Pub Date : 2024-06-07DOI: 10.51594/csitrj.v5i6.1201
Chioma Susan Nwaimo, Ayodeji Enoch Adegbola, Mayokun Daniel Adegbola
This paper explores the application of predictive analytics and machine learning techniques to enhance credit assessment and lending practices. By leveraging alternative data sources, such as mobile phone usage, social media activity, and transactional records, machine learning models can provide more accurate credit risk evaluations for individuals with limited traditional financial histories. The study demonstrates the efficacy of these models through empirical analysis, showcasing their potential to reduce default rates while increasing the approval rates for credit applicants. Furthermore, the paper discusses the ethical considerations and potential biases associated with the use of non-traditional data in credit scoring. The findings underscore the transformative impact of machine learning in fostering financial inclusion, offering practical insights for policymakers, financial institutions, and technology developers aiming to bridge the credit gap for under banked communities. This paper delves into the transformative potential of predictive analytics and machine learning in enhancing financial inclusion by improving credit access for under banked populations. Traditional credit scoring methods often fail to accurately assess the creditworthiness of individuals lacking conventional financial histories, thereby excluding a significant portion of the population from financial services. By incorporating alternative data sources such as mobile phone usage, social media interactions, utility payments, and transactional records, machine learning models can offer more comprehensive and precise credit risk evaluations. The research methodology involves developing and testing various machine learning algorithms, including decision trees, random forests, and neural networks, to predict creditworthiness. The models are trained and validated on datasets that include both traditional financial data and alternative data sources. The performance of these models is measured against standard metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic (ROC) curve. Empirical results indicate that models utilizing alternative data significantly outperform traditional credit scoring methods, leading to higher approval rates for credit applicants while maintaining or improving risk management standards. Keywords: Financial, Inclusion, Predictive, Analytics, Machine Learning, Alternative Data.
{"title":"Predictive analytics for financial inclusion: Using machine learning to improve credit access for under banked populations","authors":"Chioma Susan Nwaimo, Ayodeji Enoch Adegbola, Mayokun Daniel Adegbola","doi":"10.51594/csitrj.v5i6.1201","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1201","url":null,"abstract":"This paper explores the application of predictive analytics and machine learning techniques to enhance credit assessment and lending practices. By leveraging alternative data sources, such as mobile phone usage, social media activity, and transactional records, machine learning models can provide more accurate credit risk evaluations for individuals with limited traditional financial histories. The study demonstrates the efficacy of these models through empirical analysis, showcasing their potential to reduce default rates while increasing the approval rates for credit applicants. Furthermore, the paper discusses the ethical considerations and potential biases associated with the use of non-traditional data in credit scoring. The findings underscore the transformative impact of machine learning in fostering financial inclusion, offering practical insights for policymakers, financial institutions, and technology developers aiming to bridge the credit gap for under banked communities. This paper delves into the transformative potential of predictive analytics and machine learning in enhancing financial inclusion by improving credit access for under banked populations. Traditional credit scoring methods often fail to accurately assess the creditworthiness of individuals lacking conventional financial histories, thereby excluding a significant portion of the population from financial services. By incorporating alternative data sources such as mobile phone usage, social media interactions, utility payments, and transactional records, machine learning models can offer more comprehensive and precise credit risk evaluations. The research methodology involves developing and testing various machine learning algorithms, including decision trees, random forests, and neural networks, to predict creditworthiness. The models are trained and validated on datasets that include both traditional financial data and alternative data sources. The performance of these models is measured against standard metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic (ROC) curve. Empirical results indicate that models utilizing alternative data significantly outperform traditional credit scoring methods, leading to higher approval rates for credit applicants while maintaining or improving risk management standards. \u0000Keywords: Financial, Inclusion, Predictive, Analytics, Machine Learning, Alternative Data.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372692","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}
This review paper examines the potential implications of quantum computing for financial risk management. It explores the fundamental principles of quantum computing, including qubits, superposition, and entanglement. It discusses its advantages over classical computing for risk assessment and mitigation. The paper outlines traditional approaches to financial risk management. It explores how quantum algorithms, such as quantum Monte Carlo methods and quantum annealing, can enhance these strategies. Challenges and barriers to adopting quantum computing in the financial industry are identified, along with future research directions. Ultimately, the paper highlights the transformative potential of quantum computing for improving risk management in today's complex financial markets. Keywords: Quantum Computing, Financial Risk Management, Qubits, Quantum Algorithms, Monte Carlo Simulations, Portfolio Optimisation.
