024 5th Int. Conf. Big Data Economy and Information Management, Dec. 2024, pp. 696–701. [2] S. Fukui et al., "Applying machine learning to human resources data: Predicting job turnover among community mental health center employees," J. Ment. Health Policy Econ., vol. 26, no. 2, p. 63, 2023. [3] N. Dang, "Strategic workforce planning in hospital systems through machine-learning-based forecasting of staffing demand and skill mix," Rev. Internet Things Cyber-Phys. Syst. Appl., vol. 8, no. 9, pp. 1–22, 2023. [4] K. Singh and A. J. Nashwan, "Innovative forecasting models for nurse demand in modern healthcare systems," World J. Methodol., vol. 15, no. 3, p. 99162, 2025. [5] A. Pranata and R. Yudhantara, "Strategic human resource allocation in healthcare institutions using AI-enabled workforce analytics and predictive modeling," Int. J. Theor. Comput. Appl. Multidiscip. Sci., vol. 7, no. 12, pp. 1–24, 2023. [6] I. A. Badhan, M. N. Hasnain, and M. H. Rahman, "Advancing operational efficiency: An in-depth study of machine learning applications in industrial automation," Policy Res. J., vol. 1, no. 2, pp. 21–41, 2023. [7] I. A. Badhan, M. N. Hasnain, and M. H. Rahman, "Enhancing operational efficiency: A comprehensive analysis of machine learning integration in industrial automation," J. Bus. Insight Innov., vol. 1, no. 2, pp. 61–77, 2022. [8] A. Hossain, I. Rasul, S. Akter, S. A. Eshra, and T. S. Turja, "Exploring AI’s role in business analytics for operational efficiency: A survey across manufacturing sectors," J. Bus. Insight Innov., vol. 3, no. 2, pp. 1–17, 2024. [9] A. Sohel, M. A. Alam, A. Hossain, S. Mahmud, and S. Akter, "Artificial intelligence in predictive analytics for next-generation cancer treatment: A systematic literature review of healthcare innovations in the USA," Global Mainstream J. Innov. Eng. Emerg. Technol., vol. 1, no. 1, pp. 62–87, 2022. [10] N. A. A. H. Nahid, T. Islam, H. A. Rube, and M. I. H. Tusar, "Circular economy models for urban logistics: The role of bio-based packaging in sustainable transportation networks," in Proc. IISE Annu. Conf., 2025, pp. 1–6. [11] S. S. Akib Rahman, "A HIPAA-compliant web application design framework for next-generation telehealth systems," Int. J. Res. Technol., vol. 12, no. 4, pp. 166–184, 2024. [12] A. Rahman and S. Sultana, "Real-time threat intelligence correlation and triage for reducing security analyst burnout," J. Eng. Comput. Intell. Rev., vol. 1, no. 1, pp. 64–86, 2023. [13] M. R. Haque, M. I. Hossain, R. B. Ankhi, A. Nishan, and U. Twaha, "Liquidity traps, digital currencies and inflation targeting: A comparative analysis of policy effectiveness in advanced and emerging economies," Inverge J. Soc. Sci., vol. 2, no. 3, pp. 148–165, 2023. [14] U. Twaha, "Mitigating financial waste in the US healthcare system: An AI-driven framework for real-time fraud detection in Medicare and Medicaid," J. Eng. Comput. Intell. Rev., vol. 2, no. 2, p. 71, 2024. [15] F. Amin, M. A. But, I. Amin, and A. Khan, "The tokenized business marketplace: A blockchain and AI-powered framework for democratizing business ownership and investment," Int. J. Bus. Manag. Sci., vol. 5, no. 4, pp. 318–328, 2024. [16] F. Amin, I. Said, and M. A. Butt, "AI-based cybersecurity solutions: Securing information and privacy in the evolving digital age," J. Eng. Comput. Intell. Rev., vol. 3, no. 2, pp. 142–158, 2025. [17] N. Sultana, M. A. Nasir, C. Majumder, and A. H. K. Choain, "Exploring AI-driven approaches for safeguarding sensitive ERP, HR, and defense data within US organizations," J. Bus. Insight Innov., vol. 3, no. 2, pp. 43–59, 2024. [18] I. Alim, S. Akter, Z. Afroz, A. Al Prince, and M. A. Hasan, "Business intelligence in the age of AI: Evaluating machine learning's impact on US economic productivity," Lead Sci. J. Manag. Innov. Soc. Sci., vol. 1, no. 3, pp. 15–30, 2025. [19] W. Wu and S. Fukui, "Using human resources data to predict turnover of community mental health employees: Prediction and interpretation of machine learning methods," Int. J. Ment. Health Nurs., vol. 33, no. 6, pp. 2180–2192, 2024. [20] V. Yadav, "Machine learning in managing healthcare workforce shortage: Analyzing how machine learning can optimize workforce allocation in response to fluctuating healthcare demands," Prog. Med. Sci., vol. 7, no. 4, 2023. [21] F. Mozaffari, M. Rahimi, H. Yazdani, and B. Sohrabi, "Employee attrition prediction in a pharmaceutical company using both machine learning approach and qualitative data," Benchmarking: Int. J., vol. 30, no. 10, pp. 4140–4173, 2023. [22] M. Z. Afshar and M. Hussain Shah, "Resilient livestock supply chains in Pakistan: Adaptive strategies for climate-smart agriculture and food security," Front. Food Sci. Technol., vol. 5, p. 1658625, 2025. [23] S. Butt, I. Mubeen, and N. Yazdani, "Exploring the lived experiences of individuals to manage and cope with type 2 diabetes applying IPA," Pakistan Lang. Humanit. Rev., vol. 8, no. 2, pp. 526–539, 2024. [24] S. Garg, S. Sinha, A. K. Kar, and M. Mani, "A review of machine learning applications in human resource management," Int. J. Prod. Perform. Manag., vol. 71, no. 5, pp. 1590–1610, 2022. [25] N. K. Rajagopal et al., "Human resource demand prediction and configuration model based on grey wolf optimization and recurrent neural network," Comput. Intell. Neurosci., vol. 2022, no. 1, p. 5613407, 2022. (Retracted) [26] Y. Sun and H. Jung, "Machine learning (ML) modeling, IoT, and optimizing organizational operations through integrated strategies: The role of technology and human resource management," Sustainability, vol. 16, no. 16, p. 6751, 2024. [27] S. R. K. Indarapu, S. Vodithala, N. Kumar, S. Kiran, S. N. Reddy, and K. Dorthi, "Exploring human resource management intelligence practices using machine learning models," J. High Technol. Manag. Res., vol. 34, no. 2, p. 100466, 2023. [28] M. A. Vollmer et al., "A unified machine learning approach to time series forecasting applied to demand at emergency departments," BMC Emerg. Med., vol. 21, no. 1, p. 9, 2021. [29] H. Zhu, "Research on human resource recommendation algorithm based on machine learning," Sci. Program., vol. 2021, no. 1, p. 8387277, 2021. [30] C. E. Apeh, C. S. Odionu, B. Bristol-Alagbariya, R. Okon, and B. Austin-Gabriel, "Advancing workforce analytics and big data for decision-making: Insights from HR and pharmaceutical supply chain management," Int. J. Multidiscip. Res. Growth Eval., vol. 5, no. 1, pp. 1217–1222, 2024. [31] C. G. Okatta, F. A. Ajayi, and O. Olawale, "Navigating the future: Integrating AI and machine learning in HR practices for a digital workforce," Comput. Sci. IT Res. J., vol. 5, no. 4, pp. 1008–1030, 2024. [32] S. Ahmed and M. Asif, “Comparative analysis of attitudes toward climate change policies across urban and rural populations,” Pakistan Journal of Social Science Review, vol. 5, no. 1, pp. 747–769, 2026, doi: 10.5281/zenodo.18457821. [33] S. Ahmed and M. Asif, “Public opinion on the effectiveness of local government anti-corruption measures: A multi-city survey analysis,” International Journal of Social Sciences Bulletin, vol. 4, no. 1, pp. 1189–1201, 2026, doi: 10.5281/zenodo.18412790. [34] M. Asif and S. Ullah, “Determinants of support for federalism vs. centralization: A survey of public opinion in Punjab and Khyber Pakhtunkhwa (KP),” Social Science Review Archives, vol. 4, no. 1, pp. 2791–2807, 2026, doi: 10.70670/sra.v4i1.1843. [35] M. Asif and S. Ullah, “Performance voting vs. identity voting: An analysis of electoral behaviour in Pakistani districts,” Journal of Applied Linguistics and TESOL (JALT), vol. 9, no. 1, pp. 213–226, 2026. [36] M. Asif, A. Ali, and F. A. Shaheen, “Assessing the effects of artificial intelligence in revolutionizing human resource management: A systematic review,” Social Science Review Archives, vol. 3, no. 4, pp. 2887–2908, 2025. [37] M. Asif and R. J. Asghar, “Managerial accounting as a driver of financial performance and sustainability in small and medium enterprises in Pakistan,” Center for Management Science Research, vol. 3, no. 7, pp. 150–163, 2025. [38] D. Mohiuddin, “Adaptive marketing systems and consumer feedback loops: Implications for market development in emerging economies,” Journal of Business Insight and Innovation, vol. 5, no. 1, pp. 37–48, 2026. [39] D. Mohiuddin, “HR tech adoption in digital banking: Implications for workforce development and financial sector growth in emerging economies,” Journal of Business Insight and Innovation, vol. 4, no. 2, pp. 77–90, 2025. [40] D. Mohiuddin and D. N. Farhan, “Artificial intelligence in marketing: Ethical challenges and solutions for consumers and society,” Journal of Business Insight and Innovation, vol. 4, no. 1, pp. 73–87, 2025. [41] D. Mohiuddin, “Algorithmic hyper-personalization: The double-edged sword of predictive personalization—An empirical investigation,” Journal of Engineering and Computational Intelligence Review, vol. 2, no. 2, pp. 82–94, 2024. [42] D. Mohiuddin, “Consumer perceptions and trust in AI-generated advertising: An experimental study in the Pakistani context,” Apex Journal of Social Sciences, vol. 3, no. 1, pp. 53–68, 2024.
