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.
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. 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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. 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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. 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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. 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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. 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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. 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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. 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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
The Role of Threat Intelligence in Preventing Financially Motivated Cyberattacks
The escalating prevalence of financially motivated cyberattacks poses critical risks to global financial institutions, driving urgent demand for proactive defense mechanisms (Haruna et al., 2022). This study employs a quantitative analysis of cyber incidents (2018-2023) across 50 financial entities to evaluate the efficacy of threat intelligence (TI) in mitigating attacks. We develop a predictive logistic model to assess breach probability, offering a robust framework for risk assessment in an increasingly digital financial landscape. Our analysis correlates TI maturity with 33.7% reduced breach likelihood (p < 0.01) and 28% faster incident response. The model further quantifies how regulatory compliance (e.g., GDPR/PSD2) and advanced authentication mechanisms amplify TI’s protective effect (Ali et al., 2024). Results demonstrate that institutions with TI maturity > 0.8 experienced ≥45% lower financial losses versus peers (p = 0.003). This research provides a validated framework for financial entities to prioritize TI investments, optimize cyber-resilience, and preempt evolving threats (Bouveret, 2018; Moon et al., 2022). To enhance the model’s applicability, we incorporated additional variables such as the frequency of security audits, the diversity of threat intelligence feeds and the integration of artificial intelligence-driven analytics. These factors significantly bolster the predictive power, enabling institutions to anticipate emerging threats with greater precision. Furthermore, the study highlights the importance of continuous training for cybersecurity teams, ensuring they can effectively leverage TI tools in real-time scenarios. The analysis also considers the impact of geopolitical factors and economic conditions, which can influence the sophistication and frequency of cyberattacks. By addressing these multifaceted dimensions, the framework offers a comprehensive approach to cybersecurity strategy development. Future research could explore the long-term effects of TI adoption on organizational culture and the scalability of the model across different sectors, providing a broader perspective on global cyber defense. References [1] N. H. Al-Kumaim and S. K. Alshamsi, "Determinants of cyberattack prevention in UAE financial organizations: assessing the mediating role of cybersecurity leadership," Appl. Sci., vol. 13, no. 10, p. 5839, 2023. [2] A. Alqudhaibi, M. Albarrak, A. Aloseel, S. Jagtap, and K. Salonitis, "Predicting cybersecurity threats in critical infrastructure for industry 4.0: a proactive approach based on attacker motivations," Sensors, vol. 23, no. 9, p. 4539, 2023. [3] N. Banerjee, "Exploring the future of AI in cyber threat intelligence," in AI-Enabled Threat Intelligence and Cyber Risk Assessment, CRC Press, 2025, pp. 126–148. [4] D. A. Brooks, Cyber Threat Intelligence Driven Phishing Awareness Programs: A Qualitative Exploratory Study, Capitol Technology University, 2023. [5] M. Colajanni and M. Marchetti, "Cyber-attacks and defenses: current capabilities and future trends," in Technology and International Relations, Edward Elgar Publishing, 2021, pp. 132–151. [6] B. Cinar, "Cyber threat intelligence: Current trends and future perspectives," J. Eng. Res. Rep., vol. 25, no. 4, pp. 91–105, 2023. [7] G. Cascavilla, "The rise of cybercrime and cyber-threat intelligence: Perspectives and challenges from law enforcement," IEEE Secur. Priv., vol. 23, no. 1, pp. 17–26, 2024. [8] Y. 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. [9] O. Eltayeb, "The Crucial Significance of Cyber Threat Intelligence in Mitigating Cyber Attacks," J. Ecohumanism, vol. 3, no. 4, pp. 2422–2434, 2024. [10] F. Fatima, A Qualitative Exploratory Study of Cyber Threats to Financial Organizations, University of the Cumberlands, 2024. [11] E. Gaitan, Developing and Sharing Threat Intelligence: Strategies for Small and Medium-Sized Businesses, Doctoral dissertation, Capella University, 2022. [12] M. A. Joyner, Strategies Using Threat Intelligence to Detect Advanced Persistent Threats: A Qualitative Case Study, Doctoral dissertation, Walden University, 2022. [13] I. 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 [14] M. O. Ijiga, H. S. Olarinoye, F. A. B. Yeboah, and J. N. Okolo, "Integrating Behavioral Science and Cyber Threat Intelligence (CTI) to Counter Advanced Persistent Threats (APTs) and Reduce Human-Enabled Security Breaches," Int. J. Sci. Res. Mod. Technol., vol. 4, no. 3, pp. 1–15, 2025. [15] 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. [16] Z. Lanz, "Cybersecurity risk in US critical infrastructure: An analysis of publicly available US government alerts and advisories," Int. J. Cybersecurity Intell. Cybercrime, vol. 5, no. 1, pp. 43–70, 2022. [17] A. Shaheen, "Cybersecurity in the Modern Era: An Overview of Recent Trends," J. Eng. Comput. Intell. Rev., vol. 1, no. 1, pp. 39–50, 2023. [18] V. Mavroeidis, R. Hohimer, T. Casey, and A. Jesang, "Threat actor type inference and characterization within cyber threat intelligence," in Proc. 2021 13th Int. Conf. Cyber Conflict (CyCon), 2021, pp. 327–352. [19] E. R. Ndukwe and B. Baridam, "A graphical and qualitative review of literature on AI-based cyber-threat intelligence (CTI) in banking sector," Eur. J. Eng. Technol. Res., vol. 8, no. 5, pp. 59–69, 2023. [20] I. V. Patel, The Necessity of Cyber Threat Intelligence, Master's thesis, Utica College, 2021. [21] N. Rahimi and H. Jones, "Cyber Warfare: Strategies, Impacts and Future Directions in the Digital Battlefield," J. Inf. Secur., vol. 16, no. 2, pp. 252–269, 2025. [22] N. Arshad, "A Comprehensive Review of Emerging Challenges in Cloud Computing Security," J. Eng. Comput. Intell. Rev., vol. 2, no. 1, pp. 27–37, 2024. [23] M. Z. Afshar and D. M. H. Shah, "Strategic evaluation using PESTLE and SWOT frameworks: Public sector perspective," ISRG J. Econ. Bus. Manag., vol. 3, pp. 108–114, 2025. [24] N. Sun et al., "Cyber threat intelligence mining for proactive cybersecurity defense: A survey and new perspectives," IEEE Commun. Surv. Tutor., vol. 25, no. 3, pp. 1748–1774, 2023. [25] 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. 2, pp. 325–337, 2025. [26] M. Tahmasebi, "Beyond defense: Proactive approaches to disaster recovery and threat intelligence in modern enterprises," J. Inf. Secur., vol. 15, no. 2, pp. 106–133, 2024. [27] C. Yatagan, Interaction between the US Intelligence Community and the Private Sector in Sharing Cyber Threat Intelligence, American University, 2022. [28] M. Asif, M. A. Pasha, A. Mumtaz, and B. Sabir, “Causes of Youth Unemployment in Pakistan”, IJSS, vol. 2, no. 1, pp. 41–50, Mar. 2023. [29] M. Asif, “Integration of Information Technology in Financial Services and its Adoption by the Financial Sector in Pakistan”, IJSS, vol. 1, no. 2, pp. 23–35, Dec. 2022.