{"title":"Quantum computing and financial risk management: A theoretical review and implications","authors":"Mayokun Daniel Adegbola, Ayodeji Enoch Adegbola, Prisca Amajuoyi, Lucky Bamidele Benjamin, Kudirat Bukola Adeusi","doi":"10.51594/csitrj.v5i6.1194","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1194","url":null,"abstract":"This review paper examines the potential implications of quantum computing for financial risk management. It explores the fundamental principles of quantum computing, including qubits, superposition, and entanglement. It discusses its advantages over classical computing for risk assessment and mitigation. The paper outlines traditional approaches to financial risk management. It explores how quantum algorithms, such as quantum Monte Carlo methods and quantum annealing, can enhance these strategies. Challenges and barriers to adopting quantum computing in the financial industry are identified, along with future research directions. Ultimately, the paper highlights the transformative potential of quantum computing for improving risk management in today's complex financial markets. \u0000Keywords: Quantum Computing, Financial Risk Management, Qubits, Quantum Algorithms, Monte Carlo Simulations, Portfolio Optimisation.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372783","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}
This study delves into the integration of Artificial Intelligence (AI) in cybersecurity measures within smart cities, aiming to uncover both the challenges and opportunities this fusion presents. With the burgeoning reliance on interconnected digital infrastructures and the vast data ecosystems within urban environments, smart cities are increasingly susceptible to sophisticated cyber threats. Through a systematic literature review and content analysis, this research identifies the unique cybersecurity vulnerabilities faced by smart cities and evaluates how AI technologies can fortify urban cybersecurity frameworks. The methodology encompasses a comprehensive review of recent scholarly articles, industry reports, and case studies to assess the role of AI in enhancing threat detection, response, and prevention mechanisms. Key findings reveal that AI-driven cybersecurity solutions significantly enhance the resilience of smart cities against cyber threats by providing advanced analytical capabilities and real-time threat intelligence. However, the study also highlights the critical need for robust ethical and privacy considerations in the deployment of AI technologies. Strategic recommendations are provided for policymakers, urban planners, and technology leaders, emphasizing the importance of integrating secure AI-enabled infrastructure and fostering public-private partnerships. The study concludes with suggestions for future research directions, focusing on the ethical implications of AI in cybersecurity and the development of scalable AI solutions for diverse urban contexts. Keywords: Artificial Intelligence, Cybersecurity, Smart Cities, Urban Resilience.
{"title":"The integration of artificial intelligence in cybersecurity measures for sustainable finance platforms: An analysis","authors":"Ezekiel Onyekachukwu Udeh, Prisca Amajuoyi, Kudirat Bukola Adeusi, Anwulika Ogechukwu Scott","doi":"10.51594/csitrj.v5i6.1195","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1195","url":null,"abstract":"This study delves into the integration of Artificial Intelligence (AI) in cybersecurity measures within smart cities, aiming to uncover both the challenges and opportunities this fusion presents. With the burgeoning reliance on interconnected digital infrastructures and the vast data ecosystems within urban environments, smart cities are increasingly susceptible to sophisticated cyber threats. Through a systematic literature review and content analysis, this research identifies the unique cybersecurity vulnerabilities faced by smart cities and evaluates how AI technologies can fortify urban cybersecurity frameworks. The methodology encompasses a comprehensive review of recent scholarly articles, industry reports, and case studies to assess the role of AI in enhancing threat detection, response, and prevention mechanisms. Key findings reveal that AI-driven cybersecurity solutions significantly enhance the resilience of smart cities against cyber threats by providing advanced analytical capabilities and real-time threat intelligence. However, the study also highlights the critical need for robust ethical and privacy considerations in the deployment of AI technologies. Strategic recommendations are provided for policymakers, urban planners, and technology leaders, emphasizing the importance of integrating secure AI-enabled infrastructure and fostering public-private partnerships. The study concludes with suggestions for future research directions, focusing on the ethical implications of AI in cybersecurity and the development of scalable AI solutions for diverse urban contexts. \u0000Keywords: Artificial Intelligence, Cybersecurity, Smart Cities, Urban Resilience.","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":"141371737","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.v5i6.1199
Janet Aderonke Olaboye, Chukwudi Cosmos Maha, Tolulope Olagoke Kolawole, Samira Abdul
