Pub Date : 2024-07-27DOI: 10.51594/csitrj.v5i7.1358
Kyloeua Oumaihka
The global population continues to be affected by the ongoing coronavirus pandemic, resulting in a gradual depletion of the limited healthcare resources. In order to fully realize the potential benefits of clinical artificial intelligence (AI), it is necessary to ensure its widespread adoption and use. The current body of research investigates the inclination to use clinical Artificial Intelligence & Machine Learning using a comprehensive survey and identifies the factors that influence its adoption. This study examines the United States and Canada, two North American nations, using a sample size of 1068 individuals. The findings indicate that participants have a significant aversion towards artificial intelligence (AI). In a hypothetical scenario including pre-hospital triage for the coronavirus, just one out of ten individuals expressed a preference for clinical AI and machine learning over clinicians. The level of trust individuals place in clinical AI & ML, together with their level of receptiveness, are two crucial factors that impact the extent to which these technologies are embraced. Our study indicates that individuals who lack social ties and suffer sentiments of mistrust and neglect from human physicians are more likely to adopt clinical AI & ML. These findings indicate that widespread acceptance of clinical AI and machine learning may need individuals to reduce their emotional attachment to humans and demonstrate less reliance on human physicians. Based on our findings, we recommend that prioritizing the establishment of trust, rather than diminishing confidence in physicians, should be the primary focus in any law regarding the use of clinical AI & ML. Keywords: Healthcare, Artificial Intelligence, Machine Learning, Healthcare, Pandemic.
{"title":"Role of pandemic in driving adoption of artificial intelligence in healthcare industry","authors":"Kyloeua Oumaihka","doi":"10.51594/csitrj.v5i7.1358","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1358","url":null,"abstract":"The global population continues to be affected by the ongoing coronavirus pandemic, resulting in a gradual depletion of the limited healthcare resources. In order to fully realize the potential benefits of clinical artificial intelligence (AI), it is necessary to ensure its widespread adoption and use. The current body of research investigates the inclination to use clinical Artificial Intelligence & Machine Learning using a comprehensive survey and identifies the factors that influence its adoption. This study examines the United States and Canada, two North American nations, using a sample size of 1068 individuals. The findings indicate that participants have a significant aversion towards artificial intelligence (AI). In a hypothetical scenario including pre-hospital triage for the coronavirus, just one out of ten individuals expressed a preference for clinical AI and machine learning over clinicians. The level of trust individuals place in clinical AI & ML, together with their level of receptiveness, are two crucial factors that impact the extent to which these technologies are embraced. Our study indicates that individuals who lack social ties and suffer sentiments of mistrust and neglect from human physicians are more likely to adopt clinical AI & ML. These findings indicate that widespread acceptance of clinical AI and machine learning may need individuals to reduce their emotional attachment to humans and demonstrate less reliance on human physicians. Based on our findings, we recommend that prioritizing the establishment of trust, rather than diminishing confidence in physicians, should be the primary focus in any law regarding the use of clinical AI & ML. \u0000Keywords: Healthcare, Artificial Intelligence, Machine Learning, Healthcare, Pandemic.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"11 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141797618","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 : 2024-07-25DOI: 10.51594/csitrj.v5i7.1353
Nwankwo Charles Uzondu, Dominic Dummene Lele
This study provides a comprehensive analysis of the cybersecurity challenges and strategies within smart environmental applications, emphasizing the critical importance of robust cybersecurity measures to protect these increasingly interconnected systems. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, conference proceedings, and industry reports from 2006 onwards, focusing on cybersecurity vulnerabilities, strategic approaches to security, and case studies of both successful and failed cybersecurity implementations. The methodology ensures a thorough examination of the evolving landscape of cyber threats and the effectiveness of various cybersecurity measures in smart environmental systems. Key findings highlight a diverse range of security vulnerabilities, from technical exploits to human factors, underscoring the necessity of encryption, authentication, and network security measures. The study also identifies emerging threats and opportunities presented by advancements in technologies such as artificial intelligence, machine learning, and blockchain, which offer promising avenues for enhancing cybersecurity. Based on the analysis, the study recommends future research directions, including the development of adaptive cybersecurity frameworks and the exploration of interdisciplinary approaches that integrate insights from cybersecurity, environmental science, and urban planning. The conclusion emphasizes the importance of a holistic approach to cybersecurity, advocating for collaborative efforts among industry stakeholders, regulatory bodies, and the academic community to strengthen the resilience of smart environmental systems against cyber threats. This study contributes to the ongoing discourse on cybersecurity in smart environmental applications, providing valuable insights for practitioners, policymakers, and researchers in the field. Keywords: Cybersecurity, Security Vulnerabilities, Smart Environmental Systems, Emerging Technologies.