Copyright (c) 2025 Sanjida Alam Eshra, Fatema Tuz Zohora, Sonia Akter, Iftekhar Rasul, Amjad Hossain (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
The Role of Artificial Intelligence in Carbon Pricing Policies: Economic and Environmental Implications
The use of AI in carbon pricing policies can offer both opportunities and future challenges for the United States as it acts on climate change. This study examines experts’ perspectives on AI’s potential to enhance the effectiveness, enforcement, and equity of carbon pricing. Based on a survey of 200 U.S. experts, the research explores key factors shaping support for AI-driven climate policies, including technological feasibility, public acceptance, and regulatory hurdles. Statistical analyses, including chi-square, ANOVA, correlation, multiple regression, and logistic regression, reveal that AI’s policy role is most influenced by knowledge and trust, whereas algorithmic bias, data limitations, and legal uncertainties remain significant barriers. The findings highlight urgent U.S. priorities in climate resilience, digital infrastructure, and environmental justice, offering critical insights into the social and technical readiness for AI-integrated carbon policies. Furthermore, the study identifies disparities in expert opinions, with technologists emphasizing innovation while policymakers stress accountability. Public-private collaboration and interdisciplinary research are deemed essential to overcoming implementation challenges. The study also underscores the need for transparent governance frameworks to address ethical concerns, mitigate risks of automation bias, and foster public trust. By identifying key drivers of policy acceptance, this research provides actionable recommendations for lawmakers, including adaptive regulations and stakeholder engagement strategies. Ultimately, this study advances understanding of AI’s role in climate action, informing both policy practitioners and academic researchers while paving the way for responsible innovation in carbon pricing mechanisms. Future research should explore real-world case studies and international comparisons to refine best practices for AI-enabled climate governance. 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Copyright (c) 2025 Mohammad Rabidul Islam, MD Maruf Islam, Istiaque Ahmed Badhan, MD Nurul Hasnain (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Cybersecurity in the Modern Era: An Overview of Recent Trends
In the rapidly evolving digital landscape, cybersecurity has emerged as a critical concern for organizations, governments, and individuals alike. This article provides a comprehensive overview of contemporary cybersecurity challenges and solutions, examining the dynamic interplay between technological advancements, human factors, and regulatory frameworks. We analyze the evolving threat landscape, including sophisticated ransomware attacks, AI-powered threats, and supply chain vulnerabilities, while exploring cutting-edge defensive strategies such as zero trust architectures, behavioral analytics, and predictive security models. The discussion highlights sector-specific cybersecurity challenges in finance, healthcare, critical infrastructure, and government operations, emphasizing the growing risks posed by remote work and cloud adoption. Special attention is given to the human dimension of cybersecurity, including social engineering threats and the importance of security awareness training. The article also evaluates emerging regulatory landscapes and compliance requirements across different jurisdictions. Finally, we identify key future directions in cybersecurity, including quantum-resistant cryptography, AI-enabled defense systems, and international cooperation frameworks. By synthesizing current research and practical insights, this article offers valuable perspectives for cybersecurity professionals, policymakers, and organizational leaders navigating the complex challenges of digital security in an increasingly interconnected world. The findings underscore the urgent need for adaptive, multi-layered security approaches that balance technological innovation with human-centric solutions and robust governance models. References [1] J. Kaur and K. R. Ramkumar, "The recent trends in cyber security: A review," J. 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Copyright (c) 2023 Asma Shaheen (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Comprehensive Review of Blockchain Applications across Diverse Domains
Blockchain technology has emerged as a disruptive force with transformative potential across multiple industries. This comprehensive review examines blockchain applications in finance, healthcare, supply chain, government, and emerging sectors, while analyzing key challenges and future directions. The study reveals how blockchain's core features - decentralization, transparency, and immutability - address critical pain points including financial intermediation, data security, provenance tracking, and bureaucratic inefficiencies. Notable implementations demonstrate 90% reductions in land registry disputes, 97% faster food traceability, and new paradigms for digital identity management. However, significant barriers persist, particularly regarding scalability limitations, excessive energy consumption, regulatory uncertainty, and interoperability issues between competing platforms. The review highlights promising convergence with AI and IoT, yielding innovative solutions in smart contracts and asset tokenization. Future research directions emphasize the need for quantum-resistant cryptography, energy-efficient consensus mechanisms, and standardized frameworks to facilitate mainstream adoption. For practitioners, the findings suggest a strategic approach favoring hybrid models that combine blockchain with legacy systems, while policymakers must balance innovation with consumer protection. As blockchain transitions from experimental technology to enterprise infrastructure, its ultimate value may lie in enabling trust-minimized systems and redistributing power from centralized intermediaries to network participants. This analysis provides both a structured assessment of current applications and a forward-looking perspective on blockchain's evolving role in the digital economy. References [1] U. Bodkhe et al., "Blockchain for industry 4.0: A comprehensive review," IEEE Access, vol. 8, pp. 79764–79800, 2020. [2] J. Abou Jaoude and R. G. Saade, "Blockchain applications–usage in different domains," IEEE Access, vol. 7, pp. 45360–45381, 2019. [3] F. A. Sunny et al., "A systematic review of blockchain applications," IEEE Access, vol. 10, pp. 59155–59177, 2022. [4] A. Sharma, S. Kaur, and M. Singh, "A comprehensive review on blockchain and Internet of Things in healthcare," Trans. Emerg. Telecommun. Technol., vol. 32, no. 10, p. e4333, 2021. [5] A. Pal, C. K. Tiwari, and A. Behl, "Blockchain technology in financial services: A comprehensive review of the literature," J. Glob. Oper. Strateg. Sourcing, vol. 14, no. 1, pp. 61–80, 2021. [6] W. Chen et al., "A survey of blockchain applications in different domains," in Proc. 2018 Int. Conf. Blockchain Technol. Appl., 2018, pp. 17–21. [7] W. 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Copyright (c) 2023 Kamil Khan, Sabeeha Alvi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Big Data Analytics with Machine Learning: Challenges, Innovations, and Applications
