This research paper explores the enhancement of digital access and inclusion for Small and Medium Enterprises (SMEs) in the financial services industry by implementing Cybersecurity Governance, Risk Management, and Compliance (GRC) frameworks. It begins by analyzing the current state of digital access for SMEs, highlighting the significant benefits of digital transformation, including operational efficiencies, enhanced customer experiences, and new market opportunities. The paper identifies key barriers to digital access, such as cybersecurity risks, regulatory compliance challenges, financial constraints, and the lack of digital expertise. The study then reviews existing initiatives aimed at enhancing digital access for SMEs, including government programs, industry collaborations, cybersecurity awareness and training efforts, and fintech solutions. The paper argues that while these initiatives are beneficial, they often fail without a comprehensive GRC framework to address underlying security and compliance issues. A detailed conceptual model is proposed, comprising digital strategy and planning, a robust cybersecurity framework, effective governance and compliance practices, proactive risk management, continuous training and awareness, technological innovation, and collaboration and partnerships. Each component systematically addresses the identified barriers and facilitates a safer, more inclusive digital ecosystem for SMEs. The implementation of this model is outlined in four phases: initiation, implementation, operational, and evaluation, ensuring a structured approach to digital transformation. The paper concludes with recommendations for SMEs to adopt this model, emphasizing the importance of stakeholder engagement, resource allocation, and continuous evaluation. Keywords: Digital Transformation, SMEs, Cybersecurity, GRC, Financial Services, Digital Access.
{"title":"Enhancing digital access and inclusion for SMEs in the financial services industry through Cybersecurity GRC: A pathway to safer digital ecosystems","authors":"Adedamola Oluokun, Courage Idemudia, Toluwalase Vanessa Iyelolu","doi":"10.51594/csitrj.v5i7.1277","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1277","url":null,"abstract":"This research paper explores the enhancement of digital access and inclusion for Small and Medium Enterprises (SMEs) in the financial services industry by implementing Cybersecurity Governance, Risk Management, and Compliance (GRC) frameworks. It begins by analyzing the current state of digital access for SMEs, highlighting the significant benefits of digital transformation, including operational efficiencies, enhanced customer experiences, and new market opportunities. The paper identifies key barriers to digital access, such as cybersecurity risks, regulatory compliance challenges, financial constraints, and the lack of digital expertise. The study then reviews existing initiatives aimed at enhancing digital access for SMEs, including government programs, industry collaborations, cybersecurity awareness and training efforts, and fintech solutions. The paper argues that while these initiatives are beneficial, they often fail without a comprehensive GRC framework to address underlying security and compliance issues. A detailed conceptual model is proposed, comprising digital strategy and planning, a robust cybersecurity framework, effective governance and compliance practices, proactive risk management, continuous training and awareness, technological innovation, and collaboration and partnerships. Each component systematically addresses the identified barriers and facilitates a safer, more inclusive digital ecosystem for SMEs. The implementation of this model is outlined in four phases: initiation, implementation, operational, and evaluation, ensuring a structured approach to digital transformation. The paper concludes with recommendations for SMEs to adopt this model, emphasizing the importance of stakeholder engagement, resource allocation, and continuous evaluation. \u0000Keywords: Digital Transformation, SMEs, Cybersecurity, GRC, Financial Services, Digital Access.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"3 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141836604","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}
Financial fraud poses a significant threat to the stability and integrity of global financial systems. This paper explores the potential of machine learning (ML) algorithms to enhance the detection and prevention of financial fraud in real-time. We employed a quantitative research methodology, utilizing a combination of supervised and unsupervised ML techniques applied to a dataset comprising transactional data from a multinational bank over a five-year period. Key algorithms tested include Random Forest, Support Vector Machines, and Neural Networks, alongside anomaly detection methods like Isolation Forest and Autoencoders. Our findings reveal that ML algorithms can effectively identify patterns and anomalies that signify fraudulent activities, with Neural Networks demonstrating the highest accuracy in detection. The study also uncovered that real-time processing of transactions using these algorithms significantly reduces the detection time, thus preventing potential fraud before it can cause substantial harm. Furthermore, integrating ensemble techniques improved the robustness and accuracy of fraud detection systems. The paper concludes that the implementation of ML algorithms in financial institutions is not only feasible but also imperative for real-time fraud prevention. It recommends ongoing training of models with updated transaction data and increased collaboration between data scientists and financial security experts to continually enhance the effectiveness of fraud detection systems. This research contributes to the evolving field of financial security by providing a clearer understanding of how ML can be strategically utilized to combat financial fraud dynamically and effectively. Keywords: Machine Learning, Fraud Detection, Financial Institutions, Ethical Considerations, Privacy Protection, Regulatory Compliance, Technology Integration, Collaborative Frameworks, Deep Learning, Blockchain Technology, Data Security, Adaptive Systems, Real-time Processing, Algorithmic Bias, Data Anonymization.