{"title":"Challenges and strategies in securing smart environmental applications: A comprehensive review of cybersecurity measures","authors":"Nwankwo Charles Uzondu, Dominic Dummene Lele","doi":"10.51594/csitrj.v5i7.1353","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1353","url":null,"abstract":"This study provides a comprehensive analysis of the cybersecurity challenges and strategies within smart environmental applications, emphasizing the critical importance of robust cybersecurity measures to protect these increasingly interconnected systems. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, conference proceedings, and industry reports from 2006 onwards, focusing on cybersecurity vulnerabilities, strategic approaches to security, and case studies of both successful and failed cybersecurity implementations. The methodology ensures a thorough examination of the evolving landscape of cyber threats and the effectiveness of various cybersecurity measures in smart environmental systems. Key findings highlight a diverse range of security vulnerabilities, from technical exploits to human factors, underscoring the necessity of encryption, authentication, and network security measures. The study also identifies emerging threats and opportunities presented by advancements in technologies such as artificial intelligence, machine learning, and blockchain, which offer promising avenues for enhancing cybersecurity. Based on the analysis, the study recommends future research directions, including the development of adaptive cybersecurity frameworks and the exploration of interdisciplinary approaches that integrate insights from cybersecurity, environmental science, and urban planning. The conclusion emphasizes the importance of a holistic approach to cybersecurity, advocating for collaborative efforts among industry stakeholders, regulatory bodies, and the academic community to strengthen the resilience of smart environmental systems against cyber threats. This study contributes to the ongoing discourse on cybersecurity in smart environmental applications, providing valuable insights for practitioners, policymakers, and researchers in the field. \u0000Keywords: Cybersecurity, Security Vulnerabilities, Smart Environmental Systems, Emerging Technologies.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"32 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803303","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 era of data-driven decision-making, ensuring data quality is paramount for organizations seeking to leverage their data assets effectively. This paper explores comprehensive strategies for enhancing data quality through robust governance, methodologies, tools, and continuous improvement techniques. It highlights the critical dimensions of data quality, including accuracy, completeness, consistency, timeliness, validity, and uniqueness. It discusses various assessment techniques, such as data profiling, auditing, and quality metrics. The paper also examines the role of data cleansing, enrichment, integration, and interoperability in maintaining high data quality. Additionally, it provides an overview of leading data quality management tools, their evaluation criteria, and best practices for implementation. Finally, it underscores the importance of continuous monitoring, feedback loops, root cause analysis, and fostering an organization's data quality culture. By adopting these strategies, organizations can ensure the reliability and integrity of their data, leading to improved business outcomes. Keywords: Data Quality, Data Governance, Data Profiling, Data Cleansing, Continuous Improvement.
{"title":"Enhancing data quality through comprehensive governance: Methodologies, tools, and continuous improvement techniques","authors":"Courage Idemudia, Adebimpe Bolatito Ige, Victor Ibukun Adebayo, Osemeike Gloria Eyieyien","doi":"10.51594/csitrj.v5i7.1352","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1352","url":null,"abstract":"In the era of data-driven decision-making, ensuring data quality is paramount for organizations seeking to leverage their data assets effectively. This paper explores comprehensive strategies for enhancing data quality through robust governance, methodologies, tools, and continuous improvement techniques. It highlights the critical dimensions of data quality, including accuracy, completeness, consistency, timeliness, validity, and uniqueness. It discusses various assessment techniques, such as data profiling, auditing, and quality metrics. The paper also examines the role of data cleansing, enrichment, integration, and interoperability in maintaining high data quality. Additionally, it provides an overview of leading data quality management tools, their evaluation criteria, and best practices for implementation. Finally, it underscores the importance of continuous monitoring, feedback loops, root cause analysis, and fostering an organization's data quality culture. By adopting these strategies, organizations can ensure the reliability and integrity of their data, leading to improved business outcomes. \u0000Keywords: Data Quality, Data Governance, Data Profiling, Data Cleansing, Continuous Improvement.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"52 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805759","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 : 2024-07-25DOI: 10.51594/csitrj.v5i7.1350
Adindu Donatus Ogbu, Kate A. Iwe, Williams Ozowe, Augusta Heavens Ikevuje
