Computer Science & IT Research Journal最新文献

{"title":"CONFIGURATION MANAGEMENT IN THE MODERN ERA: BEST PRACTICES, INNOVATIONS, AND CHALLENGES","authors":"Oluwatoyin Ajoke Farayola, Azeez Olanipekun Hassan, Olubukola Rhoda Adaramodu, Ololade Gilbert Fakeyede, Monisola Oladeinde","doi":"10.51594/csitrj.v4i2.613","DOIUrl":"https://doi.org/10.51594/csitrj.v4i2.613","url":null,"abstract":"This research paper explores the multifaceted realm of Configuration Management (CM) in the modern era, examining theoretical frameworks, best practices, innovations, and challenges. Theoretical models, including the Three-Component Model and ITIL, form the foundational understanding of CM, guiding effective identification, control, and status accounting of configuration items. Innovations such as DevOps integration, Infrastructure as Code (IaC), and containerization technologies reshape traditional CM practices, providing scalability, automation, and adaptability solutions. However, challenges such as security concerns, compliance issues, and the complexities of collaboration necessitate strategic recommendations. By embracing a holistic approach, prioritizing security, promoting collaboration, and staying informed on emerging technologies, organizations can navigate these challenges and establish resilient CM practices, ensuring the stability and reliability of their IT systems in the ever-evolving technological landscape. Keywords: Configuration Management, DevOps, Three-Component Model, Security.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139231402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"INNOVATIVE BUSINESS MODELS DRIVEN BY AI TECHNOLOGIES: A REVIEW","authors":"Oluwatoyin Ajoke Farayola, Adekunle Abiola Abdul, Blessing Otohan Irabor, Evelyn Chinedu Okeleke","doi":"10.51594/csitrj.v4i2.608","DOIUrl":"https://doi.org/10.51594/csitrj.v4i2.608","url":null,"abstract":"In an era where artificial intelligence (AI) is revolutionizing business paradigms, this study delves into the intricacies of AI-driven business models, offering a nuanced understanding of their emergence, evolution, and impact on traditional business strategies. This scholarly inquiry aims to dissect the role of AI in reshaping business models, highlighting the interplay between technological innovation and business strategy. The study meticulously examines the integration of AI into various business facets by employing a systematic and thematic analysis of a diverse range of literature, including academic journals, industry reports, and case studies. This methodological approach facilitates a comprehensive understanding of AI's role in business innovation, addressing both the opportunities and challenges it presents. The findings reveal that AI-driven business models are characterized by enhanced operational efficiency, data-driven decision-making, and customer-centric approaches. These models signify a transformative shift from conventional business strategies, demanding a reevaluation of leadership roles and ethical considerations in the digital age. The study identifies key challenges in AI implementation, such as technical complexities and ethical dilemmas, while uncovering AI's vast opportunities for business growth and competitive advantage. Conclusively, the study recommends a balanced approach to AI integration, emphasizing the need for ethical AI practices, continuous adaptation, and a synergy between AI capabilities and human insights. It advocates for business leaders to embrace AI not just as a technological tool, but as a catalyst for sustainable and innovative business growth. This scholarly work contributes significantly to the discourse on AI in business, providing a foundational framework for future research and practical application in AI-driven business innovation.   Keywords: Artificial Intelligence, Business Models, Digital Transformation, AI Integration, Leadership in AI, Ethical AI Practices.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GRC STRATEGIES IN MODERN CLOUD INFRASTRUCTURES: A REVIEW OF COMPLIANCE CHALLENGES","authors":"Apeh Jonathan Apeh, Azeez Olanipekun Hassan, Olajumoke Omotola Oyewole, Ololade Gilbert Fakeyede, Patrick Azuka Okeleke, Olubukola Rhoda Adaramodu","doi":"10.51594/csitrj.v4i2.609","DOIUrl":"https://doi.org/10.51594/csitrj.v4i2.609","url":null,"abstract":"This research paper delves into the intricate landscape of Governance, Risk, and Compliance (GRC) in modern cloud infrastructures. As organisations increasingly migrate critical operations to the cloud, they encounter data security, legal compliance, and effective vendor management challenges. The paper explores a comprehensive range of GRC strategies, technological solutions, and best practices to address these challenges. It investigates the evolving regulatory landscape, the shared responsibility model, and the dynamic nature of cloud environments. Technological solutions, including cloud-native GRC platforms, AI and ML for threat detection, and automated security tools, emerge as pivotal components for fortifying GRC in the cloud. The paper concludes with strategic recommendations for organisations seeking to enhance their GRC strategies and navigate the complexities of modern cloud computing. Keywords: Governance, Risk Management, Compliance Challenges, Automated Security Tools, CSPM Solutions.