Asian Journal of Research in Computer Science
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792 research outputs found
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Search Query Refinement Using Context, Knowledge and Long-term Memorization
No full textIn the era of information overload, search tools are crucial, yet users often struggle to articulate their precise dataneeds, resulting in sub-optimal search outcomes. This is often due to users associating products with influencers or celebrities rather than knowing the specific brand or product name. This research aims to enable users to find products based on real-life associ- ations, emphasizing the importance of upgrading search query refinement for accuracy and relevance. A significant challenge faced by existing web tools is refining queries involving unrelated entities. This research addresses this gap by proposing a compre- hensive approach that integrates context, knowledge represen- tation, and long-term memorization. The framework combines contextual information with advanced knowledge representation techniques, enhancing the system’s understanding of user intent and domain-specific concepts. Long-term memorization reduces the time complexity of query refinement by leveraging past search experiences. This study underscores the potential of incorporating context, knowledge representation, and long-term memorization in refining search queries, offering more accurate results. As the digital landscape evolves, our approach has the potential to upgrade the search engine experience, providing personalized and contextually relevant results globally
AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems
No full textThis comprehensive study explores the integration and effectiveness of Artificial Intelligence (AI) in Identity and Access Management (IAM) within cloud environments. It primarily focuses on how AI can enhance user authentication, authorization, and access control, addressing the challenges and possibilities in cloud computing. The study adopts a mixed-methods approach, employing both quantitative and qualitative analyses. A survey involving 582 cybersecurity experts provides insights into the current state and potential of AI in IAM, while multiple regression analysis examines the impact of various factors on system effectiveness. Four hypotheses are explored: the impact of hardware and software configurations on system accuracy (H1), the influence of computational environments on reliability (H2), the role of demographic factors in user acceptance (H3), and the effect of technological enhancements on system performance and acceptance (H4). Findings indicate significant correlations between these factors and the effectiveness of AI in IAM. Notably, hardware configurations and security concerns influence system accuracy; computational environment variations affect system reliability; demographic factors impact user acceptance; and enhancements such as user feedback, advancements in AI technology, continuous learning algorithms, and system transparency improve performance and acceptance. These insights underscore the need for advanced hardware, standardized software, user-centric design, and continuous improvement in AI technologies for effective IAM in cloud environments. The study provides actionable recommendations for cloud service providers and developers, emphasizing the importance of involving users in development processes, ensuring transparency, and adopting adaptive algorithms. Future research directions include longitudinal studies on the impact of technological advancements and exploring demographic-specific responses to AI-integrated IAM solutions
Synergizing Random Forest and K Means Algorithms: An Analytical Study for Precise Crop Recommendation in Southeast Asia
No full textCrop detection and classification are pivotal for optimizing agricultural practices and ensuring sustainable farming. This research presents a sophisticated approach to identifying optimal environments for various crops using advanced machine-learning techniques. The study employs a Random Forest classifier framework to categorize crops based on crucial environmental parameters, including soil nitrogen, phosphorus, potassium levels, temperature, humidity, soil pH, and rainfall. Additionally, a K-Means clustering algorithm groups crops with similar growth conditions. The model demonstrates superior performance compared to existing state-of-the-art approaches, achieving an accuracy of 0.97 and macro average scores of 0.94 for precision, 0.95 for recall, and 0.94 for F1-score. Findings underscore distinct environmental requirements for different crop groups, such as those thriving in arid conditions with minimal rainfall and nutrient content, versus those favoring humid conditions with abundant rainfall and nutrient richness. This study emphasizes the potential of machine learning models to enhance agricultural productivity by aligning crop selection with suitable environmental conditions, facilitating precise agricultural decision-making. The high accuracy and detailed classification underscore the model\u27s efficacy in identifying optimal crop environments, which can significantly improve crop yield and resource management
