�ݺ�ߣshows by User: journalsats

�ݺ�ߣshows by User: journalsats / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: journalsats / Tue, 10 Sep 2024 16:47:31 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: journalsats Advancements in Structural Integrity: Enhancing Frame Strength and Compression Index Through Innovative Material Composites /slideshow/advancements-in-structural-integrity-enhancing-frame-strength-and-compression-index-through-innovative-material-composites/271707753 ijcatr13091007-240910164731-3859bc1c
Recent advancements in material science have significantly impacted structural integrity, with a particular focus on enhancing frame strength and compression index. This paper explores cutting-edge material composites that offer superior performance in these areas, emphasizing their potential to revolutionize engineering and construction practices. Key innovations include the development of high-strength fibre-reinforced polymers (FRPs), advanced nanocomposites, and hybrid materials that combine the best properties of various substances. These composites are engineered to improve load-bearing capacities, resistance to environmental stressors, and overall durability. By integrating these innovative materials into structural frames, engineers can achieve enhanced safety, longevity, and efficiency. This paper reviews the latest research, case studies, and practical applications, highlighting the transformative impact of these advancements on modern construction. The findings underscore the importance of ongoing research and development in this field to address future structural challenges and to push the boundaries of what is achievable in structural design. ]]>
Recent advancements in material science have significantly impacted structural integrity, with a particular focus on enhancing frame strength and compression index. This paper explores cutting-edge material composites that offer superior performance in these areas, emphasizing their potential to revolutionize engineering and construction practices. Key innovations include the development of high-strength fibre-reinforced polymers (FRPs), advanced nanocomposites, and hybrid materials that combine the best properties of various substances. These composites are engineered to improve load-bearing capacities, resistance to environmental stressors, and overall durability. By integrating these innovative materials into structural frames, engineers can achieve enhanced safety, longevity, and efficiency. This paper reviews the latest research, case studies, and practical applications, highlighting the transformative impact of these advancements on modern construction. The findings underscore the importance of ongoing research and development in this field to address future structural challenges and to push the boundaries of what is achievable in structural design. ]]> Tue, 10 Sep 2024 16:47:31 GMT /slideshow/advancements-in-structural-integrity-enhancing-frame-strength-and-compression-index-through-innovative-material-composites/271707753 journalsats@slideshare.net(journalsats) Advancements in Structural Integrity: Enhancing Frame Strength and Compression Index Through Innovative Material Composites journalsats Recent advancements in material science have significantly impacted structural integrity, with a particular focus on enhancing frame strength and compression index. This paper explores cutting-edge material composites that offer superior performance in these areas, emphasizing their potential to revolutionize engineering and construction practices. Key innovations include the development of high-strength fibre-reinforced polymers (FRPs), advanced nanocomposites, and hybrid materials that combine the best properties of various substances. These composites are engineered to improve load-bearing capacities, resistance to environmental stressors, and overall durability. By integrating these innovative materials into structural frames, engineers can achieve enhanced safety, longevity, and efficiency. This paper reviews the latest research, case studies, and practical applications, highlighting the transformative impact of these advancements on modern construction. The findings underscore the importance of ongoing research and development in this field to address future structural challenges and to push the boundaries of what is achievable in structural design. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr13091007-240910164731-3859bc1c-thumbnail.jpg?width=120&height=120&fit=bounds" /> Recent advancements in material science have significantly impacted structural integrity, with a particular focus on enhancing frame strength and compression index. This paper explores cutting-edge material composites that offer superior performance in these areas, emphasizing their potential to revolutionize engineering and construction practices. Key innovations include the development of high-strength fibre-reinforced polymers (FRPs), advanced nanocomposites, and hybrid materials that combine the best properties of various substances. These composites are engineered to improve load-bearing capacities, resistance to environmental stressors, and overall durability. By integrating these innovative materials into structural frames, engineers can achieve enhanced safety, longevity, and efficiency. This paper reviews the latest research, case studies, and practical applications, highlighting the transformative impact of these advancements on modern construction. The findings underscore the importance of ongoing research and development in this field to address future structural challenges and to push the boundaries of what is achievable in structural design.

Advancements in Structural Integrity: Enhancing Frame Strength and Compression Index Through Innovative Material Composites from Editor IJCATR

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0 Maritime Cybersecurity: Protecting Critical Infrastructure in The Digital Age /slideshow/maritime-cybersecurity-protecting-critical-infrastructure-in-the-digital-age/271707733 ijcatr13091006-240910164537-40bfcb13
The maritime industry, a critical component of global trade and security, is increasingly vulnerable to cyber threats as it adopts more advanced digital technologies. This paper explores the multifaceted challenges of maritime cybersecurity, highlighting the vulnerabilities in maritime infrastructure, including ports, ships, and naval operations. The study examines the nature of cyber threats, ranging from ransomware attacks to state-sponsored espionage, and their potential impact on global maritime security. Through an analysis of current cybersecurity practices and international regulations, the paper identifies key gaps in the existing frameworks and offers recommendations for enhancing cybersecurity resilience within the maritime sector. By addressing these vulnerabilities, the maritime industry can better safeguard its critical infrastructure against the growing tide of cyber threats. ]]>
The maritime industry, a critical component of global trade and security, is increasingly vulnerable to cyber threats as it adopts more advanced digital technologies. This paper explores the multifaceted challenges of maritime cybersecurity, highlighting the vulnerabilities in maritime infrastructure, including ports, ships, and naval operations. The study examines the nature of cyber threats, ranging from ransomware attacks to state-sponsored espionage, and their potential impact on global maritime security. Through an analysis of current cybersecurity practices and international regulations, the paper identifies key gaps in the existing frameworks and offers recommendations for enhancing cybersecurity resilience within the maritime sector. By addressing these vulnerabilities, the maritime industry can better safeguard its critical infrastructure against the growing tide of cyber threats. ]]> Tue, 10 Sep 2024 16:45:37 GMT /slideshow/maritime-cybersecurity-protecting-critical-infrastructure-in-the-digital-age/271707733 journalsats@slideshare.net(journalsats) Maritime Cybersecurity: Protecting Critical Infrastructure in The Digital Age journalsats The maritime industry, a critical component of global trade and security, is increasingly vulnerable to cyber threats as it adopts more advanced digital technologies. This paper explores the multifaceted challenges of maritime cybersecurity, highlighting the vulnerabilities in maritime infrastructure, including ports, ships, and naval operations. The study examines the nature of cyber threats, ranging from ransomware attacks to state-sponsored espionage, and their potential impact on global maritime security. Through an analysis of current cybersecurity practices and international regulations, the paper identifies key gaps in the existing frameworks and offers recommendations for enhancing cybersecurity resilience within the maritime sector. By addressing these vulnerabilities, the maritime industry can better safeguard its critical infrastructure against the growing tide of cyber threats. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr13091006-240910164537-40bfcb13-thumbnail.jpg?width=120&height=120&fit=bounds" /> The maritime industry, a critical component of global trade and security, is increasingly vulnerable to cyber threats as it adopts more advanced digital technologies. This paper explores the multifaceted challenges of maritime cybersecurity, highlighting the vulnerabilities in maritime infrastructure, including ports, ships, and naval operations. The study examines the nature of cyber threats, ranging from ransomware attacks to state-sponsored espionage, and their potential impact on global maritime security. Through an analysis of current cybersecurity practices and international regulations, the paper identifies key gaps in the existing frameworks and offers recommendations for enhancing cybersecurity resilience within the maritime sector. By addressing these vulnerabilities, the maritime industry can better safeguard its critical infrastructure against the growing tide of cyber threats.

Maritime Cybersecurity: Protecting Critical Infrastructure in The Digital Age from Editor IJCATR

