iosr-JCE   Volume-27 ~ Issue-6 ~ Nov. - Dec. 2025

Abstract: For many years the growing intricacy of modern systems demands effective threat modeling strategies, as the systems are faced with cyber security threat that impedes system’s performance, compromise sensitive data, facilitate malware propagation, enable unauthorized access, trigger data breaches, etc. This paper explores the role of Object-Oriented Programming (OOP) techniques in modeling cyber security threats, providing a comprehensive....

Index Terms – Cyber security, Cyber threats, Modeling, Object-Oriented Programming (OOP), Security modeling

[1]. Cyber security and Infrastructure Security Agency. (2020). Threat Modeling.
[2]. Davis, D. (2018). Oriented Programming for Cyber Security: A Survey. Journal of Cyber Security Technology, 4(2), 1-15.
[3]. Gamma, E., Helm, R., Johnson, R. E., & Vlissides, J. M. (1995). Design patterns: Elements of reusable object-oriented software. Addison-Wesley Longman Publishing Co., Inc.
[4]. International Association for Machine Learning and Artificial Intelligence. (2020). Threat Modeling for AI Systems.
[5]. J. Smith, "Applying Object-Oriented Programming to Cyber Security Threat Modeling," Journal of Cyber Security, vol. 10, no. 3, pp. 123-135, 2020

Abstract: Three key Gumbel distribution parameters—shape (α), scale (β), and position (γ)—are the main focus of this work. In reliability analysis, these characteristics aid in characterising severe circumstances such very high stress levels or lengthy failure durations. The primary goal is to compare two estimate techniques: the Approximate Bayesian estimate (ABS), which is derived using Lindley's approximation under the cautious loss function, and the conventional Maximum Likelihood Estimation (MLE).....

Index Terms – Three-parameter Gumbel distribution; Reliability estimation; Maximum Likelihood Estimation (MLE); Approximate Bayesian Estimation (ABS); Lindley’s approximation

[1]. Sinha SK. Reliability and life testing. New Delhi: Wiley Eastern; 1986.
[2]. Lindley DV. Approximate Bayesian methods. Trabajos de Estadística y de Investigación Operativa. 1980;31(1):223–245. doi:10.1007/BF02888369
[3]. Barlow RE, Proschan F. Mathematical Theory of Reliability. Philadelphia: SIAM; 1996.
[4]. Kotz S, Nadarajah S. Extreme Value Distributions: Theory and Applications. London: Imperial College Press; 2000.
[5]. Coles S. An Introduction to Statistical Modelling of Extreme Values. London: Springer; 2001.

Abstract: EchoCrypt is an AI-based audio steganography system that embeds secret information inside AI-generated white noise. Unlike traditional approaches that modify existing audio, EchoCrypt uses a coverless strategy enabled by Generative Adversarial Networks (GANs). The model integrates a Generator, Discriminator, and Extractor along with STFT-based audio processing. Experimental results demonstrate a Bit Error Rate (BER) of 0.00000000, a Message Error Rate (MER) of 0.0000.....

Index Terms – Audio Steganography, GANs, Coverless Steganography, Extractor Network, Encoder-Decoder, Deep Learning, STFT, SNR, Bit Error Rate, Message Error Rate

[1] Z. Zhang, X. Zhao, R. Ni, and Y. Zhao, “High-quality audio cover generation using adversarial learning...”, 2020.
[2] J. Yang, H. Zheng, X. Kang, and Y.-Q. Shi, “GAN-based optimal embedding location learning...”, 2019.
[3] A. Smith and B. Lee, “Deep generative models for multimedia security...”, 2021.
[4] M. Kumar, S. Rao, and T. Patel, “Adversarial feature learning for robust steganography...”, 2022.
[5] L. Hernandez and F. Gomez, “A hybrid CNN-GAN framework for audio signal manipulation...”, 2021

Abstract: The rapid evolution of high-speed networks has created new challenges for maintaining efficient and fair congestion control. Traditional loss-based algorithms, such as TCP Reno, were designed for low-bandwidth networks and fail to achieve optimal performance in modern high-bandwidth, low-latency environments. This study provides a comprehensive analysis of congestion control mechanisms suitable for high-speed networks, focusing on both end-host algorithms and Active Queue Management (AQM) schemes.
Using simulation.....