Copyright (c) 2025 Syed Tanvirul Hasan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Real-Time Threat Intelligence Correlation and Triage for Reducing Security Analyst Burnout
Security Operations Centers (SOCs) face a critical crisis as cybersecurity analysts suffer from overwhelming burnout, with over 60% reporting exhaustion due to the manual processing of thousands of daily alerts. This chronic stress leads to decision fatigue, increased oversight of genuine threats, and compromised organizational security. To address this, we present AutoTI-Triage, an autonomous system for real-time threat intelligence correlation and triage designed to alleviate cognitive load and augment human decision-making. AutoTI-Triage employs a hybrid architecture combining Graph Neural Networks (GNNs) and Reinforcement Learning (RL). The GNN component constructs dynamic threat graphs to map complex relationships between threat actors, indicators of compromise (IOCs), and assets, revealing hidden attack patterns. Concurrently, the RL agent learns optimal, adaptive triage policies from analyst feedback and incident outcomes, continuously refining prioritization accuracy. We validate our system using a comprehensive dataset of 1.2 million threat intelligence events from sources like AlienVault OTX and MISP, representing the largest public benchmark for TI correlation. Quantitative evaluation demonstrates a 0.92 F1-score for threat classification, a 65% reduction in Mean Time to Resolution (MTTR), and significant gains in analyst productivity. By automating complex correlation and enabling adaptive prioritization, AutoTI-Triage offers a scalable solution to combat analyst burnout while enhancing the efficacy of modern security operations. REFERENCES [1] D. Reinsel, J. Gantz, and J. Rydning, The Digitization of the World: From Edge to Core. IDC, 2018. [2] S. Brown, J. Harris, and E. Hutchins, “Threat intelligence platforms: The next generation,” Gartner Research, 2019. [3] W. Tounsi and H. Rais, “A survey on technical threat intelligence in the age of sophisticated cyber attacks,” Comput. Security, vol. 72, pp. 212–233, 2018. [4] A. B. Bakker and E. Demerouti, “Job demands–resources theory: Taking stock and looking forward,” J. Occup. Health Psychol., vol. 22, no. 3, pp. 273–285, 2017. [5] B. E. Strom et al., MITRE ATT&CK: Design and Philosophy. MITRE Corp., 2018. [6] R. A. Martin, “Managing vulnerabilities in networked systems,” Computer, vol. 34, no. 11, pp. 32–38, 2001. [7] W. U. Hassan et al., “NoDoze: Combatting alert fatigue with automated analytics across SOC workflows,” in Proc. ACM SIGSAC CCS, 2019, pp. 253–270. [8] Verizon, Data Breach Investigations Report 2023. Verizon Business, 2023. [9] S. C. Sundaramurthy et al., “Turning contradictions into innovations: An ethnographic study of a security operations center,” in Proc. CHI, 2016, pp. 4567–4578. [10] S. Brown, J. Harris, and E. Hutchins, “Threat intelligence platforms: The next generation,” Gartner Research, 2019 [11] S. Qamar et al., “Threat intelligence platforms: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 20, no. 4, pp. 3289–3315, 2017. [12] T. D. Wagner et al., “The correlation of cyber threat intelligence: A review,” Comput. Security, vol. 88, 2019. [13] C. Sauerwein et al., “Threat intelligence sharing platforms: An empirical study,” Comput. Security, vol. 102, 2021. [14] S. Samtani, R. Chinn, and H. Chen, “Exploring emerging hacker assets and key hackers in dark web forums,” Decis. Support Syst., vol. 135, 2020. [15] IBM Security, Cost of a Data Breach Report 2023. IBM Corp., 2023. [16] ISC², “Cybersecurity workforce study 2022,” 2022. [17] Cybersecurity Ventures, “Cybersecurity workforce shortage report 2023,” 2023. [18] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. ICLR, 2017. [19] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. MIT Press, 2018. [20] C. Sauerwein et al., “A systematic literature review of threat intelligence sharing,” J. Cybersecurity, vol. 5, no. 1, 2019. [21] C. Maslach and S. E. Jackson, “The measurement of experienced burnout,” J. Organ. Behav., vol. 2, no. 2, pp. 99–113, 1981. [22] F. B. Kokulu et al., “The human factor in security operations,” in Proc. 28th USENIX Security Symp., 2019, pp. 1385–1402. [23] L. Chen, Y. Zhang, and X. Wang, “Measuring analyst workload in modern SOCs,” IEEE Trans. Dependable Secure Comput., vol. 17, no. 4, pp. 789–802, 2020. [24] J. Cohen, “A coefficient of agreement for nominal scales,” Educ. Psychol. Meas., vol. 20, no. 1, pp. 37–46, 1960. [25] OASIS, “STIX™ Version 2.0,” 2017. [26] K. Rieck, P. Trinius, C. Willems, and T. Holz, “Automatic analysis of malware behavior using machine learning,” J. Comput. Security, vol. 19, no. 4, pp. 639–668, 2011. [27] W. L. Hamilton, Graph Representation Learning. Morgan & Claypool, 2020. [28] J. Zhao et al., “TIMiner: Automatically extracting and analyzing categorized cyber threat intelligence from social data,” Comput. Security, vol. 95, 2020. [29] S. Bhatt, P. K. Manadhata, and L. Zomlot, “The operational role of Security Information and Event Management systems,” IEEE Secur. Privacy, vol. 12, no. 5, pp. 35–41, 2014. [30] D. Swift, Successful SIEM and Log Management Strategies. SANS Institute, 2010. [31] G. Apruzzese, M. Andreolini, M. Marchetti, A. Venturi, and M. Colajanni, “Identifying and tracking malicious cyber campaigns via attacker profiling: A deep learning approach,” IEEE Trans. Inf. Forensics Secur., vol. 17, pp. 2313–2328, 2022. [32] D. Arp, E. Quiring, K. Rieck, and C. Wressnegger, “Dos and don’ts of machine learning in computer security,” in Proc. 31st USENIX Security Symp., 2022, pp. 3971–3988. [33] T. Nguyen and V. J. Reddi, “Deep reinforcement learning for cyber security,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 7, pp. 2865–2879, 2021. [34] Y. Li, Q. Liu, and Z. Wang, “Reinforcement learning for alert prioritization in SOCs,” IEEE Trans. Inf. Forensics Secur., vol. 17, pp. 2987–3001, 2022. [35] H. Chen, J. Liu, Y. Zhang, and X. Li, “Hybrid GNN-RL architecture for adaptive threat triage,” in Proc. 32nd USENIX Security Symp., 2023, pp. 1123–1140. [36] M. Vielberth et al., “Security operations center: A systematic literature review,” Comput. Security, vol. 97, 2020. Ponemon Institute, The State of Cybersecurity Analyst Burnout 2022. Ponemon Institute LLC, 2022. [37] M. Husák et al., “SoK: The impact of alerting on security operations centers,” in IEEE Symp. Security Privacy (SP), 2022, pp. 105–122. [38] P. Jacobs, D. Williams, and J. Smith, “Automation and analyst burnout in SOC environments,” J. Cybersecurity Privacy, vol. 1, no. 3, pp. 456–478, 2021. [39] C. Maslach, S. E. Jackson, and M. P. Leiter, Maslach Burnout Inventory Manual, 3rd ed. Consulting Psychologists Press, 1996. [40] C. Maslach, W. B. Schaufeli, and M. P. Leiter, “Job burnout,” Annu. Rev. Psychol., vol. 52, no. 1, pp. 397–422, 2001. [41] E. Demerouti, A. B. Bakker, F. Nachreiner, and W. B. Schaufeli, “The job demands resources model of burnout,” J. Appl. Psychol., vol. 86, no. 3, pp. 499–512, 2001. [42] C. Islam, M. A. Babar, and S. Nepal, “A survey of machine learning techniques applied to Security Operations Centers,” ACM Comput. Surveys, vol. 52, no. 4, Art. 78, 2019. [43] A. D’Amico, K. Buchanan, J. Goodall, and D. Tesone, “Critical incident analysis using visualization and human factors,” in Proc. Hum. Factors Ergonomics Soc. Annu. Meeting, vol. 49, no. 3, pp. 456–460, 2005. [44] P. Velickovic et al., “Deep Graph Infomax,” in Proc. ICLR, 2019. [45] J. Kreps, S. Narkhede, and J. Rao, “Kafka: A distributed messaging system for log processing,” in Proc. NetDB Workshop, 2011. [46] OASIS, “STIX™ Version 2.1,” 2021. [47] J. Barnes, “Data quality considerations for cyber threat intelligence,” J. Cybersecurity, vol. 6, no. 1, 2020. [48] C. Wagner et al., “MISP: The design and implementation of a collaborative threat intelligence sharing platform,” in Proc. ACM Workshop ISC, 2016, pp. 49–56. [49] J. R. Landis and G. G. Koch, “The measurement of observer agreement for categorical data,” Biometrics, vol. 33, no. 1, pp. 159–174, 1977 [50] P. Veličković et al., “Graph Attention Networks,” in Proc. ICLR, 2018. [51] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,” Trans. Assoc. Comput. Linguistics, vol. 5, pp. 135–146, 2017. [52] J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450, 2016. [53] M. Fey and J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” arXiv preprint arXiv:1903.02428, 2019. [54] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. ICLR, 2015. [55] M. Asif and A. Shaheen, “Creating a high-performance workplace by the determination of importance of job satisfaction, employee engagement, and leadership,” Journal of Business Insight and Innovation, vol. 1, no. 2, pp. 9–15, 2022. [56] N. Shahid, M. Asif, and A. Pasha, “Effect of internet addiction on school going children,” Inverge Journal of Social Sciences, vol. 1, no. 1, pp. 12–47, 2022, doi: 10.63544/ijss.v1i1.3. [57] H. A. Usama, M. Riaz, A. Khan, N. Begum, M. Asif, and M. Hamza, “Prohibition of alcohol in Quran and Bible (A research and analytical review),” PalArch’s Journal of Archaeology of Egypt/Egyptology, vol. 19, no. 4, pp. 1202–1211, 2022. [58] S. H. Alizai, M. Asif, and Z. K. Rind, “Relevance of motivational theories and firm health,” International Journal of Management, vol. 12, no. 3, pp. 1130–1137, 2021. [59] M. Asif, “Contingent effect of conflict management towards psychological capital and employees’ engagement in financial sector of Islamabad,” Ph.D. dissertation, Preston University, 2021, doi: 10.13140/RG.2.2.17616.79360. [60] Aurangzeb, M. Asif, and M. K. Amin, “Resources management and SME’s performance,” Humanities & Social Sciences Reviews, vol. 9, no. 3, pp. 679–689, 2021, doi: 10.18510/hssr.2021.9367. [61] D. Aurangzeb and M. Asif, “Role of leadership in digital transformation: A case of Pakistani SMEs,” in Proc. Fourth Int. Conf. Emerging Trends in Engineering, 2021. [62] Aurangzeb, T. Mushtaque, M. N. Tunio, Z. Rehman, and M. Asif, “Influence of administrative expertise of human resource practitioners on the job performance: Mediating role of achievement motivation,” International Journal of Management, vol. 12, no. 4, pp. 408–421, 2021, doi: 10.34218/IJM.12.4.2021.035. [63] M. Asif, A. Khan, and M. A. Pasha, “Psychological capital of employees’ engagement: Moderating impact of conflict management in the financial sector of Pakistan,” Global Social Sciences Review, vol. 4, no. 3, pp. 160–172, 2019, doi: 10.31703/gssr.2019(IV-III).15. [64] M. A. Pasha, M. Ramzan, and M. Asif, “Impact of economic value-added dynamics on stock prices fact or fallacy: New evidence from nested panel analysis,” Global Social Sciences Review, vol. 4, no. 3, pp. 135–147, 2019, doi: 10.31703/gssr.2019(IV-III).13.
Copyright (c) 2023 Akib Rahman, Sharmin Sultana (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Based Cybersecurity Solutions: Securing Information and Privacy in the Evolving Digital Age
The fast development of digital technologies has caused the volume and complexity of cyber threats to become very serious risk to information security and user privacy. Traditional methods of cybersecurity are becoming insufficient to handle advanced and emerging attacks. The purpose of the present study is to assess the level of success of Artificial Intelligence (AI)-based cybersecurity solutions and to determine the main obstacles to their adoption and how they affect the privacy of data and the evolution of security practices in the future. The research design was that of quantitative research through the use of a structured questionnaire that was administered to IT professionals, cybersecurity experts, academic researchers, and students. There were 250 valid responses, which were collected using convenience sampling. Descriptive statistical methods, such as frequencies, percentages, mean, and standard deviation, were used to analyze the data. The findings show that most of the respondents believe that AI is more efficient than conventional approaches to threat detection and prevention. Awareness was found to be high, and moderate levels of adoption of AI tools were observed. Nevertheless, the implementation cost, shortage of skills as well as privacy were noted to be major challenges. Moreover, the majority of respondents admitted that AI brings both new risks and advantages with it. The paper summarizes that AI is a key to improving cybersecurity through proactive and intelligent threat management. Although it has its merits, effective implementation involves technical, ethical, and organizational challenges that need to be addressed. It has also been suggested that organizations should invest in skills, find cost-effective AI solutions, enhance data governance processes, and implement regulatory frameworks to ensure secure and ethical use of AI in cybersecurity. REFERENCES [1] N. G. Camacho, “The role of AI in cybersecurity: Addressing threats in the digital age,” J. Artif. Intell. Gen. Sci. (JAIGS), vol. 3, no. 1, pp. 143–154, 2024. [2] H. Rehan, “AI-driven cloud security: The future of safeguarding sensitive data in the digital age,” J. Artif. Intell. Gen. Sci. (JAIGS), vol. 1, no. 1, pp. 132–151, 2024. [3] M. Aslam, “AI and cybersecurity: An ever-evolving landscape,” Int. J. Adv. Eng. Technol. Innov., vol. 1, 2024. [4] A. Shahana, R. Hasan, S. F. Farabi, J. Akter, M. A. A. Mahmud, F. T. Johora, and G. Suzer, “AI-driven cybersecurity: Balancing advancements and safeguards,” J. Comput. Sci. Technol. Stud., vol. 6, no. 2, pp. 76–85, 2024. [5] J. N. Chukwunweike, M. Yussuf, O. Okusi, and T. Oluwatobi, “The role of deep learning in ensuring privacy integrity and security: Applications in AI-driven cybersecurity solutions,” World J. Adv. Res. Rev., vol. 23, no. 2, p. 