The convergence of big data analytics and machine learning (ML) has revolutionized decision-making across industries, enabling breakthroughs in healthcare diagnostics, financial forecasting, smart infrastructure, and personalized services. This article explores the transformative potential of these technologies while addressing critical challenges such as scalability, data quality, and ethical implications. We examine cutting-edge innovations, including distributed learning frameworks, real-time analytics, and automated machine learning (AML) systems, which enhance computational efficiency and model performance. The discussion highlights applications across healthcare, finance, retail, smart cities, and social media, demonstrating how organizations leverage large-scale data for predictive insights and operational optimization. Additionally, the paper identifies emerging research directions, such as neuro-symbolic artificial intelligence (AI) integration and responsible AI governance, which aim to balance technological advancement with societal accountability. By synthesizing current trends and future opportunities, this article provides a comprehensive roadmap for researchers and practitioners navigating the evolving landscape of data-driven intelligence. References [1] I. K. Nti, J. A. Quarcoo, J. Aning, and G. K. Fosu, “A mini-review of machine learning in big data analytics: Applications, challenges, and prospects,” Big Data Mining and Analytics, vol. 5, no. 2, pp. 81–97, 2022. [2] K. Vassakis, E. Petrakis, and I. Kopanakis, “Big data analytics: Applications, prospects and challenges,” in Mobile Big Data: A Roadmap from Models to Technologies. Springer, 2017, pp. 3–20. [3] L. Zhou, S. Pan, J. Wang, and A. V. 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Copyright (c) 2024 Sameer Niazi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Review on the Role of IoT in Modern Electrical Engineering Education
The rapid evolution of Internet of Things (IoT) technologies has significantly transformed electrical engineering education, creating new paradigms for experiential learning and industry-aligned skill development. This review paper systematically examines the integration of IoT in modern electrical engineering curricula, analyzing its implementation challenges, pedagogical benefits, and future directions. Through comprehensive evaluation of case studies and current research, we demonstrate how IoT-enabled smart laboratories, project-based learning approaches, and remote experimentation platforms enhance student engagement and practical competencies in key areas like power systems, automation, and renewable energy technologies. The paper highlights successful implementations at leading institutions while addressing critical barriers including infrastructure costs, faculty training requirements, and cyber security concerns. Emerging trends such as AI-powered digital twins, 5G-enabled labs, and edge computing applications are explored as future growth areas. Practical recommendations are provided for educators and policymakers to effectively incorporate IoT into electrical engineering programs, emphasizing curriculum design, industry partnerships, and sustainable implementation strategies. The findings underscore IoT's crucial role in preparing the next generation of electrical engineers for Industry 4.0 challenges, while identifying key research gaps that require further investigation. This review serves as both a roadmap for academic institutions seeking to modernize their programs and a call to action for broader adoption of IoT technologies in engineering education worldwide. References [1] R. S. Abd-Ali, S. A. Radhi, and Z. I. Rasool, "A survey: the role of the internet of things in the development of education," Indones. J. Electr. Eng. Comput. Sci., vol. 19, no. 1, pp. 215–221, 2020. [2] D. D. 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Educ., vol. 36, no. 6, pp. 1814–1832, 2020. [8] F. Zantalis, G. Koulouras, S. Karabetsos, and D. Kandris, "A review of machine learning and IoT in smart transportation," Future Internet, vol. 11, no. 4, p. 94, 2019. [9] D. G. Broo, O. Kaynak, and S. M. Sait, "Rethinking engineering education at the age of industry 5.0," J. Ind. Inf. Integr., vol. 25, p. 100311, 2022. [10] L. Xiao et al., "IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?," IEEE Signal Process. Mag., vol. 35, no. 5, pp. 41–49, 2018. [11] K. Shafique et al., "Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios," IEEE Access, vol. 8, pp. 23022–23040, 2020. [12] M. Kassab, J. DeFranco, and P. Laplante, "A systematic literature review on Internet of things in education: Benefits and challenges," J. Comput. Assist. Learn., vol. 36, no. 2, pp. 115–127, 2020. [13] M. 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Ghazal et al., "IoT for smart cities: Machine learning approaches in smart healthcare—A review," Future Internet, vol. 13, no. 8, p. 218, 2021. [24] S. Abdulmalek et al., "IoT-based healthcare-monitoring system towards improving quality of life: A review," Healthcare, vol. 10, no. 10, p. 1993, 2022. [25] R. A. Khalil et al., "Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications," IEEE Internet Things J., vol. 8, no. 14, pp. 11016–11040, 2021. [26] S. Abbas et al., "Modeling, simulation and optimization of power plant energy sustainability for IoT enabled smart cities empowered with deep extreme learning machine," IEEE Access, vol. 8, pp. 39982–39997, 2020. [27] A. A. Laghari et al., "A review and state of art of Internet of Things (IoT)," Arch. Comput. Methods Eng., pp. 1–19, 2021. [28] S. K. Sharma and X. Wang, "Toward massive machine type communications in ultra-dense cellular IoT networks: Current issues and machine learning-assisted solutions," IEEE Commun. Surv. Tutor., vol. 22, no. 1, pp. 426–471, 2019. [29] C. E. Cornel, "The role of internet of things for a continuous improvement in education," Hyperion Econ. J., vol. 2, no. 3, pp. 24–31, 2015. [30] P. Suresh et al., "A state of the art review on the Internet of Things (IoT) history, technology and fields of deployment," in 2014 Int. Conf. Sci. Eng. Manag. Res. (ICSEMR), 2014, pp. 1–8. [31] D. Bradley et al., "The Internet of Things–The future or the end of mechatronics," Mechatronics, vol. 27, pp. 57–74, 2015. [32] S. Amendola et al., "RFID technology for IoT-based personal healthcare in smart spaces," IEEE Internet Things J., vol. 1, no. 2, pp. 144–152, 2014. [33] S. Madakam, R. Ramaswamy, and S. Tripathi, "Internet of Things (IoT): A literature review," J. Comput. Commun., vol. 3, no. 5, pp. 164–173, 2015.