The integration of artificial intelligence (AI) into HIV treatment regimens has revolutionized the approach to personalized care and optimization strategies. This study presents an in-depth analysis of the role of AI in transforming HIV treatment, focusing on its ability to tailor therapy to individual patient needs and enhance treatment outcomes. AI-driven optimization in HIV treatment involves the utilization of advanced algorithms and computational techniques to analyze vast amounts of patient data, including genetic information, viral load measurements, and treatment history. By harnessing the power of machine learning and predictive analytics, AI algorithms can identify patterns and trends in patient data that may not be readily apparent to human clinicians. One of the key benefits of AI-driven optimization is its ability to personalize treatment regimens based on individual patient characteristics and disease progression. By considering factors such as drug resistance profiles, comorbidities, and lifestyle factors, AI algorithms can recommend the most effective and well-tolerated treatment options for each patient, leading to improved adherence and clinical outcomes. Furthermore, AI enables continuous monitoring and adjustment of treatment regimens in real time, allowing healthcare providers to respond rapidly to changes in patient status and evolving viral dynamics. This proactive approach to HIV management can help prevent treatment failure and the development of drug resistance, ultimately leading to better long-term outcomes for patients. Despite its transformative potential, AI-driven optimization in HIV treatment is not without challenges. Ethical considerations, data privacy concerns, and the need for robust validation and regulatory oversight are all important factors that must be addressed to ensure the safe and effective implementation of AI algorithms in clinical practice. In conclusion, the integrative analysis presented in this study underscores the significant impact of AI-driven optimization on the personalization and optimization of HIV treatment regimens. By leveraging AI technologies, healthcare providers can tailor treatment approaches to individual patient needs, leading to improved outcomes and quality of life for people living with HIV. Keywords: Integrative Analysis, AI- Driven, Optimization, HIV Treatment, Regimens.
将人工智能（AI）融入艾滋病治疗方案彻底改变了个性化护理和优化策略的方法。本研究深入分析了人工智能在改变艾滋病治疗中的作用，重点关注其根据患者个体需求定制治疗方案和提高治疗效果的能力。人工智能驱动的艾滋病治疗优化涉及利用先进的算法和计算技术来分析大量患者数据，包括基因信息、病毒载量测量和治疗史。通过利用机器学习和预测分析的力量，人工智能算法可以识别患者数据中的模式和趋势，而这些模式和趋势对于人类临床医生来说可能并不显而易见。人工智能驱动优化的主要优势之一是能够根据患者个体特征和疾病进展情况制定个性化治疗方案。通过考虑耐药性概况、合并症和生活方式因素等因素，人工智能算法可以为每位患者推荐最有效、耐受性最好的治疗方案，从而提高依从性和临床疗效。此外，人工智能还能对治疗方案进行持续监测和实时调整，使医疗服务提供者能够快速应对患者状态的变化和病毒动态的发展。这种积极主动的艾滋病管理方法有助于防止治疗失败和耐药性的产生，最终为患者带来更好的长期治疗效果。尽管人工智能驱动的艾滋病治疗优化具有变革潜力，但也并非没有挑战。伦理方面的考虑、数据隐私方面的关注以及对强有力的验证和监管监督的需求都是必须解决的重要因素，以确保在临床实践中安全有效地实施人工智能算法。总之，本研究提出的综合分析强调了人工智能驱动的优化对艾滋病治疗方案的个性化和优化的重大影响。通过利用人工智能技术，医疗服务提供者可以根据患者的个体需求定制治疗方法，从而改善艾滋病患者的治疗效果和生活质量。关键词综合分析 人工智能驱动 优化 HIV 治疗方案
{"title":"Integrative analysis of AI-driven optimization in HIV treatment regimens","authors":"Janet Aderonke Olaboye, Chukwudi Cosmos Maha, Tolulope Olagoke Kolawole, Samira Abdul","doi":"10.51594/csitrj.v5i6.1199","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1199","url":null,"abstract":"The integration of artificial intelligence (AI) into HIV treatment regimens has revolutionized the approach to personalized care and optimization strategies. This study presents an in-depth analysis of the role of AI in transforming HIV treatment, focusing on its ability to tailor therapy to individual patient needs and enhance treatment outcomes. AI-driven optimization in HIV treatment involves the utilization of advanced algorithms and computational techniques to analyze vast amounts of patient data, including genetic information, viral load measurements, and treatment history. By harnessing the power of machine learning and predictive analytics, AI algorithms can identify patterns and trends in patient data that may not be readily apparent to human clinicians. One of the key benefits of AI-driven optimization is its ability to personalize treatment regimens based on individual patient characteristics and disease progression. By considering factors such as drug resistance profiles, comorbidities, and lifestyle factors, AI algorithms can recommend the most effective and well-tolerated treatment options for each patient, leading to improved adherence and clinical outcomes. Furthermore, AI enables continuous monitoring and adjustment of treatment regimens in real time, allowing healthcare providers to respond rapidly to changes in patient status and evolving viral dynamics. This proactive approach to HIV management can help prevent treatment failure and the development of drug resistance, ultimately leading to better long-term outcomes for patients. Despite its transformative potential, AI-driven optimization in HIV treatment is not without challenges. Ethical considerations, data privacy concerns, and the need for robust validation and regulatory oversight are all important factors that must be addressed to ensure the safe and effective implementation of AI algorithms in clinical practice. In conclusion, the integrative analysis presented in this study underscores the significant impact of AI-driven optimization on the personalization and optimization of HIV treatment regimens. By leveraging AI technologies, healthcare providers can tailor treatment approaches to individual patient needs, leading to improved outcomes and quality of life for people living with HIV. \u0000Keywords: Integrative Analysis, AI- Driven, Optimization, HIV Treatment, Regimens.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141374452","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}
