iosr-JCE  Volume-28 ~ Issue-4 ~ Jul. - Aug. 2026

Abstract: This work algebraically demonstrates the equivalence between Cramer's rule and the Gauss-Jordan method for solving 2x2 systems of linear equations. Through matrix and symbolic development, it is shown that both procedures lead to algebraically equivalent expressions when the system has a unique solution. A comparative analysis.....

Key Word: Gabriel Cramer's rule, Gauss-Jordan, equivalence, algebraics, linear algebra, system of linear equations, computational complexity.

[1]. Abdelfattah, A., Anzt, H., Boman, E. G., Carson, E., Cojean, T., Dongarra, J., ... Yang, U. M. (2021). A Survey Of Numerical Linear Algebra Methods Utilizing Mixed-Precision Arithmetic. The International Journal Of High Performance Computing Applications, 35(4), 344–369. Https://Doi.Org/10.1177/10943420211003313
[2]. Babajee, D. K. R. (2013). A Comparison Between Cramer's Rule And A Proposed 2 × 2 Cramer-Elimination Method To Solve Systems Of Three Or More Linear Equations. Scientific & Academic Research Council, ANPRAS (Allied Network For Policy, Research And Actions For Sustainability). Https://Www.Researchgate.Net/Publication/255981900
[3]. Estrada Analco, J. M. (2018). The Rhind Papyrus. A&H Digital Arts And Humanities Journal. Retrieved From
Https://Www.Upaep.Mx/Images/Revista_Artes_Humanidades/Pdf/AH_07_02.Pdf
[4]. Fandiño Albiáñez, R. (2019). Cramer's Rule And The Number E. Mathematics Bulletin, 26(1), 15–32. Unirioja.Es
[5]. Higham, N. J., & Mary, T. (2022). Solving Block Low-Rank Linear Systems By LU Factorization Is Numerically Stable. IMA Journal Of Numerical Analysis, 42(2), 951–980. Https://Doi.Org/10.1093/Imanum/Drab020.

Abstract: Diabetes mellitus is still one of the most common chronic diseases worldwide and associated with high rates of hospital readmission and increased costs of health care. Early prediction of diabetes risk and re-admission to the hospital can greatly enhance patient management and reduce the load on health systems. Recent advances in artificial intelligence have produced predictive models can analyse complex health care data. We propose a hybrid deep learning framework that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM....

Key Word: Hybrid Deep Learning; CNN–LSTM; Diabetes Prediction; Hospital Readmission; Multimodal Healthcare Data; Predictive Healthcare Analytics.

[1] Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature. 521, 436–444. Https://Doi.Org/10.1038/Nature14539
[2] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8) 1735–1780.
Https://Doi.Org/10.1162/Neco.1997.9.8.1735
[3] Karras T., Laine. S., & Aila, T. (2019). A Style-Based Generator Architecture For Generative Adversarial Networks. IEEE Transactions On Pattern Analysis And Machine Intelligence. Https://Doi.Org/10.1109/TPAMI.2020.2970919
[4] Luo, Y., Zhang Y., Cai, X., & Yuan, X. (2022). E²GAN: End-To-End Generative Adversarial Network For Multivariate Time Series Imputation. IJCAI 2022, 3094–3100. Https://Doi.Org/10.24963/Ijcai.2022/430
[5] Douzas G., & Bacao, F. (2019). Geometric SMOTE: A Geometrically Enhanced Drop-In Replacement For SMOTE. Information Sciences 501, 118–135. Https://Doi.Org/10.1016/J.Ins.2019.06.007.

Abstract: India's accelerated digital transformation across e-governance, healthcare, banking, education, telecommunications, and critical infrastructure has substantially expanded the nation's cyberattack surface. The increasing sophistication of ransomware, phishing, supply-chain compromise, application-layer attacks, and advanced persistent threats has highlighted the necessity for proactive cybersecurity strategies that extend beyond conventional perimeter defenses. Ethical hacking, implemented through structured penetration testing, has emerged as an indispensable....

Key Word: Ethical hacking; Penetration testing; Cybersecurity; Network security; Vulnerability assessment; Nmap; Wireshark; Nessus; OpenVAS; OWASP ZAP; Critical infrastructure protection; Digital resilience; India

[1]. CERT-In. (2024). Annual Cyber Security Incident Report. Indian Computer Emergency Response Team. Data Security Council Of India. (2024). India Cybersecurity Skills Report.
[2]. Government Of India. (2023). Digital Personal Data Protection Act, 2023. Government Of India.
[3]. Kim, D., & Solomon, M. G. (2016). Fundamentals Of Information Systems Security (3rd Ed.). Jones & Bartlett Learning.
[4]. Ministry Of Electronics And Information Technology. (2024). Digital India Programme. Government Of India.
[5]. Muhammad Rabnawaz. (2023). Performance Analysis Of Ethical Hacking Tools And Its Approaches (Master’s Thesis, Uskudar University).