Copyright (c) 2023 Muhammad Hamza Iqbal (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Comprehensive Review of Emerging Challenges in Cloud Computing Security
The rapid expansion of Big Data has strained classical computing systems, necessitating innovative solutions for efficient data processing. Quantum computing, leveraging principles like superposition and entanglement, presents transformative potential for addressing complex, large-scale problems that are intractable for classical methods. This review explores the role of quantum computing in Big Data processing, examining key algorithms such as Grover’s search, Shor’s factorization, and quantum machine learning techniques. It discusses foundational concepts like qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing hardware limitations such as de-coherence and error rates in NISQ-era devices. Promising applications in optimization, secure communication, and high-dimensional data analysis are highlighted, alongside challenges in data encoding, algorithmic readiness, and practical implementation. Emerging research directions, including near-term NISQ applications, fault-tolerant quantum computing, and cross-disciplinary opportunities in NLP and IoT, are also explored. By synthesizing theoretical advances with practical constraints, this review provides a balanced perspective on quantum computing is potential to reshape Big Data analytics, emphasizing both its revolutionary capabilities and the significant barriers to widespread adoption. While quantum advantage remains limited to specific use cases today, continued progress in hardware and algorithms may redefine large-scale data processing in the future. References [1]. C. Stouffer, "23 cloud security risks, threats, and best practices," Norton, 2023. [Online]. Available: https://us.norton.com/blog/privacy/cloud-security-risks [2]. A. I. 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Suman, "Towards insighting cybersecurity for healthcare domains: A comprehensive review of recent practices and trends," Cybersecur. Appl., vol. 1, p. 100016, 2023. [25]. M. M. Sadeeq et al., "IoT and Cloud computing issues, challenges and opportunities: A review," Qubahan Acad. J., vol. 1, no. 2, pp. 1–7, 2021. [26]. A. A. Abbasi et al., "Software-defined cloud computing: A systematic review on latest trends and developments," IEEE Access, vol. 7, pp. 93294–93314, 2019. [27]. A. Heidari and M. A. Jabraeil Jamali, "Internet of Things intrusion detection systems: A comprehensive review and future directions," Cluster Comput., vol. 26, no. 6, pp. 3753–3780, 2023. [28]. S. Ahmed, I. Ahmed, M. Kamruzzaman, and R. Saha, "Cybersecurity challenges in IT infrastructure and data management: A comprehensive review of threats, mitigation strategies, and future trend," Glob. Mainstream J. Innov. Eng. Emerg. Technol., vol. 1, no. 01, pp. 36–61, 2022. [29]. O. I. Abiodun et al., "Comprehensive review of artificial neural network applications to pattern recognition," IEEE Access, vol. 7, pp. 158820–158846, 2019. [30]. A. Fahim, M. Hasan, and M. A. Chowdhury, "Smart parking systems: Comprehensive review based on various aspects," Heliyon, vol. 7, no. 5, 2021. [31]. A. Khanna and S. Kaur, "Internet of things (IoT), applications and challenges: A comprehensive review," Wirel. Pers. Commun., vol. 114, pp. 1687–1762, 2020. [32]. A. K. Jain, S. R. Sahoo, and J. Kaubiyal, "Online social networks security and privacy: Comprehensive review and analysis," Complex Intell. Syst., vol. 7, no. 5, pp. 2157–2177, 2021. [33]. L. Tightiz and H. Yang, "A comprehensive review on IoT protocols’ features in smart grid communication," Energies, vol. 13, no. 11, p. 2762, 2020. [34]. P. J. Sun, "Privacy protection and data security in cloud computing: A survey, challenges, and solutions," IEEE Access, vol. 7, pp. 147420–147452, 2019. [35]. U. Agarwal et al., "Blockchain technology for secure supply chain management: A comprehensive review," IEEE Access, vol. 10, pp. 85493–85517, 2022. [36]. M. Amani et al., "Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 5326–5350, 2020.
Copyright (c) 2024 Nayab Arshad (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
A Systematic Review of Electric Vehicle Innovations and Implementation Barriers
The global transition to electric vehicles (EVs) represents a critical pathway toward sustainable transportation and net-zero emissions. This comprehensive review synthesizes 150+ studies to analyse EV innovations, adoption barriers, and future trends across technological, economic, and policy dimensions. Key findings reveal that while battery energy density has improved by 200% since 2010 (reaching 270 Wh/kg) and costs have dropped 89%, significant challenges persist: inadequate charging infrastructure (requiring 40 million global chargers by 2030), supply chain vulnerabilities for critical minerals, and consumer range anxiety. The study highlights successful case studies like Norway (90% EV market share) and Shenzhen (16,000 electric buses), demonstrating that policy consistency and public investment drive adoption. Emerging technologies, including solid-state batteries (500+ Wh/kg) and vehicle-to-grid networks promise to address current limitations, but require coordinated standards and workforce development. Three critical gaps are identified: (1) recycling systems recovering <60% of battery materials, (2) grid capacity constraints at 30% EV penetration, and (3) equity issues in emerging markets (<3% adoption). The review proposes a tripartite framework for accelerated deployment: technology (solid-state batteries, AI-optimized charging), policy (harmonized standards, critical mineral strategies), and social (inclusive financing, skilled labour programs). With transport contributing 24% of global CO₂ emissions, these insights provide policymakers and industry leader’s actionable strategies to overcome adoption barriers while ensuring an equitable transition. The analysis concludes that systemic integration of EVs with renewable energy and smart infrastructure not just vehicle substitution will determine their climate impact. References [1] B. M. Sopha, D. M. Purnamasari, and S. Ma'mun, "Barriers and enablers of circular economy implementation for electric-vehicle batteries: From systematic literature review to conceptual framework," Sustainability, vol. 14, no. 10, p. 6359, 2022. [2] A. Mahdavian et al., "Drivers and barriers to implementation of connected, automated, shared, and electric vehicles: An agenda for future research," IEEE Access, vol. 9, pp. 22195-22213, 2021. [3] M. Adhikari et al., "Identification and analysis of barriers against electric vehicle use," Sustainability, vol. 12, no. 12, p. 4850, 2020. [4] P. K. Tarei, P. Chand, and H. Gupta, "Barriers to the adoption of electric vehicles: Evidence from India," J. Clean. Prod., vol. 291, p. 125847, 2021. [5] M. Z. Afshar, "Exploring factors impacting organizational adaptation capacity of Punjab Agriculture & Meat Company (PAMCO)," Int. J. Emerg. Issues Soc. Sci. Arts Humanit., vol. 2, no. 1, pp. 1-10, 2023. [6] M. R. Haque et al., "The role of macroeconomic discourse in shaping inflation