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0 Leveraging Machine Learning for Proactive Threat Analysis in Cybersecurity /slideshow/leveraging-machine-learning-for-proactive-threat-analysis-in-cybersecurity/271707700 ijcatr13091005-240910164344-cfe958a4
In the evolving cybersecurity landscape, traditional reactive methods are increasingly inadequate. This article explores the transformative potential of machine learning (ML) in proactive threat analysis, aiming to pre-emptively identify and neutralize threats before they emerge. By employing ML algorithms, cybersecurity systems can analyse vast datasets in real time, recognize patterns, and detect anomalies indicating potential threats. The article reviews current cybersecurity challenges, examines how ML techniques—such as decision trees, neural networks, and clustering—are utilized in threat analysis, and assesses various ML-driven cybersecurity solutions through literature, case studies, and analysis. It highlights ML's benefits, including enhanced detection accuracy, quicker responses, and future threat prediction capabilities. However, challenges such as data quality, adversarial attacks, and high computational demands are also discussed. The article concludes by addressing these limitations and suggesting that while ML offers a promising approach, its success depends on overcoming these hurdles. Emerging trends and future directions emphasize the need for continued research and development in ML for cybersecurity. ]]>
In the evolving cybersecurity landscape, traditional reactive methods are increasingly inadequate. This article explores the transformative potential of machine learning (ML) in proactive threat analysis, aiming to pre-emptively identify and neutralize threats before they emerge. By employing ML algorithms, cybersecurity systems can analyse vast datasets in real time, recognize patterns, and detect anomalies indicating potential threats. The article reviews current cybersecurity challenges, examines how ML techniques—such as decision trees, neural networks, and clustering—are utilized in threat analysis, and assesses various ML-driven cybersecurity solutions through literature, case studies, and analysis. It highlights ML's benefits, including enhanced detection accuracy, quicker responses, and future threat prediction capabilities. However, challenges such as data quality, adversarial attacks, and high computational demands are also discussed. The article concludes by addressing these limitations and suggesting that while ML offers a promising approach, its success depends on overcoming these hurdles. Emerging trends and future directions emphasize the need for continued research and development in ML for cybersecurity. ]]> Tue, 10 Sep 2024 16:43:44 GMT /slideshow/leveraging-machine-learning-for-proactive-threat-analysis-in-cybersecurity/271707700 journalsats@slideshare.net(journalsats) Leveraging Machine Learning for Proactive Threat Analysis in Cybersecurity journalsats In the evolving cybersecurity landscape, traditional reactive methods are increasingly inadequate. This article explores the transformative potential of machine learning (ML) in proactive threat analysis, aiming to pre-emptively identify and neutralize threats before they emerge. By employing ML algorithms, cybersecurity systems can analyse vast datasets in real time, recognize patterns, and detect anomalies indicating potential threats. The article reviews current cybersecurity challenges, examines how ML techniques—such as decision trees, neural networks, and clustering—are utilized in threat analysis, and assesses various ML-driven cybersecurity solutions through literature, case studies, and analysis. It highlights ML's benefits, including enhanced detection accuracy, quicker responses, and future threat prediction capabilities. However, challenges such as data quality, adversarial attacks, and high computational demands are also discussed. The article concludes by addressing these limitations and suggesting that while ML offers a promising approach, its success depends on overcoming these hurdles. Emerging trends and future directions emphasize the need for continued research and development in ML for cybersecurity. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr13091005-240910164344-cfe958a4-thumbnail.jpg?width=120&height=120&fit=bounds" /> In the evolving cybersecurity landscape, traditional reactive methods are increasingly inadequate. This article explores the transformative potential of machine learning (ML) in proactive threat analysis, aiming to pre-emptively identify and neutralize threats before they emerge. By employing ML algorithms, cybersecurity systems can analyse vast datasets in real time, recognize patterns, and detect anomalies indicating potential threats. The article reviews current cybersecurity challenges, examines how ML techniques—such as decision trees, neural networks, and clustering—are utilized in threat analysis, and assesses various ML-driven cybersecurity solutions through literature, case studies, and analysis. It highlights ML's benefits, including enhanced detection accuracy, quicker responses, and future threat prediction capabilities. However, challenges such as data quality, adversarial attacks, and high computational demands are also discussed. The article concludes by addressing these limitations and suggesting that while ML offers a promising approach, its success depends on overcoming these hurdles. Emerging trends and future directions emphasize the need for continued research and development in ML for cybersecurity.

Leveraging Machine Learning for Proactive Threat Analysis in Cybersecurity from Editor IJCATR

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0 Leveraging Topological Data Analysis and AI for Advanced Manufacturing: Integrating Machine Learning and Automation for Predictive Maintenance and Process Optimization /slideshow/leveraging-topological-data-analysis-and-ai-for-advanced-manufacturing-integrating-machine-learning-and-automation-for-predictive-maintenance-and-process-optimization/271707679 ijcatr13091003-240910164211-cac9ef2f
This article explores the transformative impact of TDA when integrated with AIand machine learning within advanced manufacturing. TDA, a branch of computational topology, provides a framework for analysing complex, high-dimensional data by capturing the shape and structure of data in a way that is robust to noise and variability. The significance of TDA lies in its ability to reveal underlying patterns and relationships in manufacturing data that are otherwise difficult to discern. The purpose of this article is to highlight the synergy between TDA and AI, focusing specifically on their application in predictive maintenance and process optimization. Predictive maintenance leverages TDA's capacity to identify early signs of equipment failure by analysing historical performance data, thus enabling proactive interventions that minimize downtime and reduce maintenance costs. In process optimization, TDA assists in understanding and improving manufacturing processes by providing insights into the complex interactions between variables and their impact on production efficiency. The integration of TDA with AI enhances machine learning models by incorporating topological features, which improves the models' ability to predict and adapt to changing conditions. This combination not only enhances the accuracy of predictive analytics but also enables more effective and adaptive process control strategies. Through case studies and practical examples, the article demonstrates how these advanced analytical techniques can lead to significant improvements in manufacturing efficiency and reliability. ]]>
This article explores the transformative impact of TDA when integrated with AIand machine learning within advanced manufacturing. TDA, a branch of computational topology, provides a framework for analysing complex, high-dimensional data by capturing the shape and structure of data in a way that is robust to noise and variability. The significance of TDA lies in its ability to reveal underlying patterns and relationships in manufacturing data that are otherwise difficult to discern. The purpose of this article is to highlight the synergy between TDA and AI, focusing specifically on their application in predictive maintenance and process optimization. Predictive maintenance leverages TDA's capacity to identify early signs of equipment failure by analysing historical performance data, thus enabling proactive interventions that minimize downtime and reduce maintenance costs. In process optimization, TDA assists in understanding and improving manufacturing processes by providing insights into the complex interactions between variables and their impact on production efficiency. The integration of TDA with AI enhances machine learning models by incorporating topological features, which improves the models' ability to predict and adapt to changing conditions. This combination not only enhances the accuracy of predictive analytics but also enables more effective and adaptive process control strategies. Through case studies and practical examples, the article demonstrates how these advanced analytical techniques can lead to significant improvements in manufacturing efficiency and reliability. ]]> Tue, 10 Sep 2024 16:42:11 GMT /slideshow/leveraging-topological-data-analysis-and-ai-for-advanced-manufacturing-integrating-machine-learning-and-automation-for-predictive-maintenance-and-process-optimization/271707679 journalsats@slideshare.net(journalsats) Leveraging Topological Data Analysis and AI for Advanced Manufacturing: Integrating Machine Learning and Automation for Predictive Maintenance and Process Optimization journalsats This article explores the transformative impact of TDA when integrated with AIand machine learning within advanced manufacturing. TDA, a branch of computational topology, provides a framework for analysing complex, high-dimensional data by capturing the shape and structure of data in a way that is robust to noise and variability. The significance of TDA lies in its ability to reveal underlying patterns and relationships in manufacturing data that are otherwise difficult to discern. The purpose of this article is to highlight the synergy between TDA and AI, focusing specifically on their application in predictive maintenance and process optimization. Predictive maintenance leverages TDA's capacity to identify early signs of equipment failure by analysing historical performance data, thus enabling proactive interventions that minimize downtime and reduce maintenance costs. In process optimization, TDA assists in understanding and improving manufacturing processes by providing insights into the complex interactions between variables and their impact on production efficiency. The integration of TDA with AI enhances machine learning models by incorporating topological features, which improves the models' ability to predict and adapt to changing conditions. This combination not only enhances the accuracy of predictive analytics but also enables more effective and adaptive process control strategies. Through case studies and practical examples, the article demonstrates how these advanced analytical techniques can lead to significant improvements in manufacturing efficiency and reliability. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr13091003-240910164211-cac9ef2f-thumbnail.jpg?width=120&height=120&fit=bounds" /> This article explores the transformative impact of TDA when integrated with AIand machine learning within advanced manufacturing. TDA, a branch of computational topology, provides a framework for analysing complex, high-dimensional data by capturing the shape and structure of data in a way that is robust to noise and variability. The significance of TDA lies in its ability to reveal underlying patterns and relationships in manufacturing data that are otherwise difficult to discern. The purpose of this article is to highlight the synergy between TDA and AI, focusing specifically on their application in predictive maintenance and process optimization. Predictive maintenance leverages TDA's capacity to identify early signs of equipment failure by analysing historical performance data, thus enabling proactive interventions that minimize downtime and reduce maintenance costs. In process optimization, TDA assists in understanding and improving manufacturing processes by providing insights into the complex interactions between variables and their impact on production efficiency. The integration of TDA with AI enhances machine learning models by incorporating topological features, which improves the models' ability to predict and adapt to changing conditions. This combination not only enhances the accuracy of predictive analytics but also enables more effective and adaptive process control strategies. Through case studies and practical examples, the article demonstrates how these advanced analytical techniques can lead to significant improvements in manufacturing efficiency and reliability.

Leveraging Topological Data Analysis and AI for Advanced Manufacturing: Integrating Machine Learning and Automation for Predictive Maintenance and Process Optimization from Editor IJCATR