Advances in machine learning (ML) have revolutionized pore pressure prediction in complex geological settings, addressing critical challenges in oil and gas exploration and production. Traditionally, predicting pore pressure accurately in heterogeneous and anisotropic formations has been fraught with uncertainties due to the limitations of conventional geophysical and petrophysical methods. Recent developments in ML techniques offer enhanced precision and reliability in pore pressure estimation, leveraging vast datasets and sophisticated algorithms to analyze and interpret geological complexities. ML-driven approaches utilize a variety of data sources, including well logs, seismic data, and drilling parameters, to train predictive models that can handle the non-linear and multi-dimensional nature of subsurface conditions. Techniques such as neural networks, support vector machines, and ensemble learning methods have shown significant promise in capturing the intricate relationships between geological variables and pore pressure. These models can adaptively learn from new data, improving their predictive capabilities over time. A notable advantage of ML-driven pore pressure prediction is its ability to integrate disparate data types and scales, providing a holistic understanding of subsurface pressure regimes. This integration enhances the accuracy of pressure forecasts, which is crucial for wellbore stability, drilling safety, and hydrocarbon recovery. For instance, real-time data from drilling operations can be fed into ML models to dynamically update pore pressure estimates, allowing for immediate adjustments to drilling plans and reducing the risk of blowouts or other drilling hazards. Moreover, ML techniques facilitate the identification of subtle patterns and trends that might be overlooked by traditional methods. This capability is particularly valuable in complex geological settings, such as deep-water environments, tectonically active regions, and unconventional reservoirs, where conventional predictive models often fall short. Despite the promising advances, challenges remain in the widespread adoption of ML-driven pore pressure prediction. These include the need for extensive training datasets, the interpretability of ML models, and the integration of ML workflows into existing geoscientific practices. Addressing these challenges requires interdisciplinary collaboration between geoscientists, data scientists, and engineers to develop robust, user-friendly ML solutions. In summary, ML-driven pore pressure prediction represents a significant advancement in managing the complexities of subsurface geology. By enhancing predictive accuracy and reliability, these technologies are poised to improve safety, efficiency, and productivity in the oil and gas industry, particularly in challenging geological settings. Keywords: Advance, ML, Pore Pressure, Prediction, Geological Settings.
机器学习（ML）技术的进步彻底改变了复杂地质环境下的孔隙压力预测，解决了油气勘探和生产中的关键难题。传统上，由于传统地球物理和岩石物理方法的局限性，准确预测异质和各向异性地层中的孔隙压力充满了不确定性。ML 技术的最新发展提高了孔隙压力估算的精度和可靠性，利用庞大的数据集和复杂的算法来分析和解释地质复杂性。ML 驱动的方法利用各种数据源，包括测井记录、地震数据和钻井参数，来训练能够处理地下条件的非线性和多维性质的预测模型。神经网络、支持向量机和集合学习法等技术在捕捉地质变量与孔隙压力之间错综复杂的关系方面显示出巨大的潜力。这些模型可以自适应地学习新数据，随着时间的推移提高预测能力。ML 驱动的孔隙压力预测的一个显著优势是能够整合不同的数据类型和尺度，提供对地下压力机制的整体理解。这种整合提高了压力预测的准确性，这对井筒稳定性、钻井安全和碳氢化合物回收至关重要。例如，钻井作业的实时数据可以输入 ML 模型，以动态更新孔隙压力估计值，从而可以立即调整钻井计划，降低井喷或其他钻井危险的风险。此外，ML 技术还有助于识别传统方法可能忽略的微妙模式和趋势。这种能力在复杂的地质环境（如深水环境、构造活跃地区和非常规储层）中尤为重要，因为在这些环境中，传统的预测模型往往无法发挥作用。尽管取得了令人鼓舞的进展，但在广泛采用 ML 驱动的孔隙压力预测方面仍然存在挑战。这些挑战包括对大量训练数据集的需求、ML 模型的可解释性以及将 ML 工作流程集成到现有地球科学实践中。应对这些挑战需要地球科学家、数据科学家和工程师之间的跨学科合作，以开发出强大、用户友好的 ML 解决方案。总之，ML 驱动的孔隙压力预测是在管理复杂的地下地质方面取得的重大进展。通过提高预测的准确性和可靠性，这些技术有望提高油气行业的安全性、效率和生产力，尤其是在具有挑战性的地质环境中。关键词先进、ML、孔隙压力、预测、地质环境。
{"title":"Advances in machine learning-driven pore pressure prediction in complex geological settings","authors":"Adindu Donatus Ogbu, Kate A. Iwe, Williams Ozowe, Augusta Heavens Ikevuje","doi":"10.51594/csitrj.v5i7.1350","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1350","url":null,"abstract":"Advances in machine learning (ML) have revolutionized pore pressure prediction in complex geological settings, addressing critical challenges in oil and gas exploration and production. Traditionally, predicting pore pressure accurately in heterogeneous and anisotropic formations has been fraught with uncertainties due to the limitations of conventional geophysical and petrophysical methods. Recent developments in ML techniques offer enhanced precision and reliability in pore pressure estimation, leveraging vast datasets and sophisticated algorithms to analyze and interpret geological complexities. ML-driven approaches utilize a variety of data sources, including well logs, seismic data, and drilling parameters, to train predictive models that can handle the non-linear and multi-dimensional nature of subsurface conditions. Techniques such as neural networks, support vector machines, and ensemble learning methods have shown significant promise in capturing the intricate relationships between geological variables and pore pressure. These models can adaptively learn from new data, improving their predictive capabilities over time. A notable advantage of ML-driven pore pressure prediction is its ability to integrate disparate data types and scales, providing a holistic understanding of subsurface pressure regimes. This integration enhances the accuracy of pressure forecasts, which is crucial for wellbore stability, drilling safety, and hydrocarbon