views: Measuring public trust in Federal Reserve policies," J. Bus. Insight Innov., vol. 2, no. 2, pp. 88-106, 2023. [7] N. Adnan et al., "A comprehensive review on theoretical framework-based electric vehicle consumer adoption research," Int. J. Energy Res., vol. 41, no. 3, pp. 317-335, 2017. [8] F. G. Venegas, M. Petit, and Y. Perez, "Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services," Renew. Sustain. Energy Rev., vol. 145, p. 111060, 2021. [9] M. A. Sayem et al., "AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices," Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 10, pp. 1109-1122, 2023. [10] F. Liao, E. Molin, and B. van Wee, "Consumer preferences for electric vehicles: A literature review," Transp. Rev., vol. 37, no. 3, pp. 252-275, 2017. [11] L. Noel et al., "Fear and loathing of electric vehicles: The reactionary rhetoric of range anxiety," Energy Res. Soc. Sci., vol. 48, pp. 96-107, 2019. [12] S. Z. Rajper and J. Albrecht, "Prospects of electric vehicles in the developing countries: A literature review," Sustainability, vol. 12, no. 5, p. 1906, 2020. [13] A. Lagorio et al., "A systematic literature review of innovative technologies adopted in logistics management," Int. J. Logist. Res. Appl., vol. 25, no. 7, pp. 1043-1066, 2022. [14] F. Alanazi, "Electric vehicles: Benefits, challenges, and potential solutions for widespread adaptation," Appl. Sci., vol. 13, no. 10, p. 6016, 2023. [15] K. J. Shah et al., "Green transportation for sustainability: Review of current barriers, strategies, and innovative technologies," J. Clean. Prod., vol. 326, p. 129392, 2021. [16] N. Berkeley et al., "Assessing the transition towards Battery Electric Vehicles: A Multi-Level Perspective on drivers of, and barriers to, take up," Transp. Res. A Policy Pract., vol. 106, pp. 320-332, 2017. [17] C. M. D. Oliveira et al., "Sustainable vehicles-based alternatives in last mile distribution of urban freight transport: A systematic literature review," Sustainability, vol. 9, no. 8, p. 1324, 2017. [18] A. Parmentola et al., "Is blockchain able to enhance environmental sustainability? A systematic review and research agenda from the perspective of Sustainable Development Goals (SDGs)," Bus. Strategy Environ., vol. 31, no. 1, pp. 194-217, 2022. [19] O. Sadeghian et al., "A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges," J. Energy Storage, vol. 54, p. 105241, 2022. [20] E. H. Nemoto et al., "How to measure the impacts of shared automated electric vehicles on urban mobility," Transp. Res. D Transp. Environ., vol. 93, p. 102766, 2021. [21] S. M. Patella et al., "The adoption of green vehicles in last mile logistics: A systematic review," Sustainability, vol. 13, no. 1, p. 6, 2020. [22] D. Kirli et al., "Smart contracts in energy systems: A systematic review of fundamental approaches and implementations," Renew. Sustain. Energy Rev., vol. 158, p. 112013, 2022. [23] J. Kim et al., "Decarbonizing the iron and steel industry: A systematic review of sociotechnical systems, technological innovations, and policy options," Energy Res. Soc. Sci., vol. 89, p. 102565, 2022. [24] M. E. Biresselioglu, M. D. Kaplan, and B. K. Yilmaz, "Electric mobility in Europe: A comprehensive review of motivators and barriers in decision making processes," Transp. Res. A Policy Pract., vol. 109, pp. 1-13, 2018. [25] T. Bosona, "Urban freight last mile logistics—Challenges and opportunities to improve sustainability: A literature review," Sustainability, vol. 12, no. 21, p. 8769, 2020. [26] L. Albertsen et al., "Circular business models for electric vehicle lithium-ion batteries: An analysis of current practices of vehicle manufacturers and policies in the EU," Resour. Conserv. Recycl., vol. 172, p. 105658, 2021. [27] V. Singh, V. Singh, and S. Vaibhav, "A review and simple meta-analysis of factors influencing adoption of electric vehicles," Transp. Res. D Transp. Environ., vol. 86, p. 102436, 2020. [28] K. Govindan and M. Hasanagic, "A systematic review on drivers, barriers, and practices towards circular economy: A supply chain perspective," Int. J. Prod. Res., vol. 56, no. 1-2, pp. 278-311, 2018. [29] C. Chauhan, V. Parida, and A. Dhir, "Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises," Technol. Forecast. Soc. Change, vol. 177, p. 121508, 2022. [30] S. Habib et al., "A comprehensive study of implemented international standards, technical challenges, impacts and prospects for electric vehicles," IEEE Access, vol. 6, pp. 13866-13890, 2018. [31] R. Vidhi and P. Shrivastava, "A review of electric vehicle lifecycle emissions and policy recommendations to increase EV penetration in India," Energies, vol. 11, no. 3, p. 483, 2018.
Copyright (c) 2024 Muhammad Ali Shah (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
The Role of Quantum Computing in Future Big Data Processing: A Comprehensive Review
The rapid growth of Big Data has pushed classical computing systems to their limits, necessitating innovative approaches to data processing. Quantum computing, with its inherent parallelism and entanglement capabilities, offers transformative potential for solving complex, large-scale problems that are intractable for classical systems. This review paper examines the role of quantum computing in future Big Data processing, analyzing key quantum algorithms such as Grover's search, Shor's factorization, and quantum machine learning techniques. We explore fundamental concepts including qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing current hardware limitations like de-coherence and error rates in NISQ-era devices. The paper highlights promising applications in optimization, secure communication, and high-dimensional data analysis, alongside significant challenges in data encoding, algorithmic readiness, and practical implementation. Emerging research directions are discussed, including near-term NISQ applications, fault-tolerant quantum computing prospects, and cross-disciplinary opportunities in NLP and IoT. By synthesizing theoretical advances with practical constraints, this review provides a balanced perspective on quantum computing's evolving role in Big Data, offering insights into both its revolutionary potential and the substantial barriers that must be overcome for widespread adoption. The findings suggest that while quantum advantage remains limited to specific use cases today, continued progress in hardware and algorithms may eventually redefine the landscape of large-scale data processing. References [1] S. S. Gill et al., "Quantum computing: A taxonomy, systematic review and future directions," Softw. Pract. Exper., vol. 52, no. 1, pp. 66-114, 2022. [2] R. Ur Rasool et al., "Quantum computing for healthcare: A review," Future Internet, vol. 15, no. 3, p. 94, 2023. [3] S. Zhu et al., "Intelligent computing: The latest advances, challenges, and future," Intell. Comput., vol. 2, p. 0006, 2023. [4] M. Möller and C. Vuik, "On the impact of quantum computing technology on future developments in high-performance scientific computing," Ethics Inf. Technol., vol. 19, pp. 253-269, 2017. [5] W. Zhang et al., "Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge," Gondwana