Journal of Engineering and Computational Intelligence Review
type: channel
description: 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 meani…
publisher: Inverge Consultancy (Pvt) Ltd.
language: en
publication name: Journal of Engineering and Computational Intelligence Review
issn: 3106-969X
items: FinRisk AgentNet: Multi-Agent LLM & ML Architecture for Agentic Fraud Risk Detection & Quantitative Financial Risk Management
items: Transformer-Based Anomaly Detection in IoT Networks Using Multimodal Sensor Data
items: Transfer Learning for Small Data Analytics: Methodological Solutions to Sample Size Constraints
items: Synthesis and Characterization of Metal–Organic Frameworks (MOFs) for Environmental and Catalytic Applications
items: From Data Lakes to Business Value: A Capability-Based Framework for Analytics Maturity
items: AI-Powered Cybersecurity: Graph-Based Anomaly Detection in Network Traffic
items: A Scalable and Privacy-Preserving Machine Learning Framework for Early Detection of Chronic Diseases
items: AI-Powered Supplier Risk Intelligence: Predicting Financial and Geopolitical Supply Chain Disruptions in U.S. Critical Industries
items: AI-Enhanced Network Optimization for Electric Vehicle Charging Infrastructure Expansion in the United States Using Graph Theory and Demand Analytics
items: AI-Driven Predictive Maintenance for U.S. Smart Manufacturing: Deep Learning Models for Equipment Failure Prediction and Operational Resilience
items: Autonomous Threat Detection Using Multi-Agent AI and LLM-Assisted Network Traffic Analysis
items: Deep Learning-Enhanced Low-Dose CT Reconstruction for Pediatric Chest Imaging: Diagnostic Accuracy and Radiation Dose Optimization
items: Enhancing Large Language Model Reasoning via Retrieval-Augmented Generation and Self-Verification Mechanisms
items: Mitigating Advanced Persistent Threats: A Framework for Enhancing Organizational Cybersecurity Posture
items: AI-Powered Numerical Methods: Transforming Mathematical Problem-Solving
items: Machine Learning-Based Comparative Analysis of Dimensionality Reduction and Clustering Techniques: Evidence from MNIST and Rice Datasets
items: Agentic AI Deployment in Infrastructure-limited Environments: Observability Gaps, Failure Modes, and AI Governance Primitives
items: Machine Learning Models for Forecasting Employee Demand in Healthcare HR
items: Real-Time Threat Intelligence Correlation and Triage for Reducing Security Analyst Burnout
items: AI-Based Cybersecurity Solutions: Securing Information and Privacy in the Evolving Digital Age
items: Algorithmic Hyper-Personalization: The Double-Edged Sword of Predictive Personalization - An Empirical Investigation
items: Mitigating Financial Waste in the U.S. Healthcare System: An AI-Driven Framework for Real-Time Fraud Detection in Medicare and Medicaid Claims
items: A Diabetes Prediction Decision Support System Using Machine Learning
items: Combined Geophysical and Geochemical Evaluation of the Kirthar Fold Belt for Mineral and Hydrocarbon Resources
items: Integrating Blockchain-Enhanced Enterprise Systems for U.S. Banking, Pharmaceutical Supply Chains, Economic Resilience, and Secure Operational Models
items: Leveraging Machine Learning Techniques for Enhancing Signature Verification to Unveiling the Forgery
items: Performance Evaluation of Battery Management Systems in Lithium-Ion Battery-Powered Electric Vehicles
items: Design and Control of an Autonomous Drone Navigation System Using Embedded AI
items: A Trust Management-based Energy Efficient Message Scheduling Algorithm in the Internet of Things System
items: AI and Blockchain Integration: Driving Strategic Business Advancements in the Intelligent Era