2550, 2024. [6] Z. B. Akhtar and A. T. Rawol, “Enhancing cybersecurity through AI-powered security mechanisms,” IT J. Res. Dev., vol. 9, no. 1, pp. 50–67, 2024. [7] M. Mahfuri, S. Ghwanmeh, R. Almajed, W. Alhasan, M. Salahat, J. H. Lee, and T. M. Ghazal, “Transforming cybersecurity in the digital era: The power of AI,” in 2024 2nd Int. Conf. Cyber Resilience (ICCR), IEEE, pp. 1–8, Feb. 2024. [8] S. Rangaraju, “Secure by intelligence: Enhancing products with AI-driven security measures,” EPH-Int. J. Sci. Eng., vol. 9, no. 3, pp. 36–41, 2023. [9] K. Achuthan, S. Ramanathan, S. Srinivas, and R. Raman, “Advancing cybersecurity and privacy with artificial intelligence: Current trends and future research directions,” Front. Big Data, vol. 7, p. 1497535, 2024. [10] I. O. Owolabi, C. K. Mbabie, and J. C. Obiri, “AI-driven cybersecurity in FinTech & cloud: Combating evolving threats with intelligent defense mechanisms,” Int. J. Multidiscip. Res. Sci. Eng. Technol., vol. 7, p. 12, 2024. [11] V. Baladari, “Adaptive cybersecurity strategies: Mitigating cyber threats and protecting data privacy,” J. Sci. Eng. Res., vol. 7, no. 8, pp. 279–288, 2020. [12] A. Tanikonda, B. K. Pandey, S. R. Peddinti, and S. R. Katragadda, “Advanced AI-driven cybersecurity solutions for proactive threat detection and response in complex ecosystems,” J. Sci. Technol., vol. 3, no. 1, 2022. [13] A. S. George, “Riding the AI waves: An analysis of artificial intelligence's evolving role in combating cyber threats,” Partners Univ. Int. Innov. J., vol. 2, no. 1, pp. 39–50, 2024. [14] V. Kolluri, “An extensive investigation into guardians of the digital realm: AI-driven antivirus and cyber threat intelligence,” Int. J. Adv. Res. Interdiscip. Sci. Endeav., vol. 1, no. 2, pp. 71–77, 2024. [15] M. A. Pasha, M. Ramzan, and M. Asif, “Impact of economic value-added dynamics on stock prices fact or fallacy: New evidence from nested panel analysis,” Global Social Sciences Review, vol. 4, no. 3, pp. 135–147, 2019, doi: 10.31703/gssr.2019(IV-III).13 [16] Z. B. Akhtar, “Artificial intelligence (AI) within the realm of cyber security,” Insight Electr. Electron. Eng., vol. 1, no. 1, pp. 1–11, 2024. [17] J. Singh, “The evolution of cybersecurity in the big data era: Moving beyond data protection to data-driven security,” in 2023 IEEE Int. Conf. ICT in Bus. Ind. & Gov. (ICTBIG), IEEE, pp. 1–6, Dec. 2023. [18] I. H. Sarker, M. H. Furhad, and R. Nowrozy, “AI-driven cybersecurity: An overview, security intelligence modeling and research directions,” SN Comput. Sci., vol. 2, no. 3, p. 173, 2021. [19] M. Asif, A. Khan, and M. A. Pasha, “Psychological capital of employees’ engagement: Moderating impact of conflict management in the financial sector of Pakistan,” Global Social Sciences Review, vol. 4, no. 3, pp. 160–172, 2019, doi: 10.31703/gssr.2019(IV-III).15. [20] A. Karunamurthy, R. Kiruthivasan, and S. Gauthamkrishna, “Human-in-the-loop intelligence: Advancing AI-centric cybersecurity for the future,” Quing: Int. J. Multidiscip. Sci. Res. Dev., vol. 2, no. 3, pp. 20–43, 2023. [21] D. Mohiuddin, “Consumer perceptions and trust in AI-generated advertising: An experimental study in the Pakistani context,” Apex Journal of Social Sciences, vol. 3, no. 1, pp. 53–68, 2024. [22] S. Thapaliya and A. Bokani, “Leveraging artificial intelligence for enhanced cybersecurity: Insights and innovations,” Sadgamaya, vol. 1, no. 1, pp. 46–52, 2024. [23] G. V. S. Meghana, S. S. Afroz, R. Gurindapalli, S. Katari, and K. Swetha, “A survey paper on understanding the rise of AI-driven cyber crime and strategies for proactive digital defenders,” in 2024 4th Int. Conf. Pervasive Comput. Soc. Netw. (ICPCSN), IEEE, pp. 25–30, May 2024. [24] K. Kaushik, A. Khan, A. Kumari, I. Sharma, and R. Dubey, “Ethical considerations in AI-based cybersecurity,” in Next-Generation Cybersecurity: AI, ML, and Blockchain, Singapore: Springer Nature, pp. 437–470, 2024. [25] N. Arshad, M. U. Baber, and A. Ullah, “Assessing the transformative influence of ChatGPT on research practices among scholars in Pakistan,” Mesopotamian J. Big Data, pp. 1–10, 2024. [26] Aurangzeb, M. Asif, and M. K. Amin, “Resources management and SME’s performance,” Humanities & Social Sciences Reviews, vol. 9, no. 3, pp. 679–689, 2021, doi: 10.18510/hssr.2021.9367. [27] Aurangzeb, T. Mushtaque, M. N. Tunio, Z. Rehman, and M. Asif, “Influence of administrative expertise of human resource practitioners on the job performance: Mediating role of achievement motivation,” International Journal of Management, vol. 12, no. 4, pp. 408–421, 2021, doi: 10.34218/IJM.12.4.2021.035. [28] D. Aurangzeb and M. Asif, “Role of leadership in digital transformation: A case of Pakistani SMEs,” in Proc. Fourth Int. Conf. Emerging Trends in Engineering, 2021. [29] Aurangzeb, M. Asif, and M. K. Amin, “Resources management and SME’s performance,” Humanities & Social Sciences Reviews, vol. 9, no. 3, pp. 679–689, 2021, doi: 10.18510/hssr.2021.9367. [30] M. Asif, “Contingent effect of conflict management towards psychological capital and employees’ engagement in financial sector of Islamabad,” Ph.D. dissertation, Preston University, 2021, doi: 10.13140/RG.2.2.17616.79360. [31] M. Asif and M. S. Sandhu, “Social media marketing revolution in Pakistan: A study of its adoption and impact on business performance,” Journal of Business Insight and Innovation, vol. 2, no. 2, pp. 67–77, 2023, doi: 10.52783/eel.v13i5.901. [32] S. S. A. Rahman, “A HIPAA-compliant web application design framework for next-generation telehealth systems,” Int. J. Res. Technol., vol. 12, no. 4, pp. 166–184, 2024. [33] M. Asif, “The complexities of bioterrorism: Challenges and considerations,” International Journal of Contemporary Issues in Social Sciences, vol. 3, no. 3, pp. 2175–2184, 2024. [34] D. Mohiuddin, “Algorithmic hyper-personalization: The double-edged sword of predictive personalization—An empirical investigation,” Journal of Engineering and Computational Intelligence Review, vol. 2, no. 2, pp. 82–94, 2024. [35] S. H. Alizai, M. Asif, and Z. K. Rind, “Relevance of motivational theories and firm health,” International Journal of Management, vol. 12, no. 3, pp. 1130–1137, 2021. [36] I. A. Badhan, M. N. Hasnain, and M. H. Rahman, “Advancing operational efficiency: An in-depth study of machine learning applications in industrial automation,” Policy Res. J., vol. 1, no. 2, pp. 21–41, 2023. [37] H. A. Usama, M. Riaz, A. Khan, N. Begum, M. Asif, and M. Hamza, “Prohibition of alcohol in Quran and Bible (A research and analytical review),” PalArch’s Journal of Archaeology of Egypt/Egyptology, vol. 19, no. 4, pp. 1202–1211, 2022. [38] N. Sultana, M. A. Nasir, C. Majumder, and A. H. K. Choain, “Exploring AI-driven approaches for safeguarding sensitive ERP, HR, and defense data within US organizations,” J. Bus. Insight Innov., vol. 3, no. 2, pp. 43–59, 2024. [39] N. Shahid, M. Asif, and A. Pasha, “Effect of internet addiction on school going children,” Inverge Journal of Social Sciences, vol. 1, no. 1, pp. 12–47, 2022, doi: 10.63544/ijss.v1i1.3. [40] F. Jimmy, “Emerging threats: The latest cybersecurity risks and the role of artificial intelligence in enhancing cybersecurity defenses,” Valley Int. J. Digit. Libr., vol. 1, pp. 564–574, 2021. [41] J. P. Bharadiya, “AI-driven security: How machine learning will shape the future of cybersecurity and Web 3.0,” Am. J. Neural Netw. Appl., vol. 9, no. 1, pp. 1–7, 2023. [42] G. Waizel, “Bridging the AI divide: The evolving arms race between AI-driven cyber-attacks and AI-powered cybersecurity defenses,” in Int. Conf. Mach. Intell. & Security Smart Cities (TRUST) Proc., vol. 1, pp. 141–156, Jul. 2024. [43] E. O. Abolaji and O. T. Akinwande, “AI powered privacy protection: A survey of current state and future directions,” World J. Adv. Res. Rev., vol. 23, no. 3, pp. 2687–2696, 2024. [44] I. H. Sarker, Introduction to AI-driven cybersecurity and threat intelligence, Cham: Springer Nature, pp. 3–19, 2024. [45] M. Asif and A. Shaheen, “Creating a high-performance workplace by the determination of importance of job satisfaction, employee engagement, and leadership,” Journal of Business Insight and Innovation, vol. 1, no. 2, pp. 9–15, 2022. [46] M. Asif, “Integration of information technology in financial services and its adoption by the financial sector in Pakistan,” Inverge Journal of Social Sciences, vol. 1, no. 2, pp. 23–35, 2022, doi: 10.63544/ijss.v1i2.31. [47] A. Mumtaz, N. Munir, R. Mumtaz, M. Farooq, and M. Asif, “Impact of psychological & economic factors on investment decision-making in Pakistan stock exchange,” Journal of Positive School Psychology, vol. 7, no. 4, pp. 130–135, 2023. [48] D. Mohiuddin, “Algorithmic hyper-personalization: The double-edged sword of predictive personalization—An empirical investigation,” Journal of Engineering and Computational Intelligence Review, vol. 2, no. 2, pp. 82–94, 2024.
Copyright (c) 2025 Fahad Amin, Ihsan Said (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Algorithmic Hyper-Personalization: The Double-Edged Sword of Predictive Personalization - An Empirical Investigation
Algorithmic hyper-personalization has emerged as a dominant strategy in digital marketing, enabling brands to deliver unprecedented levels of relevance and convenience. However, the same technologies that create value for consumers also risk triggering perceptions of surveillance, invasion of privacy, and psychological discomfort often described as "creepiness." This study investigates the paradoxical nature of hyper-personalization, examining the tipping point where perceived benefits are outweighed by psychological costs. Drawing on privacy calculus theory, reactance theory, and the persuasion knowledge model, we develop and test a theoretical model that balances personalization benefits against privacy concerns. Using survey data from 487 consumers in Pakistan, we find that hyper-personalization follows an inverted U-shaped relationship with customer loyalty: moderate levels of personalization maximize loyalty, while excessive personalization diminishes it through increased perceptions of creepiness. Perceived creepiness fully mediates the relationship between hyper-personalization and loyalty, with privacy concerns and perceived surveillance serving as key mechanisms. Consumer characteristics including privacy literacy, trust propensity, and prior brand relationship moderate these effects. The findings contribute to personalization literature by identifying the curvilinear nature of personalization effects and establishing creepiness as a critical mediating mechanism. For practitioners, we provide actionable guidelines for calibrating personalization intensity, implementing transparency measures, and designing consent mechanisms that preserve consumer trust. REFERENCES [1] E. Aguirre, D. Mahr, D. Grewal, K. de Ruyter, and M. Wetzels, “Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness,” Journal of Retailing, vol. 91, no. 1, pp. 34–49, 2015, doi: 10.1016/j.jretai.2014.09.005. [2] J. W. Brehm, A Theory of Psychological Reactance. New York, NY, USA: Academic Press, 1966. [3] S. S. Brehm and J. W. Brehm, Psychological Reactance: A Theory of Freedom and Control. New York, NY, USA: Academic Press, 1981. [4] M. J. Culnan and P. K. Armstrong, “Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation,” Organization Science, vol. 10, no. 1, pp. 104–115, 1999, doi: 10.1287/orsc.10.1.104. [5] T. Dinev and P. Hart, “An extended privacy calculus model for e-commerce transactions,” Information Systems Research, vol. 17, no. 1, pp. 61–80, 2006, doi: 10.1287/isre.1060.0080. [6] M. Friestad and P. Wright, “The persuasion knowledge model: How people cope with persuasion attempts,” Journal of Consumer Research, vol. 21, no. 1, pp. 1–31, 1994, doi: 10.1086/209380. [7] D. Gefen, “E-commerce: The role of familiarity and trust,” Omega, vol. 28, no. 6, pp. 725–737, 2000, doi: 10.1016/S0305-0483(00)00021-9. [8] A. D. Miyazaki, “Online privacy and the disclosure of cookie use: Effects on consumer trust and anticipated patronage,” Journal of Public Policy & Marketing, vol. 27, no. 1, pp. 19–33, 2008, doi: 10.1509/jppm.27.1.19. [9] D. Mohiuddin, “Consumer perceptions and trust in AI-generated advertising: An experimental study in the Pakistani context,” Apex Journal of Social Sciences, vol. 3, no. 1, pp. 53–68, 2024. [Online]. Available: https://apexjss.com/index.php/AJSS/article/view/24 [10] S. Moore and D. Moore, “Creepiness in digital marketing: Conceptualization and measurement,” Journal of Interactive Marketing, vol. 58, no. 2, pp. 123–141, 2023. [11] Y. J. Park, “Digital literacy and privacy behavior online,” Communication Research, vol. 40, no. 2, pp. 215–236, 2013, doi: 10.1177/0093650211418338. [12] J. Short, E. Williams, and B. Christie, The Social Psychology of Telecommunications. London, U.K.: Wiley, 1976. [13] O. Tene and J. Polonetsky, “Big data for all: Privacy and user control in the age of analytics,” Northwestern Journal of Technology and Intellectual Property, vol. 11, no. 5, pp. 239–273, 2012. [14] J. Vesanen, “What is personalization? A conceptual framework,” European Journal of Marketing, vol. 41, no. 5/6, pp. 409–418, 2007, doi: 10.1108/03090560710737534. [15] T. B. White, D. L. Zahay, H. Thorbjørnsen, and S. Shavitt, “Getting too personal: Reactance to highly personalized email solicitations,” Marketing Letters, vol. 19, no. 1, pp. 39–50, 2008, doi: 10.1007/s11002-007-9027-9. [16] V. A. Zeithaml, L. L. Berry, and A. Parasuraman, “The behavioral consequences of service quality,” Journal of Marketing, vol. 60, no. 2, pp. 31–46, 1996, doi: 10.1177/002224299606000203. [17] S. H. Alizai, M. Asif, and Z. K. Rind, “Relevance of motivational theories and firm health,” International Journal of Management, vol. 12, no. 3, pp. 1130–1137, 2021.