Res., vol. 109, pp. 1-17, 2022. [6] S. D. Pasham, "Privacy-preserving data sharing in big data analytics: A distributed computing approach," Metascience, vol. 1, no. 1, pp. 149-184, 2023. [7] F. Bova, A. Goldfarb, and R. G. Melko, "Commercial applications of quantum computing," EPJ Quantum Technol., vol. 8, no. 1, p. 2, 2021. [8] U. Tariq et al., "A critical cybersecurity analysis and future research directions for the internet of things: A comprehensive review," Sensors, vol. 23, no. 8, p. 4117, 2023. [9] M. R. Haque et al., "The role of macroeconomic discourse in shaping inflation views: Measuring public trust in Federal Reserve policies," J. Bus. Insight Innov., vol. 2, no. 2, pp. 88-106, 2023. [10] M. A. Sayem et al., "AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices," Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 10, pp. 1109-1122, 2023. [11] M. Z. Afshar, "Exploring factors impacting organizational adaptation capacity of Punjab Agriculture & Meat Company (PAMCO)," Int. J. Emerg. Issues Soc. Sci. Arts Humanit., vol. 2, no. 1, pp. 1-10, 2023. [12] T. A. Shaikh and R. Ali, "Quantum computing in big data analytics: A survey," in Proc. IEEE Int. Conf. Comput. Inf. Technol. (CIT), 2016, pp. 112-115. [13] A. Pandey and V. Ramesh, "Quantum computing for big data analysis," Indian J. Sci., vol. 14, no. 43, pp. 98-104, 2015. [14] J. Singh and M. Singh, "Evolution in quantum computing," in Proc. Int. Conf. Syst. Model. Adv. Res. Trends (SMART), 2016, pp. 267-270. [15] C. P. Chen and C. Y. Zhang, "Data-intensive applications, challenges, techniques and technologies: A survey on big data," Inf. Sci., vol. 275, pp. 314-347, 2014. [16] X. D. Cai et al., "Entanglement-based machine learning on a quantum computer," Phys. Rev. Lett., vol. 114, no. 11, p. 110504, 2015. [17] D. P. Acharjya and K. Ahmed, "A survey on big data analytics: Challenges, open research issues and tools," Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 2, pp. 511-518, 2016. [18] S. Yu, "Big privacy: Challenges and opportunities of privacy study in the age of big data," IEEE Access, vol. 4, pp. 2751-2763, 2016. [19] O. N. Garcia and G. Bajwa, "Integration of quantum information systems in computer science and in electrical engineering," J. Integr. Des. Process Sci., vol. 19, no. 4, pp. 9-20, 2016. [20] A. JavadiAbhari et al., "ScaffCC: A framework for compilation and analysis of quantum computing programs," in Proc. 11th ACM Conf. Comput. Front., 2014, pp. 1-10. [21] S. Khanra, A. Dhir, A. N. Islam, and M. Mäntymäki, "Big data analytics in healthcare: A systematic literature review," Enterp. Inf. Syst., vol. 14, no. 7, pp. 878-912, 2020. [22] S. Ahmed, I. Ahmed, M. Kamruzzaman, and R. Saha, "Cybersecurity challenges in IT infrastructure and data management: A comprehensive review of threats, mitigation strategies, and future trend," Glob. Mainstream J. Innov. Eng. Emerg. Technol., vol. 1, no. 1, pp. 36-61, 2022. [23] O. M. Araz, T. M. Choi, D. L. Olson, and F. S. Salman, "Role of analytics for operational risk management in the era of big data," Decis. Sci., vol. 51, no. 6, pp. 1320-1346, 2020. [24] R. Orús, S. Mugel, and E. Lizaso, "Quantum computing for finance: Overview and prospects," Rev. Phys., vol. 4, p. 100028, 2019. [25] D. Herman et al., "A survey of quantum computing for finance," arXiv:2201.02773, 2022. [26] S. S. Gill et al., "AI for next generation computing: Emerging trends and future directions," Internet Things, vol. 19, p. 100514, 2022. [27] A. Deshmukh, D. S. Patil, P. D. Pawar, and S. Kumari, "Recent trends for smart environments with AI and IoT-based technologies: A comprehensive review," in Handbook of Research on Quantum Computing for Smart Environments, 2023, pp. 435-452. [28] S. J. Nawaz et al., "Quantum machine learning for 6G communication networks: State-of-the-art and vision for the future," IEEE Access, vol. 7, pp. 46317-46350, 2019. [29] M. Asif, "Integration of Information Technology in Financial Services and its Adoption by the Financial Sector in Pakistan," Inverge J. Soc. Sci., vol. 1, no. 2, pp. 23-35, 2022.
Copyright (c) 2024 Maria Ilyas, Rabia Ilyas (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
Solar Energy Development in Pakistan: Overcoming Barriers through Policy Reform and Stakeholder Engagement
Pakistan is facing an escalating energy crisis due to its heavy reliance on imported fossil fuels and an outdated energy infrastructure. With an increasing population and industrial growth, the demand for energy is rising rapidly, placing significant pressure on the country’s energy system. Solar energy, with its vast potential, has emerged as a promising solution to address the energy deficit, yet its adoption remains limited due to various barriers. This study examines the barriers hindering the widespread adoption of solar energy in Pakistan and explores the policy reforms needed to overcome these challenges. The primary objective of this research is to identify and analyze the key barriers to solar energy adoption in Pakistan. These barriers are explored from both technical and financial perspectives, as well as through institutional and social lenses. The research also aims to propose evidence-based policy recommendations that could facilitate the growth of solar energy in the country, aligning with Pakistan’s commitments to sustainable energy and climate action goals. A mixed-methods approach was employed to collect both qualitative and quantitative data. The qualitative data were gathered through semi-structured interviews with solar vendors, government representatives, and energy experts, while the quantitative data were collected through surveys administered to consumers and vendors. The qualitative data were analyzed thematically to identify key barriers, while the quantitative data were analyzed using descriptive and inferential statistics to quantify the adoption trends and barriers. The integration of both methods allowed for a comprehensive understanding of the barriers to solar energy adoption in Pakistan. The findings highlight several key barriers to solar energy adoption in Pakistan, including high upfront costs, limited financing options, insufficient skilled labor, outdated grid infrastructure, and fragmented policy frameworks. The research also identifies gaps in public awareness regarding solar energy policies, such as net metering and government incentives, with a significant portion of consumers and vendors unaware of these opportunities. Additionally, the study finds that the lack of coordination between governmental agencies and the private sector exacerbates the challenges in promoting solar energy. The research concludes that while Pakistan has significant solar potential, its adoption is hindered by technical, financial, institutional, and informational barriers. The study recommends policy reforms that include financial incentives, improved public-private collaboration, grid modernization, and enhanced public awareness campaigns. By addressing these barriers, Pakistan can unlock the full potential of solar energy, contributing to sustainable energy security, reducing dependence on imported