iosr-JECE   Volume-21 ~ Issue-3 ~ May - Jun. 2026

Abstract: The fast rise in the number of network-based cyberattacks has boosted the need to improve network resilience as a key area of research concern in contemporary systems of communication. This paper offers a machine learning-based solution to enhance a network resilience via the threat detection and prevention strategies via the Support Vector Machine (SVM) algorithm. The proposed model was trained and tested on the UNSW-NB15 dataset that includes real-....

Keywords: Network Resilience; Intrusion Detection; Threat Prevention; Support Vector Machine (SVM); Machine Learning

[1]. Aljawarneh, S., Aldwairi, M., & Yassein, M. B. (2019). Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science, 25, 64–76. https://doi.org/10.1016/j.jocs.2017.03.006
[2]. Almseidin, M., Alzubi, J., Kovacs, S., & Alkasassbeh, M. (2017). Evaluation of machine learning algorithms for intrusion detection system. Procedia Computer Science, 127, 26–31. https://doi.org/10.1016/j.procs.2017.05.055
[3]. Alshamrani, A., Myneni, S., Chowdhary, A., & Huang, D. (2020). A survey on advanced persistent threats: Techniques, solutions, challenges, and research opportunities. IEEE Communications Surveys & Tutorials, 22(2), 1449–1476. https://doi.org/10.1109/COMST.2020.2971394
[4]. Anderson, J. (2024). AI-driven threat detection in Zero Trust network segmentation: Enhancing cyber resilience. Retrieved from https://www.researchgate.net/publication/389166859
[5]. Khan, M. A., Alazab, M., & Jolfaei, A. (2021). A survey of security orchestration, automation and response (SOAR): Trends and challenges. Computers & Security, 109, 102386. https://doi.org/10.1016/j.cose.2021.102386

Abstract: This study examines the relative effectiveness of highly developed Artificial Intelligence (AI) autopilots and trained human pilots in dealing with any in-flight emergency scenarios. The growing role of automation in the cockpit, which was fuelled by the prospect of contributing to a decrease in human error, which is one of the causal factors of most of the aviation accidents, has cast a crucial question: can an AI system be better at crisis management than human cognition? The paper is a synthesis of a wide body of flight data that is gathered through accident and....

[1]. Abbeel, P., & Tani, J. (2021). Deep reinforcement learning in robotics and aviation. Annual Review of Control, Robotics, and Autonomous Systems, 4, 121-149.
[2]. Billings, C. E. (1996). Aviation automation: The search for a human-centered approach. Lawrence Erlbaum Associates.
[3]. Bureau of Enquiry and Analysis for Civil Aviation Safety (BEA). (1993). Final report on the accident which occurred on 20 January 1992 at Mont Sainte-Odile (Bas-Rhin) to the Airbus A320 registered F-GGED operated by Air Inter (Report f-ed920120a). Paris, France: BEA.
[4]. Bureau of Enquiry and Analysis for Civil Aviation Safety (BEA). (2012). Final report on the accident on 1st June 2009 to the Airbus A330-203 registered F-GZCP operated by Air France (Flight AF 447). Paris, France: BEA.
[5]. Casner, S. M. (2019). The automated cockpit: A cognitive perspective on pilot performance and skill. Cognitive Research: Principles and Implications, 4(1), 1-14

Abstract: Personal financial planning encompasses establishing financial objectives, managing budgets, saving and investing, preparing for retirement, handling debts, optimizing taxes, and mitigating risks. Integrating Artificial Intelligence (AI) into this process enhances personalization, automation, efficiency, adaptability, and proactivity. The current study focuses on how individuals invest in the stock market with the assistance of AI. AI-powered tools, such as robo-advisors, provide automated financial advice and investment management tailored to an individual's financial goals and risk tolerance. These platforms utilize algorithms to build and manage personalized investment portfolios, dynamically optimizing asset allocation. By leveraging AI, investors can benefit from data-driven insights and real-time market analysis, facilitating informed decision-making in stock market investments.

[1]. Kotecha, N. (2025). Artificial Intelligence in the Stock Market: The Trends and Challenges Regarding AI-Driven Investments. Open Journal of Business and Management, 13(2), 709-734.
[2]. Roger, J. D. D. (2024). AI-Driven Financial Modeling Techniques: Transforming Investment Strategies. The Journal of Applied Business and Economics, 26(4), 63-74.
[3]. Devapitchai, J. J., Krishnapriya, S. V., Karuppiah, S. P., & Saranya, S. (2024). Using AI-driven decision-making tools in corporate investment planning. In Generative AI for transformational management (pp. 137-160). IGI Global.
[4]. Garg, N., Raghav, A., Adhana, N., & Sharma, K. (2024, November). InvestMate: A Hybrid AI-Driven Financial Chatbot for Personalized Stock Predictions and Investor Education. In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) (Vol. 1, pp. 738-743). IEEE.
[5]. Challa, S. R. (2023). The Role of Artificial Intelligence in Wealth Advisory: Enhancing Personalized Investment Strategies Through DataDriven Decision Making. International Journal of Finance (IJFIN), 36(6), 26-46.

Abstract: In the present work, quantitative evaluation has been made of the repair of damage/ latent tracks in CR-39 detectors subjected to annealing using altogether a new approach for the study of annealing kinetics. This approach has a general application and not limited to the use of CR-39 detectors only.
It has been....

Keywords: CR-39 (DOP), CR-39 (no additive), Track Annealing, Track Etch Rate (VT), Bulk Etch Rate (VB), Etch Rate Ratio (V), Track Diameter (D).

[1]. Fleischer R.L., Price P.B. and Walker R.M. Nuclear Tracks in Solids : Principle and Applications (Berkeley : University of California Press), 1975.
[2]. A.M. Bhagwat : Solid State Nuclear Track Detection : Theory and Applications, Indian Society for Radiation Physics 1993.
[3]. G. Fiedler : Applications of SSNTDs to Heavy Ion Radiation Studies, Proc. 11th International Conference Bristol, 7-12 September, 1981
[4]. Khan H.A., Qurashi I.E. : SSNTD applications is Science and Technology – A Brief Review, Radiation Measurement Vol .31, 1-6, 1999
[5]. Mukhtar A. Rana et al: Activation Energy for the annealing of Nuclear Tracks in SSNTDs Nuclear Instruments and Method, Vol. 179, 2001

Abstract: The expansion of telecommunications infrastructure in Nigeria has created unprecedented opportunities for advancing both digital literacy and e-governance. As mobile and broadband penetration increases, telecoms have become instrumental in bridging the digital divide and enabling access to public services and civic participation. This paper explores the multifaceted role of the telecom sector in Nigeria’s digital transformation, focusing on how it contributes to the promotion of digital literacy across diverse populations and supports the delivery of transparent....

Keywords: Digital Literacy, E-Governance, Telecommunications, Nigeria, ICT Policy, Digital Inclusion

[1] Nigerian Communications Commission. (2024). Subscriber statistics report Q4 2024. https://www.ncc.gov.ng/statistics-reports/subscriber-data
[2] Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016
[3] United Nations. (2022). E-Government Survey 2022: The Future of Digital Government. United Nations Department of Economic and Social Affairs. https://publicadministration.un.org/egovkb/
[4] National Information Technology Development Agency (NITDA) , National Digital Economy Policy and Strategy (2020–2030). https://nitda.gov.ng
[5] National Bureau of Statistics. (2024). Telecoms Sector Report Q1 2024. https://www.nigerianstat.gov.ng