recovery. For instance, real-time data from drilling operations can be fed into ML models to dynamically update pore pressure estimates, allowing for immediate adjustments to drilling plans and reducing the risk of blowouts or other drilling hazards. Moreover, ML techniques facilitate the identification of subtle patterns and trends that might be overlooked by traditional methods. This capability is particularly valuable in complex geological settings, such as deep-water environments, tectonically active regions, and unconventional reservoirs, where conventional predictive models often fall short. Despite the promising advances, challenges remain in the widespread adoption of ML-driven pore pressure prediction. These include the need for extensive training datasets, the interpretability of ML models, and the integration of ML workflows into existing geoscientific practices. Addressing these challenges requires interdisciplinary collaboration between geoscientists, data scientists, and engineers to develop robust, user-friendly ML solutions. In summary, ML-driven pore pressure prediction represents a significant advancement in managing the complexities of subsurface geology. By enhancing predictive accuracy and reliability, these technologies are poised to improve safety, efficiency, and productivity in the oil and gas industry, particularly in challenging geological settings. \u0000Keywords: Advance, ML, Pore Pressure, Prediction, Geological Settings.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"53 52","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804667","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 : 2024-07-25DOI: 10.51594/csitrj.v5i7.1349
Wagobera Edgar Kedi, Chibundom Ejimuda, Courage Idemudia, Tochukwu Ignatius Ijomah
This review paper explores the transformative role of machine learning in optimizing social media marketing strategies for small and medium-sized enterprises (SMEs). It begins by highlighting the significance of social media marketing for SMEs, outlining the historical context of traditional marketing strategies, and examining current trends and emerging machine learning applications. The paper delves into the technical challenges of implementing machine learning, such as data quality, algorithm complexity, and system integration, as well as ethical concerns surrounding data privacy and algorithmic bias. SME-specific limitations are also discussed, including budget constraints and lack of technical expertise. Future directions focus on emerging technologies like deep learning and reinforcement learning, offering practical recommendations for SMEs to leverage these advancements effectively. The conclusion emphasizes the importance of embracing machine learning to achieve sustainable growth and competitive advantage in the digital marketplace. Keywords: Machine Learning, Social Media Marketing, SMEs, Data Privacy, Audience Targeting.
{"title":"Machine learning software for optimizing SME social media marketing campaigns","authors":"Wagobera Edgar Kedi, Chibundom Ejimuda, Courage Idemudia, Tochukwu Ignatius Ijomah","doi":"10.51594/csitrj.v5i7.1349","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1349","url":null,"abstract":"This review paper explores the transformative role of machine learning in optimizing social media marketing strategies for small and medium-sized enterprises (SMEs). It begins by highlighting the significance of social media marketing for SMEs, outlining the historical context of traditional marketing strategies, and examining current trends and emerging machine learning applications. The paper delves into the technical challenges of implementing machine learning, such as data quality, algorithm complexity, and system integration, as well as ethical concerns surrounding data privacy and algorithmic bias. SME-specific limitations are also discussed, including budget constraints and lack of technical expertise. Future directions focus on emerging technologies like deep learning and reinforcement learning, offering practical recommendations for SMEs to leverage these advancements effectively. The conclusion emphasizes the importance of embracing machine learning to achieve sustainable growth and competitive advantage in the digital marketplace. \u0000Keywords: Machine Learning, Social Media Marketing, SMEs, Data Privacy, Audience Targeting. ","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"47 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805287","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 paper explores frameworks for effective data governance, emphasizing the importance of robust policies, processes, roles, and metrics. It outlines best practices for ensuring high data quality, data privacy, and security while highlighting stakeholder engagement and the role of technology. The paper also discusses implementation challenges, including organizational, technical, regulatory, and cultural obstacles. It presents tailored strategies for various industries such as financial services, healthcare, retail, manufacturing, and the public sector. Future directions for research include the integration of AI and machine learning, evolving data privacy regulations, and the challenges posed by big data and IoT. Effective data governance is crucial for managing risks, ensuring compliance, and unlocking the full potential of data assets across industries. Keywords: Data Governance, Data Quality Management, Data Privacy, Regulatory Compliance.
