Asian Journal of Research in Computer Science
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792 research outputs found
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Detecting Change in a Volatile Curve United State Stock Market (US SM) with the Use of Automated Decomposition for Time Series Components
No full textThe main reason for this investigation is to use manual process of identification of time series components with two types of automated decomposition for time series known as automated BFTSC (break for time series components) and, automated GFTSC (Group for time series components) in detecting change in a volatile curve united states stock market (US SM). In identification of components of time series present in the seasonal data of US stock market. The data US Stock Market was a monthly data from January 2001 until December 2018 and a total of 18 years. The stock market of US is also available as a secondary data at the DataBank of University Utara Malaysia Library. The weaknesses of BFAST were corrected by the extension of BFAST to BFTSC and GFTSC. Both were created to capture the cyclical and irregular components that were not captured by BFAST technique and it was included in the methodology of this study. BFTSC and GFTSC were considered to provide a combined image of all the four components of time series while GFTSC had additional advantage of providing equations to the components automated. Evaluation using simulation data and empirical data vindicated the accuracy of BFTSC and GFTSC based on linear trend less volatile data. They are effective and better than BFAST because it was able to identify 100% of the data with the basic four time series components monthly. Both techniques detects 99 % of the entire components in the time series data in a linear trend data
Automating Item Activation Process to Overcome Challenges for Error-free Inventory Operations
No full textThis article highlights the need for the successful implementation of a custom engine within Oracle Governance, Risk, and Compliance (GRC) Preventive Controls Governor (PCG) to address challenges encountered during the item activation process in Inventory. The existing manual processes exhibited a high error incident rate, leading to various issues in Bill of Materials (BOM), Costing, and Financial Accounting due to incorrect inventory item attributes.
A comprehensive approach was implemented to address these issues. The processes were streamlined and optimized by leveraging controllership principles and implementing multilevel workflow-based approvals. Additionally, user-friendly webpages were developed using the Oracle Application Framework (OAF) to enhance the overall user experience.
The implementation of the custom engine within Oracle GRC PCG proved to be a transformative game-changer. It enabled effective control and governance over the item activation process, ensuring data accuracy and integrity. Remarkably, the error incident rate was reduced to zero, resulting in significant improvements in productivity and operational efficiency.
This article delves into the strategies employed, the achievements realized, and the profound impact on productivity, highlighting the successful integration of Oracle GRC PCG in optimizing critical business processes
Machine Learning-Based Human Movements Mimicking System for Animation and Virtual Reality
No full textA trend, which can be noticed over the last several years, is the increasing interest to the machine learning methods in the animation and virtual reality (VR) industries to process the human motion data. The present abstract studies the possible contribution of human like machine learning simulations on human motions, which aim to improve animation and motion synthesis personalization. In this research, we propose a novel framework that utilizes camera-captured human motion data. This technique, which takes advantage of comes of motion data to simulate these animated characters movements in real time, will allow animators to create more creature-like animated figures with subtle actions as seen in real life. Additionally, in virtual reality, it links users’ movements with their avatars so the whole interaction is more engaging and realistic
Corpus-based Approaches for Sentiment Analysis: A Review
No full textThe investigation studied the state of art on corpus-based approaches for sentiment analysis. Thus, detailing its methodologies, evaluation metrics, limitations, and future directions. The importance of sentiment analysis in fields such as marketing, customer feedback analysis, social media monitoring, financial analysis, and political science is emphasized. The methodology for corpus-based approaches in sentiment analysis includes the following key steps: data collection, preprocessing, feature extraction, and sentiment classification. The lexicon-based approaches include the corpus-based or bag of words (BOW) and dictionary (also called opinion lexicon). Evaluation of the corpus-based sentiment analysis approach is addressed through performance metrics such as accuracy, precision, recall, F1-score, and comparative analysis with other approaches including hybrid and rule-based systems. Limitations of corpus-based sentiment analysis, such as data sparsity and domain adaptation, are acknowledged, alongside potential enhancements and research directions including ensemble learning, deep learning architectures, and multimodal data integration. The conclusion emphasizes the versatility and scalability of corpus-based sentiment analysis, while ongoing research efforts aim to address its limitations and further enhance its applicability in diverse domains