FinRisk AgentNet: Multi-Agent LLM & ML Architecture for Agentic Fraud Risk Detection & Quantitative Financial Risk Management
type: item
description: 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 trace…
subject: Articles
  topic: section
subject: Multi-Agent Systems
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Fraud Risk Detection
  topic: keywords
subject: Financial Risk Management
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Quantitative Risk Analytics
  topic: keywords
creator: Kayode L. Ogunsusi
creator: Toyyibat Titilope Yussuph
creator: Basirat Adebimpe Hammed
rights: Copyright (c) 2025 Kayode L. Ogunsusi, Toyyibat Titilope Yussuph, Basirat Adebimpe Hammed (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-01-30
publication date: 2025-01-30
starting page: 155
ending page: 172

Transformer-Based Anomaly Detection in IoT Networks Using Multimodal Sensor Data
type: item
description: 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.…
subject: Articles
  topic: section
subject: Transformer Model
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: IOT Networks
  topic: keywords
subject: Multimodal Sensor Data
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: Time-Series Analysis
  topic: keywords
subject: Cyber-Physical Systems
  topic: keywords
subject: Sensor Fusion
  topic: keywords
subject: Smart Systems
  topic: keywords
creator: Tahir Ullah
creator: Muheeb Ullah
creator: Abdullah Khan
creator: Sohail Khan
creator: Talha khan
creator: Hamza Khan
rights: 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
date: 2026-06-27
publication date: 2026-06-27
starting page: 112
ending page: 122

Transfer Learning for Small Data Analytics: Methodological Solutions to Sample Size Constraints
type: item
description: 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…
subject: Articles
  topic: section
subject: Transfer Learning
  topic: keywords
subject: Small-Data Analytics
  topic: keywords
subject: Sample Size Constraints
  topic: keywords
subject: Hierarchical Bayesian Modelling
  topic: keywords
subject: Conformal Prediction
  topic: keywords
subject: HDLSS
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Scarcity
  topic: keywords
subject: Methodological Solutions
  topic: keywords
creator: Asher Saqib
rights: Copyright (c) 2025 Asher Saqib (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-20
publication date: 2025-07-20
starting page: 194
ending page: 203

Synthesis and Characterization of Metal–Organic Frameworks (MOFs) for Environmental and Catalytic Applications
type: item
description: 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…
subject: Articles
  topic: section
subject: Metal–Organic Frameworks (MOFs)
  topic: keywords
subject: Environmental Remediation
  topic: keywords
subject: Catalysis
  topic: keywords
subject: Wastewater Treatment
  topic: keywords
subject: Photocatalysis
  topic: keywords
subject: Hydrogen Production
  topic: keywords
subject: Adsorption
  topic: keywords
subject: Surface Area
  topic: keywords
subject: Crystallinity
  topic: keywords
subject: Advanced Materials
  topic: keywords
creator: Mohammad Younas
creator: Anees Ur Rehman
creator: Zainab Rehman
creator: Asma Asif
rights: Copyright (c) 2026 Mohammad Younas, Anees Ur Rehman , Zainab Rehman, Asma Asif (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-21
publication date: 2026-06-21
starting page: 101
ending page: 111

From Data Lakes to Business Value: A Capability-Based Framework for Analytics Maturity
type: item
description: 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). Kno…
subject: Articles
  topic: section
subject: Analytics Maturity
  topic: keywords
subject: Data Lakes
  topic: keywords
subject: Capability-Based Framework
  topic: keywords
subject: Business Value
  topic: keywords
subject: Structural Equation Modelling
  topic: keywords
subject: Resource-Based View
  topic: keywords
subject: Socio-Technical Systems
  topic: keywords
creator: Asher Saqib
rights: Copyright (c) 2025 Asher Saqib (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-02-20
publication date: 2025-02-20
starting page: 139
ending page: 154

AI-Powered Cybersecurity: Graph-Based Anomaly Detection in Network Traffic
type: item
description: 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.…
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Graph-Based Anomaly Detection
  topic: keywords
subject: Network Traffic Analysis
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Threat Detection
  topic: keywords
creator: Syed Mehzeyar Ali Zaidi
creator: Sufyan Muhammad Khan
rights: Copyright (c) 2026 Sufyan Muhammad Khan, Syed Mehzeyar Ali Zaidi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-19
publication date: 2026-06-19
starting page: 91
ending page: 100