{"title":"Frameworks for effective data governance: best practices, challenges, and implementation strategies across industries","authors":"Naomi Chukwurah, Adebimpe Bolatito Ige, Victor Ibukun Adebayo, Osemeike Gloria Eyieyien","doi":"10.51594/csitrj.v5i7.1351","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1351","url":null,"abstract":"This paper explores frameworks for effective data governance, emphasizing the importance of robust policies, processes, roles, and metrics. It outlines best practices for ensuring high data quality, data privacy, and security while highlighting stakeholder engagement and the role of technology. The paper also discusses implementation challenges, including organizational, technical, regulatory, and cultural obstacles. It presents tailored strategies for various industries such as financial services, healthcare, retail, manufacturing, and the public sector. Future directions for research include the integration of AI and machine learning, evolving data privacy regulations, and the challenges posed by big data and IoT. Effective data governance is crucial for managing risks, ensuring compliance, and unlocking the full potential of data assets across industries. \u0000Keywords: Data Governance, Data Quality Management, Data Privacy, Regulatory Compliance.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"44 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805491","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 : 2024-07-25DOI: 10.51594/csitrj.v5i7.1348
Williams Ozowe, Adindu Donatus Ogbu, Augusta Heavens Ikevuje
Data science has emerged as a critical tool in the oil and gas industry, revolutionizing traditional approaches to oil recovery while addressing environmental concerns. This review explores the pivotal role of data science in enhancing oil recovery methods while minimizing environmental footprints. The oil and gas industry faces the challenge of maximizing oil recovery from reservoirs while minimizing environmental impacts. Data science offers a transformative approach by leveraging advanced analytics, machine learning, and big data technologies to optimize oil recovery processes. One key area where data science has been instrumental is in reservoir characterization. Advanced data analytics techniques enable the integration of diverse data sources, such as seismic, well log, and production data, to create detailed reservoir models. These models provide insights into reservoir properties, helping engineers design more effective recovery strategies. Data science also plays a crucial role in reservoir monitoring and management. Real-time data from sensors and monitoring devices are analyzed using machine learning algorithms to detect anomalies and optimize production operations. This proactive approach minimizes downtime and reduces the risk of environmental incidents. In addition to reservoir management, data science is transforming drilling and completion operations. Machine learning algorithms analyze drilling data to optimize well trajectories, reduce drilling time, and improve wellbore stability. This leads to more efficient drilling operations and reduces the environmental impact of drilling activities. Furthermore, data science is driving innovation in enhanced oil recovery (EOR) techniques. By analyzing reservoir data and simulating different EOR scenarios, engineers can identify the most effective EOR methods for a particular reservoir. This targeted approach maximizes oil recovery while minimizing the use of chemicals and energy, thus reducing environmental footprints. Overall, data science is revolutionizing the oil and gas industry by optimizing production operations, enhancing reservoir management, and reducing environmental impacts. As the industry continues to embrace digital transformation, data science will play an increasingly pivotal role in driving sustainable oil recovery practices. Keywords: Data, Oil Recovery, Environmental, Footprints, Minimizing.
{"title":"Data science's pivotal role in enhancing oil recovery methods while minimizing environmental footprints: An insightful review","authors":"Williams Ozowe, Adindu Donatus Ogbu, Augusta Heavens Ikevuje","doi":"10.51594/csitrj.v5i7.1348","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1348","url":null,"abstract":"Data science has emerged as a critical tool in the oil and gas industry, revolutionizing traditional approaches to oil recovery while addressing environmental concerns. This review explores the pivotal role of data science in enhancing oil recovery methods while minimizing environmental footprints. The oil and gas industry faces the challenge of maximizing oil recovery from reservoirs while minimizing environmental impacts. Data science offers a transformative approach by leveraging advanced analytics, machine learning, and big data technologies to optimize oil recovery processes. One key area where data science has been instrumental is in reservoir characterization. Advanced data analytics techniques enable the integration of diverse data sources, such as seismic, well log, and production data, to create detailed reservoir models. These models provide insights into reservoir properties, helping engineers design more effective recovery strategies. Data science also plays a crucial role in reservoir monitoring and management. Real-time data from sensors and monitoring devices are analyzed using machine learning algorithms to detect anomalies and optimize production