金融欺诈对全球金融体系的稳定性和完整性构成重大威胁。本文探讨了机器学习（ML）算法在加强实时检测和预防金融欺诈方面的潜力。我们采用了定量研究方法，将有监督和无监督的 ML 技术结合应用于一个数据集，该数据集由一家跨国银行五年内的交易数据组成。测试的主要算法包括随机森林、支持向量机和神经网络，以及隔离森林和自动编码器等异常检测方法。我们的研究结果表明，ML 算法可以有效识别欺诈活动的模式和异常，其中神经网络的检测准确率最高。研究还发现，使用这些算法对交易进行实时处理可大大缩短检测时间，从而在潜在欺诈行为造成重大损害之前将其防范于未然。此外，整合集合技术提高了欺诈检测系统的稳健性和准确性。本文的结论是，在金融机构中实施 ML 算法不仅可行，而且对于实时预防欺诈也是势在必行。论文建议利用最新交易数据对模型进行持续训练，并加强数据科学家与金融安全专家之间的合作，以不断提高欺诈检测系统的有效性。这项研究让人们更清楚地了解如何战略性地利用人工智能来动态、有效地打击金融欺诈，从而为不断发展的金融安全领域做出贡献。关键词机器学习、欺诈检测、金融机构、道德考量、隐私保护、监管合规、技术集成、协作框架、深度学习、区块链技术、数据安全、自适应系统、实时处理、算法偏差、数据匿名化。
{"title":"Implementing machine learning algorithms to detect and prevent financial fraud in real-time","authors":"Halima Oluwabunmi Bello, Courage Idemudia, Toluwalase Vanessa Iyelolu","doi":"10.51594/csitrj.v5i7.1274","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1274","url":null,"abstract":"Financial fraud poses a significant threat to the stability and integrity of global financial systems. This paper explores the potential of machine learning (ML) algorithms to enhance the detection and prevention of financial fraud in real-time. We employed a quantitative research methodology, utilizing a combination of supervised and unsupervised ML techniques applied to a dataset comprising transactional data from a multinational bank over a five-year period. Key algorithms tested include Random Forest, Support Vector Machines, and Neural Networks, alongside anomaly detection methods like Isolation Forest and Autoencoders. Our findings reveal that ML algorithms can effectively identify patterns and anomalies that signify fraudulent activities, with Neural Networks demonstrating the highest accuracy in detection. The study also uncovered that real-time processing of transactions using these algorithms significantly reduces the detection time, thus preventing potential fraud before it can cause substantial harm. Furthermore, integrating ensemble techniques improved the robustness and accuracy of fraud detection systems. \u0000The paper concludes that the implementation of ML algorithms in financial institutions is not only feasible but also imperative for real-time fraud prevention. It recommends ongoing training of models with updated transaction data and increased collaboration between data scientists and financial security experts to continually enhance the effectiveness of fraud detection systems. This research contributes to the evolving field of financial security by providing a clearer understanding of how ML can be strategically utilized to combat financial fraud dynamically and effectively. \u0000Keywords: Machine Learning, Fraud Detection, Financial Institutions, Ethical Considerations, Privacy Protection, Regulatory Compliance, Technology Integration, Collaborative Frameworks, Deep Learning, Blockchain Technology, Data Security, Adaptive Systems, Real-time Processing, Algorithmic Bias, Data Anonymization.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141670573","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}
Pub Date : 2024-06-14DOI: 10.51594/csitrj.v5i6.1226
Omowonuola Ireoluwapo Kehinde Olanrewaju, Darlington Eze Ekechukwu, Peter Simpa
Driving the transition to sustainable energy is a critical global imperative, and financial innovation plays a pivotal role in accelerating this process. This paper examines the intersection of financial innovation, big data, and Environmental, Social, and Governance (ESG) metrics in advancing the energy transition. By harnessing the power of big data and integrating ESG considerations into investment decisions, financial institutions can drive meaningful change towards a more sustainable energy future. The paper begins by exploring the concept of energy transition, highlighting its importance, drivers, and challenges. It then delves into the role of financial innovation, discussing examples and the opportunities it presents for driving the transition. Subsequently, it examines the significance of big data in understanding energy consumption patterns and optimizing energy efficiency, along with the role of ESG metrics in influencing investment decisions and corporate behavior. The critical role of big data and ESG metrics is emphasized, with a focus on their synergistic potential in driving sustainable investments and informing decision-making processes. Case studies are presented to illustrate successful applications of big data and ESG metrics in the energy sector. Finally, the paper discusses challenges and future directions, including regulatory considerations, technological advancements, and opportunities for collaboration. It concludes by underscoring the importance of continued financial innovation in driving the energy transition and calls for collective action towards a sustainable energy future. Keywords: Energy Transition, Financial Innovation, Big Data, ESG Metrics, Sustainability, Investment Decisions, Sustainable Energy, Renewable Energy, Climate Change
{"title":"Driving energy transition through financial innovation: The critical role of Big Data and ESG metrics","authors":"Omowonuola Ireoluwapo Kehinde Olanrewaju, Darlington Eze Ekechukwu, Peter Simpa","doi":"10.51594/csitrj.v5i6.1226","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1226","url":null,"abstract":"Driving the transition to sustainable energy is a critical global imperative, and financial innovation plays a pivotal role in accelerating this process. This paper examines the intersection of financial innovation, big data, and Environmental, Social, and Governance (ESG) metrics in advancing the energy transition. By harnessing the power of big data and integrating ESG considerations into investment decisions, financial institutions can drive meaningful change towards a more sustainable energy future. The paper begins by exploring the concept of energy transition, highlighting its importance, drivers, and challenges. It then delves into the role of financial innovation, discussing examples and the opportunities it presents for driving the transition. Subsequently, it examines the significance of big data in understanding energy consumption patterns and optimizing energy efficiency, along with the role of ESG metrics in influencing investment decisions and corporate behavior. The critical role of big data and ESG metrics is emphasized, with a focus on their synergistic potential in driving sustainable investments and informing decision-making processes. Case studies are presented to illustrate successful applications of big data and ESG metrics in the energy sector. Finally, the paper discusses challenges and future directions, including regulatory considerations, technological advancements, and opportunities for collaboration. It concludes by underscoring the importance of continued financial innovation in driving the energy transition and calls for collective action towards a sustainable energy future. \u0000Keywords: Energy Transition, Financial Innovation, Big Data, ESG Metrics, Sustainability, Investment Decisions, Sustainable Energy, Renewable Energy, Climate Change","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"57 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141344913","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}