An IoT-Based Water Leakage Detection and Localization System
No full textIn this research, we present a proposed Internet of Things based model, which we have named iWaLDeL, for the detection and localization of water leakages which we simulated for the Ghana Water Company (GWC) pipe network, using YF-S201 Flow Rate Sensors and Static Leak Detection techniques. Through an extensive review of existing methods, the research highlights the limitations of traditional detection approaches and emphasizes the potential of modern technologies. The iWaLDeL system harnesses the power of IoT devices, deploying an array of strategically placed sensors capable of detecting leakages and pressure variations. These sensors form a distributed network which communicates with a central hub, ensuring comprehensive coverage of the monitored area. Crucially, the research delves into the innovative aspect of leak localization. By combining data from multiple sensors and pipes, the system can estimate the precise location of a leakage following a mathematical model we developed. This localization capability significantly reduces the time required for maintenance teams to address leakage issues, minimizing water loss and further damages. To validate its effectiveness, the system was prototyped and tested, which demonstrated a successful leak detection and localization, showcasing its adaptability in both residential and commercial settings. The system’s seamless integration with existing smart infrastructure enhances its feasibility for real-world implementation
Securing Tomorrow: The Intersection of AI, Data, and Analytics in Fraud Prevention
No full textAim: This research investigates the interconnections among Data Analytics, Artificial Intelligence, and other cutting-edge technologies to enhance comprehension of fraud prevention. The advantages of integrating machine learning and data analytics into artificial intelligence systems for industry-wide fraud detection and prevention are examined in this study.
Study Design: My approach involved conducting an extensive examination of existing literature and analysing numerous case studies to gather information on the role of artificial intelligence, data, and analytics in fraud prevention.
Place and Duration of the Study: A broad spectrum of academic, corporate, and governmental sources is utilised to supply the research study with its international scope. This study examines publications and developments from 2019 to 2023.
Methodology: The research procedure incorporated an exhaustive literature review. This assessment was composed of academic journals, conference proceedings, and official publications. A qualitative analysis was conducted to assess the data, identify commonalities, and evaluate the strengths and weaknesses of AI fraud protection solutions. A more comprehensive examination of practical implementations was facilitated by case studies, which enhanced comprehension of fraud prevention strategies propelled by AI.
Results: The research revealed important findings concerning the various ways in which analytics, data, and artificial intelligence can be implemented to prevent fraudulent activities. An examination of comparisons between generative AI for social engineering, credit card analytics, and cyber-physical security for Internet of Things (IoT) networks illuminated the merits and demerits of different Artificial Intelligence (AI) approaches.
Conclusion: According to the findings of the study, AI, data, and analytics may alter system defences against fraud. The above-mentioned results underscore the significance of flexible fraud prevention strategies. Constant collaboration, innovative technology, and ongoing investigation are required to remain ahead of evolving fraud techniques. The paper concludes by emphasising the significance of future challenges and orientations
Design and Implementation of an Online Blood Donation System
No full textBlood donation is a critical component of healthcare, saving countless lives each year. However, many regions, including Ghana, face chronic shortages of blood supply, often resulting in life-threatening delays for patients in need. Traditional blood donation methods are plagued with inefficiencies, causing delays in matching donors with recipients, and hindered by cumbersome paperwork that discourages potential donors. To address these challenges, this paper introduces an Online Blood Donation System, which offers an accessible, efficient, and secure platform for connecting blood donors with those in need. The project encompasses a comprehensive study, employing the Agile methodology, HTML, CSS, JavaScript, PHP, and MySQL, to design and develop the system. The system\u27s objectives encompass facilitating blood donation, enhancing efficiency, increasing donor participation, ensuring data security, promoting awareness, and overall healthcare system improvement. This project aims to contribute to the healthcare sector by providing an innovative solution to blood donation challenges, ultimately saving lives and improving healthcare efficiency
AI-Powered Information Governance: Balancing Automation and Human Oversight for Optimal Organization Productivity