operations. This proactive approach minimizes downtime and reduces the risk of environmental incidents. In addition to reservoir management, data science is transforming drilling and completion operations. Machine learning algorithms analyze drilling data to optimize well trajectories, reduce drilling time, and improve wellbore stability. This leads to more efficient drilling operations and reduces the environmental impact of drilling activities. Furthermore, data science is driving innovation in enhanced oil recovery (EOR) techniques. By analyzing reservoir data and simulating different EOR scenarios, engineers can identify the most effective EOR methods for a particular reservoir. This targeted approach maximizes oil recovery while minimizing the use of chemicals and energy, thus reducing environmental footprints. Overall, data science is revolutionizing the oil and gas industry by optimizing production operations, enhancing reservoir management, and reducing environmental impacts. As the industry continues to embrace digital transformation, data science will play an increasingly pivotal role in driving sustainable oil recovery practices. \u0000Keywords: Data, Oil Recovery, Environmental, Footprints, Minimizing.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":"32 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805268","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 : 2024-07-17DOI: 10.51594/csitrj.v5i7.1306
Hind I. Mohammed, Sabah A. Abdulkareem, Shaimaa Khamees Ahmed
Breast cancer is a frequent cancer that develops when normal cells in the breast transform into malignant cells. Breast cancer can arise from glandular tissue, muscular tissue, or fatty tissue in the breast. Many variables contribute to the risk of breast cancer, including genetics, environmental exposure, food, and lifestyle. Breast cancer should be detected early through breast self-examination, regular clinical evaluation, and mammography to identify any abnormal changes, In recent years, early detection of breast cancer in women has emerged as a beacon of hope and a pivotal point in the treatment of this dangerous disease, and its timely identification has become paramount. Modern advancements in technology, especially artificial intelligence algorithms, have played a vital role in developing systems that facilitate automated disease detection, diagnosis, rapid response, and a reduced risk of fatalities. This paper delves into a comparative study of various machine learning (ML) techniques, namely logistic regression (LR), support vector machines (SVM), linear SVM, Gaussian Naive Bayes (GNB), and artificial neural networks (ANNs). The evaluation metrics used in this study are accuracy and elapsed time. The results show that Gaussian Naive Bayes achieved the highest accuracy of 94.07% in just 0.005495 seconds, outperforming SVM (91.85%), linear SVM (90.19%), logistic regression (87.04%), and ANN (37.04%). These findings highlight the potential of Gaussian Naive Bayes in aiding the early detection of breast cancer, leading to more effective and timely interventions, ultimately improving patient outcomes. Keywords: Breast Cancer, Machine learning (ML), Logistic Regression (LR), Support Vector Machine (SVM), Linear SVM, Gaussian Naive Bayes (GNB) and Artificial Neural Networks (ANNs).
{"title":"Prediction of breast cancer based on machine learning","authors":"Hind I. Mohammed, Sabah A. Abdulkareem, Shaimaa Khamees Ahmed","doi":"10.51594/csitrj.v5i7.1306","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1306","url":null,"abstract":"Breast cancer is a frequent cancer that develops when normal cells in the breast transform into malignant cells. Breast cancer can arise from glandular tissue, muscular tissue, or fatty tissue in the breast. Many variables contribute to the risk of breast cancer, including genetics, environmental exposure, food, and lifestyle. Breast cancer should be detected early through breast self-examination, regular clinical evaluation, and mammography to identify any abnormal changes, In recent years, early detection of breast cancer in women has emerged as a beacon of hope and a pivotal point in the treatment of this dangerous disease, and its timely identification has become paramount. Modern advancements in technology, especially artificial intelligence algorithms, have played a vital role in developing systems that facilitate automated disease detection, diagnosis, rapid response, and a reduced risk of fatalities. This paper delves into a comparative study of various machine learning (ML) techniques, namely logistic regression (LR), support vector machines (SVM), linear SVM, Gaussian Naive Bayes (GNB), and artificial neural networks (ANNs). The evaluation metrics used in this study are accuracy and elapsed time. The results show that Gaussian Naive Bayes achieved the highest accuracy of 94.07% in just 0.005495 seconds, outperforming SVM (91.85%), linear SVM (90.19%), logistic regression (87.04%), and ANN (37.04%). These findings highlight the potential of Gaussian Naive Bayes in aiding the early detection of breast cancer, leading to more effective and timely interventions, ultimately improving patient outcomes. \u0000Keywords: Breast Cancer, Machine learning (ML), Logistic Regression (LR), Support Vector Machine (SVM), Linear SVM, Gaussian Naive Bayes (GNB) and Artificial Neural Networks (ANNs).","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829344","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}
