International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
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459 research outputs found
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Random Walk in the Age of GNNs: Unveiling Its Continued Relevance and Applications
Full text linkThis 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
Enhancing Wireless Charging Systems through Dynamic Power Management with the Innovative Power Control Algorithm
Full text linkAbstract— The Innovative Power Control Algorithm (IPCA) represents a significant theoretical advancement in the domain of wireless charging, addressing the inefficiencies and rigidity of traditional static power management systems. Rooted in dynamic power management principles, IPCA leverages real-time data analytics and adaptive feedback mechanisms to optimize power delivery, ensuring efficiency and adaptability across varying operational conditions. This paper delineates the theoretical framework of IPCA, elucidating its algorithmic structure, mathematical modeling, and simulated performance outcomes. Through comprehensive simulations, IPCA demonstrates a potential increase in charging efficiency and adaptability when compared to conventional methods. The theoretical implications of IPCA extend to diverse application scenarios, including consumer electronics, electric vehicles, and industrial automation, promising significant enhancements in wireless charging systems. Despite its theoretical nature, this research lays a robust groundwork for future empirical studies, aiming to validate and realize the practical deployment of IPCA in real- world wireless charging systems
Realistic Sketch-based Face Photo Synthesis using Generative Adversarial Networks (GANs)
Full text linkFacial photo-image synthesis and sketch-based face recognition are highly advantageous, particularly in the fields of security forces and forensics. Furthermore, it makes it more feasible for law enforcement to reduce the number of possible suspects in criminal identification operations. However, since pencil drawings and photographs have different properties by nature, creating a synthesis of photographs based on sketches presents a difficult topic. In the last few decades, generative adversarial network-based systems have achieved enormous advances towards improving the performance of image synthesis. It can speed up identification times while improving matching outcomes by reducing gaps among sketch and photo representations. We perform investigations on the well-known photo-sketch pair database CUHK. First, we demonstrate how a generative adversarial network transforms hand-drawn sketches into realistic photos. Secondly, we employ suspect identification by using the pre-trained VGG16-based feature extractor network and KNN classifier. Our technique focuses on the use of deep learning-based networks, which are well-known for their capacity to process data and extract hierarchical features. The presented image-to-image translation framework minimizes the modality differences between hand-drawn face sketches and color images while improving visual quality. Tests on sketch-photo matching demonstrate significant improvements over current state-of-the-art methods on the challenging task of matching sketches with corresponding photos
Data Science and Machine Learning for Network Management in Telecommunication Systems: Trends and Opportunities
Full text linkThis paper examines the transformative impact of data science, machine learning (ML), and artificial intelligence (AI) on network management in telecommunications, focusing on techniques such as network monitoring, predictive maintenance, anomaly detection, automated network configuration, and self-healing mechanisms. We analyze specific methodologies, including deep learning for anomaly detection and federated learning for predictive maintenance, and address current challenges such as data quality, system integration, and model interpretability. Emerging technologies like edge computing, federated learning, and quantum computing are explored for their potential to enhance predictive maintenance and network management. The paper provides an overview of how AI-driven solutions are revolutionizing telecom networks, offering unprecedented efficiency, reliability, and performance while highlighting the need for ongoing research to tackle complex issues of explainability and privacy
Two-factor Authentication through Flash Calls: Technical and Economic Analysis
Full text linkThis study explores flash call verification as an innovative approach to two-factor authentication (2FA), addressing modern security and cost-efficiency challenges. The research examines the theoretical foundations and technical implementation of flash call authentication, comparing it against conventional SMS-based methods. The proposed methodology utilizes telephone network infrastructure to deliver authentication codes through incoming call numbers, showcasing notable improvements in both security and economic viability. Results indicate that flash calls offer substantial cost savings relative to SMS authentication, while preserving high-security standards through channel isolation. Although the method is particularly relevant for markets with elevated SMS costs, it holds global applicability in diverse digital systems. By providing a comprehensive analysis of flash call architecture, security mechanisms, and operational benefits, this study contributes new insights into scalable and cost-effective 2FA solutions
Methods for Reducing Testing Costs through Automation
Full text linkSoftware testing automation is a strategically important method that significantly reduces the cost of quality assurance and increases the efficiency of the development process. The article discusses the main methods and approaches that contribute to the optimization of testing, including the choice of tools and technologies, cost-effectiveness assessment and integration of automated processes. The main attention is paid to the issues of planning, selection of tools, as well as structuring and parameterization of tests. Examples of successful automation implementation are given, demonstrating a significant reduction in testing time and improvement of product quality. An assessment of economic efficiency has shown that automation not only pays back the invested funds, but also brings significant profits, especially in large-scale projects. In conclusion, the importance of a flexible approach to the selection of tools and methodologies that allow automation to be adapted to the specific needs and objectives of the project is emphasized
The Mega Healthcare Data Breaches in the United States (2009 – 2023): A Comparative Document Analysis
Full text linkThis paper presents a comprehensive analysis of the predominant healthcare data breaches in the United States from October 2009 to September 2023, utilizing a mixed-methods approach centered on seven publicly available breach reports. It aims to identify patterns, common factors, and measures to enhance cybersecurity within the sector. Through comparative document analysis, the study examines the nature, causes, and repercussions of these breaches, recognizing external attacks, internal errors, and software vulnerabilities as critical weaknesses. The consequences range from financial and reputational damage to erosion of patient trust. The findings stress the necessity for improved preventive strategies, bolstering of security practices, employee training, vendor oversight, and effective incident response mechanisms. The paper also offers insights into the legal and ethical implications of breaches. It suggests robust cybersecurity measures, including the adoption of emerging technologies like blockchain and AI/ML to deter threats. The recommendations guide healthcare organizations toward establishing robust protections for sensitive health data, ensuring regulatory compliance, and facilitating continuity of trust and care. The paper serves as a call to action for ongoing study into the multidimensional impact of data compromises in healthcare.