Copyright (c) 2024 Danyal Mohiuddin (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Mitigating Financial Waste in the U.S. Healthcare System: An AI-Driven Framework for Real-Time Fraud Detection in Medicare and Medicaid Claims
The financial waste associated with fraud, abuse, and inefficient claims processing remains a significant challenge to the sustainability of the United States healthcare system, specifically in Medicare and Medicaid programs. The traditional fraud detection systems are mostly reactive, and they are unable to cope up with the increasing volume and complexity of the healthcare claims data. The recent developments in the field of artificial intelligence (AI) present the potential of improving the process of detecting fraud by applying real-time surveillance and predictive analytics. This paper will discuss the perception of stakeholders on the problem of financial waste in the U.S. healthcare system and evaluate how AI-based, real-time fraud detection systems are perceived to address the issue of fraud in Medicare and Medicaid claims. The research design used was cross-sectional, quantitative and it was based on a structured survey instrument. The sample included 250 healthcare stakeholders who included administrators, billing and coding specialists, health IT professionals, policymakers and researchers. The survey involved the assessment of the awareness of financial waste, AI adoption, perceived efficiency of AI-driven fraud detection models, the challenges related to it, and ethics. Reliability analysis and descriptive statistics were used to analyze the data. The statistics indicate that the awareness rate of the respondents regarding financial waste in healthcare is high. There was also high agreement among the respondents about the use of AI and its success in reducing financial waste particularly on real-time fraud detection. Reliability analysis indicated a high level of internal consistency among measurement constructs. Despite the predominantly positive attitudes towards AI effectiveness, the respondents mentioned the challenges related to ethics, regulation, and implementation of AI in respect of data privacy, transparency, and system integration. The paper concludes that AI-informed real-time fraud detection systems are seen by many people as a powerful tool to decrease the financial waste on Medicare and Medicaid claims. Nevertheless, a powerful ethical governance, sufficient infrastructure, and readiness of organizations are necessary to implement it successfully to provide sustainable and responsible adoption. REFERENCES [1] P. K. Pemmasani, M. Osaka, and D. Henry, “AI-powered fraud detection in healthcare systems: A data-driven approach,” The Computertech, pp. 18–23, 2021. [2] G. Frimpong, A. C. Peter-Anyebe, and O. M. Ijiga, “Artificial intelligence driven compliance automation improving audit readiness and fraud detection within healthcare revenue cycle management systems,” Global Journal of Engineering, Science & Social Science Studies, vol. 9, no. 9, 2023. [3] M. Restrepo and L. Córdoba, “The role of artificial intelligence in transforming financial management and cost optimization strategies in healthcare organizations,” Journal of Computational Intelligence for Hybrid Cloud and Edge Computing Networks, vol. 7, no. 10, pp. 1–13, 2023. [4] K. Kapadiya et al., “Blockchain and AI-empowered healthcare insurance fraud detection: An analysis, architecture, and future prospects,” IEEE Access, vol. 10, pp. 79606–79627, 2022. [5] M. T. Hasan, “Graph neural network models for detecting fraudulent insurance claims in healthcare systems,” American Journal of Advanced Technology and Engineering Solutions, vol. 2, no. 1, pp. 88–109, 2022. [6] Q. U. A. Nazir, “AI-powered radiology: Innovations and challenges in medical imaging,” Journal of Engineering and Computational Intelligence Review, vol. 1, no. 1, pp. 7–13, 2023. [7] M. Shakeel, K. Khan, and A. Latif, “Role of financial inclusion, institutional quality and human capital in economic growth: An empirical analysis from selected Asian countries,” Journal of Policy Research, vol. 9, no. 2, pp. 274–278, 2023. [8] A. Latif, S. Haider, and S. Farid, “Family social support and community engagement as a buffer against stress among youth,” Journal of Policy Research, vol. 8, no. 4, pp. 498–502, 2022. [9] M. Shahinuzzaman, T. A. Shiva, M. S. Sumon, and K. Saifuddin, “Mental health of women breast cancer survivor at different stages of the disease,” Jagannath University Journal of Earth and Life Sciences, vol. 5, no. 1, pp. 1–12, 2019. [10] K. Khan and S. Alvi, “A comprehensive review of blockchain applications across diverse domains,” Journal of Engineering and Computational Intelligence Review, vol. 1, no. 1, pp. 23–38, 2023. [11] A. Abernethy et al., “The promise of digital health: Then, now, and the future,” NAM Perspectives, 2022, Art. no. 10-31478. [12] A. K. Wagner, P. A. Ubel, and J. F. Wharam, “Financial pollution in the US health care system,” JAMA Health Forum, vol. 2, no. 3, Art. no. e210195, Mar. 2021. [13] N. R. Sahni, P. Gupta, M. Peterson, and D. M. Cutler, “Active steps to reduce administrative spending associated with financial transactions in US health care,” Health Affairs Scholar, vol. 1, no. 5, Art. no. qxad053, 2023. [14] M. A. Sayem et al., “AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices,” International Journal of Recent Innovations in Trends in Computing and Communication, vol. 11, no. 10, pp. 1109–1122, 2023. [15] J. Geyman, “Investor-owned health care: The hidden blight on America’s ‘system’,” International Journal of Health Services, vol. 51, no. 4, pp. 494–500, 2021. [16] L. N. Srinivasagopalan, “AI-enhanced fraud detection in healthcare insurance: A novel approach to combatting financial losses through advanced machine learning models,” European Journal of Advances in Engineering and Technology, vol. 9, no. 8, pp. 82–91, 2022. [17] S. Bayyapu, R. R. Turpu, and R. R. Vangala, “Advancing healthcare decision-making: The fusion of machine learning, predictive analytics, and cloud technology,” International Journal of Computer Engineering and Technology, vol. 10, no. 5, pp. 157–170, 2019. [18] S. Rani and A. Mittal, “Securing digital payments: A comprehensive analysis of AI-driven fraud detection with real-time transaction monitoring and anomaly detection,” in Proc. 6th Int. Conf. Contemporary Computing and Informatics (IC3I), Sep. 2023, pp. 2345–2349. [19] V. Dunka, “AI-driven claims fraud detection using hybrid deep learning models: Integrating convolutional neural networks and recurrent neural networks for real-time fraud detection in insurance claims,” Essex Journal of AI Ethics and Responsible Innovation, vol. 3, pp. 276–311, 2023. [20] H. Rehan, “Leveraging AI and cloud computing for real-time fraud detection in financial systems,” Journal of Science & Technology, vol. 2, no. 5, p. 127, 2021. [21] M. Z. Islam, S. K. Shil, and M. R. Buiya, “AI-driven fraud detection in the US financial sector: Enhancing security and trust,” International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, vol. 14, no. 1, pp. 775–797, 2023. [22] Aurangzeb, M. Asif, and M. K. Amin, “Resources Management and SME’s Performance,” Humanities & Social Sciences Reviews, vol. 9, no. 3, pp. 679–689, 2021. doi: 10.18510/hssr.2021.9367. [23] Aurangzeb, T. Mushtaque, M. N. Tunio, Zia-ur-Rehman, and M. Asif, “Influence of administrative expertise of human resource practitioners on the job performance: Mediating role of achievement motivation,” International Journal of Management, vol. 12, no. 4, pp. 408–421, 2021. doi: 10.34218/IJM.12.4.2021.035. [24] M. Asif and M. S. Sandhu, “Social media marketing revolution in Pakistan: A study of its adoption and impact on business performance,” Journal of Business Insight and Innovation, vol. 2, no. 2, pp. 67–77, 2023.
Copyright (c) 2024 Umma Twaha (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Diabetes Prediction Decision Support System Using Machine Learning
Diabetes is a chronic metabolic disorder affecting millions worldwide and is a leading cause of serious health complications such as heart disease, kidney failure, and vision loss. Early detection and accurate risk prediction are vital for effective disease management and reduction of long-term health burdens. This study presents a robust diabetes prediction decision support system leveraging advanced machine learning techniques applied to a curated dataset obtained from Kaggle. The primary objective is to address the critical need for early and accurate diabetes diagnosis by developing a reliable and efficient prediction model. Through comprehensive data preprocessing, detailed feature engineering, and systematic model training, a Random Forest Classifier was developed and optimized using GridSearchCV. The resulting model achieved an impressive accuracy of 94.70%, demonstrating a precision of 0.98, recall of 0.96, and F1-score of 0.97 for non-diabetic cases (class 0), and a precision of 0.65, recall of 0.80, and F1-score of 0.72 for diabetic cases (class 1). These results underscore the model's high reliability in detecting non-diabetic instances and its reasonable effectiveness in identifying diabetic cases. The findings offer significant implications for clinical decision-making, enabling healthcare professionals to implement proactive interventions and personalized treatment strategies. Furthermore, the model provides a valuable foundation for future enhancements in medical data analysis and health informatics applications aimed at improving chronic disease management. References [1] H. Wu, S. Yang, Z. Huang, J. He, and X. Wang, “Type 2 diabetes mellitus prediction model based on data mining,” Inform. Med. Unlocked, vol. 10, pp. 100–107, 2018. [2] M. M. Islam, R. Ferdousi, S. Rahman, and H. Y. Bushra, “Likelihood prediction of diabetes at early stage using data mining techniques,” in Computer Vision and Machine Intelligence in Medical Image Analysis. Singapore: Springer, 2020, pp. 113–125. [3] F. G. Woldemichael and S. Menaria, “Prediction of diabetes using data mining techniques,” in 2018 2nd Int. Conf. Trends Electron. Inform. (ICOEI), May 2018, pp. 414–418. [4] C. Fiarni, E. M. Sipayung, and S. Maemunah, “Analysis and prediction of diabetes complication disease using data mining algorithm,” Procedia Comput. Sci., vol. 161, pp. 449–457, 2019. [5] A. Aldallal and A. A. A. Al-Moosa, “Using data mining techniques to predict diabetes and heart diseases,” in 2018 4th Int. Conf. Frontiers Signal Process. (ICFSP), Sep. 2018, pp. 150–154. [6] F. A. Khan, K. Zeb, M. Al-Rakhami, A. Derhab, and S. A. C. Bukhari, “Detection and prediction of diabetes using data mining: a comprehensive review,” IEEE Access, vol. 9, pp. 43 711–43 735, 2021. [7] I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine learning and data mining methods in diabetes research,” Comput. Struct. Biotechnol. J., vol. 15, pp. 104–116, 2017. [8] P. Kumar, P. Kumar, A. Srivastava, V. D. A. Kumar, K. Vengatesan, and A. Singhal, “Comparative analysis of data mining techniques to predict heart disease for diabetic patients,” in Adv. Comput. Data Sci., Singapore: Springer, Apr. 2020, pp. 507–518. [9] T. R. Mahesh et al., “Blended ensemble learning prediction model for strengthening diagnosis and treatment of chronic diabetes disease,” Comput. Intell. Neurosci., vol. 2022, 2022. [10] N. Oza and A. Bokhare, “Diabetes prediction using logistic regression and K-nearest neighbor,” in Congr. Intell. Syst., Singapore: Springer, 2022, pp. 407–418. [11] K. S. Anil and R. Jain, “Data mining techniques in diabetes prediction and diagnosis: a review,” in 2022 6th Int. Conf. Trends Electron. Inform. (ICOEI), Apr. 2022, pp. 1696–1701.
Copyright (c) 2025 Muhammad Faisal, Imtiaz Ali Shah, Faiza Aurang Zeb (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Combined Geophysical and Geochemical Evaluation of the Kirthar Fold Belt for Mineral and Hydrocarbon Resources
This research seeks to characterize the mineral and hydrocarbon potential of the Kirthar Fold Belt (KFB) using an integrated approach of geophysics and geochemistry. KFB, which is located in the southwestern part of Pakistan, is one of the most tectonically active regions of the country and is made of a multi-tectonic lithology and diverse structural configurations. The geophysical approach using seismic, gravity, magnetic-resistivity surveys, and hydrocarbon and oil geochemical halo maps as well as major and trace, isotope, and hydrocarbon biomarker geochemical analysis helped to characterize potential mineralized and hydrocarbon-accumulating regions of the KFB. Interpreting both geophysical as well as geochemical information from the KFB helped in architectural reconstruction of the subsurface and identification of high probability prospective regions. Some of the highlighted findings include the identification of fault zones, as well as flexures, that are anticlines, having associated mineralized and hydrocarbon signatures indicating the presence of both resources in these zones. These findings, in conjunction with the study, serve as a precursor to the planning of integrated, resource-targeted, and technologically advanced development surveys. In this respect, higher tier energy and resource development of the BKC will be obtained from road and resource-based development of the surveyed areas which will minimize environmental strain. Higher resolution surveys and subsequent exploratory drilling are the next logical steps in research to enable resource assessment and development. REFERENCES [1]. M. B. Allen and S. R. Allen, Geology of the Kirthar Fold Belt and the Impact of Tectonic Processes on Resource Formation. Geological Society of Pakistan, 2010. [2]. J. L. Brown and T. E. Smith, “The role of seismic surveys in fold-and-thrust belt exploration: A case study from the Zagros Mountains, Iran,” Journal of Petroleum Geology, vol. 38, no. 2, pp. 123–140, 2015, doi: 10.1016/j.jpg.2015.02.007. [3]. A. Choudhury and M. Joshi, Hydrocarbon Exploration in the Indo-Pakistan Region: Geological Context and Prospects. Springer, 2018. [4]. P. Davis and R. Turner, “Magnetic anomalies and mineralization in fold-thrust belts: Evidence from the Appalachian Mountains,” Mineral Exploration Review, vol. 22, no. 4, pp. 249–265, 2013, doi: 10.1002/min.10024. [5]. R. Gupta and S. Sharma, “A study on geochemical anomalies in the Kirthar Fold Belt: Implications for hydrocarbon migration,” Geochemical Journal, vol. 40, no. 3, pp. 233–244, 2017, doi: 10.1016/j.gj.2017.03.004. [6]. X. Huang and W. Zhao, Integrating Geophysical and Geochemical Surveys for Resource Evaluation: A Case Study of the Himalayas. Elsevier, 2016. [7]. M. Jackson and D. Zhang, “Structural traps and their significance for hydrocarbon exploration in complex tectonic settings,” Journal of Structural Geology, vol. 56, pp. 92–104, 2019, doi: 10.1016/j.jsg.2019.02.009. [8]. S. M. K. Shuvra, M. N. Gony, and K. Fatema, “Adaptive machine learning for resource-constrained environments: A path toward sustainable AI,” International Journal of Research and Applied Innovations, vol. 7, no. 6, pp. 8004–8014, 2024. [9]. M. T. Khan and A. Raza, Geophysical and Geochemical Methods in Resource Exploration: Application in the Kirthar Fold Belt. Pakistan Geological Survey Reports, 2020. [10]. Q. Lin and L. Liu, “The application of gravity and magnetic data in mineral exploration in the Zagros fold-belt: Insights for Kirthar Fold Belt,” Journal of Geophysics, vol. 72, no. 3, pp. 281–298, 2017, doi: 10.1007/s10967-017-5219-2. [11]. D. McKenzie and D. Mallard, Regional Tectonics and Resource Formation: Implications for Mineral and Hydrocarbon Deposits in Fold Belts. Wiley-Blackwell, 2014. [12]. A. Shaheen and M. Noor, “Exploration techniques for hydrocarbon resources in the Kirthar Fold Belt: Past, present, and future,” Petroleum Exploration and Development, vol. 43, no. 1, pp. 15–28, 2016, doi: 10.1016/j.petrol.2016.01.004. [13]. M. Shahinuzzaman, T. A. Shiva, M. S. Sumon, and K. Saifuddin, “Mental health of women breast cancer survivors at different stages of the disease,” Jagannath University Journal of Earth and Life Sciences, vol. 5, no. 1, pp. 1–12, 2019. [14]. M. S. Islam and T. A. Shiva, “Virtual cognitive behavioural therapy in rural US communities: Effectiveness and reach,” Journal of Business Insight and Innovation, vol. 3, no. 2, pp. 60–76, 2024. [15]. F. Wang and J. Zhang, Geophysical Exploration Techniques in Fold-and-Thrust Belts: Insights from the Sulaiman Range, Pakistan. Geological Survey of Pakistan, 2015. [16]. L. Zhang and L. Wu, “Integration of geophysical and geochemical data for identifying mineral deposits: The role of advanced technologies in resource exploration,” Journal of Earth Sciences, vol. 33, no. 6, pp. 487–495, 2018, doi: 10.1007/s12583-018-0866-4. [17]. D. Ali and T. Ahmed, “Computational analysis of geotherma-enhanced heavy oil production,” Journal of Engineering and Computational Intelligence Review, vol. 2, no. 2, pp. 10–20, 2024. [18] D. Aurangzeb and M. Asif, “Role of leadership in digital transformation: A case of Pakistani SMEs,” in Proc. Fourth Int. Conf. Emerging Trends Eng., 2021, pp. 17. [19] M. Asif, “The complexities of bioterrorism: Challenges and considerations,” Int. J. Contemp. Issues Social Sci., vol. 3, no. 3, pp. 2175–2184, 2024. [20] H. A. Usama, M. Riaz, A. Khan, N. Begum, M. Asif, and M. Hamza, “Prohibition of alcohol in Quran and Bible (A research and analytical review),” PalArch’s J. Archaeol. Egypt/Egyptology, vol. 19, no. 4, pp. 1202–1211, 2022. [21] M. Asif and R. J. Asghar, “Managerial accounting as a driver of financial performance and sustainability in small and medium enterprises in Pakistan,” Cent. Manag. Sci. Res., vol. 3, no. 7, pp. 150–163.