A Scalable and Privacy-Preserving Machine Learning Framework for Early Detection of Chronic Diseases
type: item
description: 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 o…
subject: Articles
  topic: section
subject: Privacy-Preserving Machine Learning
  topic: keywords
subject: Chronic Disease Detection
  topic: keywords
subject: Differential Privacy
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Gradient Boosting
  topic: keywords
subject: Healthcare AI
  topic: keywords
subject: Clinical Decision Support System
  topic: keywords
subject: Privacy–Utility Trade-off
  topic: keywords
subject: IoT Healthcare Systems
  topic: keywords
creator: Shuvo Dutta
creator: Rajesh Sikder
creator: Mithun Ranjan Kar
rights: Copyright (c) 2026 Shuvo Dutta, Rajesh Sikder, Mithun Ranjan Kar (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-09
publication date: 2026-06-09
starting page: 73
ending page: 90

AI-Powered Supplier Risk Intelligence: Predicting Financial and Geopolitical Supply Chain Disruptions in U.S. Critical Industries
type: item
description: 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, …
subject: Articles
  topic: section
subject: AI-Powered Risk Intelligence
  topic: keywords
subject: Supplier Risk Management
  topic: keywords
subject: Financial Distress Prediction
  topic: keywords
subject: Geopolitical Analytics
  topic: keywords
subject: XGBOOST
  topic: keywords
subject: BERT
  topic: keywords
subject: Supply Chain Disruptions
  topic: keywords
subject: Early Warning Systems
  topic: keywords
subject: Multi-Modal Fusion
  topic: keywords
subject: U.S. Critical industries
  topic: keywords
creator: Umair Iqbal
rights: Copyright (c) 2025 Umair Iqbal (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-31
publication date: 2025-07-31
starting page: 173
ending page: 193

AI-Enhanced Network Optimization for Electric Vehicle Charging Infrastructure Expansion in the United States Using Graph Theory and Demand Analytics
type: item
description: 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 usi…
subject: Articles
  topic: section
subject: Electric vehicle charging infrastructure
  topic: keywords
subject: AI-enhanced optimization
  topic: keywords
subject: graph theory
  topic: keywords
subject: demand analytics
  topic: keywords
subject: D-ST-GNN
  topic: keywords
subject: Bayesian optimization
  topic: keywords
subject: network coverage
  topic: keywords
subject: equity
  topic: keywords
subject: sustainable transportation
  topic: keywords
creator: Umair Iqbal
rights: Copyright (c) 2024 Umair Iqbal (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-09-30
publication date: 2024-09-30
starting page: 112
ending page: 129

AI-Driven Predictive Maintenance for U.S. Smart Manufacturing: Deep Learning Models for Equipment Failure Prediction and Operational Resilience
type: item
description: 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…
subject: Articles
  topic: section
subject: AI-Driven Predictive Maintenance
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Equipment Failure Prediction
  topic: keywords
subject: Operational Resilience
  topic: keywords
subject: Survival Analysis
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: IoT Sensors
  topic: keywords
subject: Maintenance Optimization
  topic: keywords
creator: Umair Iqbal
rights: Copyright (c) 2025 Umair Iqbal (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-01-04
publication date: 2025-01-04
starting page: 114
ending page: 138

Autonomous Threat Detection Using Multi-Agent AI and LLM-Assisted Network Traffic Analysis
type: item
description: 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…
subject: Articles
  topic: section
subject: Autonomous Threat Detection
  topic: keywords
subject: Multi-Agent AI
  topic: keywords
subject: Large Language Models (LLM)
  topic: keywords
subject: Network Traffic Analysis
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Abdul Hanan
creator: Abdul Hanan Imtiaz Ahmed Khan
creator: Faez Akhtar
creator: Zerminey Saleem
rights: Copyright (c) 2026 Abdul Hanan, Abdul Hanan Imtiaz Ahmed Khan, Faez Akhtar, Zerminey Saleem (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-07
publication date: 2026-06-07
starting page: 55
ending page: 72