Digital transformation in public sector organizations has become a pivotal driver for improving service delivery, operational efficiency, and transparency. SAP implementations offer robust solutions that can address the unique challenges faced by these entities, including complex bureaucratic processes, legacy systems, and the need for enhanced data security. This review explores the profound impact of SAP implementations on the digital transformation of public sector organizations and outlines key solutions provided by SAP to facilitate this transformation. SAP's integrated suite of applications streamlines operations by automating routine tasks, thus reducing administrative overhead and enabling public sector employees to focus on more strategic initiatives. With modules designed for finance, human resources, procurement, and citizen services, SAP systems enhance inter-departmental collaboration and data sharing, breaking down silos that traditionally hinder effective governance. Additionally, the real-time data processing capabilities of SAP solutions empower public sector organizations with actionable insights, aiding in timely decision-making and policy implementation. One of the most significant impacts of SAP implementations is the improvement in service delivery to citizens. By digitizing and automating processes such as tax collection, social services distribution, and public records management, SAP systems ensure faster, more reliable services. This not only enhances citizen satisfaction but also promotes greater transparency and accountability in public sector operations. However, the journey to digital transformation is not without challenges. Public sector organizations often grapple with budget constraints, resistance to change, and the integration of SAP with existing legacy systems. To overcome these obstacles, SAP offers tailored implementation strategies, including phased deployment and extensive training programs for public sector employees. Leveraging cloud-based solutions, SAP also provides scalable and cost-effective options that mitigate budgetary pressures. In conclusion, SAP implementations play a crucial role in achieving digital transformation in public sector organizations. By enhancing operational efficiency, improving service delivery, and fostering transparency, SAP systems enable public sector entities to meet the evolving demands of citizens. Addressing the challenges through strategic implementation and continuous support, SAP paves the way for a more efficient, responsive, and transparent public sector. This review underscores the transformative potential of SAP solutions in driving digital innovation and improving public sector performance. Keywords: Digital Transformation, SAP Implementation, Impact, Public Sector Organization, Solutions.
公共部门组织的数字化转型已成为改善服务交付、提高运营效率和透明度的关键驱动力。SAP 实施提供了强大的解决方案，可以应对这些实体所面临的独特挑战，包括复杂的官僚流程、遗留系统以及对增强数据安全性的需求。本评论探讨了 SAP 实施对公共部门组织数字化转型的深远影响，并概述了 SAP 为促进这一转型而提供的关键解决方案。SAP 的集成应用套件通过自动化日常任务来简化运营，从而减少行政开销，使公共部门员工能够专注于更具战略性的举措。SAP 系统拥有专为财务、人力资源、采购和公民服务设计的模块，可加强部门间协作和数据共享，打破传统上阻碍有效治理的 "孤岛"。此外，SAP 解决方案的实时数据处理功能使公共部门组织能够获得可操作的见解，有助于及时决策和政策实施。SAP 实施的最重要影响之一是改善了为公民提供的服务。通过税收、社会服务分配和公共档案管理等流程的数字化和自动化，SAP 系统可确保提供更快、更可靠的服务。这不仅能提高公民满意度，还能增强公共部门运作的透明度和问责制。然而，数字化转型之路并非没有挑战。公共部门组织经常要面对预算限制、变革阻力以及 SAP 与现有遗留系统的集成等问题。为了克服这些障碍，SAP 提供了量身定制的实施策略，包括分阶段部署和针对公共部门员工的广泛培训计划。利用基于云的解决方案，SAP 还提供了可扩展且具有成本效益的选择，从而减轻了预算压力。总之，SAP 的实施在公共部门组织实现数字化转型方面发挥着至关重要的作用。通过提高运营效率、改善服务提供和提高透明度，SAP 系统使公共部门实体能够满足公民不断变化的需求。通过战略实施和持续支持来应对挑战，SAP 为公共部门实现更高效、更灵敏、更透明的发展铺平了道路。本综述强调了 SAP 解决方案在推动数字创新和提高公共部门绩效方面的变革潜力。关键词数字化转型 SAP 实施 影响 公共部门组织 解决方案
{"title":"Achieving digital transformation in public sector organizations: The impact and solutions of SAP implementations","authors":"Oluwatosin Abdul-Azeez, Alexsandra Ogadimma Ihechere, Courage Idemudia","doi":"10.51594/csitrj.v5i7.1273","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1273","url":null,"abstract":"Digital transformation in public sector organizations has become a pivotal driver for improving service delivery, operational efficiency, and transparency. SAP implementations offer robust solutions that can address the unique challenges faced by these entities, including complex bureaucratic processes, legacy systems, and the need for enhanced data security. This review explores the profound impact of SAP implementations on the digital transformation of public sector organizations and outlines key solutions provided by SAP to facilitate this transformation. SAP's integrated suite of applications streamlines operations by automating routine tasks, thus reducing administrative overhead and enabling public sector employees to focus on more strategic initiatives. With modules designed for finance, human resources, procurement, and citizen services, SAP systems enhance inter-departmental collaboration and data sharing, breaking down silos that traditionally hinder effective governance. Additionally, the real-time data processing capabilities of SAP solutions empower public sector organizations with actionable insights, aiding in timely decision-making and policy implementation. One of the most significant impacts of SAP implementations is the improvement in service delivery to citizens. By digitizing and automating processes such as tax collection, social services distribution, and public records management, SAP systems ensure faster, more reliable services. This not only enhances citizen satisfaction but also promotes greater