This review paper explores the intersection of advanced machine-learning techniques and personalised technology education. It examines how machine learning models can be leveraged to tailor educational content and teaching methods to individual learning styles and needs, focusing on adaptive learning systems and intelligent tutoring systems. The paper discusses challenges associated with implementing machine learning in education, including data quality, algorithmic bias, scalability, and ethical considerations related to data privacy and equitable access to personalised learning. Future research directions and strategies for overcoming these challenges are proposed, highlighting the importance of improving data quality, developing ethical guidelines, promoting educator training, and fostering stakeholder collaboration. Personalised technology education can enhance student empowerment and equal access to high-quality education by tackling these issues and adopting moral values. Keywords: Machine Learning, Personalised Education, Adaptive Learning Systems, Intelligent Tutoring Systems, Ethical Considerations, Educational Technology.
{"title":"Advanced machine learning techniques for personalising technology education","authors":"Enitan Shukurat Animashaun, Babajide Tolulope Familoni, Nneamaka Chisom Onyebuchi","doi":"10.51594/csitrj.v5i6.1198","DOIUrl":"https://doi.org/10.51594/csitrj.v5i6.1198","url":null,"abstract":"This review paper explores the intersection of advanced machine-learning techniques and personalised technology education. It examines how machine learning models can be leveraged to tailor educational content and teaching methods to individual learning styles and needs, focusing on adaptive learning systems and intelligent tutoring systems. The paper discusses challenges associated with implementing machine learning in education, including data quality, algorithmic bias, scalability, and ethical considerations related to data privacy and equitable access to personalised learning. Future research directions and strategies for overcoming these challenges are proposed, highlighting the importance of improving data quality, developing ethical guidelines, promoting educator training, and fostering stakeholder collaboration. Personalised technology education can enhance student empowerment and equal access to high-quality education by tackling these issues and adopting moral values. \u0000Keywords: Machine Learning, Personalised Education, Adaptive Learning Systems, Intelligent Tutoring Systems, Ethical Considerations, Educational Technology.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 54","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141374942","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 : 2024-05-21DOI: 10.51594/csitrj.v5i5.1156
Uwaga Monica Adanma, Emmanuel Olurotimi Ogunbiyi
This study explores the integration of Artificial Intelligence (AI) into environmental conservation efforts, aiming to assess AI's transformative potential in enhancing sustainability practices. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, reports, and case studies from 2014 to 2024, focusing on the application of AI in biodiversity preservation, climate change mitigation, and sustainable resource management. The methodology hinges on a comprehensive search strategy, adhering to strict inclusion and exclusion criteria to ensure the relevance and quality of the literature analyzed. Key findings reveal that AI significantly contributes to environmental conservation by optimizing resource management, improving predictive analytics for biodiversity conservation, and facilitating advanced monitoring and analysis to mitigate environmental impacts. However, the deployment of AI technologies also presents ethical and cybersecurity challenges, necessitating robust frameworks for responsible use. The study underscores the importance of interdisciplinary collaboration, stakeholder engagement, and the development of ethical AI solutions to address these challenges effectively. Finally, AI holds immense promise for advancing environmental sustainability efforts. Strategic recommendations include fostering partnerships across disciplines, prioritizing ethical considerations in AI development, and enhancing AI literacy among conservationists. Future research directions emphasize the need for innovative AI applications in conservation and addressing the socio-technical complexities of integrating AI into environmental strategies. This study contributes valuable insights into leveraging AI for a sustainable and resilient future, highlighting the critical balance between technological advancements and ethical considerations. Keywords: Artificial Intelligence (AI), Environmental Conservation, Sustainability, Cyber Risks.