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0 Leveraging AI and Principal Component Analysis (PCA) For In-Depth Analysis in Drilling Engineering: Optimizing Production Metrics through Well Logs and Reservoir Data /slideshow/leveraging-ai-and-principal-component-analysis-pca-for-in-depth-analysis-in-drilling-engineering-optimizing-production-metrics-through-well-logs-and-reservoir-data/271707656 ijcatr13091004-240910164057-711aff46
In recent years, the integration of Artificial Intelligence (AI) and Principal Component Analysis (PCA) has significantly transformed drilling engineering, driving notable advancements in both the efficiency and accuracy of subsurface exploration and production. The fusion of these technologies offers a powerful approach to managing and interpreting the vast, complex datasets typically associated with drilling operations. This research looks into the application of AI techniques in conjunction with PCA to analyse well logs, reservoir data, and production metrics, aiming to uncover critical patterns and insights that traditional methods might overlook. By utilizing AI algorithms, particularly machine learning models, this study harnesses the ability of AI to process and learn from large volumes of data, making it possible to predict and optimize drilling outcomes with greater precision. PCA, as a dimensionality reduction technique, plays a crucial role by simplifying these complex datasets, enabling more efficient data processing and enhancing the interpretability of results. The combination of AI and PCA not only streamlines the analysis but also facilitates the identification of key variables and trends that influence drilling performance. Ultimately, this research contributes to the development of more intelligent and data-driven approaches in drilling engineering, promising to optimize operations and reduce risks in subsurface exploration. ]]>
In recent years, the integration of Artificial Intelligence (AI) and Principal Component Analysis (PCA) has significantly transformed drilling engineering, driving notable advancements in both the efficiency and accuracy of subsurface exploration and production. The fusion of these technologies offers a powerful approach to managing and interpreting the vast, complex datasets typically associated with drilling operations. This research looks into the application of AI techniques in conjunction with PCA to analyse well logs, reservoir data, and production metrics, aiming to uncover critical patterns and insights that traditional methods might overlook. By utilizing AI algorithms, particularly machine learning models, this study harnesses the ability of AI to process and learn from large volumes of data, making it possible to predict and optimize drilling outcomes with greater precision. PCA, as a dimensionality reduction technique, plays a crucial role by simplifying these complex datasets, enabling more efficient data processing and enhancing the interpretability of results. The combination of AI and PCA not only streamlines the analysis but also facilitates the identification of key variables and trends that influence drilling performance. Ultimately, this research contributes to the development of more intelligent and data-driven approaches in drilling engineering, promising to optimize operations and reduce risks in subsurface exploration. ]]> Tue, 10 Sep 2024 16:40:57 GMT /slideshow/leveraging-ai-and-principal-component-analysis-pca-for-in-depth-analysis-in-drilling-engineering-optimizing-production-metrics-through-well-logs-and-reservoir-data/271707656 journalsats@slideshare.net(journalsats) Leveraging AI and Principal Component Analysis (PCA) For In-Depth Analysis in Drilling Engineering: Optimizing Production Metrics through Well Logs and Reservoir Data journalsats In recent years, the integration of Artificial Intelligence (AI) and Principal Component Analysis (PCA) has significantly transformed drilling engineering, driving notable advancements in both the efficiency and accuracy of subsurface exploration and production. The fusion of these technologies offers a powerful approach to managing and interpreting the vast, complex datasets typically associated with drilling operations. This research looks into the application of AI techniques in conjunction with PCA to analyse well logs, reservoir data, and production metrics, aiming to uncover critical patterns and insights that traditional methods might overlook. By utilizing AI algorithms, particularly machine learning models, this study harnesses the ability of AI to process and learn from large volumes of data, making it possible to predict and optimize drilling outcomes with greater precision. PCA, as a dimensionality reduction technique, plays a crucial role by simplifying these complex datasets, enabling more efficient data processing and enhancing the interpretability of results. The combination of AI and PCA not only streamlines the analysis but also facilitates the identification of key variables and trends that influence drilling performance. Ultimately, this research contributes to the development of more intelligent and data-driven approaches in drilling engineering, promising to optimize operations and reduce risks in subsurface exploration. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr13091004-240910164057-711aff46-thumbnail.jpg?width=120&height=120&fit=bounds" /> In recent years, the integration of Artificial Intelligence (AI) and Principal Component Analysis (PCA) has significantly transformed drilling engineering, driving notable advancements in both the efficiency and accuracy of subsurface exploration and production. The fusion of these technologies offers a powerful approach to managing and interpreting the vast, complex datasets typically associated with drilling operations. This research looks into the application of AI techniques in conjunction with PCA to analyse well logs, reservoir data, and production metrics, aiming to uncover critical patterns and insights that traditional methods might overlook. By utilizing AI algorithms, particularly machine learning models, this study harnesses the ability of AI to process and learn from large volumes of data, making it possible to predict and optimize drilling outcomes with greater precision. PCA, as a dimensionality reduction technique, plays a crucial role by simplifying these complex datasets, enabling more efficient data processing and enhancing the interpretability of results. The combination of AI and PCA not only streamlines the analysis but also facilitates the identification of key variables and trends that influence drilling performance. Ultimately, this research contributes to the development of more intelligent and data-driven approaches in drilling engineering, promising to optimize operations and reduce risks in subsurface exploration.

Leveraging AI and Principal Component Analysis (PCA) For In-Depth Analysis in Drilling Engineering: Optimizing Production Metrics through Well Logs and Reservoir Data from Editor IJCATR

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0 The Intersection of Artificial Intelligence and Cybersecurity: Safeguarding Data Privacy and Information Integrity in The Digital Age /slideshow/the-intersection-of-artificial-intelligence-and-cybersecurity-safeguarding-data-privacy-and-information-integrity-in-the-digital-age/271707628 ijcatr13091002-240910163915-ba4faf9d
As artificial intelligence (AI) becomes increasingly integrated into various sectors, its impact on cybersecurity, data privacy, and information protection has grown significantly. This article explores the symbiotic relationship between AI and cybersecurity, focusing on how AI-driven solutions can both enhance and challenge data privacy and information integrity. It delves into the dual-edged nature of AI in cybersecurity, examining its potential to strengthen defenses against cyber threats while also raising concerns about privacy and security. Key areas of focus include AI's role in threat detection and response, the implications of AI for data privacy regulations, and the ethical considerations surrounding AI's use in information protection. The article also discusses strategies for balancing innovation in AI with the need for robust privacy and security measures, ensuring that the integrity of personal and organizational data is maintained in an increasingly interconnected world ]]>
As artificial intelligence (AI) becomes increasingly integrated into various sectors, its impact on cybersecurity, data privacy, and information protection has grown significantly. This article explores the symbiotic relationship between AI and cybersecurity, focusing on how AI-driven solutions can both enhance and challenge data privacy and information integrity. It delves into the dual-edged nature of AI in cybersecurity, examining its potential to strengthen defenses against cyber threats while also raising concerns about privacy and security. Key areas of focus include AI's role in threat detection and response, the implications of AI for data privacy regulations, and the ethical considerations surrounding AI's use in information protection. The article also discusses strategies for balancing innovation in AI with the need for robust privacy and security measures, ensuring that the integrity of personal and organizational data is maintained in an increasingly interconnected world ]]> Tue, 10 Sep 2024 16:39:14 GMT /slideshow/the-intersection-of-artificial-intelligence-and-cybersecurity-safeguarding-data-privacy-and-information-integrity-in-the-digital-age/271707628 journalsats@slideshare.net(journalsats) The Intersection of Artificial Intelligence and Cybersecurity: Safeguarding Data Privacy and Information Integrity in The Digital Age journalsats As artificial intelligence (AI) becomes increasingly integrated into various sectors, its impact on cybersecurity, data privacy, and information protection has grown significantly. This article explores the symbiotic relationship between AI and cybersecurity, focusing on how AI-driven solutions can both enhance and challenge data privacy and information integrity. It delves into the dual-edged nature of AI in cybersecurity, examining its potential to strengthen defenses against cyber threats while also raising concerns about privacy and security. Key areas of focus include AI's role in threat detection and response, the implications of AI for data privacy regulations, and the ethical considerations surrounding AI's use in information protection. The article also discusses strategies for balancing innovation in AI with the need for robust privacy and security measures, ensuring that the integrity of personal and organizational data is maintained in an increasingly interconnected world <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr13091002-240910163915-ba4faf9d-thumbnail.jpg?width=120&height=120&fit=bounds" /> As artificial intelligence (AI) becomes increasingly integrated into various sectors, its impact on cybersecurity, data privacy, and information protection has grown significantly. This article explores the symbiotic relationship between AI and cybersecurity, focusing on how AI-driven solutions can both enhance and challenge data privacy and information integrity. It delves into the dual-edged nature of AI in cybersecurity, examining its potential to strengthen defenses against cyber threats while also raising concerns about privacy and security. Key areas of focus include AI's role in threat detection and response, the implications of AI for data privacy regulations, and the ethical considerations surrounding AI's use in information protection. The article also discusses strategies for balancing innovation in AI with the need for robust privacy and security measures, ensuring that the integrity of personal and organizational data is maintained in an increasingly interconnected world

The Intersection of Artificial Intelligence and Cybersecurity: Safeguarding Data Privacy and Information Integrity in The Digital Age from Editor IJCATR