Copyright (c) 2025 Sayed Qaisar Abbas, Muhammad Naveed Khalil (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Integrating Blockchain-Enhanced Enterprise Systems for U.S. Banking, Pharmaceutical Supply Chains, Economic Resilience, and Secure Operational Models
Background: Blockchain technology has emerged as a transformative force with the potential to revolutionize enterprise systems beyond its cryptocurrency origins. Critical U.S. sectors, including banking and pharmaceutical supply chains, face persistent challenges related to fraud, transparency, traceability, and cybersecurity, for which blockchain offers promising decentralized solutions. However, the adoption landscape remains fragmented, hindered by significant barriers and varying levels of awareness. Objective: This study aims to investigate the integration of blockchain-enhanced enterprise systems within the U.S., focusing on assessing awareness levels, evaluating its perceived impact on banking and pharmaceutical supply chains, analyzing its contribution to economic resilience and secure operational models, and identifying key adoption challenges and future prospects. Methods: A quantitative research design was employed, utilizing a structured questionnaire administered to 300 professionals from the banking, pharmaceutical supply chain, IT/technology, and government/regulatory sectors. The instrument demonstrated strong reliability, with Cronbach's Alpha scores ranging from 0.78 to 0.86. Data analysis included descriptive statistics, one-way ANOVA, and correlation analysis to examine sectoral differences and interrelationships between key constructs. Results: Findings indicate a moderate to high level of optimism, with the "Future Prospects" construct scoring the highest (M=3.71). A significant variance in awareness was observed across sectors (F=3.42, p=0.018), with pharmaceutical and banking professionals reporting higher awareness than their government/regulatory counterparts. Correlation analysis revealed strong positive relationships between blockchain adoption, economic resilience (r=0.52, p<0.01), and future prospects (r=0.49, p<0.01). However, "Adoption Barriers" such as cost and regulatory uncertainty were confirmed as significant impediments. Conclusion: The study concludes that while blockchain technology holds considerable promise for enhancing transparency, security, and resilience in U.S. enterprise systems, its full potential is currently constrained by sectoral awareness gaps and persistent implementation challenges. They can be reformed by strategic suggestions such as encouraging cross-sector cooperation, elaborating better regulatory frameworks, and encouraging pilot programs to narrow the gap in perceived potential and practical integration. REFERENCES [1] T. T. Adewale, T. D. Olorunyomi, and T. N. Odonkor, “Blockchain-enhanced financial transparency: A conceptual approach to reporting and compliance,” Int. J. Frontiers Sci. Technol. Res., vol. 2, no. 1, pp. 024–045, 2022. [2] V. Paliwal, S. Chandra, and S. Sharma, “Blockchain technology for sustainable supply chain management: A systematic literature review and a classification framework,” Sustainability, vol. 12, no. 18, p. 7638, 2020. [3] A. Rejeb, J. G. Keogh, S. J. Simske, T. Stafford, and H. Treiblmaier, “Potentials of blockchain technologies for supply chain collaboration: a conceptual framework,” Int. J. Logist. Manag., vol. 32, no. 3, pp. 973–994, 2021. [4] X. Guo, G. Zhang, and Y. Zhang, “A comprehensive review of blockchain technology-enabled smart manufacturing: a framework, challenges and future research directions,” Sensors, vol. 23, no. 1, p. 155, 2022. [5] N. Krapels, “Blockchain will animate tomorrow's integrated global logistics systems,” in The Digital Transformation of Logistics: Demystifying Impacts of the Fourth Industrial Revolution, 2021, pp. 79–96. [6] Z. Shu, W. Liu, B. Fu, Z. Li, and M. He, “Blockchain-enhanced trading systems for construction industry to control carbon emissions,” Clean Technol. Environ. Policy, vol. 24, no. 6, pp. 1851–1870, 2022. [7] E. C. Chukwuma-Eke, O. Y. Ogunsola, and N. J. Isibor, “Developing an integrated framework for SAP-based cost control and financial reporting in energy companies,” Int. J. Multidiscipl. Res. Growth Evaluation, vol. 3, no. 1, pp. 805–818, 2022. [8] M. O. Okoye et al., “A blockchain-enhanced transaction model for microgrid energy trading,” IEEE Access, vol. 8, pp. 143 777–143 786, 2020. [9] Y. Xiong, H. K. Lam, A. Kumar, E. W. T. Ngai, C. Xiu, and X. Wang, “The mitigating role of blockchain-enabled supply chains during the COVID-19 pandemic,” Int. J. Oper. Prod. Manag., vol. 41, no. 9, pp. 1495–1521, 2021. [10] B. Cao et al., “Blockchain systems, technologies, and applications: A methodology perspective,” IEEE Commun. Surv. Tutorials, vol. 25, no. 1, pp. 353–385, 2022. [11] A. Sheel and V. Nath, “Effect of blockchain technology adoption on supply chain adaptability, agility, alignment and performance,” Manag. Res. Rev., vol. 42, no. 12, pp. 1353–1374, 2019. [12] K. Yu et al., “A blockchain-based shamir’s threshold cryptography scheme for data protection in industrial internet of things settings,” IEEE Internet Things J., vol. 9, no. 11, pp. 8154–8167, 2021. [13] A. Chaluvadi and M. Karthick, “Leveraging cloud computing and big data for enhanced healthcare decision-making: Integrating LSTM for predictive modelling,” Int. J. Bus. Manag. Econ. Rev., vol. 4, no. 5, p. 142, 2021. [14] J. Liu, Z. Liu, Q. Yang, M. Osmani, and P. Demian, “A conceptual blockchain enhanced information model of product service systems framework for sustainable furniture,” Buildings, vol. 13, p. 85, 2022. [15] S. Jiang and T. B. Ræder, “Experience on using ArchiMate models for modelling blockchain-enhanced value chains,” in Proc. 26th Int. Conf. Evaluation Assessment Softw. Eng., 2022, pp. 375–382. [16] P. D. Dozier and T. A. Montgomery, “Banking on blockchain: An evaluation of innovation decision making,” IEEE Trans. Eng. Manag., vol. 67, no. 4, pp. 1129–1141, 2019. [17] A. Begum, M. S. K. Munira, and S. Juthi, “Systematic review of blockchain technology in trade finance and banking security,” Amer. J. Scholarly Res. Innov., vol. 1, no. 01, pp. 25–52, 2022. [18] J. Mattke, A. Hund, C. Maier, and T. Weitzel, “How an enterprise blockchain application in the US pharmaceuticals supply chain is saving lives,” MIS Q. Exec., vol. 18, no. 4, 2019. [19] M. M. Akhtar and D. R. Rizvi, “Traceability and detection of counterfeit medicines in pharmaceutical supply chain using blockchain-based architectures,” in Sustainable and Energy Efficient Computing Paradigms for Society. Cham, Switzerland: Springer, 2020, pp. 1–31. [20] H. Min, “Blockchain technology for enhancing supply chain resilience,” Bus. Horiz., vol. 62, no. 1, pp. 35–45, 2019. [21] A. Bayramova, D. J. Edwards, and C. Roberts, “The role of blockchain technology in augmenting supply chain resilience to cybercrime,” Buildings, vol. 11, no. 7, p. 283, 2021. [22] B. Lokesh and N. Rajagopalan, “A blockchain-based security model for SDNs,” in 2020 IEEE Int. Conf. Electron., Comput. Commun. Technol. (CONECCT), 2020, pp. 1–6. [23] A. Happy, K. W. Rahman, M. M. H. Chowdhury, M. Chowdhury, and M. Scerri, “Blockchain adoption challenges in supply chain,” in The Routledge Companion to Global Value Chains. Routledge, 2021, pp. 137–155. [24] D. Levis, F. Fontana, and E. Ughetto, “A look into the future of blockchain technology,” PLoS ONE, vol. 16, no. 11, p. e0258995, 2021. [25] P. Maidamwar, P. Saraf, and N. Chavhan, “Blockchain applications, challenges, and opportunities: a survey of a decade of research and future outlook,” in 2021 Int. Conf. Comput. Intell. Comput. Appl. (ICCICA), 2021, pp. 1–5. [26] P. Cui, U. Guin, A. Skjellum, and D. Umphress, “Blockchain in IoT: current trends, challenges, and future roadmap,” J. Hardw. Syst. Secur., vol. 3, no. 4, pp. 338–364, 2019. [27] R. Belchior, A. Vasconcelos, S. Guerreiro, and M. Correia, "A survey on blockchain interoperability: Past, present, and future trends," ACM Comput. Surv., vol. 54, no. 8, pp. 1–41, 2021. [28] H. Treiblmaier, "Exploring the next wave of blockchain and distributed ledger technology: The overlooked potential of scenario analysis," Future Internet, vol. 13, no. 7, p. 183, 2021. [29] M. Asif, "Contingent effect of conflict management towards psychological capital and employees’ engagement in financial sector of Islamabad," Ph.D. thesis, Dept. Manag. Sci., Preston Univ., Kohat, Islamabad, Pakistan, 2021. [30] M. A. Pasha, M. Ramzan, and M. Asif, "Impact of Economic Value Added Dynamics on Stock Prices Fact or Fallacy: New Evidence from Nested Panel Analysis," Global Social Sciences Review, vol. 4, no. 3, pp. 135-147, 2019. [31] D. Aurangzeb and M. Asif, "Role of leadership in digital transformation: A case of Pakistani SMEs," in 2021 Fourth International Conference on Emerging Trends in Engineering, Management and Sciences (ICETEMS), 2021, vol. 4, no. 1, pp. 219-229.