Securing OTP-based Access in Film-sharing Platforms Using RSA Encryption and QR Codes
No full textLogin data refers to information used by individuals who have purchased or subscribed to a specific membership. Typically, this data includes an OTP code. In this research, the OTP code is utilized to grant users access to view the list of movies associated with the account that added them as a member. OTPs are particularly vulnerable to attacks, as they are often transmitted via phone numbers or email. To mitigate risks such as eavesdropping or interception, implementing robust data security systems is essential. Cryptography, the science of data security and authenticity, plays a crucial role in protecting sensitive information. Rivest-Shamir-Adleman (RSA) is one of the algorithms employed to secure data. In this research, the RSA algorithm is used to encrypt the OTP code provided to members. This encrypted OTP code is then converted into a QR code format and sent to the user via email. An application of the RSA algorithm in this research has proven effective in securing data. This is supported by the results of avalanche effect testing, which showed an average security improvement of 51.9%. Based on this percentage, it can be concluded that the system provides a high level of data security, making it challenging for unauthorized individuals to breach
Classification of Lung Cancer Using SVM with Feature Selection Based on PSO-ROC
No full textThe global issue of lung cancer has grown to be very serious. Using machine learning to classify lung cancer is one method. The challenges in this study are how to apply Particle Swarm Optimization rate of change (PSO-ROC) as a feature selection method and support vector machine (SVM) as a classifier in the context of lung cancer classification; how to compare the accuracy values and running times between SVM without first reducing or selecting the features, SVM with PSO feature selection, and SVM with SVM with PSO-ROC feature selection in the context of lung cancer classification. The purpose of this work is to use SVM with feature selection based on the PSO-ROC algorithm to classify lung cancer. Three methods of classification were used in this study: first, Support Vector Machine (SVM) classification without feature reduction or feature selection; second, SVM and PSO feature selection method; and third, SVM and PSO -ROC feature selection. There are two categories for cancer: malignant and non-cancerous. The findings of this study should help the medical community categorize cancer more quickly and accurately, especially lung cancer. The PSO-ROC based feature selection selects limited number of attributes and yields high classification accuracy compare to others
Enhancing Accessibility and Engagement in Computer Science Education for Diverse Learners
No full textThis study investigates strategies to transform perceptions of computer science, with the primary objective of identifying educational interventions that can effectively reduce barriers faced by underrepresented groups in computer science education. Historically, this field has been perceived as dominated by complex mathematics and programming, which, reinforced by societal stereotypes, deters many from pursuing careers in technology. Using a mixed-methods approach that includes surveys, interviews, and case studies, the research identifies key barriers, such as gender disparities and misconceptions, while evaluating the effectiveness of various pedagogical approaches and outreach initiatives. Findings reveal that students often struggle with foundational courses, particularly in mathematics and programming concepts. Specific challenges include difficulties in grasping abstract concepts and a lack of confidence in technical skills. However, interventions like interactive teaching methods, mentorship programs, and the incorporation of real-world applications significantly enhance student engagement and academic performance. Active learning strategies and gender-sensitive curricula not only foster more inclusive environments but also enable all students to thrive. The research emphasizes practical recommendations for educators and policymakers, highlighting the need for systemic changes to promote inclusivity in computer science education. Ultimately, these findings contribute to fostering a more diverse and inclusive tech industry, underscoring the broader significance of creating equitable opportunities for all learners in the field
Renewable Energy in Agriculture: Enhancing Aquaculture and Post-Harvest Technologies with Solar and AI Integration