Abstract: Background: The Centralized Admission Process (CAP) for engineering institutions presents a complex decision matrix for prospective students, characterized by severe information asymmetry and volatile historical cutoffs across multiple admission cycles. Existing collegiate search platforms predominantly rely on static, rule-based querying, which fails to accommodate strict multi-criteria constraints, user orientation, or peer-validated institutional quality....

Key Word: Hybrid Recommendation; Collaborative Filtering; TF-IDF; Machine Learning; Educational Technology; Data Engineering; Engineering Admissions

[1]. Thongchotchat V, Kudo Y, Okada Y, Sato K. Educational Recommendation System Utilizing Learning Styles: A Systematic Literature Review. Ieee Access. 2023;11:8988–8999.
[2]. Jeevamol J, Renumol Vg. An Ontology-Based Hybrid E-Learning Content Recommender System For Alleviating The Cold-Start Problem. Education And Information Technologies. 2021;26(4):4993–5022.
[3]. Zawia Jm, Et Al. Comprehensive Review Of Meta-Learning Methods For Cold-Start Issue In Recommendation Systems. Ieee Access. 2025;13:24622–24641.
[4]. Pu H, Hua Y. Adaptive Course Recommendation Using Federated Learning And Graph Convolutional Networks In Iot-Enhanced E-Learning. Scientific Reports. 2025;15(1):42040.
[5]. El-Morshid Ma, El-Sawy Aa, El-Bakry Hm. A Hybrid Recommender System Framework For Academic Program Selection. Ieee Transactions On Learning Technologies. 2023;16(3):312–325

Abstract: The rapid proliferation of the Internet of Things (IoT) has transformed modern digital ecosystems by enabling seamless connectivity among smart devices, sensors, industrial systems, healthcare technologies, and consumer applications. While IoT has driven significant advancements across domains such as smart cities, healthcare, transportation, manufacturing, and critical infrastructure, its widespread adoption has also introduced substantial....

Key Word: Internet of Things (IoT); IoT Security; Cybersecurity; Cyber Threats; Risk Assessment; Data Privacy; Intrusion Detection Systems (IDS); Artificial Intelligence (AI); Blockchain; Network Security challenges; Mitigation Strategies

[1]. Arachchige, P. C. M., Bertok, P., Khalil, I., Liu, D., Camtepe, S., & Atiquzzaman, M. (2020). A trustworthy privacy-preserving framework for machine learning in industrial IoT systems. IEEE Transactions on Industrial Informatics, 16(9), 6092–6102.
[2]. Chandra, A. (2024). Privacy-preserving data sharing in cloud computing environments. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 13(1), 104–111. https://www.eduzonejournal.com/index.php/eiprmj/article/view/5572
[3]. Chandra, A., & Manukondakrupa. (2024). Ensemble-Enhanced Threat Intelligence Network (EETIN): A unified approach for IoT attack detection.
[4]. Mohassel, P., & Zhang, Y. (2017). SecureML: A system for scalable privacy-preserving machine learning. In 2017 IEEE Symposium on Security and Privacy (SP) (pp. 19–38). IEEE. https://doi.org/10.1109/SP.2017.12
[5]. Langley, D. J., van Doorn, J., Ng, I. C. L., Stieglitz, S., Lazovik, A., & Boonstra, A. (2021). The Internet of Everything: Smart things and their impact on business models. Journal of Business Research, 122, 853–863. https://doi.org/10.1016/j.jbusres.2019.12.035

Abstract: Academic placement into Science, Arts, or Commercial classes is an important decision in Nigerian senior secondary schools. In many schools, the decision still depends mainly on examination scores, teacher judgement, and school rules. This can ignore important student characteristics and may lead to unsuitable placement. Recent studies show that machine learning can analyse educational data and support more objective decisions. This study developed a machine learning framework using student records collected from selected secondary schools....

Key Word: Academic placement; Educational data mining; Machine learning; Random Forest; Secondary education; Decision support