Deep Learning-Enhanced Low-Dose CT Reconstruction for Pediatric Chest Imaging: Diagnostic Accuracy and Radiation Dose Optimization
type: item
description: 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…
subject: Articles
  topic: section
subject: Radiology
  topic: keywords
subject: Low-dose CT
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Pediatric imaging
  topic: keywords
subject: Image reconstruction
  topic: keywords
subject: Radiation dose reduction
  topic: keywords
subject: Chest CT
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Diagnostic accuracy
  topic: keywords
subject: ALARA
  topic: keywords
creator: Piam Zahra
rights: Copyright (c) 2026 Piam Zahra (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-04
publication date: 2026-06-04
starting page: 46
ending page: 54

Enhancing Large Language Model Reasoning via Retrieval-Augmented Generation and Self-Verification Mechanisms
type: item
description: 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. Nar…
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: Retrieval Augmented Generation
  topic: keywords
subject: Self-Verification
  topic: keywords
subject: Hallucination Mitigation
  topic: keywords
subject: Evidence Based Reasoning
  topic: keywords
subject: Knowledge Intensive Tasks
  topic: keywords
subject: Factual Accuracy
  topic: keywords
subject: Natural Language Processing
  topic: keywords
creator: Zerminey Saleem
creator: Muhammad Noor ul Haq
creator: Sifat Ullah
creator: Ali Raza
rights: Copyright (c) 2026 Zerminey Saleem, Muhammad Noor ul Haq, Sifat Ullah, Ali Raza (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-19
publication date: 2026-05-19
starting page: 34
ending page: 45

Mitigating Advanced Persistent Threats: A Framework for Enhancing Organizational Cybersecurity Posture
type: item
description: 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.stim…
subject: Articles
  topic: section
subject: Advanced Persistent Threats (APTs)
  topic: keywords
subject: Cybersecurity Posture
  topic: keywords
subject: Zero Trust Architecture
  topic: keywords
subject: APT Detection and Mitigation
  topic: keywords
subject: Organizational Cybersecurity
  topic: keywords
subject: MITRE Attack Framework
  topic: keywords
creator: Fahad Amin
creator: Usman Imtiaz
rights: Copyright (c) 2026 Fahad Amin, Usman Imtiaz (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-04
publication date: 2025-06-04
starting page: 99
ending page: 113

AI-Powered Numerical Methods: Transforming Mathematical Problem-Solving
type: item
description: 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. 4…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Numerical Methods
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Hybrid Models
  topic: keywords
subject: Computational Efficiency
  topic: keywords
subject: Mathematical Problem Solving
  topic: keywords
creator: Muhammad Zeeshan Ashraf
creator: Rabia Kamal
creator: Ali Ghulam
rights: Copyright (c) 2026 Muhammad Zeeshan Ashraf, Rabia Kamal, Ali Ghulam (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-08
publication date: 2026-05-08
starting page: 25
ending page: 33

Machine Learning-Based Comparative Analysis of Dimensionality Reduction and Clustering Techniques: Evidence from MNIST and Rice Datasets
type: item
description: 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. …
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: MNIST
  topic: keywords
subject: Clustering
  topic: keywords
subject: Expectation Maximization
  topic: keywords
subject: Principal Component Analysis
  topic: keywords
subject: Independent Component
  topic: keywords
subject: Randomized Projection
  topic: keywords
subject: Manifold Learning
  topic: keywords
creator: Imran Ullah
creator: Maryam Javaid
creator: Urooj Tariq
rights: Copyright (c) 2026 Imran Ullah, Maryam Javaid , Urooj Tariq (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-26
publication date: 2026-04-26
starting page: 12
ending page: 24

Agentic AI Deployment in Infrastructure-limited Environments: Observability Gaps, Failure Modes, and AI Governance Primitives
type: item
description: 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…
subject: Articles
  topic: section
subject: Agentic AI
  topic: keywords
subject: Observability
  topic: keywords
subject: Failure Modes
  topic: keywords
subject: AI Governance
  topic: keywords
subject: Infrastructure-Limited Environments
  topic: keywords
subject: Edge Computing
  topic: keywords
creator: Omar Azhar Malik
rights: Copyright (c) 2026 Omar Azhar Malik (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-21
publication date: 2026-04-21
starting page: 1
ending page: 11