{"title":"Artificial intelligence in environmental conservation: evaluating cyber risks and opportunities for sustainable practices","authors":"Uwaga Monica Adanma, Emmanuel Olurotimi Ogunbiyi","doi":"10.51594/csitrj.v5i5.1156","DOIUrl":"https://doi.org/10.51594/csitrj.v5i5.1156","url":null,"abstract":"This study explores the integration of Artificial Intelligence (AI) into environmental conservation efforts, aiming to assess AI's transformative potential in enhancing sustainability practices. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, reports, and case studies from 2014 to 2024, focusing on the application of AI in biodiversity preservation, climate change mitigation, and sustainable resource management. The methodology hinges on a comprehensive search strategy, adhering to strict inclusion and exclusion criteria to ensure the relevance and quality of the literature analyzed. Key findings reveal that AI significantly contributes to environmental conservation by optimizing resource management, improving predictive analytics for biodiversity conservation, and facilitating advanced monitoring and analysis to mitigate environmental impacts. However, the deployment of AI technologies also presents ethical and cybersecurity challenges, necessitating robust frameworks for responsible use. The study underscores the importance of interdisciplinary collaboration, stakeholder engagement, and the development of ethical AI solutions to address these challenges effectively. Finally, AI holds immense promise for advancing environmental sustainability efforts. Strategic recommendations include fostering partnerships across disciplines, prioritizing ethical considerations in AI development, and enhancing AI literacy among conservationists. Future research directions emphasize the need for innovative AI applications in conservation and addressing the socio-technical complexities of integrating AI into environmental strategies. This study contributes valuable insights into leveraging AI for a sustainable and resilient future, highlighting the critical balance between technological advancements and ethical considerations. \u0000Keywords: Artificial Intelligence (AI), Environmental Conservation, Sustainability, Cyber Risks.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"25 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141117538","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}
This study investigates the pivotal role of cybersecurity in bolstering environmental protection and sustainable development, a critical yet underexplored nexus in contemporary research. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, conference proceedings, and industry reports from 2015 to 2023, sourced from databases such as IEEE Xplore, ScienceDirect, and Google Scholar. The methodology is anchored in a rigorous search strategy, leveraging keywords related to cybersecurity, sustainability, and communication technologies, and adheres to defined inclusion and exclusion criteria to ensure the relevance and quality of the literature reviewed. Key findings highlight cybersecurity as an indispensable enabler of sustainable development initiatives, safeguarding the technological infrastructure essential for environmental conservation efforts. The study identifies evolving cyber threats as a significant challenge, necessitating adaptive security measures that anticipate and mitigate potential vulnerabilities. Furthermore, it underscores the opportunities presented by advanced cybersecurity technologies, such as artificial intelligence and blockchain, in enhancing the security and efficiency of sustainable practices. Strategic recommendations emphasize the need for comprehensive cybersecurity frameworks, stakeholder collaboration, cybersecurity education, and alignment with regulatory standards to fortify the resilience of sustainability initiatives against cyber threats. The study concludes that integrating robust cybersecurity measures is paramount in the pursuit of sustainable development goals, calling for ongoing vigilance, innovation, and interdisciplinary collaboration to navigate the complex landscape of digital threats and opportunities. This research contributes valuable insights into the critical intersection of cybersecurity and sustainability, offering a foundation for future studies and strategic initiatives aimed at securing sustainable development in the digital age. Keywords: Cybersecurity, Sustainable Development, Environmental Protection, Advanced Security Technologies.
