https://ijcjournal.org/index.php/InternationalJournalOfComputer/issue/feed International Journal of Computer (IJC) 2025-01-11T13:33:19+00:00 Prof. Feras Fares editor1@ijcjournal.org Open Journal Systems <p>The <a title="International Journal of Computer (IJC) home page" href="https://ijcjournal.org/index.php/InternationalJournalOfComputer/index" target="_blank" rel="noopener"><strong>International Journal of Computer (IJC)</strong></a> is an open access International Journal for scientists and researchers to publish their scientific papers in Computer Science related fields. <a title="International Journal of Computer (IJC)" href="https://ijcjournal.org/index.php/InternationalJournalOfComputer/index" target="_blank" rel="noopener">IJC</a> plays its role as a refereed international journal to publish research results conducted by researchers.</p> <p>This journal accepts scientific papers for publication after passing the journal's double peer review process (within 4 weeks). For detailed information about the journal kindly check <a title="About the Journal" href="https://ijcjournal.org/index.php/InternationalJournalOfComputer/about">About the Journal</a> page. </p> <p>All <a title="International Journal of Computer (IJC)" href="https://ijcjournal.org/index.php/InternationalJournalOfComputer/index" target="_blank" rel="noopener">IJC</a> published papers in Computer Science will be available for scientific readers for free; no fees are required to download published papers in this international journal.</p> <p> </p> https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2306 Implementation of machine learning in Android Applications 2024-11-15T05:41:16+00:00 Vladislav Terekhov deanace50@gmail.com <p>The introduction of machine learning into Android applications based on the Java platform allows you to significantly expand the functionality of mobile applications, improving the user experience and increasing the efficiency of data processing. The use of various libraries, such as TensorFlow Lite and ML Kit, gives developers flexible tools for integrating machine learning models. This allows you to implement image recognition, text analysis, and user segmentation functions, providing a more personalized service. However, developers face challenges related to the limitations of computing resources of mobile devices, which require optimization of models to work in conditions of low power consumption and limited RAM. Nevertheless, machine learning on Android shows high development prospects, contributing to the creation of more intelligent and adaptive mobile solutions.</p> 2025-01-20T00:00:00+00:00 Copyright (c) 2024 Vladislav Terekhov https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2333 Makespan Minimization for Efficient Placement of Distributed Computations on Virtual Dynamic Environment 2025-01-11T13:33:19+00:00 Albert Djakene Wandala djakene.albert@univ-ndere.cm Omer Yenke Blaise boyenke@univ-ndere.cm <p>Nowadays, virtualization, containerization technology and computer development make it possible to build distributed systems with virtual nodes, offering considerable performance for the execution of distributed computations. However, building such infrastructure faces various challenges of distributed systems, including load balancing, fault tolerance and wise placement of distributed computations on compute nodes. In this paper, we focus on the efficient placement of distributed computations in a virtual distributed system with the aim of minimizing the makespan. Several approaches have been proposed to reduce the placement makespan , but the need of improvement still remains. Consequently, in this work, we propose a new approach that minimizes the makespan of distributed computations on compute nodes by performing fine-grained intelligent placement. The results obtained during tests have shown a better placement of distributed computations on core nodes than existing approaches, regardless of the characteristics of the processes, cores, distributed computations and compute nodes.</p> 2025-01-26T00:00:00+00:00 Copyright (c) 2024 Albert Djakene Wandala, Omer Yenke Blaise https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2327 Hybrid Skin Lesion Detection Integrating CNN and XGBoost for Accurate Diagnosis 2025-01-02T11:48:41+00:00 Adekunle O. Ajiboye deanace50@gmail.com <p>Skin cancer, particularly melanoma, remains one of the most challenging medical conditions due to its rapid progression and high mortality rate when not detected early. The growing prevalence of skin cancer highlights a significant problem in medical diagnostics: the need for automated, accurate, and efficient classification systems that can aid dermatologists in diagnosing various types of skin lesions. This issue is exacerbated by the imbalance in available datasets, underrepresentation of certain lesion classes, and a lack of generalizable diagnostic tools, ultimately impacting patient outcomes and healthcare efficiency.</p> <p>This study aimed to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) for feature extraction and XGBoost for classification to address the problem of skin lesion classification. This study's guiding conceptual framework was applying deep learning techniques combined with ensemble models to enhance classification accuracy and model interpretability.