Machine Learning Models for Forecasting Employee Demand in Healthcare HR
type: item
description: 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 forecasti…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Workforce Demand Forecasting
  topic: keywords
subject: Healthcare HR
  topic: keywords
subject: HR Analytics
  topic: keywords
subject: Patient Inflow
  topic: keywords
subject: Emergency Cases
  topic: keywords
subject: Data-Driven Decision Making
  topic: keywords
subject: Forecast Accuracy
  topic: keywords
creator: Syed Tanvirul Hasan
rights: Copyright (c) 2025 Syed Tanvirul Hasan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 159
ending page: 172

Real-Time Threat Intelligence Correlation and Triage for Reducing Security Analyst Burnout
type: item
description: 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, “Jo…
subject: Articles
  topic: section
subject: Cyber Threat Intelligence
  topic: keywords
subject: Real-Time Correlation
  topic: keywords
subject: Triage Automation
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Analyst Burnout
  topic: keywords
subject: Security Operations Center
  topic: keywords
subject: Threat Prioritization
  topic: keywords
subject: Machine Learning Security.
  topic: keywords
creator: Akib Rahman
creator: Sharmin Sultana
rights: Copyright (c) 2023 Akib Rahman, Sharmin Sultana (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-12-31
publication date: 2023-12-31
starting page: 64
ending page: 86

AI-Based Cybersecurity Solutions: Securing Information and Privacy in the Evolving Digital Age
type: item
description: 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 …
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Threat Detection
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Digital Security
  topic: keywords
creator: Fahad Amin
creator: Ihsan Said
creator: Mahad Ali Butt
rights: Copyright (c) 2025 Fahad Amin, Ihsan Said (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 95
ending page: 111

Algorithmic Hyper-Personalization: The Double-Edged Sword of Predictive Personalization - An Empirical Investigation
type: item
description: 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…
subject: Articles
  topic: section
subject: Hyper-Personalization
  topic: keywords
subject: Algorithmic Personalization
  topic: keywords
subject: Creepiness
  topic: keywords
subject: Privacy Concerns
  topic: keywords
subject: Customer Loyalty
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Consumer Privacy
  topic: keywords
creator: Danyal Mohiuddin
rights: Copyright (c) 2024 Danyal Mohiuddin (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 82
ending page: 94

Mitigating Financial Waste in the U.S. Healthcare System: An AI-Driven Framework for Real-Time Fraud Detection in Medicare and Medicaid Claims
type: item
description: 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 …
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Healthcare Fraud
  topic: keywords
subject: Financial Waste
  topic: keywords
subject: Medicare
  topic: keywords
subject: Medicaid
  topic: keywords
subject: Real-Time Fraud Detection
  topic: keywords
subject: Claims Management
  topic: keywords
creator: Umma Twaha
rights: Copyright (c) 2024 Umma Twaha (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 71
ending page: 81

A Diabetes Prediction Decision Support System Using Machine Learning
type: item
description: 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: …
subject: Articles
  topic: section
subject: Diabetes Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Random Forest Classifier
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subject: Health Informatics
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subject: Medical Data Analysis
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subject: Predictive
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creator: Muhammad Faisal
creator: Imtiaz Ali Shah
creator: Faiza Aurang Zeb
rights: Copyright (c) 2025 Muhammad Faisal, Imtiaz Ali Shah, Faiza Aurang Zeb (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 129
ending page: 141

Combined Geophysical and Geochemical Evaluation of the Kirthar Fold Belt for Mineral and Hydrocarbon Resources
type: item
description: 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, do…
subject: Articles
  topic: section
subject: Kirthar Fold Belt
  topic: keywords
subject: Geophysical Methods
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subject: Geochemical Exploration
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subject: Mineralization
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subject: Hydrocarbon Potential
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creator: Sayed Qaisar Abbas
creator: Muhammad Naveed Khalil
rights: Copyright (c) 2025 Sayed Qaisar Abbas, Muhammad Naveed Khalil (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-10
publication date: 2025-12-10
starting page: 115
ending page: 128