{"title":"Cybersecurity’s Role in Environmental Protection and Sustainable Development: Bridging Technology and Sustainability Goals","authors":"Scholar Chinenye Obasi, Nko Okina Solomon, Olubunmi Adeolu Adenekan, Peter Simpa","doi":"10.51594/csitrj.v5i5.1140","DOIUrl":"https://doi.org/10.51594/csitrj.v5i5.1140","url":null,"abstract":"This study investigates the pivotal role of cybersecurity in bolstering environmental protection and sustainable development, a critical yet underexplored nexus in contemporary research. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, conference proceedings, and industry reports from 2015 to 2023, sourced from databases such as IEEE Xplore, ScienceDirect, and Google Scholar. The methodology is anchored in a rigorous search strategy, leveraging keywords related to cybersecurity, sustainability, and communication technologies, and adheres to defined inclusion and exclusion criteria to ensure the relevance and quality of the literature reviewed. Key findings highlight cybersecurity as an indispensable enabler of sustainable development initiatives, safeguarding the technological infrastructure essential for environmental conservation efforts. The study identifies evolving cyber threats as a significant challenge, necessitating adaptive security measures that anticipate and mitigate potential vulnerabilities. Furthermore, it underscores the opportunities presented by advanced cybersecurity technologies, such as artificial intelligence and blockchain, in enhancing the security and efficiency of sustainable practices. Strategic recommendations emphasize the need for comprehensive cybersecurity frameworks, stakeholder collaboration, cybersecurity education, and alignment with regulatory standards to fortify the resilience of sustainability initiatives against cyber threats. The study concludes that integrating robust cybersecurity measures is paramount in the pursuit of sustainable development goals, calling for ongoing vigilance, innovation, and interdisciplinary collaboration to navigate the complex landscape of digital threats and opportunities. This research contributes valuable insights into the critical intersection of cybersecurity and sustainability, offering a foundation for future studies and strategic initiatives aimed at securing sustainable development in the digital age. \u0000Keywords: Cybersecurity, Sustainable Development, Environmental Protection, Advanced Security Technologies.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"3 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985202","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}
The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning algorithms to analyze equipment data and predict when maintenance is required before a breakdown occurs. By monitoring equipment performance in real-time, AI can identify potential issues early, allowing operators to take proactive maintenance actions. This approach helps minimize downtime, reduce maintenance costs, and improve overall equipment reliability and safety. The implementation of AI-driven predictive maintenance requires a comprehensive strategy that includes data collection, analysis, and integration with existing maintenance practices. Successful adoption of AI-driven predictive maintenance can lead to significant benefits for oil and gas companies, including increased equipment uptime, extended asset lifespan, and enhanced operational efficiency. This paper reviews the current landscape of equipment management in the oil and gas industry, highlighting the limitations of traditional maintenance practices and the need for a more proactive approach. It then examines the principles and benefits of AI-driven predictive maintenance, showcasing real-world examples of its successful implementation. Finally, the paper discusses the challenges and considerations for implementing AI-driven predictive maintenance and provides recommendations for oil and gas companies looking to transform their equipment management practices. Keywords: Transforming Equipment; Management; Oil and Gas; AI-Driven; Predictive Maintenance.