No full textThis study employs a mixed-methods approach to examine the optimal balance between AI-powered automation and human oversight in information governance frameworks, aiming to enhance organizational productivity, efficiency, and compliance. Quantitative data collected from 384 respondents were analyzed using Pearson correlation, regression models, and Structural Equation Modeling (SEM). The results reveal strong positive correlations between AI automation levels and both organization size (r = 0.55, p < .01) and AI adoption duration (r = 0.62, p < .01). Regression analysis indicates that higher levels of AI automation significantly improve error reduction (β = 1.12, p < .001) and compliance (β = 1.05, p < .001), especially in larger organizations with longer AI adoption periods. SEM findings highlight that human oversight positively impacts error reduction (β = 0.65, p < .001) and compliance improvement (β = 0.72, p < .001), and the interaction between human oversight and AI automation further enhances these outcomes (error reduction: β = 0.32, p < .001; compliance improvement: β = 0.35, p < .001). The qualitative analysis, involving thematic extraction from industry reports, reveals ethical challenges such as data quality issues, algorithmic bias, and privacy concerns. Hence, it is necessary to integrate human oversight to ensure ethical standards and build stakeholder trust in AI-driven systems. The study concludes with practical recommendations for organizations: establishing transparent AI governance frameworks, investing in continuous training for employees, and regularly auditing AI processes to mitigate risks. By addressing both the technological and ethical dimensions, organizations can implement AI-powered information governance that not only boosts productivity and efficiency but also ensures compliance and ethical integrity
The Impact of Artificial Intelligence on E-commerce
No full textThe research in the current paper seeks to discuss artificial intelligence (AI) in e-commerce with a viewpoint of its effects on the management and the customers. In the light of the current advancements in technology applied in organizations, it is important for companies particularly e-commerce firms to understand AI value for improvement of their business processes in order to establish competitive advantages. Altogether, the present study employs a qualitative method, where data were collected from ten participants in the industry through interviews supported by quantitative analysis to tests associations or trends. It can be concluded that concerning the level of the work organization and effectiveness, AI enhances business processes by reducing the number of routine tasks, increasing inventory control, and providing real-time information processing at the rate of 4.4 out of 5. Moreover, customer experience is improved by narrow casted marketing and effective chat bots, where the mean rating is 4.5. In fact, the correlation between operational efficiency and customer engagement is very high; it stands at 0.75, which means that better backend management directly lead to better customer experiences. However, the implementation costs remain high, and privacy issues also contribute to the hindrances of this flow, which makes the rate of adoption not very enthusiastic. It is crucial to focus on deepening the work in the sphere of creating more efficient AI solutions while keeping the attendees in the strongest constraints of data protection rules possible. In conclusion, this paper reveals the trends of AI within the e-commerce sector as laying the foundation for the effective implementation of AI solutions while providing specific recommendations to managers as to how they can unlock the advantages of various AI approaches for enhancing the efficiency of firms’ operations and strengthening consumers’ engagement in view of the continuously rising competition
Enhancing Fraud Detection Systems through Advanced Data Engineering Techniques
No full textAims: Fraud remains a persistent issue in various industries, particularly in finance, e-commerce, and healthcare, where traditional rule-based systems have struggled to keep pace with the evolving complexity of fraudulent activities. This study aims to develop an enhanced fraud detection framework by addressing the limitations of traditional rule-based systems, particularly in industries where sophisticated fraud schemes prevail.
Study Design: The research utilizes advanced data engineering techniques, including big data analytics, machine learning, and real-time processing, to improve the accuracy and efficiency of fraud detection systems.
Place and Duration of Study: The study was conducted over two years across industries with high fraud susceptibility, including financial services, e-commerce platforms, and healthcare organizations.
Methodology: The framework integrates various data sources, including transaction logs, user behavior, and external fraud indicators. These datasets were pre-processed through data cleaning, feature engineering, and integration. Supervised and unsupervised machine learning models, such as Random Forest and Gradient Boosting, were applied to detect fraud patterns. Real-time data processing enabled immediate detection and response. The system continuously learned from historical data, adapting to new fraud tactics and improving detection over time.
Results: The proposed framework demonstrated a significant improvement in fraud detection accuracy, with machine learning models achieving over 90% accuracy rates. There was also a 30% reduction in false positives compared to traditional methods, and detection times were shortened by 40%, enabling faster identification and mitigation of emerging fraud schemes.
Conclusion: This study concludes that integrating advanced data engineering techniques with machine learning significantly enhances fraud detection systems\u27 accuracy, scalability, and adaptability. While promising, further improvements are needed, particularly in addressing the evolving nature of fraud schemes and ensuring the scalability of real-time data processing. These areas present opportunities for future research and development