Integrating Blockchain-Enhanced Enterprise Systems for U.S. Banking, Pharmaceutical Supply Chains, Economic Resilience, and Secure Operational Models
type: item
description: 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. Conclus…
subject: Articles
  topic: section
subject: Blockchain Technology
  topic: keywords
subject: Enterprise Systems
  topic: keywords
subject: U.S. Banking
  topic: keywords
subject: Pharmaceutical Supply Chain
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subject: Economic Resilience
  topic: keywords
subject: Secure Operational Models
  topic: keywords
subject: Adoption Barriers
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subject: Digital Transformation
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creator: Arafat Hossain Khan Choain
creator: Chinmoy Majumder
creator: Md Abu Nasir Nasir
creator: Nasrin Sultana
rights: 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
date: 2023-12-31
publication date: 2023-12-31
starting page: 51
ending page: 63

Leveraging Machine Learning Techniques for Enhancing Signature Verification to Unveiling the Forgery
type: item
description: 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 …
subject: Articles
  topic: section
subject: Offline and Online signature
  topic: keywords
subject: Handwritten Signature Verification
  topic: keywords
subject: Machine Learning Algorithms
  topic: keywords
subject: Classification
  topic: keywords
creator: Shumaila Ejaz
rights: Copyright (c) 2025 Shumaila Ejaz (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-10
publication date: 2025-11-10
starting page: 96
ending page: 114

Performance Evaluation of Battery Management Systems in Lithium-Ion Battery-Powered Electric Vehicles
type: item
description: 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, “…
subject: Articles
  topic: section
subject: Battery Management System (BMS)
  topic: keywords
subject: State of Charge (SOC)
  topic: keywords
subject: State of Health (SOH)
  topic: keywords
subject: Cell Balancing
  topic: keywords
subject: Thermal Regulation
  topic: keywords
creator: Basit Azam
creator: Sanam Mehar
rights: Copyright (c) 2025 Basit Azam, Sanam Mehar (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-10
publication date: 2025-11-10
starting page: 81
ending page: 95

Design and Control of an Autonomous Drone Navigation System Using Embedded AI
type: item
description: 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.2019.29584…
subject: Articles
  topic: section
subject: Autonomous Drones
  topic: keywords
subject: Embedded AI
  topic: keywords
subject: Navigation Systems
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Path Planning
  topic: keywords
subject: Obstacle Avoidance
  topic: keywords
subject: Sensor Fusion
  topic: keywords
subject: Real-Time Decision Making
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creator: Muhammad Taha Abbas
creator: Farrukh Hanif
rights: Copyright (c) 2025 Muhammad Taha Abbas, Farrukh Hanif (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-10
publication date: 2025-11-10
starting page: 68
ending page: 80

A Trust Management-based Energy Efficient Message Scheduling Algorithm in the Internet of Things System
type: item
description: 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 modifie…
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: Wireless Sensor Network
  topic: keywords
subject: Scheduling
  topic: keywords
subject: Energy Efficient
  topic: keywords
subject: Message Scheduling
  topic: keywords
subject: Trust Management
  topic: keywords
creator: Shanza Ijaz
creator: Muhammad Talah Zubair
rights: Copyright (c) 2025 Shanza Ijaz, Muhammad Talha Zubair (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-09
publication date: 2025-11-09
starting page: 51
ending page: 67

AI and Blockchain Integration: Driving Strategic Business Advancements in the Intelligent Era
type: item
description: 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 fr…
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Blockchain Technology
  topic: keywords
subject: Business Innovation
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Operational Efficiency
  topic: keywords
creator: Ishrak Alim
creator: Nafiz Imtiaz
creator: Abdullah Al Prince
creator: MD Asif Hasan
rights: Copyright (c) 2025 Ishrak Alim, Nafiz Imtiaz, Abdullah Al Prince, MD Asif Hasan (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-14
publication date: 2025-08-14
starting page: 38
ending page: 50