Pub Date : 2024-06-14DOI: 10.51594/csitrj.v5i6.1225
Henry Nwapali Ndidi Naiho, Oluwabunmi Layode, Gbenga Sheriff Adeleke, Ezekiel Onyekachukwu Udeh, Talabi Temitope Labake
This study systematically reviews the intersection of cybersecurity and waste management technologies, aiming to identify current practices, challenges, and future directions for enhancing cybersecurity within this essential sector. Employing a systematic literature review methodology, the research analyzed peer-reviewed articles, conference proceedings, and industry reports published between 2014 to 2024. The methodology involved a structured search strategy, rigorous inclusion and exclusion criteria, and thematic synthesis of findings. Key insights reveal the growing importance of cybersecurity in waste management, driven by the sector's increasing reliance on digital technologies. Significant challenges identified include data breaches, system vulnerabilities, and the absence of standardized cybersecurity practices. The future of cybersecure waste management is characterized by both challenges, such as the rapid pace of technological advancements and opportunities for innovation, including the development of advanced cybersecurity frameworks and the integration of AI for threat detection. Strategic recommendations for industry leaders and policymakers include developing standardized cybersecurity frameworks, investing in advanced technologies, fostering collaboration, enhancing training and awareness, and strengthening regulatory compliance. The study underscores the necessity of robust cybersecurity measures to protect sensitive data, ensure operational continuity, and support environmental sustainability in waste management. This research contributes valuable insights into the critical role of cybersecurity in waste management, offering a foundation for future research and practice enhancements in creating secure, sustainable, and efficient waste management systems. Keywords: Cybersecurity, Waste Management, Digital Technologies, Systematic Literature Review.
{"title":"Cybersecurity considerations in the implementation of innovative waste management technologies: \"A critical review\"","authors":"Henry Nwapali Ndidi Naiho, Oluwabunmi Layode, Gbenga Sheriff Adeleke, Ezekiel Onyekachukwu Udeh, Talabi Temitope Labake","doi":"10.51594/csitrj.v5i6.1225","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1225","url":null,"abstract":"This study systematically reviews the intersection of cybersecurity and waste management technologies, aiming to identify current practices, challenges, and future directions for enhancing cybersecurity within this essential sector. Employing a systematic literature review methodology, the research analyzed peer-reviewed articles, conference proceedings, and industry reports published between 2014 to 2024. The methodology involved a structured search strategy, rigorous inclusion and exclusion criteria, and thematic synthesis of findings. Key insights reveal the growing importance of cybersecurity in waste management, driven by the sector's increasing reliance on digital technologies. Significant challenges identified include data breaches, system vulnerabilities, and the absence of standardized cybersecurity practices. The future of cybersecure waste management is characterized by both challenges, such as the rapid pace of technological advancements and opportunities for innovation, including the development of advanced cybersecurity frameworks and the integration of AI for threat detection. Strategic recommendations for industry leaders and policymakers include developing standardized cybersecurity frameworks, investing in advanced technologies, fostering collaboration, enhancing training and awareness, and strengthening regulatory compliance. The study underscores the necessity of robust cybersecurity measures to protect sensitive data, ensure operational continuity, and support environmental sustainability in waste management. This research contributes valuable insights into the critical role of cybersecurity in waste management, offering a foundation for future research and practice enhancements in creating secure, sustainable, and efficient waste management systems. \u0000Keywords: Cybersecurity, Waste Management, Digital Technologies, Systematic Literature Review.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"2 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141344704","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}
In the fast-paced and data-driven environment of the tech industry, strategic decision-making is paramount for organizations to maintain relevance and competitiveness. This paper investigates the pivotal role of data visualization in this process, leveraging a series of illuminating case studies to underscore its significance. Through real-world examples drawn from various sectors of the tech industry, including software development, e-commerce, and artificial intelligence, it elucidates how data visualization techniques enable organizations to unlock actionable insights from complex data sets. By transforming raw data into visually intuitive representations, decision-makers can identify patterns, trends, and correlations that inform strategic direction and drive improved business outcomes. The case studies presented highlight diverse applications of data visualization, from optimizing user experience on digital platforms to predicting maintenance needs in IoT devices. These examples showcase how data visualization empowers organizations to make informed decisions across a spectrum of strategic initiatives, including product development, market analysis, and resource allocation. Furthermore, this paper delves into the challenges and opportunities associated with implementing data visualization strategies, such as data quality assurance, tool selection, and user training. It provides practical recommendations for overcoming these obstacles and maximizing the effectiveness of data visualization in strategic decision-making processes. By shedding light on the transformative potential of data visualization, this study not only underscores its critical role in the tech industry but also offers valuable insights for organizations seeking to harness its power in navigating the complexities of today's business landscape. This comprehensive examination highlights the need for tech companies to adopt advanced data visualization practices to stay competitive and innovative, ultimately contributing to a more data-informed and agile organizational culture. The paper explores the technological advancements that have propelled data visualization capabilities forward, such as machine learning, augmented reality, and interactive dashboards. These innovations have enhanced the ability of organizations to analyze and interpret vast amounts of data quickly and accurately, further solidifying data visualization as a cornerstone of strategic planning. The study addresses the human factors involved in data visualization, emphasizing the importance of design principles that ensure clarity, accessibility, and user engagement. Effective data visualizations not only present data but also tell a compelling story that resonates with stakeholders, facilitating a shared understanding and alignment around strategic goals. This paper provides a roadmap for tech companies looking to integrate data visualization into their strategic decision-making frameworks. By adopting best pra