</p> <p>The study utilized the HAM10000 dataset, comprising 10,015 dermatoscopic images across seven skin lesion classes. Dynamic resampling based on power analysis ensured class balance by selecting 158 samples per class. Image preprocessing techniques, such as resizing, hair removal, and Gaussian blurring, were applied to standardize the data. The CNN model extracted hierarchical features, while the XGBoost model performed classification on these features. The research methodology involved a quantitative approach using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to evaluate the model’s effectiveness.</p> <p>The results demonstrated that the CNN-XGBoost hybrid model achieved superior classification performance with an accuracy of 86.46% on the test dataset, outperforming the standalone CNN model. The hybrid model effectively addressed class imbalance and exhibited high discriminatory power across all lesion classes, as confirmed by an average ROC-AUC score of 0.98.</p> <p>The study concludes that the hybrid CNN-XGBoost model holds significant potential for assisting dermatologists in early skin lesion detection and improving diagnostic accuracy. Recommendations for future research include validation using diverse datasets, incorporating clinical metadata, and enhancing model interpretability for real-world deployment. These findings contribute to advancing AI-driven healthcare solutions, offering promising implications for dermatological diagnostics and patient care.</p> 2025-01-10T00:00:00+00:00 Copyright (c) 2024 Adekunle O. Ajiboye https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2313 Application of International Standards to Improve Competitiveness in the Gaming Industry 2024-11-27T09:04:52+00:00 Andrei Saprykin sandeep.matharoo@quantumarc.ca <p>This review article explores the importance of international standards in optimizing processes and enhancing the competitiveness of companies in the rapidly growing market for video games. The author delves into the existing standards created by renowned organizations such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and the Institute of Electrical and Electronics Engineers (IEEE). Among the standards examined are ISO/IEC 25010, which covers systems and software quality models, ISO/IEC 33020, which assesses process capability, ISO/IEC 29110, which outlines lifecycle profiles for small businesses, IEEE 2861 for evaluating and optimizing mobile gaming performance, and ISO/IEC/IEEE 29119 for software testing. The article highlights the key features of these standards and explains how they contribute to process optimization, quality improvement, and enhanced user experience (UX). It also addresses the risks associated with implementing these standards and suggests strategies to minimize or eliminate them.</p> 2025-01-03T00:00:00+00:00 Copyright (c) 2024 Andrei Saprykin https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2328 Harnessing Generative AI for Optimizing Power Generation Innovations and Applications in Energy Efficiency 2025-01-02T11:56:59+00:00 Sandeep Matharoo sandeep.matharoo@quantumarc.ca <p>This article explores modern approaches to the integration of advanced technologies such as generative artificial intelligence (AI), the Internet of Things (IoT), and 5G, aimed at developing digital infrastructure and strategic partnerships with technology companies in the energy sector. The focus is on methods such as the use of generative AI models for big data analysis, failure prediction, and the optimization of energy grid operational processes. Special attention is given to the integration of IoT and 5G to create a flexible and resilient infrastructure capable of adapting to real-time changes. The key conclusions from this work show that these technologies not only reduce operational costs but also significantly enhance environmental sustainability through the integration of renewable energy sources. Furthermore, the analysis indicates that the implementation of Vehicle-to-Grid systems contributes to more efficient energy management, and when combined with IoT and Phasor Measurement Units (PMUs), improves the monitoring and control of electrical networks. The article emphasizes that despite the need for adaptation of existing infrastructure and significant computational resources, the potential of these technologies will continue to grow, offering innovative solutions for reducing energy consumption and enhancing productivity in the long term.</p> 2025-01-26T00:00:00+00:00 Copyright (c) 2024 Sandeep Matharoo https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/2321 Random Walk in the Age of GNNs: Unveiling Its Continued Relevance and Applications 2024-12-13T11:24:15+00:00 Aleksandr Timashov aleks.k.tim@gmail.com <p>This article emphasizes the ongoing importance of Random Walk in improving Graph Neural Networks (GNNs). We illustrate how Random Walk enhances GNNs by offering a deeper structural understanding, better feature learning, and increased efficiency in handling large-scale graphs. The incorporation of Random Walk strategies significantly enhances performance in practical applications like drug discovery and fraud detection. Our results indicate that Random Walk continues to be an essential tool for enhancing the interpretability, scalability, and dynamic modeling of graph-based systems, highlighting its enduring significance in contemporary AI methods.</p> 2025-01-26T00:00:00+00:00 Copyright (c) 2024 Aleksandr Timashov