{"title":"Transforming equipment management in oil and gas with AI-Driven predictive maintenance","authors":"Dazok Donald Jambol, Oludayo Olatoye Sofoluwe, Ayemere Ukato, Obinna Joshua Ochulor","doi":"10.51594/csitrj.v5i5.1117","DOIUrl":"https://doi.org/10.51594/csitrj.v5i5.1117","url":null,"abstract":"The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning algorithms to analyze equipment data and predict when maintenance is required before a breakdown occurs. By monitoring equipment performance in real-time, AI can identify potential issues early, allowing operators to take proactive maintenance actions. This approach helps minimize downtime, reduce maintenance costs, and improve overall equipment reliability and safety. The implementation of AI-driven predictive maintenance requires a comprehensive strategy that includes data collection, analysis, and integration with existing maintenance practices. Successful adoption of AI-driven predictive maintenance can lead to significant benefits for oil and gas companies, including increased equipment uptime, extended asset lifespan, and enhanced operational efficiency. This paper reviews the current landscape of equipment management in the oil and gas industry, highlighting the limitations of traditional maintenance practices and the need for a more proactive approach. It then examines the principles and benefits of AI-driven predictive maintenance, showcasing real-world examples of its successful implementation. Finally, the paper discusses the challenges and considerations for implementing AI-driven predictive maintenance and provides recommendations for oil and gas companies looking to transform their equipment management practices. \u0000Keywords: Transforming Equipment; Management; Oil and Gas; AI-Driven; Predictive Maintenance.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"22 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141012037","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 : 2024-05-05DOI: 10.51594/csitrj.v5i5.1115
Excel G Chukwurah, Chukwuekem David Okeke, Cynthia Chizoba Ekechi
This study explores the critical intersection of cybersecurity measures and green technologies, aiming to assess their combined impact on sustainability goals and stakeholder implications. Employing a systematic literature review methodology, the research scrutinizes peer-reviewed journals, conference proceedings, and reports from reputable databases, focusing on publications from the year 2010 to 2024. The review identifies key themes, including the integration challenges and opportunities of cybersecurity within sustainable technologies, the evolving landscape of cybersecurity protocols, and the strategic implications for industry leaders, policymakers, and technologists. Key insights reveal the dual imperative of pursuing sustainability alongside security, highlighting the necessity of integrating robust cybersecurity measures without compromising the environmental benefits of green technologies. The study identifies significant challenges at this nexus, such as the rapid evolution of cyber threats and the complexity of embedding cybersecurity in green innovations. It also outlines opportunities for innovation and the development of a security-aware culture that supports environmental sustainability. Strategic recommendations are provided for stakeholders to navigate these complexities, emphasizing the importance of multidisciplinary approaches, continuous learning, and the development of policies that encourage the adoption of secure and sustainable technologies. The study concludes that fostering innovation in green technology requires a concerted effort to integrate cybersecurity measures effectively, underscoring the need for future research to expand the knowledge frontiers in this critical area. This research contributes to the ongoing dialogue on achieving environmental sustainability and technological resilience, offering a foundation for further exploration and action towards these dual objectives. Keywords: Cybersecurity, Green Technologies, Sustainable Technological, Stakeholder Security Concerns.
{"title":"Innovation green technology in the age of cybersecurity: Balancing sustainability goals with security concerns","authors":"Excel G Chukwurah, Chukwuekem David Okeke, Cynthia Chizoba Ekechi","doi":"10.51594/csitrj.v5i5.1115","DOIUrl":"https://doi.org/10.51594/csitrj.v5i5.1115","url":null,"abstract":"This study explores the critical intersection of cybersecurity measures and green technologies, aiming to assess their combined impact on sustainability goals and stakeholder implications. Employing a systematic literature review methodology, the research scrutinizes peer-reviewed journals, conference proceedings, and reports from reputable databases, focusing on publications from the year 2010 to 2024. The review identifies key themes, including the integration challenges and opportunities of cybersecurity within sustainable technologies, the evolving landscape of cybersecurity protocols, and the strategic implications for industry leaders, policymakers, and technologists. Key insights reveal the dual imperative of pursuing sustainability alongside security, highlighting the necessity of integrating robust cybersecurity measures without compromising the environmental benefits of green technologies. The study identifies significant challenges at this nexus, such as the rapid evolution of cyber threats and the complexity of embedding cybersecurity in green innovations. It also outlines opportunities for innovation and the development of a security-aware culture that supports environmental sustainability. Strategic recommendations are provided for stakeholders to navigate these complexities, emphasizing the importance of multidisciplinary approaches, continuous learning, and the development of policies that encourage the adoption of secure and sustainable technologies. The study concludes that fostering innovation in green technology requires a concerted effort to integrate cybersecurity measures effectively, underscoring the need for future research to expand the knowledge frontiers in this critical area. This research contributes to the ongoing dialogue on achieving environmental sustainability and technological resilience, offering a foundation for further exploration and action towards these dual objectives. \u0000Keywords: Cybersecurity, Green Technologies, Sustainable Technological, Stakeholder Security Concerns. ","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"323 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141012169","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 : 2024-05-05DOI: 10.51594/csitrj.v5i5.1118