[1]. V. Jacobson, “Congestion avoidance and control,” ACM SIGCOMM Computer Communication Review, vol. 18, no. 4, pp. 314–329, Aug. 1988.
[2]. S. Floyd and V. Jacobson, “Random early detection gateways for congestion avoidance,” IEEE/ACM Transactions on Networking, vol. 1, no. 4, pp. 397–413, Aug. 1993.
[3]. .N. Cardwell, Y. Cheng, C. S. Gunn, S. H. Yeganeh, and V. Jacobson, “BBR: Congestion-based congestion control,” Communications of the ACM, vol. 60, no. 2, pp. 58–66, Feb. 2017.
[4]. .S. Ha, I. Rhee, and L. Xu, “CUBIC: A new TCP-friendly high-speed TCP variant,” ACM SIGOPS Operating Systems Review, vol. 42, no. 5, pp. 64–74, Jul. 2008.
[5]. K. Nichols and V. Jacobson, “Controlling queue delay,” ACM Queue, vol. 10, no. 5, pp. 1–15, May 2012.

Abstract: The study of electrohydrodynamic (EHD) stability in a poorly conducting fluid-saturated porous layer under the influence of combined transverse electric and magnetic fields is of significant interest in engineering, geophysics, and materials science. This research examines the interplay between fluid flow, electric fields, and magnetic fields within a porous medium, employing the Darcy-Lapwood-Brinkmann model to analyze stability conditions. By incorporating the effects of induced and applied electric fields, the study explores the emergence of self-generating....

Index Terms –Electrohydrodynamic Stability, Porous Media, Magnetic Fields, Smart Materials, Orr-Sommerfeld Equation

[1]. Lin, C.C (1955) “On the theory of hydrodynamic stability” Cambridge University press pp 102,126,134,164,231,292.
[2]. Stuart, J.T. (1954) “On the Stability of viscous flow between parallel planes in the presence of coplanar magnetic field”, Proc. Roy. Soc. (London), A 221,189.
[3]. Ishihara, Masayuki, Noda, Naotake (2003) Effect of damping on dynamic behavior of a piezo thermoelastic laminate considering the effects of transverse shear. International Journal of Solids and Structures, 40. 6077-6094 doi:10.1016/s0020-7683(03)00396-2
[4]. Rudraiah, N. (2003) “Instabilities of importance in the manufacture of nano and smart materials”, in Mode ling of Nano and Smart Materials, Book Paradise, Bangalore.
[5]. Rudraiah, N. (2003) “Effect of porous lining on reducing the Growth rate of Rayleigh-Taylor instability in the Inertial Fusion Energy Target”, American J. of Fusion Sci. and Tech., 43,307.

Abstract: Artificial Intelligence (AI) revolutionizes education through personalized learning and administrative efficiency, yet energy-intensive computational demands threaten environmental sustainability. This study presents a systematic investigation of embedding AI within green technology frameworks to simultaneously optimize educational advancement and ecological responsibility. Energy-efficient AI models integrating model pruning, quantization, and edge inference were deployed with hybrid renewable energy systems across urban, semi-urban, and rural educational sites over 12-month implementation periods. Results demonstrate enhanced student engagement (+23.5%), improved academic performance....

Index Terms – Artificial Intelligence, Green Technology, Sustainable Education, Adaptive Learning, Renewable Energy, Energy Efficiency, Carbon Reduction.

[1]. M. Chen, L. Johnson, & P. Williams, “Energy-efficient artificial intelligence algorithms for educational systems,” Journal of Educational Technology Research, vol. 45, no. 3, pp. 234-251, 2023.
[2]. A. Brown, K. Davis, & R. Wilson, “Adaptive learning system implementations in secondary education contexts,” Computers in Education Review, vol. 28, no. 2, pp. 145-162, 2022.
[3]. S. Lee, H. Kim, & J. Park, “Renewable energy integration frameworks in educational institutions,” Sustainable Technology Quarterly, vol. 19, no. 1, pp. 78-95, 2023.
[4]. M. Green, P. Thompson, & S. Martinez, “Carbon footprint reduction strategies through green educational technology,” Environmental Education Journal, vol. 51, no. 4, pp. 389-406, 2023.
[5]. W. Chen & Y. Liu, “Model optimization techniques for edge computing deployment in AI systems,” IEEE Transactions on Edge Computing, vol. 6, no. 2, pp. 156-172, 2022.

Abstract: Cloud data centers are rapidly proliferating, and with this expansion, their environmental and operational impacts have become pressing concerns for enterprises worldwide. Traditional management approaches fall short in response to dynamic workload patterns and the variability of renewable energy supply. This study presents a novel AI-powered framework for optimizing energy efficiency and sustainability in multi-cloud data centers. By leveraging LSTM-based workload forecasting and multi-agent reinforcement learning (RL) for intelligent resource provisioning, coupled with comprehensive benchmarking.......

Index Terms – cloud computing, artificial intelligence, energy optimization, sustainability, reinforcement learning, multi-cloud orchestration.

[1]. Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481), 984-986.
[2]. Jones, N. (2018). How to stop data centres from gobbling up the world's electricity. Nature, 561(7722), 163-166.
[3]. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599-616.
[4]. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., ... & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
[5]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.