International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
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Leveraging AI Techniques to Enhance Data Security in Cloud Environments: Challenges and Future Prospects
Full text linkThis paper explores the application of Artificial Intelligence (AI) techniques to enhance data security in cloud computing environments. As organizations increasingly migrate to the cloud, the need for robust security measures has become paramount. Traditional security approaches often struggle to keep pace with the dynamic nature of cloud environments and sophisticated cyber threats. This research examines how AI can address these challenges and improve cloud security. The study analyzes the current state of AI applications in cloud security, evaluates key AI techniques applicable to various cloud security challenges, and identifies future directions for AI integration in cloud security. Machine learning, natural language processing, and other AI methods are discussed in the context of threat detection, anomaly identification, and adaptive security measures. While highlighting the potential of AI in cloud security, the paper also addresses significant challenges, including data quality issues, model interpretability, adversarial attacks on AI systems, privacy concerns, integration with legacy systems, and the cybersecurity skills gap. The research concludes by proposing future directions, such as quantum-resistant AI, federated learning for collaborative security, AI-driven autonomous security systems, and the development of explainable AI for security applications. This comprehensive analysis provides valuable insights for cloud service providers, enterprise customers, cybersecurity professionals, and policymakers navigating the rapidly evolving landscape of AI-driven cloud security
Typing in JavaScript API SDK development: Benefits and Implementation Techniques Using TypeScript
Full text linkThis article aims to explore the development of a scalable and maintainable API SDK using TypeScript, with a focus on the practical implementation of modern programming techniques. The study presents a detailed methodology, including the selection of TypeScript for strict type enforcement, the use of Rollup and microbundle for optimized bundling, and the application of modular design principles through TypeScript mixins. The results highlight the advantages of these approaches in creating a lightweight, cross-platform SDK that works seamlessly in both browser and Node.js environments. Testing strategies, including the use of Nock for HTTP request simulation, are also discussed to ensure reliability and stability. The conclusions emphasize the significance of these modern practices in enhancing code quality, maintainability, and scalability. The novelty of this work lies in its comprehensive integration of these methodologies, providing a robust framework for API SDK development in contemporary software engineering
Makespan Minimization for Efficient Placement of Distributed Computations on Virtual Dynamic Environment
Full text linkNowadays, 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 ย 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
Myanmar Lexicon Based Sentiment Analysis on Hotel Reviews
Full text linkAs social media and digital communication use increases in Myanmar, sentiment analysis is being used more and more in business, politics, and social trends. Big social data analytics is a valuable tool that can be utilized to uncover significant information from social user data. This methodology integrates diverse statistical techniques, sentiment analysis, multimedia administration, and social media analytics to anticipate and predict individuals and examine patterns. Natural Language Processing (NLP) tools and frameworks are becoming more customizable and easily accessible, which makes the process of creating language models unique to Myanmar easier. The proposed system\u27s lexicon will have six categories of aspects (Room, Staff, Facilities, Location, Value, General), together with their corresponding subcategories and opinion terms. After that, word2vec is used to train the reviews of the annotated corpus and create a word embedding model. Because of the nature of the Myanmar language, it is particularly more difficult to perform aspect-level opinion mining on reviews about Myanmar. As a result, the proposed system\u27s primary goal is to employ syntactic patterns and rules to extract pertinent pairs of attributes and opinion terms from user evaluations. The proposed method could be increase the accuracy of sentiment analysis on social media postings written in Myanmar
Optimisation of University Examination Timetable Using Hybridised Genetic and Greedy Algorithms: A Case Study of Computer Science Department, University of Ibadan
Full text linkTimetable scheduling is an important aspect of decision-making in any organisation, particularly in academia. An examination timetable is expected to coordinate students, invigilators, courses, examination hall allocation, and time slots. However, the problem could be viewed as a Nondeterministic Polynomial (NP); NP-hard problem, scheduling problem has plagued humanity since its inception. Due to the complex structure of the problem in terms of hard and soft-constraints, most organisations schedule time inefficiently using manual approach. This study introduced an algorithms hybridisation method of genetic and greedy algorithms to automate the timetable scheduling process efficiently. A genetic algorithm is a heuristic search technique based on Charles Darwin\u27s theory of natural evolution. The fitness of each course, venue, and faculty content is determined by the probabilistic optimisation which is the solution candidate in the initial population of all the objects. Subsequently, the greedy algorithm\u27s activities selector selects the best solution. The output demonstrates that the method effectively handled all the constraints associated with timetable scheduling. Hybridising the two algorithms to build a scheduling system, such as the examination timetable. Therefore, it is a viable option to combine genetic and greedy algorithms to have an optimised examination timetable that is flexible to any situation