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0 Leveraging AI and Deep Learning in Predictive Genomics for MPOX Virus Research using MATLAB /slideshow/leveraging-ai-and-deep-learning-in-predictive-genomics-for-mpox-virus-research-using-matlab/271707603 ijcatr13091001-240910163723-90a3524f
The Mpox virus, a zoonotic orthopoxvirus, poses significant public health risks due to its capacity to cause outbreaks with high morbidity. Recent advancements in genomics and bioinformatics have enabled in-depth analysis of viral evolution, transmission, and pathogenicity through DNA and RNA sequencing. Integrating artificial intelligence (AI) and machine learning (ML) techniques, particularly deep learning, with genomic data offers a powerful approach to predicting viral behaviour and mutations. This study utilizes MATLAB to harness these advanced computational techniques, aiming to improve the predictive modelling of the Mpox virus. The research involves collecting and analysing Mpox DNA and RNA sequences using MATLAB's robust AI, ML, and deep learning toolboxes. By developing predictive models, this study seeks to uncover patterns that could inform predictions about viral mutation rates and evolutionary trends. MATLAB's environment allows for efficient data preprocessing, model training, and validation, ensuring accurate and interpretable results. This approach not only enhances our understanding of the Mpox virus but also provides a framework for applying AI-driven genomics in managing and preventing future viral outbreaks. The findings from this research could be instrumental in informing public health strategies and vaccine development, potentially reducing the impact of future Mpox outbreaks through early prediction and intervention. ]]>
The Mpox virus, a zoonotic orthopoxvirus, poses significant public health risks due to its capacity to cause outbreaks with high morbidity. Recent advancements in genomics and bioinformatics have enabled in-depth analysis of viral evolution, transmission, and pathogenicity through DNA and RNA sequencing. Integrating artificial intelligence (AI) and machine learning (ML) techniques, particularly deep learning, with genomic data offers a powerful approach to predicting viral behaviour and mutations. This study utilizes MATLAB to harness these advanced computational techniques, aiming to improve the predictive modelling of the Mpox virus. The research involves collecting and analysing Mpox DNA and RNA sequences using MATLAB's robust AI, ML, and deep learning toolboxes. By developing predictive models, this study seeks to uncover patterns that could inform predictions about viral mutation rates and evolutionary trends. MATLAB's environment allows for efficient data preprocessing, model training, and validation, ensuring accurate and interpretable results. This approach not only enhances our understanding of the Mpox virus but also provides a framework for applying AI-driven genomics in managing and preventing future viral outbreaks. The findings from this research could be instrumental in informing public health strategies and vaccine development, potentially reducing the impact of future Mpox outbreaks through early prediction and intervention. ]]> Tue, 10 Sep 2024 16:37:23 GMT /slideshow/leveraging-ai-and-deep-learning-in-predictive-genomics-for-mpox-virus-research-using-matlab/271707603 journalsats@slideshare.net(journalsats) Leveraging AI and Deep Learning in Predictive Genomics for MPOX Virus Research using MATLAB journalsats The Mpox virus, a zoonotic orthopoxvirus, poses significant public health risks due to its capacity to cause outbreaks with high morbidity. Recent advancements in genomics and bioinformatics have enabled in-depth analysis of viral evolution, transmission, and pathogenicity through DNA and RNA sequencing. Integrating artificial intelligence (AI) and machine learning (ML) techniques, particularly deep learning, with genomic data offers a powerful approach to predicting viral behaviour and mutations. This study utilizes MATLAB to harness these advanced computational techniques, aiming to improve the predictive modelling of the Mpox virus. The research involves collecting and analysing Mpox DNA and RNA sequences using MATLAB's robust AI, ML, and deep learning toolboxes. By developing predictive models, this study seeks to uncover patterns that could inform predictions about viral mutation rates and evolutionary trends. MATLAB's environment allows for efficient data preprocessing, model training, and validation, ensuring accurate and interpretable results. This approach not only enhances our understanding of the Mpox virus but also provides a framework for applying AI-driven genomics in managing and preventing future viral outbreaks. The findings from this research could be instrumental in informing public health strategies and vaccine development, potentially reducing the impact of future Mpox outbreaks through early prediction and intervention. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr13091001-240910163723-90a3524f-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Mpox virus, a zoonotic orthopoxvirus, poses significant public health risks due to its capacity to cause outbreaks with high morbidity. Recent advancements in genomics and bioinformatics have enabled in-depth analysis of viral evolution, transmission, and pathogenicity through DNA and RNA sequencing. Integrating artificial intelligence (AI) and machine learning (ML) techniques, particularly deep learning, with genomic data offers a powerful approach to predicting viral behaviour and mutations. This study utilizes MATLAB to harness these advanced computational techniques, aiming to improve the predictive modelling of the Mpox virus. The research involves collecting and analysing Mpox DNA and RNA sequences using MATLAB's robust AI, ML, and deep learning toolboxes. By developing predictive models, this study seeks to uncover patterns that could inform predictions about viral mutation rates and evolutionary trends. MATLAB's environment allows for efficient data preprocessing, model training, and validation, ensuring accurate and interpretable results. This approach not only enhances our understanding of the Mpox virus but also provides a framework for applying AI-driven genomics in managing and preventing future viral outbreaks. The findings from this research could be instrumental in informing public health strategies and vaccine development, potentially reducing the impact of future Mpox outbreaks through early prediction and intervention.

Leveraging AI and Deep Learning in Predictive Genomics for MPOX Virus Research using MATLAB from Editor IJCATR

]]> 39 0 https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr13091001-240910163723-90a3524f-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post

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0 Text Mining in Digital Libraries using OKAPI BM25 Model /slideshow/text-mining-in-digital-libraries-using-okapi-bm25-model-252449256/252449256 ijcatr07101003-220806133952-40a13b6c
The emergence of the internet has made vast amounts of information available and easily accessible online. As a result, most libraries have digitized their content in order to remain relevant to their users and to keep pace with the advancement of the internet. However, these digital libraries have been criticized for using inefficient information retrieval models that do not perform relevance ranking to the retrieved results. This paper proposed the use of OKAPI BM25 model in text mining so as means of improving relevance ranking of digital libraries. Okapi BM25 model was selected because it is a probability-based relevance ranking algorithm. A case study research was conducted and the model design was based on information retrieval processes. The performance of Boolean, vector space, and Okapi BM25 models was compared for data retrieval. Relevant ranked documents were retrieved and displayed at the OPAC framework search page. The results revealed that Okapi BM 25 outperformed Boolean model and Vector Space model. Therefore, this paper proposes the use of Okapi BM25 model to reward terms according to their relative frequencies in a document so as to improve the performance of text mining in digital libraries. ]]>
The emergence of the internet has made vast amounts of information available and easily accessible online. As a result, most libraries have digitized their content in order to remain relevant to their users and to keep pace with the advancement of the internet. However, these digital libraries have been criticized for using inefficient information retrieval models that do not perform relevance ranking to the retrieved results. This paper proposed the use of OKAPI BM25 model in text mining so as means of improving relevance ranking of digital libraries. Okapi BM25 model was selected because it is a probability-based relevance ranking algorithm. A case study research was conducted and the model design was based on information retrieval processes. The performance of Boolean, vector space, and Okapi BM25 models was compared for data retrieval. Relevant ranked documents were retrieved and displayed at the OPAC framework search page. The results revealed that Okapi BM 25 outperformed Boolean model and Vector Space model. Therefore, this paper proposes the use of Okapi BM25 model to reward terms according to their relative frequencies in a document so as to improve the performance of text mining in digital libraries. ]]> Sat, 06 Aug 2022 13:39:52 GMT /slideshow/text-mining-in-digital-libraries-using-okapi-bm25-model-252449256/252449256 journalsats@slideshare.net(journalsats) Text Mining in Digital Libraries using OKAPI BM25 Model journalsats The emergence of the internet has made vast amounts of information available and easily accessible online. As a result, most libraries have digitized their content in order to remain relevant to their users and to keep pace with the advancement of the internet. However, these digital libraries have been criticized for using inefficient information retrieval models that do not perform relevance ranking to the retrieved results. This paper proposed the use of OKAPI BM25 model in text mining so as means of improving relevance ranking of digital libraries. Okapi BM25 model was selected because it is a probability-based relevance ranking algorithm. A case study research was conducted and the model design was based on information retrieval processes. The performance of Boolean, vector space, and Okapi BM25 models was compared for data retrieval. Relevant ranked documents were retrieved and displayed at the OPAC framework search page. The results revealed that Okapi BM 25 outperformed Boolean model and Vector Space model. Therefore, this paper proposes the use of Okapi BM25 model to reward terms according to their relative frequencies in a document so as to improve the performance of text mining in digital libraries. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07101003-220806133952-40a13b6c-thumbnail.jpg?width=120&height=120&fit=bounds" /> The emergence of the internet has made vast amounts of information available and easily accessible online. As a result, most libraries have digitized their content in order to remain relevant to their users and to keep pace with the advancement of the internet. However, these digital libraries have been criticized for using inefficient information retrieval models that do not perform relevance ranking to the retrieved results. This paper proposed the use of OKAPI BM25 model in text mining so as means of improving relevance ranking of digital libraries. Okapi BM25 model was selected because it is a probability-based relevance ranking algorithm. A case study research was conducted and the model design was based on information retrieval processes. The performance of Boolean, vector space, and Okapi BM25 models was compared for data retrieval. Relevant ranked documents were retrieved and displayed at the OPAC framework search page. The results revealed that Okapi BM 25 outperformed Boolean model and Vector Space model. Therefore, this paper proposes the use of Okapi BM25 model to reward terms according to their relative frequencies in a document so as to improve the performance of text mining in digital libraries.