{"title":"The role of data visualization in strategic decision making: Case studies from the tech industry","authors":"Omorinsola Bibire Seyi- Lande, Ebunoluwa Johnson, Gbenga Sheriff Adeleke, Chinazor Prisca Amajuoyi, Bayode Dona Simpson","doi":"10.51594/csitrj.v5i6.1223","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1223","url":null,"abstract":"In the fast-paced and data-driven environment of the tech industry, strategic decision-making is paramount for organizations to maintain relevance and competitiveness. This paper investigates the pivotal role of data visualization in this process, leveraging a series of illuminating case studies to underscore its significance. Through real-world examples drawn from various sectors of the tech industry, including software development, e-commerce, and artificial intelligence, it elucidates how data visualization techniques enable organizations to unlock actionable insights from complex data sets. By transforming raw data into visually intuitive representations, decision-makers can identify patterns, trends, and correlations that inform strategic direction and drive improved business outcomes. The case studies presented highlight diverse applications of data visualization, from optimizing user experience on digital platforms to predicting maintenance needs in IoT devices. These examples showcase how data visualization empowers organizations to make informed decisions across a spectrum of strategic initiatives, including product development, market analysis, and resource allocation. Furthermore, this paper delves into the challenges and opportunities associated with implementing data visualization strategies, such as data quality assurance, tool selection, and user training. It provides practical recommendations for overcoming these obstacles and maximizing the effectiveness of data visualization in strategic decision-making processes. By shedding light on the transformative potential of data visualization, this study not only underscores its critical role in the tech industry but also offers valuable insights for organizations seeking to harness its power in navigating the complexities of today's business landscape. This comprehensive examination highlights the need for tech companies to adopt advanced data visualization practices to stay competitive and innovative, ultimately contributing to a more data-informed and agile organizational culture. The paper explores the technological advancements that have propelled data visualization capabilities forward, such as machine learning, augmented reality, and interactive dashboards. These innovations have enhanced the ability of organizations to analyze and interpret vast amounts of data quickly and accurately, further solidifying data visualization as a cornerstone of strategic planning. The study addresses the human factors involved in data visualization, emphasizing the importance of design principles that ensure clarity, accessibility, and user engagement. Effective data visualizations not only present data but also tell a compelling story that resonates with stakeholders, facilitating a shared understanding and alignment around strategic goals. This paper provides a roadmap for tech companies looking to integrate data visualization into their strategic decision-making frameworks. By adopting best pra","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"11 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141343479","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}
Pub Date : 2024-06-14DOI: 10.51594/csitrj.v5i6.1224
Oluwatosin Ilori, Nelly Tochi Nwosu, Henry Nwapali Ndidi Naiho
In the rapidly evolving landscape of financial institutions, robust IT governance is essential to ensure that information technology supports business goals, mitigates risks, and complies with regulatory requirements. This comprehensive review examines the effective implementation of two prominent IT governance frameworks, COBIT (Control Objectives for Information and Related Technologies) and ITIL (Information Technology Infrastructure Library), within financial institutions. The review highlights the unique features, advantages, and challenges associated with each framework, and provides insights into their synergistic application for optimizing IT governance. COBIT is a comprehensive framework that focuses on the governance and management of enterprise IT, providing a structured approach to aligning IT with business strategy, managing risk, and ensuring compliance. ITIL, on the other hand, is a set of detailed practices for IT service management (ITSM) that focuses on delivering value to customers and improving service quality. While COBIT offers a high-level governance model, ITIL provides detailed guidance on service management processes. The integration of COBIT and ITIL frameworks offers a holistic approach to IT governance, combining COBIT’s strategic oversight with ITIL’s operational focus. This synergy enhances the ability of financial institutions to achieve operational excellence, regulatory compliance, and strategic alignment. Case studies from leading financial institutions demonstrate how the combined use of COBIT and ITIL frameworks has led to improved IT governance, risk management, and service delivery. The review also addresses the challenges encountered during the implementation of these frameworks, including organizational resistance, the complexity of integration, and the need for continuous improvement. Strategies for overcoming these challenges are discussed, emphasizing the importance of executive sponsorship, stakeholder engagement, and tailored training programs. In conclusion, this comprehensive review underscores the critical role of COBIT and ITIL frameworks in strengthening IT governance within financial institutions. By effectively implementing these frameworks, financial institutions can enhance their IT governance capabilities, drive operational efficiencies, and ensure compliance with regulatory standards. The findings of this review provide valuable insights for IT leaders and practitioners seeking to optimize IT governance and support the strategic objectives of their organizations. In this review paper, I examine the significant impact of integrating COBIT and ITIL frameworks to enhance IT governance, operational efficiency, and compliance within financial institutions. I draw on detailed case studies and practical examples from my extensive experience to illustrate these concepts. Keywords: IT Governance, Financial Institutions, ITIL, COBIT, Effective Implementation.