fossil fuels, and promoting climate resilience. References [1] U. K. Mirza, N. Ahmad, K. Harijan, and T. Majeed, “Identifying and addressing barriers to renewable energy development in Pakistan,” Renew. Sustain. Energy Rev., vol. 13, no. 4, pp. 927–931, 2009. [2] Y. A. Solangi, C. Longsheng, and S. A. A. Shah, “Assessing and overcoming the renewable energy barriers for sustainable development in Pakistan: An integrated AHP and fuzzy TOPSIS approach,” Renew. Energy, vol. 173, pp. 209–222, 2021. [3] M. Asif, M. I. Khan, and A. Pandey, “Navigating the inclusive and sustainable energy transitions in South Asia: Progress, priorities and stakeholder perspectives,” Energy Convers. Manag., vol. 313, p. 118589, 2024. [4] M. Irfan, Z. Y. Zhao, M. Ahmad, and M. C. Mukeshimana, “Solar energy development in Pakistan: Barriers and policy recommendations,” Sustainability, vol. 11, no. 4, p. 1206, 2019. [5] M. Z. Afshar, “Exploring factors impacting organizational adaptation capacity of Punjab Agriculture & Meat Company (PAMCO),” Int. J. Emerg. Issues Soc. Sci. Arts Humanit. (IJEISSAH), vol. 2, no. 1, pp. 1–10, 2023. [6] 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. 01, pp. 40–47, 2025. [7] M. Z. Afshar and D. M. H. Shah, “Strategic evaluation using PESTLE and SWOT frameworks: Public sector perspective,” ISRG J. Econ. Bus. Manag. (ISRGJEBM), vol. 3, pp. 108–114, 2025. [8] M. S. Islam et al., “Machine learning-based cryptocurrency prediction: Enhancing market forecasting with advanced predictive models,” J. Ecohumanism, vol. 4, no. 2, pp. 2498–2519, 2025. [9] F. T. Zohora et al., “Optimizing credit card security using consumer behavior data: A big data and machine learning approach to fraud detection,” Frontline Mark. Manag. Econ. J., vol. 4, no. 12, pp. 26–60, 2024. [10] M. R. Haque et al., “The role of macroeconomic discourse in shaping inflation views: Measuring public trust in Federal Reserve policies,” J. Bus. Insight Innov., vol. 2, no. 2, pp. 88–106, 2023. [11] A. Shabbir et al., “Analyzing enterprise data protection and safety risks in cloud computing using ensemble learning,” Int. J. Recent Innov. Trends Comput. Commun., vol. 12, no. 2, pp. 499–507, 2024. [12] K. Shahzad et al., “Analysis of obstacles to adoption of solar energy in emerging economies using spherical fuzzy AHP decision support system: A case of Pakistan,” Energy Rep., vol. 10, pp. 381–395, 2023. [13] A. Sher and Y. Qiu, “Pakistan's solar mission: Do solar finance and subsidy remove the barriers to solar installations?,” Renew. Energy, vol. 190, pp. 993–1005, 2022. [14] S. A. A. Shah and Y. A. Solangi, “A sustainable solution for electricity crisis in Pakistan: Opportunities, barriers, and policy implications for 100% renewable energy,” Environ. Sci. Pollut. Res., vol. 26, pp. 29687–29703, 2019. [15] S. H. Malik et al., “Investigating the impact of communication factors and stakeholders engagement on renewable energy projects in Pakistan,” Sustainability, vol. 15, no. 14, p. 11289, 2023. [16] R. Asghar et al., “The important contribution of renewable energy technologies in overcoming Pakistan's energy crisis: Present challenges and potential opportunities,” Energy Environ., vol. 34, no. 8, pp. 3450–3494, 2023. [17] Z. Nishtar and J. Afzal, “History of emerging trends of renewable energy for sustainable development in Pakistan,” J. Hist. Soc. Sci., vol. 14, no. 2, pp. 296–309, 2023. [18] K. Shahzad, B. Lu, and D. Abdul, “Entrepreneur barrier analysis on renewable energy promotion in the context of Pakistan using Pythagorean fuzzy AHP method,” Environ. Sci. Pollut. Res., vol. 29, no. 36, pp. 54756–54768, 2022. [19] P. M. Falcone, “Sustainable energy policies in developing countries: A review of challenges and opportunities,” Energies, vol. 16, no. 18, p. 6682, 2023. [20] S. Sen and S. Ganguly, “Opportunities, barriers and issues with renewable energy development—A discussion,” Renew. Sustain. Energy Rev., vol. 69, pp. 1170–1181, 2017. [21] D. Asante et al., “Prioritizing strategies to eliminate barriers to renewable energy adoption and development in Ghana: A CRITIC-fuzzy TOPSIS approach,” Renew. Energy, vol. 195, pp. 47–65, 2022. [22] M. Iqbal et al., “Sustainable construction through energy management practices in developing economies: An analysis of barriers in the construction sector,” Environ. Sci. Pollut. Res., vol. 28, pp. 34793–34823, 2021. [23] S. A. A. Shah and C. 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Jan et al., “Barriers to the adoption of social welfare measures in the electricity tariff structure of developing countries: A case of Pakistan,” Energy Policy, vol. 179, p. 113648, 2023. [29] M. U. Yousuf, M. A. Irshad, and M. Umair, “Identifying barriers and drivers for energy efficiency in steel and iron industries of Karachi, Pakistan: Insights from executives and professionals,” Energy Nexus, vol. 14, p. 100284, 2024. [30] Y. Feng et al., “Assessing and prioritizing biogas energy barriers: A sustainable roadmap for energy security,” Renew. Energy, vol. 223, p. 120053, 2024. [31] J. Cai et al., “Modeling wind energy development barriers: Implications for promoting green energy sector,” Energy Sources, Part B Econ. Plan. Policy, vol. 17, no. 1, p. 2118403, 2022. [32] R. Maqbool and Y. Sudong, “Critical success factors for renewable energy projects; empirical evidence from Pakistan,” J. Clean. Prod., vol. 195, pp. 991–1002, 2018. [33] K. Shahzad et al., “Assessment of biomass energy barriers towards sustainable development: Application of Pythagorean fuzzy AHP,” Geol. J., vol. 58, no. 4, pp. 1607–1622, 2023. [34] Z. H. Hulio, W. Jiang, and G. S. Chandio, “Power policies, challenges, and recommendations of renewable resource assessment in Pakistan,” Energy Explor. Exploit., vol. 40, no. 3, pp. 947–976, 2022. [35] M. Y. Raza, M. Wasim, and M. S. Sarwar, “Development of renewable energy technologies in rural areas of Pakistan,” Energy Sources, Part A Recovery Util. Environ. Eff., vol. 42, no. 6, pp. 740–760, 2020. [36] M. I. Khan, I. A. Khan, and Y. C. Chang, “An overview of global renewable energy trends and current practices in Pakistan—A perspective of policy implications,” J. Renew. Sustain. Energy, vol. 12, no. 5, 2020. [37] M. Dehghani Madvar et al., “Analysis of stakeholder roles and the challenges of solar energy utilization in Iran,” Int. J. Low-Carbon Technol., vol. 13, no. 4, pp. 438–451, 2018. [38] A. Wahid et al., “Barriers to empowerment: Assessment of community-led local development organizations in Pakistan,” Renew. Sustain. Energy Rev., vol. 74, pp. 1361–1370, 2017. [39] R. Uddin et al., “Renewable energy perspectives of Pakistan and Turkey: Current analysis and policy recommendations,” Sustainability, vol. 13, no. 6, p. 3349, 2021. [40] S. Azeem et al., “Examining barriers and measures to promote the adoption of green building practices in Pakistan,” Smart Sustain. Built Environ., vol. 6, no. 3, pp. 86–100, 2017. [41] K. Batool, Z. Y. Zhao, N. Nureen, and M. Irfan, “Assessing and prioritizing biogas barriers to alleviate energy poverty in Pakistan: An integrated AHP and G-TOPSIS model,” Environ. Sci. Pollut. Res., vol. 30, no. 41, pp. 94669–94693, 2023.