Text Mining in Digital Libraries using OKAPI BM25 Model from Editor IJCATR

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0 Green Computing, eco trends, climate change, e-waste and eco-friendly /slideshow/green-computing-eco-trends-climate-change-ewaste-and-ecofriendly/252449213 ijcatr07101002-220806133120-fe60ce1d
This study focused on the practice of using computing resources more efficiently while maintaining or increasing overall performance. Sustainable IT services require the integration of green computing practices such as power management, virtualization, improving cooling technology, recycling, electronic waste disposal, and optimization of the IT infrastructure to meet sustainability requirements. Studies have shown that costs of power utilized by IT departments can approach 50% of the overall energy costs for an organization. While there is an expectation that green IT should lower costs and the firm’s impact on the environment, there has been far less attention directed at understanding the strategic benefits of sustainable IT services in terms of the creation of customer value, business value and societal value. This paper provides a review of the literature on sustainable IT, key areas of focus, and identifies a core set of principles to guide sustainable IT service design. ]]>
This study focused on the practice of using computing resources more efficiently while maintaining or increasing overall performance. Sustainable IT services require the integration of green computing practices such as power management, virtualization, improving cooling technology, recycling, electronic waste disposal, and optimization of the IT infrastructure to meet sustainability requirements. Studies have shown that costs of power utilized by IT departments can approach 50% of the overall energy costs for an organization. While there is an expectation that green IT should lower costs and the firm’s impact on the environment, there has been far less attention directed at understanding the strategic benefits of sustainable IT services in terms of the creation of customer value, business value and societal value. This paper provides a review of the literature on sustainable IT, key areas of focus, and identifies a core set of principles to guide sustainable IT service design. ]]> Sat, 06 Aug 2022 13:31:20 GMT /slideshow/green-computing-eco-trends-climate-change-ewaste-and-ecofriendly/252449213 journalsats@slideshare.net(journalsats) Green Computing, eco trends, climate change, e-waste and eco-friendly journalsats This study focused on the practice of using computing resources more efficiently while maintaining or increasing overall performance. Sustainable IT services require the integration of green computing practices such as power management, virtualization, improving cooling technology, recycling, electronic waste disposal, and optimization of the IT infrastructure to meet sustainability requirements. Studies have shown that costs of power utilized by IT departments can approach 50% of the overall energy costs for an organization. While there is an expectation that green IT should lower costs and the firm’s impact on the environment, there has been far less attention directed at understanding the strategic benefits of sustainable IT services in terms of the creation of customer value, business value and societal value. This paper provides a review of the literature on sustainable IT, key areas of focus, and identifies a core set of principles to guide sustainable IT service design. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07101002-220806133120-fe60ce1d-thumbnail.jpg?width=120&height=120&fit=bounds" /> This study focused on the practice of using computing resources more efficiently while maintaining or increasing overall performance. Sustainable IT services require the integration of green computing practices such as power management, virtualization, improving cooling technology, recycling, electronic waste disposal, and optimization of the IT infrastructure to meet sustainability requirements. Studies have shown that costs of power utilized by IT departments can approach 50% of the overall energy costs for an organization. While there is an expectation that green IT should lower costs and the firm’s impact on the environment, there has been far less attention directed at understanding the strategic benefits of sustainable IT services in terms of the creation of customer value, business value and societal value. This paper provides a review of the literature on sustainable IT, key areas of focus, and identifies a core set of principles to guide sustainable IT service design.

Green Computing, eco trends, climate change, e-waste and eco-friendly from Editor IJCATR

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0 Policies for Green Computing and E-Waste in Nigeria /slideshow/policies-for-green-computing-and-ewaste-in-nigeria-252449184/252449184 ijcatr07101001-220806132325-226e4eeb
Computers today are an integral part of individuals’ lives all around the world, but unfortunately these devices are toxic to the environment given the materials used, their limited battery life and technological obsolescence. Individuals are concerned about the hazardous materials ever present in computers, even if the importance of various attributes differs, and that a more environment -friendly attitude can be obtained through exposure to educational materials. In this paper, we aim to delineate the problem of e-waste in Nigeria and highlight a series of measures and the advantage they herald for our country and propose a series of action steps to develop in these areas further. It is possible for Nigeria to have an immediate economic stimulus and job creation while moving quickly to abide by the requirements of climate change legislation and energy efficiency directives. The costs of implementing energy efficiency and renewable energy measures are minimal as they are not cash expenditures but rather investments paid back by future, continuous energy savings. ]]>
Computers today are an integral part of individuals’ lives all around the world, but unfortunately these devices are toxic to the environment given the materials used, their limited battery life and technological obsolescence. Individuals are concerned about the hazardous materials ever present in computers, even if the importance of various attributes differs, and that a more environment -friendly attitude can be obtained through exposure to educational materials. In this paper, we aim to delineate the problem of e-waste in Nigeria and highlight a series of measures and the advantage they herald for our country and propose a series of action steps to develop in these areas further. It is possible for Nigeria to have an immediate economic stimulus and job creation while moving quickly to abide by the requirements of climate change legislation and energy efficiency directives. The costs of implementing energy efficiency and renewable energy measures are minimal as they are not cash expenditures but rather investments paid back by future, continuous energy savings. ]]> Sat, 06 Aug 2022 13:23:25 GMT /slideshow/policies-for-green-computing-and-ewaste-in-nigeria-252449184/252449184 journalsats@slideshare.net(journalsats) Policies for Green Computing and E-Waste in Nigeria journalsats Computers today are an integral part of individuals’ lives all around the world, but unfortunately these devices are toxic to the environment given the materials used, their limited battery life and technological obsolescence. Individuals are concerned about the hazardous materials ever present in computers, even if the importance of various attributes differs, and that a more environment -friendly attitude can be obtained through exposure to educational materials. In this paper, we aim to delineate the problem of e-waste in Nigeria and highlight a series of measures and the advantage they herald for our country and propose a series of action steps to develop in these areas further. It is possible for Nigeria to have an immediate economic stimulus and job creation while moving quickly to abide by the requirements of climate change legislation and energy efficiency directives. The costs of implementing energy efficiency and renewable energy measures are minimal as they are not cash expenditures but rather investments paid back by future, continuous energy savings. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07101001-220806132325-226e4eeb-thumbnail.jpg?width=120&height=120&fit=bounds" /> Computers today are an integral part of individuals’ lives all around the world, but unfortunately these devices are toxic to the environment given the materials used, their limited battery life and technological obsolescence. Individuals are concerned about the hazardous materials ever present in computers, even if the importance of various attributes differs, and that a more environment -friendly attitude can be obtained through exposure to educational materials. In this paper, we aim to delineate the problem of e-waste in Nigeria and highlight a series of measures and the advantage they herald for our country and propose a series of action steps to develop in these areas further. It is possible for Nigeria to have an immediate economic stimulus and job creation while moving quickly to abide by the requirements of climate change legislation and energy efficiency directives. The costs of implementing energy efficiency and renewable energy measures are minimal as they are not cash expenditures but rather investments paid back by future, continuous energy savings.

Policies for Green Computing and E-Waste in Nigeria from Editor IJCATR

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0 Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Scenarios /slideshow/performance-evaluation-of-vanets-for-evaluating-node-stability-in-dynamic-scenarios-252447647/252447647 ijcatr07091002-220806085230-1bec95b1
Vehicular ad hoc networks (VANETs) are a favorable area of exploration which empowers the interconnection amid the movable vehicles and between transportable units (vehicles) and road side units (RSU). In Vehicular Ad Hoc Networks (VANETs), mobile vehicles can be organized into assemblage to promote interconnection links. The assemblage arrangement according to dimensions and geographical extend has serious influence on attribute of interaction .Vehicular ad hoc networks (VANETs) are subclass of mobile Ad-hoc network involving more complex mobility patterns. Because of mobility the topology changes very frequently. This raises a number of technical challenges including the stability of the network .There is a need for assemblage configuration leading to more stable realistic network. The paper provides investigation of various simulation scenarios in which cluster using k-means algorithm are generated and their numbers are varied to find the more stable configuration in real scenario of road. ]]>
Vehicular ad hoc networks (VANETs) are a favorable area of exploration which empowers the interconnection amid the movable vehicles and between transportable units (vehicles) and road side units (RSU). In Vehicular Ad Hoc Networks (VANETs), mobile vehicles can be organized into assemblage to promote interconnection links. The assemblage arrangement according to dimensions and geographical extend has serious influence on attribute of interaction .Vehicular ad hoc networks (VANETs) are subclass of mobile Ad-hoc network involving more complex mobility patterns. Because of mobility the topology changes very frequently. This raises a number of technical challenges including the stability of the network .There is a need for assemblage configuration leading to more stable realistic network. The paper provides investigation of various simulation scenarios in which cluster using k-means algorithm are generated and their numbers are varied to find the more stable configuration in real scenario of road. ]]> Sat, 06 Aug 2022 08:52:30 GMT /slideshow/performance-evaluation-of-vanets-for-evaluating-node-stability-in-dynamic-scenarios-252447647/252447647 journalsats@slideshare.net(journalsats) Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Scenarios journalsats Vehicular ad hoc networks (VANETs) are a favorable area of exploration which empowers the interconnection amid the movable vehicles and between transportable units (vehicles) and road side units (RSU). In Vehicular Ad Hoc Networks (VANETs), mobile vehicles can be organized into assemblage to promote interconnection links. The assemblage arrangement according to dimensions and geographical extend has serious influence on attribute of interaction .Vehicular ad hoc networks (VANETs) are subclass of mobile Ad-hoc network involving more complex mobility patterns. Because of mobility the topology changes very frequently. This raises a number of technical challenges including the stability of the network .There is a need for assemblage configuration leading to more stable realistic network. The paper provides investigation of various simulation scenarios in which cluster using k-means algorithm are generated and their numbers are varied to find the more stable configuration in real scenario of road. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07091002-220806085230-1bec95b1-thumbnail.jpg?width=120&height=120&fit=bounds" /> Vehicular ad hoc networks (VANETs) are a favorable area of exploration which empowers the interconnection amid the movable vehicles and between transportable units (vehicles) and road side units (RSU). In Vehicular Ad Hoc Networks (VANETs), mobile vehicles can be organized into assemblage to promote interconnection links. The assemblage arrangement according to dimensions and geographical extend has serious influence on attribute of interaction .Vehicular ad hoc networks (VANETs) are subclass of mobile Ad-hoc network involving more complex mobility patterns. Because of mobility the topology changes very frequently. This raises a number of technical challenges including the stability of the network .There is a need for assemblage configuration leading to more stable realistic network. The paper provides investigation of various simulation scenarios in which cluster using k-means algorithm are generated and their numbers are varied to find the more stable configuration in real scenario of road.

Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Scenarios from Editor IJCATR

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0 Optimum Location of DG Units Considering Operation Conditions /slideshow/optimum-location-of-dg-units-considering-operation-conditions-252447640/252447640 ijcatr07091001-220806085148-b067256e
The optimal sizing and placement of Distributed Generation units (DG) are becoming very attractive to researchers these days. In this paper a two stage approach has been used for allocation and sizing of DGs in distribution system with time varying load model. The strategic placement of DGs can help in reducing energy losses and improving voltage profile. The proposed work discusses time varying loads that can be useful for selecting the location and optimizing DG operation. The method has the potential to be used for integrating the available DGs by identifying the best locations in a power system. The proposed method has been demonstrated on 9-bus test system. ]]>
The optimal sizing and placement of Distributed Generation units (DG) are becoming very attractive to researchers these days. In this paper a two stage approach has been used for allocation and sizing of DGs in distribution system with time varying load model. The strategic placement of DGs can help in reducing energy losses and improving voltage profile. The proposed work discusses time varying loads that can be useful for selecting the location and optimizing DG operation. The method has the potential to be used for integrating the available DGs by identifying the best locations in a power system. The proposed method has been demonstrated on 9-bus test system. ]]> Sat, 06 Aug 2022 08:51:48 GMT /slideshow/optimum-location-of-dg-units-considering-operation-conditions-252447640/252447640 journalsats@slideshare.net(journalsats) Optimum Location of DG Units Considering Operation Conditions journalsats The optimal sizing and placement of Distributed Generation units (DG) are becoming very attractive to researchers these days. In this paper a two stage approach has been used for allocation and sizing of DGs in distribution system with time varying load model. The strategic placement of DGs can help in reducing energy losses and improving voltage profile. The proposed work discusses time varying loads that can be useful for selecting the location and optimizing DG operation. The method has the potential to be used for integrating the available DGs by identifying the best locations in a power system. The proposed method has been demonstrated on 9-bus test system. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07091001-220806085148-b067256e-thumbnail.jpg?width=120&height=120&fit=bounds" /> The optimal sizing and placement of Distributed Generation units (DG) are becoming very attractive to researchers these days. In this paper a two stage approach has been used for allocation and sizing of DGs in distribution system with time varying load model. The strategic placement of DGs can help in reducing energy losses and improving voltage profile. The proposed work discusses time varying loads that can be useful for selecting the location and optimizing DG operation. The method has the potential to be used for integrating the available DGs by identifying the best locations in a power system. The proposed method has been demonstrated on 9-bus test system.

Optimum Location of DG Units Considering Operation Conditions from Editor IJCATR

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0 Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classification Method for Diabetes Mellitus Diagnosis /slideshow/analysis-of-comparison-of-fuzzy-knn-c45-algorithm-and-nave-bayes-classification-method-for-diabetes-mellitus-diagnosis/252447632 ijcatr07081011-220806085021-b46cbd36
Early detection of diabetes mellitus (DM) can prevent or inhibit complication. There are several laboratory test that must be done to detect DM. The result of this laboratory test then converted into data training. Data training used in this study generated from UCI Pima Database with 6 attributes that were used to classify positive or negative diabetes. There are various classification methods that are commonly used, and in this study three of them were compared, which were fuzzy KNN, C4.5 algorithm and Naïve Bayes Classifier (NBC) with one identical case. The objective of this study was to create software to classify DM using tested methods and compared the three methods based on accuracy, precision, and recall. The results showed that the best method was Fuzzy KNN with average and maximum accuracy reached 96% and 98%, respectively. In second place, NBC method had respective average and maximum accuracy of 87.5% and 90%. Lastly, C4.5 algorithm had average and maximum accuracy of 79.5% and 86%, respectively. ]]>
Early detection of diabetes mellitus (DM) can prevent or inhibit complication. There are several laboratory test that must be done to detect DM. The result of this laboratory test then converted into data training. Data training used in this study generated from UCI Pima Database with 6 attributes that were used to classify positive or negative diabetes. There are various classification methods that are commonly used, and in this study three of them were compared, which were fuzzy KNN, C4.5 algorithm and Naïve Bayes Classifier (NBC) with one identical case. The objective of this study was to create software to classify DM using tested methods and compared the three methods based on accuracy, precision, and recall. The results showed that the best method was Fuzzy KNN with average and maximum accuracy reached 96% and 98%, respectively. In second place, NBC method had respective average and maximum accuracy of 87.5% and 90%. Lastly, C4.5 algorithm had average and maximum accuracy of 79.5% and 86%, respectively. ]]> Sat, 06 Aug 2022 08:50:21 GMT /slideshow/analysis-of-comparison-of-fuzzy-knn-c45-algorithm-and-nave-bayes-classification-method-for-diabetes-mellitus-diagnosis/252447632 journalsats@slideshare.net(journalsats) Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classification Method for Diabetes Mellitus Diagnosis journalsats Early detection of diabetes mellitus (DM) can prevent or inhibit complication. There are several laboratory test that must be done to detect DM. The result of this laboratory test then converted into data training. Data training used in this study generated from UCI Pima Database with 6 attributes that were used to classify positive or negative diabetes. There are various classification methods that are commonly used, and in this study three of them were compared, which were fuzzy KNN, C4.5 algorithm and Naïve Bayes Classifier (NBC) with one identical case. The objective of this study was to create software to classify DM using tested methods and compared the three methods based on accuracy, precision, and recall. The results showed that the best method was Fuzzy KNN with average and maximum accuracy reached 96% and 98%, respectively. In second place, NBC method had respective average and maximum accuracy of 87.5% and 90%. Lastly, C4.5 algorithm had average and maximum accuracy of 79.5% and 86%, respectively. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07081011-220806085021-b46cbd36-thumbnail.jpg?width=120&height=120&fit=bounds" /> Early detection of diabetes mellitus (DM) can prevent or inhibit complication. There are several laboratory test that must be done to detect DM. The result of this laboratory test then converted into data training. Data training used in this study generated from UCI Pima Database with 6 attributes that were used to classify positive or negative diabetes. There are various classification methods that are commonly used, and in this study three of them were compared, which were fuzzy KNN, C4.5 algorithm and Naïve Bayes Classifier (NBC) with one identical case. The objective of this study was to create software to classify DM using tested methods and compared the three methods based on accuracy, precision, and recall. The results showed that the best method was Fuzzy KNN with average and maximum accuracy reached 96% and 98%, respectively. In second place, NBC method had respective average and maximum accuracy of 87.5% and 90%. Lastly, C4.5 algorithm had average and maximum accuracy of 79.5% and 86%, respectively.

Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Na誰ve Bayes Classification Method for Diabetes Mellitus Diagnosis from Editor IJCATR

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0 Web Scraping for Estimating new Record from Source Site /slideshow/web-scraping-for-estimating-new-record-from-source-site/252447625 ijcatr07081010-220806084936-ebcdbe7b
Study in the Competitive field of Intelligent, and studies in the field of Web Scraping, have a symbiotic relationship mutualism. In the information age today, the website serves as a main source. The research focus is on how to get data from websites and how to slow down the intensity of the download. The problem that arises is the website sources are autonomous so that vulnerable changes the structure of the content at any time. The next problem is the system intrusion detection snort installed on the server to detect bot crawler. So the researchers propose the use of the methods of Mining Data Records and the method of Exponential Smoothing so that adaptive to changes in the structure of the content and do a browse or fetch automatically follow the pattern of the occurrences of the news. The results of the tests, with the threshold 0.3 for MDR and similarity threshold score 0.65 for STM, using recall and precision values produce f-measure average 92.6%. While the results of the tests of the exponential estimation smoothing using ? = 0.5 produces MAE 18.2 datarecord duplicate. It slowed down to 3.6 datarecord from 21.8 datarecord results schedule download/fetch fix in an average time of occurrence news. ]]>
Study in the Competitive field of Intelligent, and studies in the field of Web Scraping, have a symbiotic relationship mutualism. In the information age today, the website serves as a main source. The research focus is on how to get data from websites and how to slow down the intensity of the download. The problem that arises is the website sources are autonomous so that vulnerable changes the structure of the content at any time. The next problem is the system intrusion detection snort installed on the server to detect bot crawler. So the researchers propose the use of the methods of Mining Data Records and the method of Exponential Smoothing so that adaptive to changes in the structure of the content and do a browse or fetch automatically follow the pattern of the occurrences of the news. The results of the tests, with the threshold 0.3 for MDR and similarity threshold score 0.65 for STM, using recall and precision values produce f-measure average 92.6%. While the results of the tests of the exponential estimation smoothing using ? = 0.5 produces MAE 18.2 datarecord duplicate. It slowed down to 3.6 datarecord from 21.8 datarecord results schedule download/fetch fix in an average time of occurrence news. ]]> Sat, 06 Aug 2022 08:49:36 GMT /slideshow/web-scraping-for-estimating-new-record-from-source-site/252447625 journalsats@slideshare.net(journalsats) Web Scraping for Estimating new Record from Source Site journalsats Study in the Competitive field of Intelligent, and studies in the field of Web Scraping, have a symbiotic relationship mutualism. In the information age today, the website serves as a main source. The research focus is on how to get data from websites and how to slow down the intensity of the download. The problem that arises is the website sources are autonomous so that vulnerable changes the structure of the content at any time. The next problem is the system intrusion detection snort installed on the server to detect bot crawler. So the researchers propose the use of the methods of Mining Data Records and the method of Exponential Smoothing so that adaptive to changes in the structure of the content and do a browse or fetch automatically follow the pattern of the occurrences of the news. The results of the tests, with the threshold 0.3 for MDR and similarity threshold score 0.65 for STM, using recall and precision values produce f-measure average 92.6%. While the results of the tests of the exponential estimation smoothing using ? = 0.5 produces MAE 18.2 datarecord duplicate. It slowed down to 3.6 datarecord from 21.8 datarecord results schedule download/fetch fix in an average time of occurrence news. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07081010-220806084936-ebcdbe7b-thumbnail.jpg?width=120&height=120&fit=bounds" /> Study in the Competitive field of Intelligent, and studies in the field of Web Scraping, have a symbiotic relationship mutualism. In the information age today, the website serves as a main source. The research focus is on how to get data from websites and how to slow down the intensity of the download. The problem that arises is the website sources are autonomous so that vulnerable changes the structure of the content at any time. The next problem is the system intrusion detection snort installed on the server to detect bot crawler. So the researchers propose the use of the methods of Mining Data Records and the method of Exponential Smoothing so that adaptive to changes in the structure of the content and do a browse or fetch automatically follow the pattern of the occurrences of the news. The results of the tests, with the threshold 0.3 for MDR and similarity threshold score 0.65 for STM, using recall and precision values produce f-measure average 92.6%. While the results of the tests of the exponential estimation smoothing using ? = 0.5 produces MAE 18.2 datarecord duplicate. It slowed down to 3.6 datarecord from 21.8 datarecord results schedule download/fetch fix in an average time of occurrence news.