Wearable Sensors for Posture and Movement in Patient Handling: A Scoping Review
Full text linkNurses experience work-related musculoskeletal disorders (WMSDs) such as lower back pain due to awkward postures or movements during patient handling. Monitoring and education for patient handling are necessary to prevent these WMSDs. Recently, measurement methods for patient handling using wearable sensors have been developed to implement these interventions at various sites. However, the status of these measurement methods has not been comprehensively summarized. The purpose of this study is to summarize the status of measurement methods for patient handling using wearable sensors. Peer-reviewed papers published between January 2013 and November 2023 that included measurements of patient handling using wearable sensors were selected from Google Scholar. Measured patient handlings, postures, and movements were summarized. The type, number, and placement of sensors were also investigated. Furthermore, the applied data processing techniques were also summarized. Inertial sensors and insole pressure sensors were applied for measurement methods. Current methods can measure trunk angle, arm movement, and foot placement during several motions such as patient transfer. In addition, load and correctness of patient handling motion are recognized by a wearable sensor-based system using machine learning techniques. These results indicate that current methods can provide effective kinematic values during patient handling to prevent WMSDs. On the other hand, there were also limitations due to number of sensors. Future studies should develop simpler measurement methods using fewer sensors
Intelligent Clock Gating for FPGA-based RISC Architectures: A Novel Approach to Switching Activity and Dynamic Power Reduction
Full text linkIn modern digital systems, dynamic power consumption remains a critical concern, particularly in Field-Programmable Gate Arrays (FPGAs) utilized in power-sensitive applications. This paper presents a novel intelligent clock gating technique specifically tailored for FPGA-based RISC architectures to effectively reduce switching activity and dynamic power dissipation. Our approach leverages a combination of hardware and software strategies to dynamically control the clock signals to inactive modules, thereby minimizing unnecessary power consumption. The proposed method integrates seamlessly with existing FPGA design flows and RISC architectures, providing a scalable and efficient solution for power management. Through comprehensive simulations and experimental evaluations on standard benchmark circuits, we demonstrate a significant reduction in dynamic power consumption while maintaining performance and functionality. At higher frequencies overall 64% power on total power is saved
Methods of Automated CSS Refactoring for Web Application Performance Optimization
Full text linkThis paper investigates the effectiveness of automated CSS refactoring techniques in optimizing web application performance. Focusing on two key methods - removal of unused CSS and implementation of scoped CSS - the study conducts experiments on both dynamic and static web pages. Performance metrics such as First Contentful Paint (FCP) and Largest Contentful Paint (LCP) are used to measure the impact of these techniques. The results reveal that removing unused CSS consistently improves performance, with a 4.77% decrease in loading time for dynamic pages and a 3.58% decrease for static pages. Surprisingly, the implementation of scoped CSS led to slight performance degradations in the test environment. This research provides insights into the relative effectiveness of these automated CSS optimization strategies and highlights the need for context-specific testing in web development practices. The findings contribute to the ongoing discussion on best practices for CSS performance optimization in modern web applications
A Comprehensive Overview of Kernels in Machine Learning: Mathematical Foundations and Applications
Full text linkKernels play a fundamental role in machine learning, enabling algorithms to operate efficiently and effectively in high-dimensional spaces. In this paper, we provide a comprehensive overview of regression kernels in machine learning, focusing on their mathematical foundations, properties, and practical applications. We begin with an introduction to the concept of regression kernels and their significance in machine learning. Then, we delve into the mathematical formulation of regression kernels, exploring Mercer\u27s theorem and positive semi-definite (PSD) kernels. Next, we discuss popular kernel functions with their respective properties and applications. After that we apply regression kernel to the bike sharing demand dataset as a case study and compare the different kernel functions. Finally, we explore kernel limitations and current research trends and emerging directions in kernel-based learning, offering insights into the future potential of this powerful methodology. This work aims to serve as a resource for both researchers and practitioners seeking a thorough understanding of regression kernel-based approaches in machine learning
Implementation of Machine Learning in Android Applications
Full text linkThe 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