在金融机构快速发展的形势下，稳健的 IT 治理对于确保信息技术支持业务目标、降低风险并符合监管要求至关重要。本综合评论探讨了在金融机构内有效实施 COBIT（信息及相关技术控制目标）和 ITIL（信息技术基础设施库）这两个著名 IT 治理框架的情况。评论强调了每个框架的独特之处、优势和相关挑战，并深入分析了它们在优化 IT 治理方面的协同应用。COBIT 是一个全面的框架，侧重于企业 IT 的治理和管理，提供了一种结构化的方法，使 IT 与业务战略保持一致、管理风险并确保合规性。另一方面，ITIL 是一套详细的 IT 服务管理（ITSM）实践，侧重于为客户提供价值和提高服务质量。COBIT 提供了一个高层次的治理模型，而 ITIL 则为服务管理流程提供了详细的指导。COBIT 和 ITIL 框架的整合提供了一种全面的 IT 治理方法，将 COBIT 的战略监督与 ITIL 的运营重点相结合。这种协同作用增强了金融机构实现卓越运营、监管合规和战略调整的能力。来自领先金融机构的案例研究展示了如何结合使用 COBIT 和 ITIL 框架来改进 IT 治理、风险管理和服务交付。评论还探讨了在实施这些框架过程中遇到的挑战，包括组织阻力、整合的复杂性以及持续改进的必要性。报告还讨论了克服这些挑战的策略，强调了高管支持、利益相关者参与和定制培训计划的重要性。总之，本综合评论强调了 COBIT 和 ITIL 框架在加强金融机构内部 IT 治理方面的关键作用。通过有效实施这些框架，金融机构可以增强其 IT 治理能力，提高运营效率，并确保符合监管标准。本综述的结论为寻求优化 IT 治理和支持组织战略目标的 IT 领导者和从业人员提供了宝贵的见解。在这篇综述论文中，我探讨了整合 COBIT 和 ITIL 框架对提高金融机构 IT 治理、运营效率和合规性的重大影响。我从自己丰富的经验中汲取了详细的案例研究和实际例子来说明这些概念。关键词IT 治理、金融机构、ITIL、COBIT、有效实施。
{"title":"A comprehensive review of it governance: effective implementation of COBIT and ITIL frameworks in financial institutions","authors":"Oluwatosin Ilori, Nelly Tochi Nwosu, Henry Nwapali Ndidi Naiho","doi":"10.51594/csitrj.v5i6.1224","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1224","url":null,"abstract":"In the rapidly evolving landscape of financial institutions, robust IT governance is essential to ensure that information technology supports business goals, mitigates risks, and complies with regulatory requirements. This comprehensive review examines the effective implementation of two prominent IT governance frameworks, COBIT (Control Objectives for Information and Related Technologies) and ITIL (Information Technology Infrastructure Library), within financial institutions. The review highlights the unique features, advantages, and challenges associated with each framework, and provides insights into their synergistic application for optimizing IT governance. COBIT is a comprehensive framework that focuses on the governance and management of enterprise IT, providing a structured approach to aligning IT with business strategy, managing risk, and ensuring compliance. ITIL, on the other hand, is a set of detailed practices for IT service management (ITSM) that focuses on delivering value to customers and improving service quality. While COBIT offers a high-level governance model, ITIL provides detailed guidance on service management processes. The integration of COBIT and ITIL frameworks offers a holistic approach to IT governance, combining COBIT’s strategic oversight with ITIL’s operational focus. This synergy enhances the ability of financial institutions to achieve operational excellence, regulatory compliance, and strategic alignment. Case studies from leading financial institutions demonstrate how the combined use of COBIT and ITIL frameworks has led to improved IT governance, risk management, and service delivery. The review also addresses the challenges encountered during the implementation of these frameworks, including organizational resistance, the complexity of integration, and the need for continuous improvement. Strategies for overcoming these challenges are discussed, emphasizing the importance of executive sponsorship, stakeholder engagement, and tailored training programs. In conclusion, this comprehensive review underscores the critical role of COBIT and ITIL frameworks in strengthening IT governance within financial institutions. By effectively implementing these frameworks, financial institutions can enhance their IT governance capabilities, drive operational efficiencies, and ensure compliance with regulatory standards. The findings of this review provide valuable insights for IT leaders and practitioners seeking to optimize IT governance and support the strategic objectives of their organizations. In this review paper, I examine the significant impact of integrating COBIT and ITIL frameworks to enhance IT governance, operational efficiency, and compliance within financial institutions. I draw on detailed case studies and practical examples from my extensive experience to illustrate these concepts. \u0000Keywords: IT Governance, Financial Institutions, ITIL, COBIT, Effective Implementation.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"18 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141342571","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}
Pub Date : 2024-06-14DOI: 10.51594/csitrj.v5i6.1227
Arman Mohammad Nakib, Yuemei Luo, Jobaydul Hasan Emon, Sakib Chowdhury
Wastage of water is a burning topic in the world. Different countries worldwide are facing the issue of the lack of fresh water, and the problem is increasing daily. This paper aims to design a system that will predict the amount of water needed by a family or in a locality depending on family members, region, temperature, season, occupation, location, and religion. It can also be possible to forecast the water demand in a locality, area, or country depending on these factors. Previous works in this field focus on something other than these factors and the distribution system mentioned in this paper. Different machine learning models will predict the amount of water required by a family or a locality based on these factors. Then, water will be supplied using these expected values so that each family or community in a locality receives the desired amount of water. The practical circuit uses an Arduino microcontroller, water flow meter, solenoids, etc. Water distribution is automatically controlled by the water flow meter and solenoid, and no family or community in a locality will receive more water than the predicted values per day. So it will reduce water wastage, and everybody will use it according to their daily needs. Different machine learning models were used in this proposed design to compare the performance of the models for this task. Linear, Ridge, Lasso, ElasticNet, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting), KNN (K-Nearest Neighbors), SVR (Support Vector Regression), MLP (Multilayer Perceptron), LightGBM (Light Gradient-Boosting Machine), CatBoost, Deep Neural Network have been used. Different model's performances have been analyzed. The analyzing factors are model training time, model prediction time, Robustness to outliers, and scalability. All these performances were analyzed to determine which model is best for this work. So, the Decision Tree and LightGBM models are the best based on comparing all the models for this task. Keywords: Factors Influencing Water Consumption, Different Machine Learning Models Comparison, Water Demand Prediction and Forecast, Precise Water Distribution, Reducing Water Wastage.