No full textRenewable energy, particularly solar energy, is an important component of sustainable agriculture because it provides energy-efficient and ecologically friendly alternatives to traditional techniques. AI reduced waste and produced predicted insights, improving agricultural operations. This review focuses on how solar energy and artificial intelligence are being used in aquaculture and post-harvest technologies. Aquaculture uses AI-driven systems to monitor real-time water quality and fish health, enhancing productivity by reducing mortality rates and minimizing environmental impact. AI algorithms optimize the utilization of sun dryers and cold storage units, reducing post-harvest losses by over 30% and ensuring high-quality produce by minimizing waste. The paper discusses the economic and environmental effects of various technologies, ranging from the high initial cost and existing constraints to the potential for decentralized energy networks and data-driven optimization. This analysis brings out the key trends, gaps, and future opportunities in integrating solar and artificial intelligence technologies for resilient, sustainable, and energy-efficient agriculture. The study establishes the groundwork for future developments in sustainable agriculture by highlighting the important implications for energy efficiency, climate resilience, and global food security
Understanding Cybercrime Modus Operandi: Techniques, Psychological Tricks, and Countermeasures
No full textCyber professionals and general users are still challenged on how to secure cyberspace and their related activities. The primary challenge remained difficulty in identifying the perpetrators of cybercrimes due to the anonymous nature of the internet and the use of sophisticated and stealthy techniques by attackers to hide their identities. The emergence of advanced crime in cyberspace such as Advanced Persistent Threats (APTs) unlike traditional cyberattacks, APTs are characterized by the use of stealthy tactics, including advanced evasion techniques and zero-day vulnerabilities, making them challenging to detect using conventional security measures. Therefore, this study aimed to explore the techniques, psychological tricks, technical tricks, and countermeasures using the two models of cyberattacking: cyber kill chain and cyberattacking process phase’s models. The study applied the Systematic Literature Review (SLR) and Autoethnography methods. The sample of 305 sent (replied) emails from the Yahoo account of the Author from 2012 to 2024. The OSINT techniques are used to verify the empirical validity of the 10 spammed message contents. The study found that in traditional phishing, the attack uses a combination of tricks such as false identity and financial lures, emotional manipulation and trust through divinity and love psychological tricks, involvement of the trusted figure usually the religious leaders, direct solicitation of personal information, urgency and action. In general, we conclude that phishers usually a combination of blended psychological and technical tricks to manipulate the recipient into providing personal information and facilitating the scam. Notably, phishing attacks start with psychological tricks to induce or deceive the target to accept the technical tricks such as clicking the link or downloading. Therefore, we recommend that cyber professionals and general users engage in cognitive training to raise cybersecurity awareness
Implementation of Wireless Charging Unit for Pacemaker Based on Vital Data Analysis Using IOT
No full textThis research paper focuses on the implementation of a wireless charging unit for pacemakers based on vital data analysis using the Internet of Things (IoT). Pacemakers are essential medical devices that require regular charging to ensure their uninterrupted operation. However, conventional charging methods involve invasive procedures, which can be uncomfortable and pose risks to patients. By leveraging IoT and vital data analysis, this study aims to develop a non-invasive and efficient wireless charging system for pacemakers. Through an analysis of vital data and real-time monitoring, the system will intelligently charge the pacemaker based on the patient\u27s needs. This paper discusses a proposed system that uses machine learning to improve the monitoring of patients with artificial pacemaker. The system collects data from implanted pacemakers, as well as other health metrics such as temperature, blood pressure, and glucose levels. This data would be stored in a database and analyzed using machine learning algorithm
A Novel Approach to Text Summarization Using Machine Learning
No full textText summarization is a key strategy in the domains of information retrieval and natural language processing (NLP). Its objective is to reduce a lengthy written document into a clearer, more succinct summary of the information it contains. When a text document is too lengthy or intricate to analyse completely, as in news stories, academic papers, or legal documents, this approach is extremely helpful. The major challenge of text summarising is to take the most important and relevant information from the original text and convey it in an understandable and concise way. In this study, extractive and abstractive summarising techniques are the two primary categories of text summary methods. The paper also presents several algorithms that have been proposed for text summarization, including TextRank, Seq2Seq, and BART. TextRank is a simple and fast algorithm that works well for short documents, Seq2Seq is a deep learning-based approach that generates high-quality summaries, and BART is a transformer-based algorithm that provides the best results on benchmark datasets. The obtained ROUGE Score after passing TextRank, BART, and Seq2SEq algorithm significant also