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"16 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANALYZING THE ROLE OF ARTIFICIAL INTELLIGENCE IN IT AUDIT: CURRENT PRACTICES AND FUTURE PROSPECTS","authors":"Uzoamaka Iwuanyanwu, Apeh Jonathan Apeh, Olubukola Rhoda Adaramodu, Evelyn Chinedu Okeleke, Ololade Gilbert Fakeyede","doi":"10.51594/csitrj.v4i2.606","DOIUrl":"https://doi.org/10.51594/csitrj.v4i2.606","url":null,"abstract":"This research paper explores the integration of Artificial Intelligence (AI) into Information Technology (IT) audits, analyzing current practices, training requirements, and prospects. The literature review traces the historical evolution of IT audits, emphasizing the transformative impact of AI. The discussion on training and skill requirements outlines the evolving role of auditors and the strategies for equipping them with essential competencies. Recommendations emphasize continuous learning, ethical considerations, and collaboration, envisioning a future where auditors adeptly leverage AI to enhance the efficiency and strategic value of IT audits within organizations Keywords: Artificial Intelligence, Information Technology Audits, IT Governance, Machine Learning, Auditing Practices, Skill Development","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"52 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SECURITY CONSIDERATIONS AND GUIDELINES FOR AUGMENTED REALITY IMPLEMENTATION IN CORPORATE ENVIRONMENTS","authors":"Olajumoke Omotola Oyewole, Ololade Gilbert Fakeyede, Evelyn Chinedu Okeleke, Apeh Jonathan Apeh, Olubukola Rhoda Adaramodu","doi":"10.51594/csitrj.v4i2.607","DOIUrl":"https://doi.org/10.51594/csitrj.v4i2.607","url":null,"abstract":"This research paper explores the intricate tapestry of security considerations in integrating augmented reality (AR) within corporate landscapes. The journey begins with an in-depth literature review, providing insights into authentication, data privacy, network security, and device vulnerabilities specific to AR systems. A conceptual framework, synthesizing the augmented reality security framework with legal, ethical, and human-centric dimensions, serves as a foundational guide. The guidelines proposed outline a strategic roadmap, emphasizing policy formulation, employee training, security audits, integration with existing infrastructures, legal compliance, and device security. The conclusion underscores the dynamic nature of AR technology, advocating for ongoing vigilance and collaboration to secure the evolving frontier of augmented reality in corporate environments. Keywords: Augmented Reality Security, Corporate Environments, Authentication, Data Privacy, Network Security, Conceptual Framework, Security Guidelines.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A COMPREHENSIVE REVIEW OF THE ROLE OF DATA ANALYTICS IN SHAPING FOOD PRICING STRATEGIES IN THE UNITED STATES: HISTORICAL PERSPECTIVES, CURRENT TRENDS, AND FUTURE PROJECTIONS","authors":"Blessing Otohan Irabor, Adekunle Abiola Abdul, Bankole Ibrahim Ashiwaju, Gbolahan Olaoluwa Oladayo","doi":"10.51594/csitrj.v4i1.603","DOIUrl":"https://doi.org/10.51594/csitrj.v4i1.603","url":null,"abstract":"This topic is designed as an extensive review that charts the historical evolution, current state, and future potential of data analytics in influencing food pricing strategies within the United States. It will encompass a thorough examination of how data analytics has transformed from basic statistical models to advanced AI and machine learning algorithms in the context of food pricing. The review will include a critical analysis of various case studies and models that have been employed in the food industry, assessing their impact on both market dynamics and consumer behavior. Furthermore, it will explore the challenges and ethical considerations surrounding data usage in pricing strategies, such as privacy concerns and market fairness. The future section will speculate on emerging trends and technologies that could further shape this field. This topic is intended to provide a holistic and in-depth perspective on the intersection of data science and food economics, highlighting its significance in the contemporary economic landscape of the U.S. Keywords: Data Analytics, Food Pricing Strategies, Trends, Future Projections Food Demand, Customer Segmentation, Supply Chain Optimization, Personalized Pricing, Dynamic Pricing, Food Fraud, Food Safety.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139323982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ENSURING CYBER SECURITY IN AIRLINES TO PREVENT DATA BREACH","authors":"Leo Tong, Ming Kwan","doi":"10.51594/csitrj.v3i3.426","DOIUrl":"https://doi.org/10.51594/csitrj.v3i3.426","url":null,"abstract":"Using the data breach issues that happened in Cathay Pacific Airways (CX) and British Airways (BA) as case studies, the aim of this study is to focus on analyzing cyber security against data breach that affects airlines passengers’ privacy and induces greater financial losses for airlines. The objective is to investigate the possible leakages in airlines’ cyber security and explore how to strengthen cyber security in airlines. Based on the results, preventative, detective, and reactive measures were revealed which contribute to strengthening cyber security for the airlines.               \u0000Keywords: Cyber Security, Data Breach, Airlines.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129722021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FRAUD PREVENTION AND DETECTION SYSTEM IN NIGERIA BANKING INDUSTRIES","authors":"Kamalu Aliyu Babando","doi":"10.51594/csitrj.v3i2.355","DOIUrl":"https://doi.org/10.51594/csitrj.v3i2.355","url":null,"abstract":"Fraud is on the rise as a result of the advent of modern technology and the global superhighways of banking transactions, resulting in billions of dollars in losses worldwide each year. Although fraud prevention technologies are the most effective method of combating fraud, fraudsters are flexible and will usually find a way around them over time. We need fraud detection approaches if we are to catch fraudsters after fraud prevention has failed. Statistics and machine learning are effective fraud detection technologies that have been used to detect money laundering, e-commerce credit card fraud, telecommunications fraud, and computer intrusion, to name a few. The program is simple to use, and anyone with permission can use it. The importance of computer technology has expanded as it has advanced in all areas of human endeavor.                        \u0000Keywords: Fraud Detection, Fraud Prevention, Banking Industries, Telecommunications.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129975007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A DIAGNOSTIC MODEL FOR THE PREDICTION OF LIVER CIRRHOSIS USING MACHINE LEARNING TECHNIQUES","authors":"Ganty Jamila, G. Wajiga, Y. M. Malgwi, Abba Hamman Maidabara","doi":"10.51594/csitrj.v3i1.296","DOIUrl":"https://doi.org/10.51594/csitrj.v3i1.296","url":null,"abstract":"Liver cirrhosis is the most common type of chronic liver disease in the globe. The ability to forecast the onset of liver cirrhosis sickness is critical for successful treatment and the prevention of catastrophic health implications. As a result, the researchers created a prediction model using machine learning techniques. This study was based on a dataset from the Federal Medical Centre, Yola, which included 583 patient instances and 11 attributes. The proposed model for the prediction of liver cirrhosis sickness employed Nave Bayes, Classification and Regression Tree (CART), and Support Vector Machine (SVM) with 10-fold cross-validation. Accuracy, precision, recall, and F1 Score were used to evaluate the model's performance. Among all the strategies used in this study, the Support Vector Machine (SVM) technique produces the best results, with accuracy of 73%, precision of 73%, recall of 100%, and F1 Score of 84%. Based on medical data from FMC, Yola, this study shows that machine learning methods, specifically the Support Vector Machine, provide a more accurate prediction for liver cirrhosis sickness. This approach can be used to help doctors make better clinical decisions.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"763 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133282327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"REAL-TIME PETROL AVAILABILITY REPORTING SYSTEM (RPARS) FOR NSUKKA TOWN, NIGERIA","authors":"Achi Unimke Aaron, Umar Zayyanu, F. Bakpo","doi":"10.51594/csitrj.v3i1.294","DOIUrl":"https://doi.org/10.51594/csitrj.v3i1.294","url":null,"abstract":"As the number of vehicle owners grows continually, the challenge of searching for petrol availability increases, as not all petrol stations may have petrol available at all times. This is owed to the fact that petrol as a commodity remains relatively scarce. Therefore, this project aims to provide a platform for reporting in real-time petrol availability in Nsukka town. Hence, vehicle owners and public transporters need not waste more petrol and time searching for filling stations with petrol availability. A mobile application is developed to capture relevant real-time information about petrol availability in sampled petrol stations in Nsukka town. The application is a real-time petrol availability reporting system. The system shows a Graphic User Interface (GUI), of a simulated real-time display of petrol availability in sampled filling stations in Nsukka town. This system will help public road vehicle transporters and private vehicle owners make informed decisions on refilling their vehicle tanks from petrol stations with petrol availability closest to the users of the system when they are running out of petrol in their vehicle. Object-Oriented Analysis and Design (OOAD) methodology was used for the analysis and design while JavaScript (JS) and DART programming languages, MongoDB, a no-SQL database, were used to implement the simulation of wireless capacitive fuel level sensor reading on a mobile App, using flutter SDK. \u0000Keywords: Realtime, Petrol Availability, Reporting System, Petrol Stations.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115209087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}