{"title":"Machine learning-based water requirement forecast and automated water distribution control system","authors":"Arman Mohammad Nakib, Yuemei Luo, Jobaydul Hasan Emon, Sakib Chowdhury","doi":"10.51594/csitrj.v5i6.1227","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1227","url":null,"abstract":"Wastage of water is a burning topic in the world. Different countries worldwide are facing the issue of the lack of fresh water, and the problem is increasing daily. This paper aims to design a system that will predict the amount of water needed by a family or in a locality depending on family members, region, temperature, season, occupation, location, and religion. It can also be possible to forecast the water demand in a locality, area, or country depending on these factors. Previous works in this field focus on something other than these factors and the distribution system mentioned in this paper. Different machine learning models will predict the amount of water required by a family or a locality based on these factors. Then, water will be supplied using these expected values so that each family or community in a locality receives the desired amount of water. The practical circuit uses an Arduino microcontroller, water flow meter, solenoids, etc. Water distribution is automatically controlled by the water flow meter and solenoid, and no family or community in a locality will receive more water than the predicted values per day. So it will reduce water wastage, and everybody will use it according to their daily needs. Different machine learning models were used in this proposed design to compare the performance of the models for this task. Linear, Ridge, Lasso, ElasticNet, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting), KNN (K-Nearest Neighbors), SVR (Support Vector Regression), MLP (Multilayer Perceptron), LightGBM (Light Gradient-Boosting Machine), CatBoost, Deep Neural Network have been used. Different model's performances have been analyzed. The analyzing factors are model training time, model prediction time, Robustness to outliers, and scalability. All these performances were analyzed to determine which model is best for this work. So, the Decision Tree and LightGBM models are the best based on comparing all the models for this task. \u0000Keywords: Factors Influencing Water Consumption, Different Machine Learning Models Comparison, Water Demand Prediction and Forecast, Precise Water Distribution, Reducing Water Wastage.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"53 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339293","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}
Pub Date : 2024-06-07DOI: 10.51594/csitrj.v5i6.1197
Darlington Eze Ekechukwu, Peter Simpa
This study provides a comprehensive examination of cybersecurity within renewable energy systems, highlighting the critical role of cybersecurity measures in ensuring the sustainability and reliability of these systems. With the increasing reliance on renewable energy sources, the need for robust cybersecurity frameworks to protect against evolving cyber threats has never been more pressing. Through a systematic literature review and content analysis, this research identifies the prevalent cyber threats and vulnerabilities specific to renewable energy infrastructures, evaluates the effectiveness of current cybersecurity measures, and explores cutting-edge technologies and practices in the field. The methodology encompasses a detailed analysis of peer-reviewed academic journals, conference proceedings, industry reports, and white papers published from 2010 to 2024. This approach facilitates the identification of gaps in current cybersecurity practices and the proposal of strategic solutions to address these challenges. Key insights reveal the significance of adopting advanced cybersecurity technologies, such as artificial intelligence and machine learning algorithms, to enhance threat detection and mitigation efforts. The study concludes with strategic recommendations for industry practitioners and policymakers, emphasizing the importance of a proactive cybersecurity posture, collaboration and information sharing, investment in cybersecurity training, and the development of specific cybersecurity standards and regulations for the renewable energy sector. Future research directions are suggested to further explore innovative cybersecurity solutions and assess their implications for renewable energy systems. This study underscores the necessity of integrating robust cybersecurity measures to safeguard the future of sustainable energy. Keywords: Cybersecurity, Renewable Energy, Cyber Threats, Vulnerabilities, Advanced Cybersecurity Technologies.