Copyright (c) 2025 Arafat Hossain Khan Choain, Chinmoy Majumder, Md Abu Nasir Nasir, Nasrin Sultana (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Leveraging Machine Learning Techniques for Enhancing Signature Verification to Unveiling the Forgery
These days, computer vision and machine learning researchers are actively studying handwritten signature verification. It makes sense to define signature verification as a machine-learning task. This is accomplished by figuring out if the signature is real or fake. It is therefore regarded as a two-class classification problem. Given the prevalence of handwritten signatures in legal documents and financial transactions, researchers must carefully consider which machine-learning technique to apply in order to authenticate handwritten signatures and prevent forgeries that could result in significant losses for their clients. Thus far, the application of machine learning algorithms has produced excellent results in terms of equal computation error rates. The research aims to develop a model of classification capable of effectively categorizing forged verified signatures using input data. The primary objective is to explore the creation of a robust signature verification classification model using machine learning and algorithms. The research design involves a step-by-step approach design includes the gathering of data, Preprocessing, classification algorithms, and evaluation of models. The process of verifying the authenticity of a signature by use of machine learning techniques is called signature verification. The present project concentrates on off-line signature verification, however signatures might be of either online or offline form. This project intends to create a methodology that uses writer-independent characteristics to differentiate between authentic and faked signatures. In order to collect signatures from people, execute signature verification, and display the outcomes, we want to develop a whole end-to-end hardware/software system. To achieve this, a number of Machine Learning approaches for off-line signature verification were created and evaluated on benchmark datasets. Our proposed technique outperforms offline signature verification approaches such as support vector machine (SVM), neural network (NN), and logistic regression in terms of accuracy. REFERENCES [1] H. A. Salman and H. Abdul wahab, “Anti-screenshot keyboard for web-based application using cloaking,” in Proc. Int. Conf. Adv. Intell. Syst. Inform., 2020, pp. 473–478, doi: 10.1007/978-3-030-21005-2_45. [2] F. M. Alsuhimat and F. S. Mohamad, “Offline signature verification using long short-term memory and histogram orientation gradient,” Bull. Electr. Eng. Informatics, vol. 12, no. 1, pp. 10–19, Feb. 2023, doi: 10.11591/eei.v12i1.4024. [3] A. Hamadene and Y. Chibani, “One-class writer-independent offline signature verification using feature dissimilarity thresholding,” IEEE Trans. Inf. Forensics Security, vol. 11, no. 6, pp. 1226–1238, Jun. 2016, doi: 10.1109/TIFS.2016.2521611. [4] Z. Hashim, H. M. Ahmed, and A. H. Alkhayyat, “A comparative study among handwritten signature verification methods using machine learning techniques,” Sci. Program., vol. 2022, p. 8170424, Jan. 2022, doi: 10.1155/2022/8170424. [5] Sam et al., “Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet Inception-v1 and Inception-v3,” Procedia Comput. Sci., vol. 161, pp. 475–483, 2019, doi: 10.1016/j.procs.2019.11.147. [6] M. Kurowski, A. Sroczyński, G. Bogdanis, and A. Czyżewski, “An automated method for biometric handwritten signature authentication employing neural networks,” Electronics, vol. 10, no. 4, p. 456, Feb. 2021, doi: 10.3390/electronics10040456. [7] I. Alim, N. Imtiaz, A. Al Prince, and M. A. Hasan, “AI and blockchain integration: Driving strategic business advancements in the intelligent era,” J. Eng. Comput. Intell. Rev., vol. 3, no. 2, pp. 38–50, 2025. [8] M. Z. Afshar and M. H. Shah, “A narrative review for revisiting BCG matrix application in performance evaluation of public sector entities,” J. Res. Rev., vol. 2, no. 02, pp. 325–337, 2025. [9] F. Leclerc and R. Plamondon, “Automatic signature verification: The state of the art - 1989-1993,” Int. J. Pattern Recognit. Artif. Intell., vol. 8, no. 3, pp. 643–660, Sep. 1994, doi: 10.1142/S0218001494000346. [10] F. S. Mohamad, F. M. Alsuhimat, M. A. Mohamed, M. Mohamad, and A. A. Jamal, “Detection and feature extraction for images signatures,” Int. J. Eng. Technol., vol. 7, no. 3, pp. 44–48, 2018. [11] R. Plamondon and G. Lorette, “Automatic signature verification and writer identification — the state of the art,” Pattern Recognit., vol. 22, no. 2, pp. 107–131, 1989, doi: 10.1016/0031-3203(89)90059-9. [12] D. Ali and T. Ahmed, “Computational analysis of geothermal-enhanced heavy oil production,” J. Eng. Comput. Intell. Rev., vol. 2, no. 2, pp. 10–20, 2024. [13] M. Z. Afshar and M. H. Shah, “Leveraging Porter's Diamond Model: Public Sector Insights,” Crit. Rev. Soc. Sci. Stud., vol. 3, no. 2, pp. 2255–2271, 2025. [14] K. S. Radhika and S. Gopika, “Online and offline signature verification: A combined approach,” Procedia Comput. Sci., vol. 46, pp. 1593–1600, 2015, doi: 10.1016/j.procs.2015.02.089. [15] R. Amin and A. A. Kazmi, “Smart grids: A comprehensive review of technologies, challenges, and future directions,” J. Eng. Comput. Intell. Rev., vol. 2, no. 2, pp. 45–70, 2024. [16] T. A. Shiva, J. G. Brown, A. A. McField, R. E. Osborne, and C. D. Oberle, “Cultural associations with prosocial behaviors and attitudes among Asian Americans,” Asian Am. J. Psychol., 2025, early access. [17] M. Sharif, M. A. Khan, M. Faisal, M. Yasmin, and S. L. Fernandes, “A framework for offline signature verification system: Best features selection approach,” Pattern Recognit. Lett., vol. 139, pp. 50–59, Nov. 2020, doi: 10.1016/j.patrec.2018.01.021. [18] D. Suryani, E. Irwansyah, and R. Chindra, “Offline signature recognition and verification system using efficient fuzzy kohonen clustering network (EFKCN) algorithm,” Procedia Comput. Sci., vol. 116, pp. 621–628, 2017, doi: 10.1016/j.procs.2017.10.025. [19] M. A. Saputra and I. Nurhaida, “Signature originality verification using a deep learning approach,” Electron. J. Educ. Soc. Econ. Technol., vol. 5, no. 1, pp. 19–29, 2024. [20] N. Patel, P. J. Patel, and A. Changa, “Exploring the effectiveness of machine learning algorithms in image forgery detection,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 10, no. 1, pp. 45–54, 2024. [21] M. Asif, "Contingent effect of conflict management towards psychological capital and employees’ engagement in financial sector of Islamabad," Ph.D. dissertation, Preston Univ., Kohat, Islamabad Campus, Pakistan, 2021. [22] M. Asif and A. Shaheen, "Creating a high-performance workplace by the determination of importance of job satisfaction, employee engagement, and leadership," Journal of Business Insight and Innovation, vol. 1, no. 2, pp. 9-15, 2022. [23] M. Asif, "Mediating role of trust between emotional intelligence and project team performance in telecommunication sector," Journal of Business Insight and Innovation, vol. 1, no. 2, pp. 9-15, 2022. (Note: Assumed journal based on context; please verify if this is correct.) [24] Aurangzeb, M. Asif, and M. K. Amin, "Resources management and SME's performance," Humanities & Social Sciences Reviews, 2021, doi: 10.18510/hssr.2021. [25] M. A. Pasha, M. Ramzan, and M. Asif, "Impact of economic value added dynamics on stock prices fact or fallacy: New evidence from nested panel analysis," Global Social Sciences Review, vol. 4, no. 3, pp. 135-147, 2019. [26] S. H. Alizai, M. Asif, and Z. K. Rind, "Relevance of motivational theories and firm health," International Journal of Management (IJM), vol. 12, no. 3, pp. 1130-1137, 2021. [27] Aurangzeb, T. Mushtaque, M. N. Tunio, Zia-ur-Rehman, and M. Asif, "Influence of administrative expertise of human resource practitioners on the job performance: Mediating role of achievement motivation," International Journal of Management (IJM), vol. 12, no. 4, pp. 408-421, 2021. [28] M. Asif, M. A. Pasha, and A. Shahid, "Energy scarcity and economic stagnation in Pakistan," Bahria University Journal of Management & Technology, vol. 8, no. 1, pp. 141-157, 2025. [29] A. Mumtaz, N. Munir, R. Mumtaz, M. Farooq, and M. Asif, "Impact of psychological & economic factors on investment decision-making in Pakistan Stock Exchange," Journal of Positive School Psychology, vol. 7, no. 4, pp. 130-135, 2023. [30] M. Asif, H. Shah, and H. A. H. Asim, "Cybersecurity and audit resilience in digital finance: Global insights and the Pakistani context," Journal of Asian Development Studies, vol. 14, no. 3, pp. 560-573, 2025. [31] M. Asif, "The complexities of bioterrorism: Challenges and considerations," International Journal of Contemporary Issues in Social Sciences, vol. 3, no. 3, pp. 2175–2184, 2024. [32] Dr. Aurangzeb and M. Asif, "Role of leadership in digital transformation: A case of Pakistani SMEs," in 2021 Fourth International Conference on Emerging Trends in Engineering, Management and Sciences (ICETEMS), Oct. 13-14, 2021, vol. 4, no. 1, pp. 219-229. [33] H. A. Usama, M. Riaz, A. Khan, N. Begum, M. Asif, and M. Hamza, "Prohibition of alcohol in Quran and Bible (A research and analytical review)," PalArch's Journal of Archaeology of Egypt/Egyptology, vol. 19, no. 4, pp. 1202-1211, 2022.
Copyright (c) 2025 Shumaila Ejaz (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Performance Evaluation of Battery Management Systems in Lithium-Ion Battery-Powered Electric Vehicles
This work provides the first exhaustive quantitative assessment of Battery Management Systems (BMS) in electric vehicles (EVs). This work evaluates BMS as each attempts to establish the State of Charge (SOC) and State of Health (SOH) estimation, cell balancing, thermal control, and fault safeguarding efficiency. This work evaluates real-world scenario BMS efficiency considering different driving conditions, environmental conditions, and scenarios to age the battery pack. A substantial amount of work has been done to assess the battery pack's cell voltages, currents, temperatures, State of Charge/State of Health, and fault logs in a 48V, 60Ah lithium-ion battery pack. The work has primarily focused on measuring the SOC error, SOH drift, balancing efficiency, and thermal deviation. This work demonstrates the BMS SOC estimation efficiency of 3% error, while SOH estimation accuracy dropped by 5% after 1000 cycles of charge and discharge. BMS cell balancing works efficiently at average driving conditions; however, it becomes much less efficient with aggressive driving. The BMS thermal regulation suffered greatly in extreme heat (45°C) as the temperature regulation control was ±5°C from the ideal temperature of 5°C. Fault BMS fault detection was remarkably fast, detecting overvoltage and overcurrent conditions in less than 200 ms. This work provides a summary of important BMS efficiency bottlenecks while offering solutions to advance BMS safety, BMS longevity, and BMS powered EVs efficiency. REFERENCES [1] M. Chen, S. Li, and Y. Zhang, “Impedance spectroscopy for monitoring state of health in lithium-ion batteries,” J. Power Sources, vol. 450, p. 227758, Feb. 2020, doi: 10.1016/j.jpowsour.2019.227758. [2] Cyient, “State of charge (SOC) estimation challenges in BMS technology,” 2020. [Online]. Available: https://www.cyient.com [3] Cyient, “Fault detection in Battery Management Systems for electric vehicles,” 2021. [Online]. Available: https://www.cyient.com [4] S. Lohum, “A comparative study of centralized and modular Battery Management Systems in electric vehicles,” J. Energy Storage, vol. 38, p. 102451, Jun. 2021, doi: 10.1016/j.est.2021.102451. [5] Z. Li, X. Wang, and J. Yao, “Neural network-based SOC estimation for lithium-ion batteries under dynamic loading conditions,” J. Energy Storage, vol. 41, p. 102838, Sep. 2021, doi: 10.1016/j.est.2021.102838. [6] L. Liu and C. Zhang, “Performance analysis of BMS under high discharge rates and extreme temperatures,” Energy, vol. 191, p. 116537, Jan. 2020, doi: 10.1016/j.energy.2019.116537. [7] C. Lo, M. Tan, and A. Wong, “Effect of driving conditions on the performance of battery management systems in electric vehicles,” Int. J. Electr. Power Energy Syst., vol. 121, p. 106071, Oct. 2020, doi: 10.1016/j.ijepes.2020.106071. [8] MathWorks, “State of charge estimation techniques for lithium-ion batteries,” 2020. [Online]. Available: https://www.mathworks.com [9] S. M. Kashif and F. Chowdhury, “A Comprehensive Review of Asset Management Systems: Trends, Technologies, and Future Directions,” J. Eng. Comput. Intell. Rev., vol. 2, no. 2, pp. 1–9, 2024. [10] M. Z. Afshar and M. H. Shah, “Examining Vision Sharing as a Driver of Organizational Resilience: Evidence from Public Sector Contexts in Developing Economies,” Indus J. Soc. Sci., vol. 3, no. 2, pp. 971–985, 2025. [11] Monolithic Power Systems, “Thermal management in lithium-ion battery packs for electric vehicles,” 2020. [Online]. Available: https://www.monolithicpower.com [12] Monolithic Power Systems, “Active thermal management for high-performance battery systems in electric vehicles,” 2021. [Online]. Available: https://www.monolithicpower.com [13] M. Shahinuzzaman, T. A. Shiva, M. S. Sumon, and K. Saifuddin, “Mental health of women breast cancer survivor at different stages of the disease,” Jagannath Univ. J. Earth Life Sci., vol. 5, no. 1, pp. 1–12, 2019. [14] H. Zhang, M. Chen, and X. Li, “Long-term aging effects on state of charge estimation in lithium-ion battery systems,” J. Power Sources, vol. 486, p. 229400, Dec. 2021, doi: 10.1016/j.jpowsour.2020.229400. [15] C. Lo and L. Wang, “Battery management system (BMS) architecture: A survey on centralized, modular, and distributed configurations,” Renew. Sustain. Energy Rev., vol. 121, p. 109683, Apr. 2020, doi: 10.1016/j.rser.2019.109683. [16] M. Z. Afshar and M. H. Shah, “Examining The Role Of Change Management In Enhancing Organizational Resilience In Public Sector Entities,” Cent. Manag. Sci. Res., vol. 3, no. 3, pp. 931–942, 2025. [17] W. Liu and J. Zhang, “BMS fault detection and diagnosis: Real-time processing and sensor precision issues,” Energy, vol. 231, p. 120857, Sep. 2021, doi: 10.1016/j.energy.2021.120857. [18] T. Zhang and X. Ma, “Impacts of environmental factors on lithium-ion battery degradation under electric vehicle driving conditions,” Energy Rep., vol. 7, pp. 1405–1415, Nov. 2021, doi: 10.1016/j.egyr.2021.02.062. [19] L. Liu and C. Zhang, “Thermal runaway prevention strategies in lithium-ion battery management systems,” J. Therm. Sci. Eng. Appl., vol. 13, no. 4, p. 044901, Aug. 2021, doi: 10.1115/1.4049073. [20] M. Asif, M. A. Pasha, and A. Shahid, "Energy scarcity and economic stagnation in Pakistan," Bahria University Journal of Management & Technology, vol. 8, no. 1, pp. 141-157, 2025. [21] A. Mumtaz, N. Munir, R. Mumtaz, M. Farooq, and M. Asif, "Impact of psychological & economic factors on investment decision-making in Pakistan Stock Exchange," Journal of Positive School Psychology, vol. 7, no. 4, pp. 130-135, 2023. [22] M. Asif, H. Shah, and H. A. H. Asim, "Cybersecurity and audit resilience in digital finance: Global insights and the Pakistani context," Journal of Asian Development Studies, vol. 14, no. 3, pp. 560-573, 2025. [23] M. Asif, "The complexities of bioterrorism: Challenges and considerations," International Journal of Contemporary Issues in Social Sciences, vol. 3, no. 3, pp. 2175–2184, 2024. [24] H. A. Usama, M. Riaz, A. Khan, N. Begum, M. Asif, and M. Hamza, "Prohibition of alcohol in Quran and Bible (A research and analytical review)," PalArch's Journal of Archaeology of Egypt/Egyptology, vol. 19, no. 4, pp. 1202-1211, 2022. [25] D. Aurangzeb and M. Asif, "Role of leadership in digital transformation: A case of Pakistani SMEs," in 2021 Fourth International Conference on Emerging Trends in Engineering, Management and Sciences (ICETEMS), 2021, pp. 219-229.