Utilizing NLP Sentiment Analysis Approach to Categorize Amazon Reviews against an Extended Testing Set
Full text linkSentiment analysis, also known as opinion mining, is a pivotal aspect of natural language processing (NLP). This method entails discerning the polarity of textual information and determining whether it conveys positive or negative sentiments. In one of the domains, e-commerce, sentiment analysis assumes paramount significance. It offers businesses a nuanced understanding of their brand and product sentiment as reflected in customer reviews, facilitating market comprehension and strategic decision-making. This study primarily focused on analyzing the Amazon food reviews dataset, augmenting the original dataset with newly generated data, and subsequently conducting data preprocessing tasks, encompassing text cleansing, removing stop words, lemmatization, and stemming. Subsequently, machine learning models were constructed, trained, and evaluated using NLP feature extraction techniques to address the sentiment analysis challenge and investigate the impact of increased data volume on model performance. Among the diverse methodologies employed for extracting features from textual data samples, this research integrated term frequency-inverse document frequency (TF-IDF), Word to Vector (W2V), and Bag of Words (BoW) techniques in the feature extraction phase. Furthermore, three distinct machine learning models, namely Logistic Regression, Decision Tree, and Random Forest, were designed, implemented, and assessed. The models\u27 performance was scrutinized following hyperparameter optimization to determine the most effective approach. The outcomes revealed that the performance of the models was consistent, yielding accuracy rates ranging from 85% to 89% on the testing dataset. Nevertheless, the Logistic Regression model, employing BoW features, demonstrated superior performance compared to the other models. Following optimization of the logistic regression model, a remarkable accuracy of 89% was attained on the testing dataset by operating the BoW extracted features
Assessing the Effect of Gamification in Increasing the Mastery Level of Grade 8 Students in Technology and Livelihood Education
Full text linkThis study aims to investigate whether the gamification in lesson can help on increasing the mastery level of grade 8 students. Gamification, it refers to the application game design elements to an educational setting where competition, points, badges, and rewards helps enhance students’ engagement and motivation. The main goal is to make learning more engaging and interesting specially for learners with short attention span. The research involved a pre-test at the beginning of the quarter, discussion of the lessons with the use of gamification and post-test at the end of the quarter to collect data if the used of gamification really helps in increasing the mastery level of students. Hence, the data supports the assertion that the use of gamification in TLE 8 lessons has resulted in a significant enhancement in student performance. The significant difference between pre-test and post-test scores indicates that gamification has positively impacted the students\u27 learning outcomes
Acoustic Based Induction Motor Fault Detection System Using Adaptive Filtering Algorithm and Fusion Based Feature Extraction Method
Full text linkThe proposed machine fault diagnostic system utilizes acoustic signal processing and machine learning for early fault detection and localization in induction motors. The growth of the fault in an induction motor tends to be quick and can result in a significant failure that can lead to economic loss and huge maintenance expenses. Therefore, developing accurate and sensitive induction motor fault diagnostic procedures for the maintenance system is crucial. The main purpose of this paper was to propose an optimized noise reduction technique for an induction motor fault diagnosis system and two novel acoustic feature vectors that can be used in machine learning algorithms. The contribution of this paper is to implement the effectiveness of the fusion features of acoustic signals by concatenating them from different domains. The acoustic dataset for an induction motor is collected in a motor workshop, and the NLMS algorithm is used for background noise cancellation due to its quick adaptation, stability, and efficient error minimization. Data are segmented and normalized during pre-processing, and the induction motor fault diagnosis system is implemented using MATLAB. Zero Crossing Rate (ZCR), Spectral Entropy (SE), and Energy Entropy (EE) feature vectors are combined, and the F1 feature vector is built. Correlation calculations are employed to assess the motor\u27s condition status, and if a fault is detected, the system proceeds with feature extraction for fault localization. In the feature extraction stage for induction motor (IM) fault localization, Gammatone Cepstral Coefficients (GTCC) and Mel Frequency Cepstral Coefficient (MFCC) features are combined to construct the second feature vector (F2). This feature vector is used as training feature data in machine learning algorithms. If the input test signal is strongly correlated with the faulty signals, the type of faults is classified using a Support Vector Machine (SVM) classifier. According to the experimental results, the proposed system achieved an average accuracy of 99% in fault detection, 97.5% in fault localization, and an error rate of 2.5%