Web Scraping for Estimating new Record from Source Site from Editor IJCATR

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0 Evaluating Semantic Similarity between Biomedical Concepts/Classes through Single Ontology /slideshow/evaluating-semantic-similarity-between-biomedical-conceptsclasses-through-single-ontology/252447617 ijcatr07081009-220806084842-46f11756
Most of the existing semantic similarity measures that use ontology structure as their primary source can measure semantic similarity between concepts/classes using single ontology. The ontology-based semantic similarity techniques such as structure-based semantic similarity techniques (Path Length Measure, Wu and Palmer’s Measure, and Leacock and Chodorow’s measure), information content-based similarity techniques (Resnik’s measure, Lin’s measure), and biomedical domain ontology techniques (Al-Mubaid and Nguyen’s measure (SimDist)) were evaluated relative to human experts’ ratings, and compared on sets of concepts using the ICD-10 “V1.0” terminology within the UMLS. The experimental results validate the efficiency of the SemDist technique in single ontology, and demonstrate that SemDist semantic similarity techniques, compared with the existing techniques, gives the best overall results of correlation with experts’ ratings. ]]>
Most of the existing semantic similarity measures that use ontology structure as their primary source can measure semantic similarity between concepts/classes using single ontology. The ontology-based semantic similarity techniques such as structure-based semantic similarity techniques (Path Length Measure, Wu and Palmer’s Measure, and Leacock and Chodorow’s measure), information content-based similarity techniques (Resnik’s measure, Lin’s measure), and biomedical domain ontology techniques (Al-Mubaid and Nguyen’s measure (SimDist)) were evaluated relative to human experts’ ratings, and compared on sets of concepts using the ICD-10 “V1.0” terminology within the UMLS. The experimental results validate the efficiency of the SemDist technique in single ontology, and demonstrate that SemDist semantic similarity techniques, compared with the existing techniques, gives the best overall results of correlation with experts’ ratings. ]]> Sat, 06 Aug 2022 08:48:42 GMT /slideshow/evaluating-semantic-similarity-between-biomedical-conceptsclasses-through-single-ontology/252447617 journalsats@slideshare.net(journalsats) Evaluating Semantic Similarity between Biomedical Concepts/Classes through Single Ontology journalsats Most of the existing semantic similarity measures that use ontology structure as their primary source can measure semantic similarity between concepts/classes using single ontology. The ontology-based semantic similarity techniques such as structure-based semantic similarity techniques (Path Length Measure, Wu and Palmer’s Measure, and Leacock and Chodorow’s measure), information content-based similarity techniques (Resnik’s measure, Lin’s measure), and biomedical domain ontology techniques (Al-Mubaid and Nguyen’s measure (SimDist)) were evaluated relative to human experts’ ratings, and compared on sets of concepts using the ICD-10 “V1.0” terminology within the UMLS. The experimental results validate the efficiency of the SemDist technique in single ontology, and demonstrate that SemDist semantic similarity techniques, compared with the existing techniques, gives the best overall results of correlation with experts’ ratings. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07081009-220806084842-46f11756-thumbnail.jpg?width=120&height=120&fit=bounds" /> Most of the existing semantic similarity measures that use ontology structure as their primary source can measure semantic similarity between concepts/classes using single ontology. The ontology-based semantic similarity techniques such as structure-based semantic similarity techniques (Path Length Measure, Wu and Palmer’s Measure, and Leacock and Chodorow’s measure), information content-based similarity techniques (Resnik’s measure, Lin’s measure), and biomedical domain ontology techniques (Al-Mubaid and Nguyen’s measure (SimDist)) were evaluated relative to human experts’ ratings, and compared on sets of concepts using the ICD-10 “V1.0” terminology within the UMLS. The experimental results validate the efficiency of the SemDist technique in single ontology, and demonstrate that SemDist semantic similarity techniques, compared with the existing techniques, gives the best overall results of correlation with experts’ ratings.

Evaluating Semantic Similarity between Biomedical Concepts/Classes through Single Ontology from Editor IJCATR

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0 Semantic Similarity Measures between Terms in the Biomedical Domain within frame work Unified Medical Language System (UMLS) /slideshow/semantic-similarity-measures-between-terms-in-the-biomedical-domain-within-frame-work-unified-medical-language-system-umls/252447603 ijcatr07061007-220806084627-b1ed0d7f
The techniques and tests are tools used to define how measure the goodness of ontology or its resources. The similarity between biomedical classes/concepts is an important task for the biomedical information extraction and knowledge discovery. However, most of the semantic similarity techniques can be adopted to be used in the biomedical domain (UMLS). Many experiments have been conducted to check the applicability of these measures. In this paper, we investigate to measure semantic similarity between two terms within single ontology or multiple ontologies in ICD-10 “V1.0” as primary source, and compare my results to human experts score by correlation coefficient. ]]>
The techniques and tests are tools used to define how measure the goodness of ontology or its resources. The similarity between biomedical classes/concepts is an important task for the biomedical information extraction and knowledge discovery. However, most of the semantic similarity techniques can be adopted to be used in the biomedical domain (UMLS). Many experiments have been conducted to check the applicability of these measures. In this paper, we investigate to measure semantic similarity between two terms within single ontology or multiple ontologies in ICD-10 “V1.0” as primary source, and compare my results to human experts score by correlation coefficient. ]]> Sat, 06 Aug 2022 08:46:27 GMT /slideshow/semantic-similarity-measures-between-terms-in-the-biomedical-domain-within-frame-work-unified-medical-language-system-umls/252447603 journalsats@slideshare.net(journalsats) Semantic Similarity Measures between Terms in the Biomedical Domain within frame work Unified Medical Language System (UMLS) journalsats The techniques and tests are tools used to define how measure the goodness of ontology or its resources. The similarity between biomedical classes/concepts is an important task for the biomedical information extraction and knowledge discovery. However, most of the semantic similarity techniques can be adopted to be used in the biomedical domain (UMLS). Many experiments have been conducted to check the applicability of these measures. In this paper, we investigate to measure semantic similarity between two terms within single ontology or multiple ontologies in ICD-10 “V1.0” as primary source, and compare my results to human experts score by correlation coefficient. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07061007-220806084627-b1ed0d7f-thumbnail.jpg?width=120&height=120&fit=bounds" /> The techniques and tests are tools used to define how measure the goodness of ontology or its resources. The similarity between biomedical classes/concepts is an important task for the biomedical information extraction and knowledge discovery. However, most of the semantic similarity techniques can be adopted to be used in the biomedical domain (UMLS). Many experiments have been conducted to check the applicability of these measures. In this paper, we investigate to measure semantic similarity between two terms within single ontology or multiple ontologies in ICD-10 “V1.0” as primary source, and compare my results to human experts score by correlation coefficient.

Semantic Similarity Measures between Terms in the Biomedical Domain within frame work Unified Medical Language System (UMLS) from Editor IJCATR

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0 A Strategy for Improving the Performance of Small Files in Openstack Swift /slideshow/a-strategy-for-improving-the-performance-of-small-files-in-openstack-swift/252447597 ijcatr07081006-220806084531-7e936fe2
This is an effective way to improve the storage access performance of small files in Openstack Swift by adding an aggregate storage module. Because Swift will lead to too much disk operation when querying metadata, the transfer performance of plenty of small files is low. In this paper, we propose an aggregated storage strategy (ASS), and implement it in Swift. ASS comprises two parts which include merge storage and index storage. At the first stage, ASS arranges the write request queue in chronological order, and then stores objects in volumes. These volumes are large files that are stored in Swift actually. During the short encounter time, the object-to-volume mapping information is stored in Key-Value store at the second stage. The experimental results show that the ASS can effectively improve Swift's small file transfer performance. ]]>
This is an effective way to improve the storage access performance of small files in Openstack Swift by adding an aggregate storage module. Because Swift will lead to too much disk operation when querying metadata, the transfer performance of plenty of small files is low. In this paper, we propose an aggregated storage strategy (ASS), and implement it in Swift. ASS comprises two parts which include merge storage and index storage. At the first stage, ASS arranges the write request queue in chronological order, and then stores objects in volumes. These volumes are large files that are stored in Swift actually. During the short encounter time, the object-to-volume mapping information is stored in Key-Value store at the second stage. The experimental results show that the ASS can effectively improve Swift's small file transfer performance. ]]> Sat, 06 Aug 2022 08:45:31 GMT /slideshow/a-strategy-for-improving-the-performance-of-small-files-in-openstack-swift/252447597 journalsats@slideshare.net(journalsats) A Strategy for Improving the Performance of Small Files in Openstack Swift journalsats This is an effective way to improve the storage access performance of small files in Openstack Swift by adding an aggregate storage module. Because Swift will lead to too much disk operation when querying metadata, the transfer performance of plenty of small files is low. In this paper, we propose an aggregated storage strategy (ASS), and implement it in Swift. ASS comprises two parts which include merge storage and index storage. At the first stage, ASS arranges the write request queue in chronological order, and then stores objects in volumes. These volumes are large files that are stored in Swift actually. During the short encounter time, the object-to-volume mapping information is stored in Key-Value store at the second stage. The experimental results show that the ASS can effectively improve Swift's small file transfer performance. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07081006-220806084531-7e936fe2-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is an effective way to improve the storage access performance of small files in Openstack Swift by adding an aggregate storage module. Because Swift will lead to too much disk operation when querying metadata, the transfer performance of plenty of small files is low. In this paper, we propose an aggregated storage strategy (ASS), and implement it in Swift. ASS comprises two parts which include merge storage and index storage. At the first stage, ASS arranges the write request queue in chronological order, and then stores objects in volumes. These volumes are large files that are stored in Swift actually. During the short encounter time, the object-to-volume mapping information is stored in Key-Value store at the second stage. The experimental results show that the ASS can effectively improve Swift's small file transfer performance.