Copyright (c) 2025 Basit Azam, Sanam Mehar (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Design and Control of an Autonomous Drone Navigation System Using Embedded AI
This research study the design and the development of an autonomous drone navigation system utilizing embedded artificial intelligence (AI) capable of real-time decision making, obstacle avoidance, and path planning. The focus of this research is the application of neural networks and reinforcement learning techniques integrated with the drone’s onboard computer and the fully autonomous navigation of the drone in sophisticated and rapidly changing environments. The stability, precision, and flexible response of the drone’s navigation system within the complex environmental frameworks is attained by the combination of classical control methods (PID, LQR) and the control frameworks provided by artificial intelligence. Other than the developments in the optimization of path planning algorithms (A*, RRT) the paper discusses the incorporation of AI for dynamic obstacle avoidance which enables real-time handling of rapid environmental changes. A balanced coverage of sensor fusion methods (EKF, SLAM) which provides accurate localization in environments where GPS is not available outlines the importance of localization in autonomous drones. The research is developed and demonstrated in multiple environments, and the system’s performance is evaluated based on speed, accuracy, and reliability in comparison with classical systems. This research has significance in demonstrating the operationalization of embedded AI with real-time decision making in improving drone autonomy for applications in delivery, surveillance and, search-and-rescue missions. REFERENCES [1] M. Aghazadeh and M. Gohari, “Real-time obstacle avoidance in autonomous drones: A survey of vision-based navigation methods,” Robot. Auton. Syst., vol. 133, p. 103617, Nov. 2020, doi: 10.1016/j.robot.2020.103617. [2] M. Al-Dhaifallah and M. Ghanem, “Sensor fusion for autonomous drone navigation in GPS-denied environments,” IEEE Trans. Ind. Electron., vol. 67, no. 4, pp. 2935–2943, Apr. 2020, doi: 10.1109/TIE.2019.2958424. [3] J. Bao, W. Wang, and J. Xu, “Development of vision-based drone navigation for obstacle avoidance using deep learning,” J. Field Robot., vol. 37, no. 1, pp. 23–38, Jan. 2020, doi: 10.1002/rob.21968. [4] M. Bai, D. Chen, and F. Liu, “A comprehensive review on SLAM techniques in autonomous drones,” Sensors, vol. 21, no. 3, p. 800, Jan. 2021, doi: 10.3390/s21030800. [5] M. Boulanger and M. Dufour, “Reinforcement learning for autonomous drone navigation: A survey,” Robotics, vol. 10, no. 4, p. 119, Dec. 2021, doi: 10.3390/robotics10040119. [6] M. Burri, D. Nikolic, and R. Siegwart, “Towards robust, real-time visual-inertial navigation for autonomous drones,” J. Field Robot., vol. 37, no. 5, pp. 859–876, Aug. 2020, doi: 10.1002/rob.21968. [7] S. Chien, S. Liu, and Y. Chen, “SLAM-based autonomous navigation system for drones in indoor environments,” Sensors, vol. 21, no. 1, p. 52, Dec. 2020, doi: 10.3390/s21010052. [8] J. David and K. Ramesh, “The role of LiDAR sensors in autonomous drone navigation for precision agriculture,” Sens. Actuators A, Phys., vol. 311, p. 112071, Aug. 2020. [9] A. Das and S. Kumar, “Object detection for autonomous drones using convolutional neural networks,” IEEE Access, vol. 8, pp. 124379–124391, 2020, doi: 10.1109/ACCESS.2020.3006352. [10] M. Z. Afshar and M. H. Shah, “Performance evaluation using balanced scorecard framework: Insights from a public sector case study,” Int. J. Hum. Soc., vol. 5, no. 1, pp. 40–47, 2025. [11] M. Danish and M. M. Siraj, “AI and Cybersecurity: Defending Data and Privacy in the Digital Age,” J. Eng. Comput. Intell. Rev., vol. 3, no. 1, pp. 25–35, 2025. [12] M. Shahinuzzaman, T. A. Shiva, M. S. Sumon, and K. Saifuddin, “Mental health of women breast cancer survivor at different stages of the disease,” Jagannath Univ. J. Earth Life Sci., vol. 5, no. 1, pp. 1–12, 2019. [13] M. Dumitrascu and B. Vasile, “Advances in artificial intelligence algorithms for drone navigation,” Artif. Intell. Rev., vol. 53, no. 3, pp. 1819–1833, Mar. 2020. [14] M. Farhadi and R. Rezaei, “A comprehensive review of AI-based approaches for autonomous drone path planning and obstacle avoidance,” J. Intell. Robot. Syst., vol. 101, no. 3, p. 1419, Mar. 2021, doi: 10.1007/s10846-021-01324-2. [15] M. Z. Afshar and M. H. Shah, “Examining Vision Sharing as a Driver of Organizational Resilience: Evidence from Public Sector Contexts in Developing Economies,” Indus J. Soc. Sci., vol. 3, no. 2, pp. 971–985, 2025. [16] L. Gao and T. Zhang, “Machine learning algorithms for real-time drone navigation and collision avoidance,” Comput., Environ. Urban Syst., vol. 85, p. 101590, Jan. 2021. [17] Y. Hsu, L. Lee, and C. Chang, “Low-power AI for embedded drone control systems,” IEEE Trans. Robot., vol. 36, no. 5, pp. 1348–1356, Oct. 2020, doi: 10.1109/TRO.2020.2980472. [18] R. Jain and P. Shukla, “Path planning and obstacle avoidance in autonomous drones using deep reinforcement learning,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 12, pp. 5243–5253, Dec. 2020, doi: 10.1109/TITS.2020.2983456. [19] W. Jiao and S. Xie, “Autonomous drone navigation using hybrid control algorithms in GPS-denied environments,” IEEE Trans. Aerosp. Electron. Syst., vol. 57, no. 2, pp. 678–689, Apr. 2021, doi: 10.1109/TAES.2021.3078311. [20] A. Kanellakis and M. Dufresne, “Real-time vision-based control of autonomous drones in dynamic environments,” J. Control Decis., vol. 8, no. 2, pp. 120–134, 2021, doi: 10.1080/23307706.2021.1903943. [21] M. Khosravi and M. Amini, “SLAM-based path planning algorithms for autonomous drones,” Sensors, vol. 21, no. 6, p. 1983, Mar. 2021, doi: 10.3390/s21061983. [22] H. Liu and X. Chen, “A review on neural network-based control systems for autonomous drone navigation,” J. Comput. Theor. Nanosci., vol. 17, no. 5, pp. 2091–2099, May 2020, doi: 10.1166/jctn.2020.8783. [23] Y. Lu and Z. Wang, “Path planning for autonomous drones using AI techniques in GPS-denied environments,” IEEE Trans. Robot., vol. 37, no. 5, pp. 1634–1643, Oct. 2021, doi: 10.1109/TRO.2021.3099027. [24] S. Niazi, “Big Data Analytics with Machine Learning: Challenges, Innovations, and Applications,” J. Eng. Comput. Intell. Rev., vol. 2, no. 1, pp. 38–48, 2024. [25] I. Alim, S. Akter, Z. Afroz, A. Al Prince, and M. A. Hasan, “Business Intelligence in the Age of AI: Evaluating Machine Learning's Impact on US Economic Productivity,” Lead Sci. J. Manag. Innov. Soc. Sci., vol. 1, no. 3, pp. 15–30, 2025. [26] A. Moosavi and F. Baghery, “Real-time obstacle avoidance for autonomous drones using deep reinforcement learning,” IEEE Access, vol. 8, pp. 137106–137116, 2020, doi: 10.1109/ACCESS.2020.3018943. [27] H. Nguyen and T. Nguyen, “LiDAR-based navigation for autonomous drones in unknown environments,” Sensors, vol. 21, no. 14, p. 4647, Jul. 2021, doi: 10.3390/s21144647. [28] A. Shah and W. Chou, “Real-time sensor fusion algorithms for autonomous drone navigation,” Robot. Auton. Syst., vol. 133, p. 103602, Nov. 2020, doi: 10.1016/j.robot.2020.103602. [29] X. Yang and Y. Li, “Advancements in AI-based control systems for drones in dynamic environments,” IEEE Trans. Control Syst. Technol., vol. 29, no. 4, pp. 1485–1496, Jul. 2021, doi: 10.1109/TCST.2021.3056548. [30] Z. Zhang and Y. Lin, “Path planning in GPS-denied environments for autonomous drones using deep reinforcement learning,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 6, pp. 2197–2209, Jun. 2021, doi: 10.1109/TNNLS.2020.3025612. [31] Dr. Aurangzeb and M. Asif, "Role of leadership in digital transformation: A case of Pakistani SMEs," in 2021 Fourth International Conference on Emerging Trends in Engineering, Management and Sciences (ICETEMS), Oct. 13-14, 2021, vol. 4, no. 1, pp. 219-229. [32] A. Mumtaz, N. Munir, R. Mumtaz, M. Farooq, and M. Asif, "Impact Of Psychological & Economic Factors On Investment Decision-Making In Pakistan Stock Exchange," Journal of Positive School Psychology, vol. 7, no. 4, pp. 130-135, 2023. [33] M. Asif, H. Shah, and H. A. H. Asim, "Cybersecurity and audit resilience in digital finance: Global insights and the Pakistani context," Journal of Asian Development Studies, vol. 14, no. 3, pp. 560-573, 2025. [34] M. Asif, "The complexities of bioterrorism: Challenges and considerations," International Journal of Contemporary Issues in Social Sciences, vol. 3, no. 3, pp. 2175–2184, 2024. [35] H. A. Usama, M. Riaz, A. Khan, N. Begum, M. Asif, and M. Hamza, "Prohibition of alcohol in Quran and Bible (A research and analytical review)," PalArch's Journal of Archaeology of Egypt/Egyptology, vol. 19, no. 4, pp. 1202-1211, 2022.