{"title":"The future of Cybersecurity in renewable energy systems: A review, identifying challenges and proposing strategic solutions","authors":"Darlington Eze Ekechukwu, Peter Simpa","doi":"10.51594/csitrj.v5i6.1197","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1197","url":null,"abstract":"This study provides a comprehensive examination of cybersecurity within renewable energy systems, highlighting the critical role of cybersecurity measures in ensuring the sustainability and reliability of these systems. With the increasing reliance on renewable energy sources, the need for robust cybersecurity frameworks to protect against evolving cyber threats has never been more pressing. Through a systematic literature review and content analysis, this research identifies the prevalent cyber threats and vulnerabilities specific to renewable energy infrastructures, evaluates the effectiveness of current cybersecurity measures, and explores cutting-edge technologies and practices in the field. The methodology encompasses a detailed analysis of peer-reviewed academic journals, conference proceedings, industry reports, and white papers published from 2010 to 2024. This approach facilitates the identification of gaps in current cybersecurity practices and the proposal of strategic solutions to address these challenges. Key insights reveal the significance of adopting advanced cybersecurity technologies, such as artificial intelligence and machine learning algorithms, to enhance threat detection and mitigation efforts. The study concludes with strategic recommendations for industry practitioners and policymakers, emphasizing the importance of a proactive cybersecurity posture, collaboration and information sharing, investment in cybersecurity training, and the development of specific cybersecurity standards and regulations for the renewable energy sector. Future research directions are suggested to further explore innovative cybersecurity solutions and assess their implications for renewable energy systems. This study underscores the necessity of integrating robust cybersecurity measures to safeguard the future of sustainable energy. \u0000Keywords: Cybersecurity, Renewable Energy, Cyber Threats, Vulnerabilities, Advanced Cybersecurity Technologies.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370691","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}
Pub Date : 2024-06-07DOI: 10.51594/csitrj.v5i6.1200
Dazok Donald Jambol, Ayemere Ukato, Chinwe Ozowe, Olusile Akinyele Babayeju
The review explores the application of machine learning (ML) to improve the accuracy of instrumentation in the oil and gas industry. The paper discusses the challenges faced in instrumentation accuracy and how ML can be utilized to address these challenges. It highlights the benefits of using ML, such as improved data accuracy, reduced maintenance costs, and enhanced operational efficiency. The review also covers the future prospects of ML in the oil and gas industry and concludes with a call to action for companies to adopt ML technologies to improve instrumentation accuracy. In the oil and gas industry, accurate instrumentation is crucial for ensuring safe and efficient operations. However, maintaining high levels of accuracy can be challenging due to factors such as environmental conditions, equipment aging, and human error. Machine learning (ML) offers a promising solution to enhance instrumentation accuracy by leveraging data-driven insights to improve monitoring and control systems. ML algorithms can analyze large volumes of data from various sensors and equipment to identify patterns and anomalies that may indicate potential issues. By continuously learning from new data, ML models can adapt to changing conditions and improve their accuracy over time. This proactive approach can help prevent equipment failures, minimize downtime, and optimize production processes. Furthermore, ML can also help reduce maintenance costs by enabling predictive maintenance strategies. By analyzing equipment performance data, ML models can predict when maintenance is likely to be needed, allowing operators to schedule maintenance activities proactively. This can help avoid costly unplanned downtime and reduce the need for unnecessary maintenance checks. Overall, leveraging ML to enhance instrumentation accuracy in oil and gas extraction offers significant benefits. It can improve operational efficiency, reduce costs, and enhance safety. As ML technologies continue to advance, the future prospects for enhancing instrumentation accuracy in the oil and gas industry look promising. Companies that embrace ML technologies stand to gain a competitive edge in the industry by improving their operational performance and reducing risks. Keywords: Leveraging, ML, Enhance, Instrumentation Accuracy, Oil and Gas Extraction.
这篇综述探讨了如何应用机器学习（ML）来提高石油和天然气行业的仪表精度。论文讨论了仪器精度面临的挑战，以及如何利用 ML 来应对这些挑战。它强调了使用 ML 的好处，如提高数据准确性、降低维护成本和提高运营效率。报告还介绍了 ML 在石油和天然气行业的未来前景，最后呼吁各公司采取行动，采用 ML 技术来提高仪表精度。在石油和天然气行业，精确的仪器对于确保安全高效的运营至关重要。然而，由于环境条件、设备老化和人为错误等因素的影响，保持高水平的准确性可能具有挑战性。机器学习 (ML) 通过利用数据驱动的洞察力来改进监测和控制系统，为提高仪表的准确性提供了一种前景广阔的解决方案。ML 算法可以分析来自各种传感器和设备的大量数据，识别可能表明潜在问题的模式和异常。通过不断学习新数据，ML 模型可以适应不断变化的条件，并随着时间的推移提高其准确性。这种积极主动的方法有助于防止设备故障、最大限度地减少停机时间并优化生产流程。此外，人工智能还能通过实施预测性维护策略帮助降低维护成本。通过分析设备性能数据，ML 模型可以预测何时可能需要维护，从而使操作员能够主动安排维护活动。这有助于避免代价高昂的计划外停机，减少不必要的维护检查。总之，利用智能语言提高石油和天然气开采中的仪表精度具有显著的优势。它可以提高运营效率、降低成本并增强安全性。随着 ML 技术的不断进步，提高油气行业仪表精度的未来前景一片光明。采用 ML 技术的公司将通过提高运营绩效和降低风险在行业中获得竞争优势。关键词利用、ML、提高、仪器精度、石油和天然气开采。
{"title":"Leveraging machine learning to enhance instrumentation accuracy in oil and gas extraction","authors":"Dazok Donald Jambol, Ayemere Ukato, Chinwe Ozowe, Olusile Akinyele Babayeju","doi":"10.51594/csitrj.v5i6.1200","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1200","url":null,"abstract":"The review explores the application of machine learning (ML) to improve the accuracy of instrumentation in the oil and gas industry. The paper discusses the challenges faced in instrumentation accuracy and how ML can be utilized to address these challenges. It highlights the benefits of using ML, such as improved data accuracy, reduced maintenance costs, and enhanced operational efficiency. The review also covers the future prospects of ML in the oil and gas industry and concludes with a call to action for companies to adopt ML technologies to improve instrumentation accuracy. In the oil and gas industry, accurate instrumentation is crucial for ensuring safe and efficient operations. However, maintaining high levels of accuracy can be challenging due to factors such as environmental conditions, equipment aging, and human error. Machine learning (ML) offers a promising solution to enhance instrumentation accuracy by leveraging data-driven insights to improve monitoring and control systems. ML algorithms can analyze large volumes of data from various sensors and equipment to identify patterns and anomalies that may indicate potential issues. By continuously learning from new data, ML models