https://ej-compute.org/index.php/compute/issue/feed European Journal of Information Technologies and Computer Science 2025-01-02T17:46:07+01:00 Editor-in-Chief editor@ej-compute.org Open Journal Systems European Journal of Information Technologies and Computer Science https://ej-compute.org/index.php/compute/article/view/147 UAV Path Planning Based on Butterfly Optimization Algorithm in Three-Dimensional Space 2024-11-14T14:47:51+01:00 Maytham Kadhim Srayyih mustafafadil837@gmail.com <p>The BOA is a novel optimization algorithm, which is inspired by the butterfly and enables the searching for the best solutions in a respective search area. The algorithm can be set to targeted goals like the amount of distance needed to cover, or/and the presence of an obstacle or the completion of the particular mission objectives. I applied the BOA to generate paths of UAVs on a three-dimensional space and considered the objectives of collision urgency, energy consumption, and near-optimal path planning. Specifically for the assessment of the algorithm, I simulated the application of MATLAB and apply multiple scenarios both on two-dimensional and on three-dimensional environments. I also benchmarked the BOA with two other algorithms including the Ant Colony Optimization and Particle Swarm Optimization (PSO). The results proved that the BOA performed better than the GA in terms of cost function and the time required to arrive at the optimal solution especially in 3D solid terrain. By analyzing the simulation results, the flexibility of the BOA in a 3D environment is evident when new changes take place in the environment. Moreover, the algorithm showed rather swift reaction in terms of path acting in response to various unexpected obstacles. The proposed BOA is viable for the path planning of UAVs in three-dimensional space and effective compared to the other optimization algorithms.</p> 2025-01-21T00:00:00+01:00 Copyright (c) 2025 Maytham Kadhim Srayyih https://ej-compute.org/index.php/compute/article/view/146 The Impact of Quantum Computing on Cryptographic Systems: Urgency of Quantum-Resistant Algorithms and Practical Applications in Cryptography 2024-11-12T09:45:40+01:00 Charles Kinyua Gitonga cgkinyua@chuka.ac.ke <p class="p1">Quantum computing presents computational powers previously thought unattainable. This brings severe threats to classical cryptographic methods, especially <em>RSA</em> and <em>ECC</em>. This paper addresses these risks through a detailed investigation of quantum-resistant algorithms, focusing on lattice- based (<em>CRYSTALS-Kyber</em>), hash-based (<em>SPHINCS+</em>), and code-based (<em>McEliece</em>) systems. Research questions guiding this study include: How vulnerable are traditional algorithms under quantum attack, and which quantum-resistant alternatives offer viable performance and security trade-offs? Through simulations, we analyzed key metrics like encryption speeds, key sizes, and efficiency under quantum threats. Additionally, we demonstrated vulnerabilities in <em>RSA-2048</em> and <em>ECC-256</em> under Shor’s algorithm, emphasizing the necessity for quantum-resistant cryptography. Our results highlighted <em>CRYSTALS-Kyber</em> as a balanced candidate, aligning with the NIST<em> PQC</em> Standardization, while Quantum Key Distribution (<em>QKD</em>) is reviewed for high-sensitivity contexts. Given the forecasted advancements in quantum hardware, we propose a transitional approach using hybrid cryptographic systems to ensure immediate security and ease the shift to quantum-safe protocols. This study also explores industry applications, particularly in finance, healthcare, and IoT, recommending a phased adoption strategy utilizing hybrid cryptographic systems for a secure, gradual transition.</p> 2025-01-14T00:00:00+01:00 Copyright (c) 2025 Charles Kinyua Gitonga https://ej-compute.org/index.php/compute/article/view/145 A New Fault Method Detection for Wireless Sensor Networks using “Autoencoder and LS-SVM” 2024-11-01T08:24:45+01:00 Falah Hasan Hani mustafafadil837@gmail.com <p style="margin: 0cm; margin-bottom: .0001pt; text-align: justify;">This study focuses on the issue of fault detection in WSNs while not disturbing the flow of data; and it presents a comprehensive, and new approach to dealing with the problem. The first steps in the context of the developed methodology for application to the data of stock exchanges include scaling of samples by the method of