Copyright (c) 2025 Muhammad Taha Abbas, Farrukh Hanif (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Trust Management-based Energy Efficient Message Scheduling Algorithm in the Internet of Things System
Internet of Things (IoT) is an emerging concept in internet development that connects diverse objects by transmitting information from there to a central hub for interpretation. Though much work has been done individually either on message scheduling or provisioning of a secure environment to assure data accuracy in IoT systems, this paper focuses on a Trust Management-Based Scheme to defend against internal attacks while parallel handling Message Scheduling to improve overall IoT system efficiency. Choosing, secure cluster heads based on residual energy and node density to balance the network load through a trust-value scheme and operating the message scheduler on cluster heads to decide the transmission of messages is a salient feature of the proposed system. Managing traffic intensity for scheduling messages in a secure environment made the proposed algorithm more reliable against malicious attacks and energy consumption. In this IoT environment, things/nodes are placed into subgroups with a trusted cluster head responsible for successful message delivery from the group to the receiver of the sensed data. The simulation results show the effectiveness and efficiency of the proposed algorithm. REFERENCES Abdullah, M. N. Asghar, M. Ashraf, and N. Abbas, "An energy-efficient message scheduling algorithm with joint routing mechanism at network layer in Internet of Things environment," Wireless Personal Communications, vol. 111, no. 3, pp. 1821–1835, 2020, doi: 10.1007/s11277-019-06959-x. Abdullah and K. Yang, "An energy-efficient message scheduling algorithm in Internet of Things environment," in Proc. 2013 9th Int. Wireless Commun. Mobile Comput. Conf. (IWCMC), 2013, pp. 311–316, doi: 10.1109/IWCMC.2013.6583578. O. Abu Salem and N. Shudifat, "Enhanced LEACH protocol for increasing a lifetime of WSNs," Personal and Ubiquitous Computing, vol. 23, no. 5-6, pp. 901–907, 2019, doi: 10.1007/s00779-019-01205-4. Ali et al., "An efficient cryptographic technique using modified Diffie–Hellman in wireless sensor networks," International Journal of Distributed Sensor Networks, vol. 16, no. 6, 2020, doi: 10.1177/1550147720925772. Almobaideen, M. Qatawneh, and O. Abualghanam, "Virtual Node Schedule for Supporting QoS in Wireless Sensor Network," in Proc. IEEE Jordan Int. Joint Conf. Elect. Eng. Inf. Technol. (JEEIT), 2019, pp. 281–285, doi: 10.1109/JEEIT.2019.8717465. Amjad et al., "Blockchain Based Authentication and Cluster Head Selection Using DDR-LEACH in Internet of Sensor Things," Sensors, vol. 22, no. 5, 2022, doi: 10.3390/s22051972. B. D. and F. Al-Turjman, "A hybrid secure routing and monitoring mechanism in IoT-based wireless sensor networks," Ad Hoc Networks, vol. 97, 2020, doi: 10.1016/j.adhoc.2019.102022. Cheng, C. Xu, X. Liu, J. Li, and J. Zhang, "LEACH Protocol Optimization Based on Weighting Strategy and the Improved Ant Colony Algorithm," Frontiers in Neurorobotics, vol. 16, 2022, doi: 10.3389/fnbot.2022.840332. Fang et al., "FETMS: Fast and Efficient Trust Management Scheme for Information-Centric Networking in Internet of Things," IEEE Access, vol. 7, pp. 13476–13485, 2019, doi: 10.1109/ACCESS.2019.2892712. Fang, W. Zhang, W. Yang, Z. Li, W. Gao, and Y. Yang, "Trust management-based and energy efficient hierarchical routing protocol in wireless sensor networks," Digital Communications and Networks, vol. 7, no. 4, pp. 470–478, 2021, doi: 10.1016/j.dcan.2021.03.005. Fotohi, S. Firoozi Bari, and M. Yusefi, "Securing Wireless Sensor Networks Against Denial-of-Sleep Attacks Using RSA Cryptography Algorithm and Interlock Protocol," International Journal of Communication Systems, vol. 33, no. 4, pp. 1–25, 2020, doi: 10.1002/dac.4234. Ganeriwal, L. K. Balzano, and M. B. Srivastava, "Reputation-based framework for high integrity sensor networks," ACM Transactions on Sensor Networks, vol. 4, no. 3, pp. 1–37, 2008, doi: 10.1145/1362542.1362546. Shaheen, "The Internet of Things (IoT): A Comprehensive Review of Technologies, Applications, Challenges, and Future Trends," Journal of Engineering and Computational Intelligence Review, vol. 2, no. 1, pp. 1-8, 2024. Gulen and S. Baktir, "Elliptic curve cryptography for wireless sensor networks using the number theoretic transform," Sensors, vol. 20, no. 5, 2020, doi: 10.3390/s20051507. B. Gupta and M. Quamara, "An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols," Concurrency and Computation: Practice and Experience, vol. 32, no. 21, 2020, doi: 10.1002/cpe.4946. K. Khan and A. Ullah, "Implication of IoT and its impact on library services: An overview," Inverge Journal of Social Sciences, vol. 3, no. 2, pp. 63-72, 2024. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 660–670, 2002, doi: 10.1109/TWC.2002.804190. Ji, L. Li, and W. Zhou, "Design and implementation of a RFID Reader/Router in RFID-WSN hybrid system," Future Internet, vol. 10, no. 11, 2018, doi: 10.3390/fi10110106. Museera and H. Khan, "Internet of Things in Food Supply Chains: Enhancing Quality and Safety through Smart Technologies," Journal of Engineering and Computational Intelligence Review, vol. 1, no. 1, pp. 1-6, 2023. Kanchana Devi and R. Ganesan, "Trust-based selfish node detection mechanism using beta distribution in wireless sensor network," Journal of ICT Research and Applications, vol. 13, no. 1, pp. 79–92, 2019, doi: 10.5614/itbj.ict.res.appl.2019.13.1.6. A. Khalid, Q. Bai, and A. Al-Anbuky, "Adaptive Trust-Based Routing Protocol for Large Scale WSNs," IEEE Access, vol. 7, pp. 143539–143549, 2019, doi: 10.1109/ACCESS.2019.2944648. K. Khan et al., "Hierarchical Routing Protocols for Wireless Sensor Networks: Functional and Performance Analysis," Journal of Sensors, vol. 2021, 2021, doi: 10.1155/2021/7459368. N. Khan et al., "Improving energy efficiency with content-based adaptive and dynamic scheduling in wireless sensor networks," IEEE Access, vol. 8, pp. 176495–176520, 2020, doi: 10.1109/ACCESS.2020.3026939. Natarajan and S. Subramanian, "A cross-layer design: energy efficient multilevel dynamic feedback scheduling in wireless sensor networks using deadline aware active time quantum for environmental monitoring," International Journal of Electronics, vol. 106, no. 1, pp. 87–108, 2019, doi: 10.1080/00207217.2018.1501615. Asif, “The complexities of bioterrorism: Challenges and considerations,” International Journal of Contemporary Issues in Social Sciences, vol. 3, no. 3, pp. 2175–2184, 2024. [Online]. Available: https://ijciss.org/index.php/ijciss/article/view/1391 Aurangzeb, D., & Asif, M. (2021). Role of leadership in digital transformation: A case of Pakistani SMEs. In Fourth International Conference on Emerging Trends in Engineering, Management and Sciences (ICETEMS-2021)(4 (1), 219-229).
Copyright (c) 2025 Shanza Ijaz, Muhammad Talha Zubair (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
AI and Blockchain Integration: Driving Strategic Business Advancements in the Intelligent Era
This research examines the adopted artificial intelligence (AI) and blockchain integration in contemporary companies systems and their capabilities to enhance efficiency, security, and innovation in the finance, healthcare, and supply chain sectors. It also seeks to analyze the adoption patterns, operational advantages, and critical challenges that obstruct the wide-spread deployment of the technology. The study’s framework is quantitative, taking the structured survey method of data collection, the target population being 384 professionals, inclusive of business professionals, technology professionals, and scholars. The sample size was calculated using the Cochran formula, achieving a 95% confidence level and 5% margin of error. The collected data were processed, and a descriptive analysis was done, with the results being displayed in charts, graphs, and tables, highlighting the trends, barriers, and quantifiable impacts of technology adoption in specific industries. The results show that 69.2% of organizations reported enhanced operational efficiency with the adoption of AI-Blockchain solutions, with fraud detection (64.6%), smart contracts (54.7%), and data analytics (57.6%) cited as the primary use cases. However, the study identified high implementation costs (71.9%), talent shortages (60.2%), and regulatory uncertainty (51.6%) as the primary barriers. Interestingly, 80.5% of the respondents expect these technologies to become the de facto industry standards by the year 2030, and 81.3% confirmed that adoption of these technologies would lead to a competitive advantage. This research closes the gap between theoretical concepts and actual application by offering tailored recommendations for organizations at various levels of digital transformation. It shows the cross-sectional adoption dynamics of sectors AI and Blockchain and develops integration hurdles—strategic suggestions on adoption, phased integration milestones, and recommendations on policy and talent frameworks. These results, while academic in nature, can also provide guidance for enterprises in the now called era of intelligence. References Arafat, "Business intelligence as a strategic asset: Measuring its role in enhancing US national interests across defense, trade, and cyber domains," J. Bus. Insight Innov., vol. 4, no. 1, pp. 1–20, 2025. Mosaddeque et al., "The role of AI and machine learning in fortifying cybersecurity systems in the US healthcare industry," Inverge J. Soc. Sci., vol. 1, no. 2, pp. 70–81, 2022. Rowshon, A. Mosaddeque, T. Ahmed, and U. Twaha, "Exploring the impact of generative AI and virtual reality on mental health: Opportunities, challenges, and implications for well-being," 2025, doi: 10.54660/.IJMRGE.2022.3.1.784-796. Twaha, A. Mosaddeque, and M. Rowshon, "Accounting implications of using AI to enhance incentives for wireless energy transmission in smart cities," 2025, doi: 10.54660/.IJMRGE.2025.6.2.1208-1218. Qayyum et al., "Revolutionizing market insights through AI and data analytics: The next era of competitive intelligence," Crit. Rev. Soc. Sci. Stud., vol. 3, no. 1, pp. 3285–3302, 2025. Khan et al., "Artificial intelligence and 6G integration: Transforming the digital technology landscape," Spectr. Eng. Sci., vol. 3, no. 6, pp. 717–737, 2025. R. Haque et al., "Liquidity traps, digital currencies and inflation targeting: A comparative analysis of policy effectiveness in advanced and emerging economies," Inverge J. Soc. Sci., vol. 2, no. 3, pp. 148–165, 2023, doi: 10.63544/ijss.v2i3.115. Z. Afshar and M. H. Shah, "Examining the role of change management in enhancing organizational resilience in public sector entities," Cent. Manag. Sci. Res., vol. 3, no. 3, pp. 931–942, 2025. Alim, "The impact of artificial intelligence on the accounting profession: Technological advancements and employment perspectives," Int. J. Sci. Res. Arch., vol. 15, no. 3, pp. 1173–1187, 2025, doi: 10.30574/ijsra.2025.15.3.1873. Z. Afshar and M. H. Shah, "Examining vision sharing as a driver of organizational resilience: Evidence from public sector contexts in developing economies," Indus J. Soc. Sci., vol. 3, no. 2, pp. 971–985, 2025. Basharat et al., "Strategic innovations and transformative impact of blockchain technology," Asian Bull. Big Data Manag., vol. 5, no. 2, pp. 87–103, 2025, doi: 10.62019/sc4xdv41. Sultan et al., "AI-driven cybersecurity: Protecting data and privacy in an evolving digital world," Spectr. Eng. Sci., vol. 3, no. 7, pp. 853–875, 2025. Ali, "Advancements and transformative applications of blockchain technology," J. Eng. Comput. Intell. Rev., vol. 3, no. 1, pp. 36–51, 2025. Z. Afshar and M. H. Shah, "Leveraging Porter's diamond model: Public sector insights," Crit. Rev. Soc. Sci. Stud., vol. 3, no. 2, pp. 2255–2271, 2025. Butt, "Impact of E-banking service quality on customers' behavior intentions mediating role of trust," Glob. Manag. J. Acad. Corp. Stud., vol. 11, no. 2, pp. 1–21, 2021. Khan and S. Alvi, "A comprehensive review of blockchain applications across diverse domains," J. Eng. Comput. Intell. Rev., vol. 1, no. 1, pp. 23–38, 2023. Z. Afshar and M. H. Shah, "Resilience through adaptation: Examining the interplay between adaptive capacity and organizational resilience in public sector organizations," Acad. Int. J. Soc. Sci., vol. 4, no. 2, pp. 1770–1789, 2025. H. Chowdhury, "The evolution of business operations: Unleashing the potential of artificial intelligence, machine learning, and blockchain," World J. Adv. Res. Rev., vol. 22, no. 3, pp. 2135–2147, 2024. Butt, "Service quality assessment and student satisfaction in business schools: Mediating role of perceived value," MOJEM, vol. 9, no. 1, pp. 58–76, 2020. Arshad, M. U. Baber, and A. Ullah, "Assessing the transformative influence of ChatGPT on research practices among scholars in Pakistan," Mesopot. J. Big Data, pp. 1–10, 2024. Aakula, C. Zhang, and T. Ahmad, "Leveraging AI and blockchain for strategic advantage in digital transformation," J. Artif. Intell. Res., vol. 4, no. 1, pp. 356–395, 2024. Paramesha, N. Rane, and J. Rane, "Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence," 2024. Butt and N. Yazdani, "Implementation of quality management practices and firm’s innovation performance: Mediation of knowledge creation processes and moderating role of digital transformation," Pak. J. Humanit. Soc. Sci., vol. 11, no. 4, pp. 3881–3902, 2023. R. Xuan and S. Ness, "Integration of blockchain and AI: Exploring application in the digital business," J. Eng. Res. Rep., vol. 25, no. 8, pp. 20–39, 2023. H. Shahzad et al., "The rise of autonomous enterprises: How AI is reshaping business models and market dynamics," Acad. Int. J. Soc. Sci., vol. 4, no. 2, pp. 677–693, 2025. Javaid, "Evaluating current attitudes regarding parental genetic testing for beta thalassemia among pregnant women in Pakistan," IJHR, vol. 3, no. 4, pp. 146–156, Jul. 2025, doi: 10.71000/nrq0rd49. Kuznetsov et al., "On the integration of artificial intelligence and blockchain technology: A perspective about security," IEEE Access, vol. 12, pp. 3881–3897, 2024. Kumar et al., "Artificial intelligence and blockchain integration in business: Trends from a bibliometric-content analysis," Inf. Syst. Front., vol. 25, no. 2, pp. 871–896, 2023. K. Tyagi, S. U. Aswathy, and A. Abraham, "Integrating blockchain technology and artificial intelligence: Synergies perspectives challenges and research directions," J. Inf. Assur. Secur., vol. 15, no. 5, pp. 1554, 2020. U. Attah, O. Y. Ogunsola, and B. M. P. Garba, "Leadership in the digital age: Emerging trends in business strategy, innovation, and technology integration," Icon. Res. Eng. J., vol. 6, no. 9, pp. 389–411, 2023. French et al., "The 4th industrial revolution powered by the integration of AI, blockchain, and 5G," Commun. Assoc. Inf. Syst., vol. 49, no. 1, pp. 6, 2021. Hemamalini et al., "Artificial intelligence–blockchain-enabled–internet of things–based cloud applications for next-generation society," in Automated Secure Computing for Next-Generation Systems, 2024, pp. 65–82. O. Eboigbe et al., "Business intelligence transformation through AI and data analytics," Eng. Sci. Technol. J., vol. 4, no. 5, pp. 285–307, 2023. G. Hardini, N. A. Yusuf, and A. R. A. Zahra, "Convergence of intelligent networks: Harnessing the power of artificial intelligence and blockchain for future innovations," ADI J. Recent Innov., vol. 5, no. 2, pp. 200–209, 2024.
Copyright (c) 2025 Ishrak Alim, Nafiz Imtiaz, Abdullah Al Prince, MD Asif Hasan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0