Copyright (c) 2025 Razi Ud Din, Nimra Bibi (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Powered Radiology: Innovations and Challenges in Medical Imaging
Artificial intelligence (AI) is revolutionizing radiology by enhancing diagnostic accuracy, optimizing workflows, and enabling personalized medicine. This review explores the evolution of AI in medical imaging, from early rule-based systems to contemporary deep learning applications, while examining both ground-breaking innovations and persistent challenges. Key advancements include AI's superior performance in detecting tumours, fractures, and hemorrhages; workflow improvements through automated segmentation and prioritized case triage; and cutting-edge techniques like low-dose imaging reconstruction and radio genomics. However, significant barriers remain, including data quality issues, "black box" algorithm limitations, clinical adoption resistance, and ethical concerns regarding bias and privacy. The future of AI in radiology points toward explainable AI (XAI) for transparent decision-making, federated learning for privacy-preserving collaboration, and integration with emerging technologies like augmented reality. Successful implementation will require addressing technical, regulatory, and socioeconomic challenges while maintaining human oversight. As AI continues to transform medical imaging, its ultimate measure of success will be its ability to improve patient outcomes across diverse healthcare settings while upholding the highest standards of safety, equity, and clinical relevance. References [1] K. Devarapu, “Blockchain-driven AI solutions for medical imaging and diagnosis in healthcare,” Technol. & Manag. Rev., vol. 5, no. 1, pp. 80–91, 2020. [2] N. K. Mishra, J. Agrawal, and M. H. Baba, “AI-powered COVID-19 health management based on radiological imaging: Bio-ethics perspective,” JK Practitioner, vol. 27, 2022. [3] C. Han et al., “Bridging the gap between AI and healthcare sides: Towards developing clinically relevant AI-powered diagnosis systems,” in Artificial Intelligence Applications and Innovations, Springer, 2020, pp. 320–333. [4] S. M. Orthi et al., “AI powered digital transformation in healthcare: Revolutionizing patient care through intelligent and adaptive information systems,” Propel J. Acad. Res., vol. 2, no. 2, pp. 329–352, 2022. [5] C. Malamateniou et al., “Artificial intelligence in radiography: Where are we now and what does the future hold?,” Radiography, vol. 27, pp. S58–S62, 2021. [6] M. Z. Afshar, “Exploring factors impacting organizational adaptation capacity of Punjab Agriculture & Meat Company (PAMCO),” Int. J. Emerg. Issues Soc. Sci. Arts Humanit., vol. 2, no. 1, pp. 1–10, 2023. [7] M. R. Haque et al., “The role of macroeconomic discourse in shaping inflation views: Measuring public trust in Federal Reserve policies,” J. Bus. Insight Innov., vol. 2, no. 2, pp. 88–106, 2023. 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Copyright (c) 2023 Qurat Ul Ain Nazir (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
The Convergence of Artificial Intelligence and 6G Networks: A Transformative Shift in Computing and Communications
This article investigates the intersection of Artificial Intelligence (AI) with 6G networks and their potential impact on computing and communication systems. It aims to analyze the effects of telecommunications, healthcare, smart cities, and other industries by showcasing how AI integration will transform 6G technology and its subsequent network optimization and management. This article takes a conceptual approach by assessing available literature AI technology’s trends and research concerning 6G networks. It assesses the foundational technologies of 6G like AI and THz communication, massive MIMO, edge and quantum computing, and how these technologies amplify the impact of future networks on society. Further, it discusses the role of AI in the different domains of AI powered 6G and considers the major implications for security. Incorporating AI into 6G networks is expected to provide unprecedented benefits like ultra-high speed connection, real-time decision-making, and unparalleled data handling. This will give rise to autonomous systems, transform healthcare with proactive monitoring, boost industrial automation, and facilitate the development of smart cities. The study however does highlight challenges posed by complex AI algorithms, issues of security and privacy, and stringent governance policies. This text explains the impact of Artificial Intelligence with respect to its impact across different sections of 6G networks, as well as with the possibilities and problems this integration might pose. This research captures how important AI is for managing 6G networks alongside the interrelated issues of security and ethics. This publication increases the existing literature on emerging communication systems and IN the same time provides thoughtful recommendations for research, policy development, and industrial practice. References [1] M. Abd Elaziz et al., "Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology," Wiley Interdiscip. Rev.: Data Min. Knowl. Discov., vol. 14, no. 1, p. e1521, 2024. [2] M. W. Akhtar et al., "The shift to 6G communications: Vision and requirements," Human-centric Comput. Inf. Sci., vol. 10, pp. 1-27, 2020. [3] M. Z. Afshar, "Exploring factors impacting organizational adaptation capacity of Punjab Agriculture & Meat Company (PAMCO)," Int. J. Emerg. Issues Soc. Sci., Arts Humanit. (IJEISSAH), vol. 2, no. 1, pp. 1-10, 2023. [4] M. Z. Afshar and D. M. H. Shah, "Strategic evaluation using PESTLE and SWOT frameworks: Public sector perspective," ISRG J. Econ., Bus. Manag. (ISRGJEBM), vol. 3, pp. 108-114, 2025. [5] M. 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Copyright (c) 2025 Imran Nazeer Butt, Sameer Shah (Author) https://creativecommons.org/licenses/by-nc-sa/4.0
The Impact of Different Computing Tools in Electrical Engineering Learning
The objective of this study is to assess the use, impact, and issues related to the application of computing devices in Electrical Engineering learning by students in Pakistan. With the ever-increasing infusion of technology in engineering disciplines, it becomes necessary to examine the contribution of computing tools like MATLAB, Simulink, Python, etc. on understanding, application, and readiness for industrial demands. The current study adopted a quantitative strategy by using a pre-tested questionnaire with 400 randomly chosen students enrolled in the Electrical Engineering program across government and private universities in Pakistan. The survey recorded demographic information, frequency of tool utilization, methods of learning, perceived effectiveness of the methods, and challenges encountered. Data analysis procedures were conducted using SPSS Version 29 where frequencies and percentages were computed along with the generation of bar and pie charts, radar diagrams, and tables, all of which fell under descriptive statistics. The results suggest that MATLAB (80%) and Simulink (62.5%) are the most popular and considered the best tools in engineering courses. Most of the students completed these tools to be helpful or extremely helpful in learning; however, significant issues such as no training (62.5%), software cost (50%), and complexity (45%) posed problems. Although most students reported university courses as their primary learning source (75%), they also resorted to self-study, online videos, and studying with friends. Furthermore, students proposed including additional modern technologies like sophisticated Python for AI/ML and PLC programming in the curriculum. 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Copyright (c) 2025 Asif Raza Bali, Yousaf Mughal (Author) https://creativecommons.org/licenses/by-nc-sa/4.0