Charles Chukwudalu Ebulue, Ogochukwu Virginia Ekkeh, Ogochukwu Roseline Ebulue, Chukwunonso Sylvester Ekesiobi
Epidemic forecasting plays a critical role in public health preparedness and response, enabling proactive measures to mitigate the impact of infectious diseases. Environmental data, encompassing factors such as temperature, humidity, air quality, and geographical features, holds valuable insights for predicting and identifying areas prone to epidemics. This paper explores the integration of predictive analytics with environmental data to enhance epidemic forecasting capabilities. By leveraging predictive analytics techniques, researchers and public health officials can analyze environmental data to identify regions at higher risk of experiencing epidemic outbreaks. Through statistical modeling, machine learning algorithms, and computational simulations, predictive analytics utilize environmental indicators to forecast the likelihood and spread of diseases. For example, areas with high temperatures and humidity may be conducive to mosquito-borne diseases, while regions with poor air quality may experience increased rates of respiratory infections. Case studies highlight the application of predictive analytics in various contexts, including forecasting mosquito-borne diseases in tropical regions and tracking respiratory infections in urban areas with poor air quality. Early warning systems, informed by environmental data, provide timely alerts to potential epidemic threats, enabling proactive interventions and resource allocation. While the integration of environmental data into epidemic forecasting offers significant benefits, challenges remain, including data quality, availability, and ethical considerations. Continued research and collaboration are essential to address these challenges and further enhance the effectiveness of predictive analytics in identifying and mitigating epidemic risks. In conclusion, this paper underscores the importance of leveraging environmental data and predictive analytics for epidemic forecasting, emphasizing their potential to improve public health outcomes and enhance preparedness efforts in the face of emerging infectious diseases and climate change. Keywords: Environmental Data, Epidemic Forecasting, Predictive Analytics.
{"title":"Environmental data in epidemic forecasting: Insights from predictive analytics","authors":"Charles Chukwudalu Ebulue, Ogochukwu Virginia Ekkeh, Ogochukwu Roseline Ebulue, Chukwunonso Sylvester Ekesiobi","doi":"10.51594/csitrj.v5i5.1118","DOIUrl":"https://doi.org/10.51594/csitrj.v5i5.1118","url":null,"abstract":"Epidemic forecasting plays a critical role in public health preparedness and response, enabling proactive measures to mitigate the impact of infectious diseases. Environmental data, encompassing factors such as temperature, humidity, air quality, and geographical features, holds valuable insights for predicting and identifying areas prone to epidemics. This paper explores the integration of predictive analytics with environmental data to enhance epidemic forecasting capabilities. By leveraging predictive analytics techniques, researchers and public health officials can analyze environmental data to identify regions at higher risk of experiencing epidemic outbreaks. Through statistical modeling, machine learning algorithms, and computational simulations, predictive analytics utilize environmental indicators to forecast the likelihood and spread of diseases. For example, areas with high temperatures and humidity may be conducive to mosquito-borne diseases, while regions with poor air quality may experience increased rates of respiratory infections. Case studies highlight the application of predictive analytics in various contexts, including forecasting mosquito-borne diseases in tropical regions and tracking respiratory infections in urban areas with poor air quality. Early warning systems, informed by environmental data, provide timely alerts to potential epidemic threats, enabling proactive interventions and resource allocation. While the integration of environmental data into epidemic forecasting offers significant benefits, challenges remain, including data quality, availability, and ethical considerations. Continued research and collaboration are essential to address these challenges and further enhance the effectiveness of predictive analytics in identifying and mitigating epidemic risks. In conclusion, this paper underscores the importance of leveraging environmental data and predictive analytics for epidemic forecasting, emphasizing their potential to improve public health outcomes and enhance preparedness efforts in the face of emerging infectious diseases and climate change. \u0000Keywords: Environmental Data, Epidemic Forecasting, Predictive Analytics.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"313 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141012264","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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