transparency and accountability in public sector operations. However, the journey to digital transformation is not without challenges. Public sector organizations often grapple with budget constraints, resistance to change, and the integration of SAP with existing legacy systems. To overcome these obstacles, SAP offers tailored implementation strategies, including phased deployment and extensive training programs for public sector employees. Leveraging cloud-based solutions, SAP also provides scalable and cost-effective options that mitigate budgetary pressures. In conclusion, SAP implementations play a crucial role in achieving digital transformation in public sector organizations. By enhancing operational efficiency, improving service delivery, and fostering transparency, SAP systems enable public sector entities to meet the evolving demands of citizens. Addressing the challenges through strategic implementation and continuous support, SAP paves the way for a more efficient, responsive, and transparent public sector. This review underscores the transformative potential of SAP solutions in driving digital innovation and improving public sector performance. \u0000Keywords: Digital Transformation, SAP Implementation, Impact, Public Sector Organization, Solutions.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141671086","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}
Advanced analytics has revolutionized decision-making processes in agriculture and business by harnessing data-driven insights to optimize operations, manage risks, and drive innovation. This paper explores the transformative role of advanced analytics in these sectors, highlighting key benefits, challenges, and future directions. In agriculture, advanced analytics enables precision farming by integrating AI, IoT sensors, and satellite imagery. Predictive models forecast crop yields, optimize irrigation, and enhance soil management practices, improving productivity and sustainability. Similarly, advanced analytics supports strategic decision-making in business by analyzing consumer behavior, predicting market trends, and optimizing supply chain operations. However, adopting advanced analytics faces challenges such as data quality, technical expertise, cost constraints, and ethical considerations. Addressing these challenges requires investments in data infrastructure, talent development, and regulatory compliance to ensure secure and ethical data usage. Emerging trends include AI-driven automation, blockchain for supply chain transparency, and augmented analytics for democratizing data access. Recommendations for stakeholders include investing in data capabilities, fostering collaborative partnerships, and promoting a culture of data-driven decision making. In conclusion, advanced analytics offers profound opportunities to enhance efficiency, inform decision making, and drive sustainable growth in agriculture and business. Embracing these technologies is essential for organizations seeking to thrive in a data-driven economy. Keywords: Advanced Analytics, Precision Farming, Predictive Analytics, Data-driven Decision Making, Business Intelligence.
{"title":"Data-Driven decision making in agriculture and business: The role of advanced analytics","authors":"Eyitayo Raji, Tochukwu Ignatius Ijomah, Osemeike Gloria Eyieyien","doi":"10.51594/csitrj.v5i7.1275","DOIUrl":"https://doi.org/10.51594/csitrj.v5i7.1275","url":null,"abstract":"Advanced analytics has revolutionized decision-making processes in agriculture and business by harnessing data-driven insights to optimize operations, manage risks, and drive innovation. This paper explores the transformative role of advanced analytics in these sectors, highlighting key benefits, challenges, and future directions. In agriculture, advanced analytics enables precision farming by integrating AI, IoT sensors, and satellite imagery. Predictive models forecast crop yields, optimize irrigation, and enhance soil management practices, improving productivity and sustainability. Similarly, advanced analytics supports strategic decision-making in business by analyzing consumer behavior, predicting market trends, and optimizing supply chain operations. However, adopting advanced analytics faces challenges such as data quality, technical expertise, cost constraints, and ethical considerations. Addressing these challenges requires investments in data infrastructure, talent development, and regulatory compliance to ensure secure and ethical data usage. Emerging trends include AI-driven automation, blockchain for supply chain transparency, and augmented analytics for democratizing data access. Recommendations for stakeholders include investing in data capabilities, fostering collaborative partnerships, and promoting a culture of data-driven decision making. In conclusion, advanced analytics offers profound opportunities to enhance efficiency, inform decision making, and drive sustainable growth in agriculture and business. Embracing these technologies is essential for organizations seeking to thrive in a data-driven economy. \u0000Keywords: Advanced Analytics, Precision Farming, Predictive Analytics, Data-driven Decision Making, Business Intelligence.","PeriodicalId":282796,"journal":{"name":"Computer Science & IT Research Journal","volume":" 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141671227","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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