[1] Z. Ersozlu, S. Taheri, and I. Koch, “A review of machine learning methods used for educational data,” Education and Information Technologies, vol. 29, pp. 22125–22145, 2024. doi: 10.1007/s10639-024-12704-0.
[2] “Analyzing students’ academic performance using educational data mining,” Computers and Education: Artificial Intelligence, vol. 7, 100263, 2024. doi: 10.1016/j.caeai.2024.100263.
[3] A. Laakel Hemdanou, M. Lamarti Sefian, Y. Achtoun, and I. Tahiri, “Comparative analysis of feature selection and extraction methods for student performance prediction across different machine learning models,” Computers and Education: Artificial Intelligence, vol. 7, 100301, 2024. doi: 10.1016/j.caeai.2024.100301.
[4] G. Al-Tameemi, J. Xue, I. Hadi, and S. Ajit, “A Hybrid Machine Learning Approach for Predicting Student Performance Using Multi-class Educational Datasets,” Procedia Computer Science, vol. 238, pp. 888–895, 2024. doi: 10.1016/j.procs.2024.06.108.
[5] S. Khan, T. Mazhar, T. Shahzad, M. Amir Khan, W. Waheed, A. Waheed, and H. Hamam, “Predictive analytics in education—enhancing student achievement through machine learning,” Social Sciences & Humanities Open, vol. 12, 101824, 2025. doi: 10.1016/j.ssaho.2025.101824.

Abstract: Lassa fever remains a recurrent public-health problem in Nigeria, with Ebonyi State among the states that have repeatedly reported confirmed cases. Conventional surveillance is essential, but it is mainly designed to identify and report cases that have already occurred. This study developed a web-based early warning and prediction system that uses time-based surveillance records and machine learning to estimate outbreak risk before wider spread. The system was developed using Object-Oriented Analysis and Design Methodology for the software components and CRISP-DM for the machine-learning process. Historical weekly surveillance variables were cleaned, transformed....

Key Word: Lassa fever; Early warning system; Machine learning; Random Forest; Outbreak prediction; Disease surveillance; Ebonyi State

[1] World Health Organization, “Lassa fever,” Fact Sheet, 5 December 2024.
[2] A. A. Olayinka et al., “A Review of the Epidemiology of Lassa Fever in Nigeria,” 2025. Available through PubMed Central.
[3] Nigeria Centre for Disease Control and Prevention, “Public Advisory on Lassa Fever,” 10 March 2025.
[4] Nigeria Centre for Disease Control and Prevention, “NCDC Calls for Strengthened State-Level Action as Lassa Fever Cases Rise During Peak Season,” 3 March 2026.
[5] W.-H. Hu, H.-M. Sun, Y.-Y. Wei, et al., “Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic,” Infectious Disease Modelling, vol. 10, no. 2, pp. 410–422, 2025, doi:10.1016/j.idm.2024.12.001

Abstract: The rapid adoption of multi-cloud computing has introduced significant security challenges due to the distributed, heterogeneous, and dynamic nature of cloud environments. Traditional perimeter-based security approaches are increasingly inadequate for protecting workloads, applications, and data across multiple cloud service providers. This paper examines the application of a Zero Trust Security Framework (ZTSF) to secure multi-cloud computing environments....

Key Word: Zero Trust Security Framework; Multi-Cloud Computing; Zero Trust Architecture (ZTA); Cloud Security; Identity and Access Management (IAM); Micro-Segmentation; Continuous Authentication; Cybersecurity; Least-Privilege Access; Distributed Cloud Environments

[1]. Lee, J., & Park, S. (2022). Securing distributed workloads with Zero Trust. International Journal of Cybersecurity, 9(1), 78–90.
[2]. Microsoft. (2023). Zero Trust in multi-cloud environments (White paper). https://www.microsoft.com/security/
[3]. National Institute of Standards and Technology. (2020). Zero Trust architecture (NIST Special Publication 800-207). U.S. Department of Commerce. https://doi.org/10.6028/NIST.SP.800-207
[4]. Palo Alto Networks. (2024). The State of Cloud-Native Security 2024 Report. https://www.paloaltonetworks.com/
[5]. Sharma, P., & Gupta, R. (2021). Zero Trust in cloud computing: Mitigating insider threats. Journal of Cloud Security, 14(3), 45–56.

Abstract: Intelligent fault alarm technology plays a critical role in enhancing the efficiency of automated production lines by enabling real-time monitoring and early fault detection. In this study, we analyzed one year of operational data from ten industrial production lines to systematically investigate fault characteristics. Key features during fault occurrences were extracted to train and evaluate multiple machine learning models. Comparative results demonstrated that the Bagged Trees model achieved superior performance, with higher classification accuracy and F1 scores than competing...

Key Word: Automatic alarm prediction, Machine learning, Accuracy, F1 scores

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[3] Mercorelli P. Recent advances in intelligent algorithms for fault detection and diagnosis[J]. Sensors, 2024, 24(8): 2656.
[4] Sudhakar P K, Muthucumaraswamy R, Selvaraju P, et al. A Regularized Mixed Integer Linear Programming framework with penalties for integrated workforce, subcontracting and production optimization[J]. JIMO, 2026, 22(4): 2014-2043.
[5] Kulkarni A J. Optimization Methods in Staff Scheduling and Allocation[M]//Optimization Methods in Manufacturing Processes: A Machine-Generated Literature Overview. Singapore: Springer Nature Singapore, 2025: 433-488.