can adapt to changing conditions and improve their accuracy over time. This proactive approach can help prevent equipment failures, minimize downtime, and optimize production processes. Furthermore, ML can also help reduce maintenance costs by enabling predictive maintenance strategies. By analyzing equipment performance data, ML models can predict when maintenance is likely to be needed, allowing operators to schedule maintenance activities proactively. This can help avoid costly unplanned downtime and reduce the need for unnecessary maintenance checks. Overall, leveraging ML to enhance instrumentation accuracy in oil and gas extraction offers significant benefits. It can improve operational efficiency, reduce costs, and enhance safety. As ML technologies continue to advance, the future prospects for enhancing instrumentation accuracy in the oil and gas industry look promising. Companies that embrace ML technologies stand to gain a competitive edge in the industry by improving their operational performance and reducing risks. \u0000Keywords: Leveraging, ML, Enhance, Instrumentation Accuracy, Oil and Gas Extraction.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141373310","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}
Pub Date : 2024-06-07DOI: 10.51594/csitrj.v5i6.1196
Chukwudi Cosmos Maha, Tolulope Olagoke Kolawole, Samira Abdul
Non-communicable diseases (NCDs), including heart disease, diabetes, and cancer, represent a significant global health challenge, particularly in the US and Africa. With rising prevalence rates, these chronic conditions strain healthcare systems and economies. This Review explores how data analytics can revolutionize the prediction and prevention of NCDs in these regions, highlighting its potential to transform public health strategies. Data analytics encompasses a range of techniques, including statistical analysis, machine learning, and predictive modeling, to extract meaningful insights from vast datasets. In the US, where healthcare systems generate massive amounts of electronic health records (EHRs), data analytics enables the identification of risk factors, early detection of diseases, and personalized intervention strategies. For instance, predictive algorithms can analyze patient data to identify individuals at high risk for developing NCDs, allowing for timely and targeted preventive measures. In Africa, the integration of data analytics faces unique challenges and opportunities. While the continent has less extensive healthcare data infrastructure compared to the US, mobile health (mHealth) technologies offer a promising solution. By leveraging mobile devices, health data can be collected, analyzed, and utilized to monitor and manage NCDs in remote and underserved communities. Data analytics can also aid in understanding the socio-economic and environmental determinants of NCDs in Africa, providing a comprehensive view of the factors contributing to disease prevalence. Comparatively, both regions can benefit from shared knowledge and collaborative efforts in harnessing data analytics for NCD prevention. Cross-continental partnerships can facilitate the exchange of expertise, technology, and best practices, fostering innovation and improving health outcomes. Furthermore, ethical considerations and data privacy must be prioritized to ensure responsible and equitable use of health data. In conclusion, data analytics holds immense potential to predict and prevent NCDs in the US and Africa. By leveraging advanced analytical techniques, healthcare systems can move towards more proactive and personalized approaches to disease management. Embracing this new frontier requires investment in data infrastructure, capacity building, and cross-regional collaboration, ultimately paving the way for healthier populations and sustainable healthcare systems. Keywords: Harnessing, Data Analytics, Frontier, Predicting Non- Communicable Diseases, Preventing.
{"title":"Harnessing data analytics: A new frontier in predicting and preventing non-communicable diseases in the US and Africa","authors":"Chukwudi Cosmos Maha, Tolulope Olagoke Kolawole, Samira Abdul","doi":"10.51594/csitrj.v5i6.1196","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1196","url":null,"abstract":"Non-communicable diseases (NCDs), including heart disease, diabetes, and cancer, represent a significant global health challenge, particularly in the US and Africa. With rising prevalence rates, these chronic conditions strain healthcare systems and economies. This Review explores how data analytics can revolutionize the prediction and prevention of NCDs in these regions, highlighting its potential to transform public health strategies. Data analytics encompasses a range of techniques, including statistical analysis, machine learning, and predictive modeling, to extract meaningful insights from vast datasets. In the US, where healthcare systems generate massive amounts of electronic health records (EHRs), data analytics enables the identification of risk factors, early detection of diseases, and personalized intervention strategies. For instance, predictive algorithms can analyze patient data to identify individuals at high risk for developing NCDs, allowing for timely and targeted preventive measures. In Africa, the integration of data analytics faces unique challenges and opportunities. While the continent has less extensive healthcare data infrastructure compared to the US, mobile health (mHealth) technologies offer a promising solution. By leveraging mobile devices, health data can be collected, analyzed, and utilized to monitor and manage NCDs in remote and underserved communities. Data analytics can also aid in understanding the socio-economic and environmental determinants of NCDs in Africa, providing a comprehensive view of the factors contributing to disease prevalence. Comparatively, both regions can benefit from shared knowledge and collaborative efforts in harnessing data analytics for NCD prevention. Cross-continental partnerships can facilitate the exchange of expertise, technology, and best practices, fostering innovation and improving health outcomes. Furthermore, ethical considerations and data privacy must be prioritized to ensure responsible and equitable use of health data. In conclusion, data analytics holds immense potential to predict and prevent NCDs in the US and Africa. By leveraging advanced analytical techniques, healthcare systems can move towards more proactive and personalized approaches to disease management. Embracing this new frontier requires investment in data infrastructure, capacity building, and cross-regional collaboration, ultimately paving the way for healthier populations and sustainable healthcare systems. \u0000Keywords: Harnessing, Data Analytics, Frontier, Predicting Non- Communicable Diseases, Preventing.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371756","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}
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