min-max, transformation of windows of samples as part of data preparation, as well as preliminary data cleaning and accurate division of data into sections. And these steps are important for dataset preparation for further analysis. The proposed method relies on the integration of Autoencoders put alongside Least Squares Support Vector Machines (LSSVM). An Autoencoder network was developed and the size of the hidden nodes was later adjusted to identify internal parameters in the dataset. It was helpful for the subsequent reconstructions of the data scene and allowed to obtain high-level features required for fault detection. With the help of these extracted features, LSSVM model was developed towards classifying no+rmal and anomalous condition in WSNs, The training outcome exhibited high effectiveness where anticipated indexes of training data set were 99.77% and for the test data set were 99%. The above outcomes support the feasibility and accuracy of the applied approach in fault recognition. The thesis greatly helps in the progression of the field by providing a methodical way of addressing the important problem of fault detection in WSNs and providing experimental evidence and analysis for the stated problem.</p> 2024-12-05T00:00:00+01:00 Copyright (c) 2024 Falah Hasan Hani https://ej-compute.org/index.php/compute/article/view/141 Investigating User Experience Disparity in E-Commerce: A Case Study of Computer Science Professionals vs. Regular Users on the Saudi Airlines Platform 2024-10-10T08:21:02+02:00 Ahmed Rashed Albashiri arzahrani@uqu.edu.sa <p>This research aims to elucidate the divergent perspectives of computer science professionals and general users on e-commerce system usability. By examining the Saudi Airlines reservation system as a case study, this study seeks to identify key disparities in user experience between these two groups. The research will contribute to a deeper understanding of how system design can be optimized to cater to a wider range of users. A user experience evaluation of the system was undertaken using the user experience questionnaire (UEQ) framework, focusing on the dimensions of attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. Findings indicate no significant disparity in user experience between regular and professional computer users when interacting with the Saudia Airlines reservation system. However, a notable trend emerged: a mean increase of 1.96 in perspicuity ratings and a mean decrease of 0.53 in novelty ratings across all user groups. The research findings offer a valuable resource for Saudia development teams in their pursuit of optimising the reservations website. Specifically, the results provide actionable insights for enhancing the system’s interface and overall user experience.</p> 2024-12-10T00:00:00+01:00 Copyright (c) 2024 Ahmed Rashed Albashiri https://ej-compute.org/index.php/compute/article/view/144 Computer Power Consumption while using Ad-Blocker on a System with AI Accelerators 2024-10-22T11:46:33+02:00 Khan Awais Khan kakhan@mun.ca Mohammad Tariq Iqbal tariq@mun.ca Mohsin Iqbal mjamil@mun.ca <p class="p1">This study investigates the impact of ad-blockers on system power consumption in a computing environment equipped with an AI accelerator. The increasing prevalence of online advertisements has raised concerns about system performance and energy efficiency, prompting many users to turn to ad-blockers. However, the effectiveness of ad-blockers on power consumption, especially in systems equipped with specialized AI accelerators, remains underexplored. In this research, we evaluate the power usage, GPU utilization, and memory consumption of computers running ad-blockers on both Windows and Ubuntu operating systems. The study compared traditional CPU/GPU methods with AI-accelerated scenarios, using popular ad-blockers such as AdBlock, Adblock Plus, uBlock, uBlock Origin, and uBlock Origin Lite. Results indicate that uBlock Origin and uBlock Origin Lite were the most efficient, significantly reducing power consumption and memory usage compared to other ad-blockers. However, multimedia-heavy websites presented challenges, with increased resource usage observed. The findings emphasize the importance of choosing appropriate ad-blockers to enhance energy efficiency, optimize system resources, and contribute to sustainable computing.</p> 2025-01-21T00:00:00+01:00 Copyright (c) 2025 Khan Awais Khan, Mohammad Tariq Iqbal, Mohsin Iqbal