A Strategy for Improving the Performance of Small Files in Openstack Swift from Editor IJCATR

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0 Integrated System for Vehicle Clearance and Registration /slideshow/integrated-system-for-vehicle-clearance-and-registration-252447590/252447590 ijcatr07081005-220806084442-ceeb7f6f
Efficient management and control of government's cash resources rely on government banking arrangements. Nigeria, like many low income countries, employed fragmented systems in handling government receipts and payments. Later in 2016, Nigeria implemented a unified structure as recommended by the IMF, where all government funds are collected in one account would reduce borrowing costs, extend credit and improve government's fiscal policy among other benefits to government. This situation motivated us to embark on this research to design and implement an integrated system for vehicle clearance and registration. This system complies with the new Treasury Single Account policy to enable proper interaction and collaboration among five different level agencies (NCS, FRSC, SBIR, VIO and NPF) saddled with vehicular administration and activities in Nigeria. Since the system is web based, Object Oriented Hypermedia Design Methodology (OOHDM) is used. Tools such as Php, JavaScript, css, html, AJAX and other web development technologies were used. The result is a web based system that gives proper information about a vehicle starting from the exact date of importation to registration and renewal of licensing. Vehicle owner information, custom duty information, plate number registration details, etc. will also be efficiently retrieved from the system by any of the agencies without contacting the other agency at any point in time. Also number plate will no longer be the only means of vehicle identification as it is presently the case in Nigeria, because the unified system will automatically generate and assigned a Unique Vehicle Identification Pin Number (UVIPN) on payment of duty in the system to the vehicle and the UVIPN will be linked to the various agencies in the management information system. ]]>
Efficient management and control of government's cash resources rely on government banking arrangements. Nigeria, like many low income countries, employed fragmented systems in handling government receipts and payments. Later in 2016, Nigeria implemented a unified structure as recommended by the IMF, where all government funds are collected in one account would reduce borrowing costs, extend credit and improve government's fiscal policy among other benefits to government. This situation motivated us to embark on this research to design and implement an integrated system for vehicle clearance and registration. This system complies with the new Treasury Single Account policy to enable proper interaction and collaboration among five different level agencies (NCS, FRSC, SBIR, VIO and NPF) saddled with vehicular administration and activities in Nigeria. Since the system is web based, Object Oriented Hypermedia Design Methodology (OOHDM) is used. Tools such as Php, JavaScript, css, html, AJAX and other web development technologies were used. The result is a web based system that gives proper information about a vehicle starting from the exact date of importation to registration and renewal of licensing. Vehicle owner information, custom duty information, plate number registration details, etc. will also be efficiently retrieved from the system by any of the agencies without contacting the other agency at any point in time. Also number plate will no longer be the only means of vehicle identification as it is presently the case in Nigeria, because the unified system will automatically generate and assigned a Unique Vehicle Identification Pin Number (UVIPN) on payment of duty in the system to the vehicle and the UVIPN will be linked to the various agencies in the management information system. ]]> Sat, 06 Aug 2022 08:44:41 GMT /slideshow/integrated-system-for-vehicle-clearance-and-registration-252447590/252447590 journalsats@slideshare.net(journalsats) Integrated System for Vehicle Clearance and Registration journalsats Efficient management and control of government's cash resources rely on government banking arrangements. Nigeria, like many low income countries, employed fragmented systems in handling government receipts and payments. Later in 2016, Nigeria implemented a unified structure as recommended by the IMF, where all government funds are collected in one account would reduce borrowing costs, extend credit and improve government's fiscal policy among other benefits to government. This situation motivated us to embark on this research to design and implement an integrated system for vehicle clearance and registration. This system complies with the new Treasury Single Account policy to enable proper interaction and collaboration among five different level agencies (NCS, FRSC, SBIR, VIO and NPF) saddled with vehicular administration and activities in Nigeria. Since the system is web based, Object Oriented Hypermedia Design Methodology (OOHDM) is used. Tools such as Php, JavaScript, css, html, AJAX and other web development technologies were used. The result is a web based system that gives proper information about a vehicle starting from the exact date of importation to registration and renewal of licensing. Vehicle owner information, custom duty information, plate number registration details, etc. will also be efficiently retrieved from the system by any of the agencies without contacting the other agency at any point in time. Also number plate will no longer be the only means of vehicle identification as it is presently the case in Nigeria, because the unified system will automatically generate and assigned a Unique Vehicle Identification Pin Number (UVIPN) on payment of duty in the system to the vehicle and the UVIPN will be linked to the various agencies in the management information system. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07081005-220806084442-ceeb7f6f-thumbnail.jpg?width=120&height=120&fit=bounds" /> Efficient management and control of government's cash resources rely on government banking arrangements. Nigeria, like many low income countries, employed fragmented systems in handling government receipts and payments. Later in 2016, Nigeria implemented a unified structure as recommended by the IMF, where all government funds are collected in one account would reduce borrowing costs, extend credit and improve government's fiscal policy among other benefits to government. This situation motivated us to embark on this research to design and implement an integrated system for vehicle clearance and registration. This system complies with the new Treasury Single Account policy to enable proper interaction and collaboration among five different level agencies (NCS, FRSC, SBIR, VIO and NPF) saddled with vehicular administration and activities in Nigeria. Since the system is web based, Object Oriented Hypermedia Design Methodology (OOHDM) is used. Tools such as Php, JavaScript, css, html, AJAX and other web development technologies were used. The result is a web based system that gives proper information about a vehicle starting from the exact date of importation to registration and renewal of licensing. Vehicle owner information, custom duty information, plate number registration details, etc. will also be efficiently retrieved from the system by any of the agencies without contacting the other agency at any point in time. Also number plate will no longer be the only means of vehicle identification as it is presently the case in Nigeria, because the unified system will automatically generate and assigned a Unique Vehicle Identification Pin Number (UVIPN) on payment of duty in the system to the vehicle and the UVIPN will be linked to the various agencies in the management information system.

Integrated System for Vehicle Clearance and Registration from Editor IJCATR

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0 Assessment of the Efficiency of Customer Order Management System: A Case Study of Sambajo General Enterprises Jigawa State, Nigeria. /slideshow/assessment-of-the-efficiency-of-customer-order-management-system-a-case-study-of-sambajo-general-enterprises-jigawa-state-nigeria-252447586/252447586 ijcatr07081004-220806084348-60391bda
The Supermarket Management System deals with the automation of buying and selling of good and services. It includes both sales and purchase of items. The project Supermarket Management System is to be developed with the objective of making the system reliable, easier, fast, and more informative. ]]>
The Supermarket Management System deals with the automation of buying and selling of good and services. It includes both sales and purchase of items. The project Supermarket Management System is to be developed with the objective of making the system reliable, easier, fast, and more informative. ]]> Sat, 06 Aug 2022 08:43:48 GMT /slideshow/assessment-of-the-efficiency-of-customer-order-management-system-a-case-study-of-sambajo-general-enterprises-jigawa-state-nigeria-252447586/252447586 journalsats@slideshare.net(journalsats) Assessment of the Efficiency of Customer Order Management System: A Case Study of Sambajo General Enterprises Jigawa State, Nigeria. journalsats The Supermarket Management System deals with the automation of buying and selling of good and services. It includes both sales and purchase of items. The project Supermarket Management System is to be developed with the objective of making the system reliable, easier, fast, and more informative. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07081004-220806084348-60391bda-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Supermarket Management System deals with the automation of buying and selling of good and services. It includes both sales and purchase of items. The project Supermarket Management System is to be developed with the objective of making the system reliable, easier, fast, and more informative.

Assessment of the Efficiency of Customer Order Management System: A Case Study of Sambajo General Enterprises Jigawa State, Nigeria. from Editor IJCATR

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0 Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A* /slideshow/energyaware-routing-in-wireless-sensor-network-using-modified-bidirectional-a-252447580/252447580 ijcatr07081003-220806084248-60681557
Energy is a key component in the Wireless Sensor Network (WSN)[1]. The system will not be able to run according to its function without the availability of adequate power units. One of the characteristics of wireless sensor network is Limitation energy[2]. A lot of research has been done to develop strategies to overcome this problem. One of them is clustering technique. The popular clustering technique is Low Energy Adaptive Clustering Hierarchy (LEACH)[3]. In LEACH, clustering techniques are used to determine Cluster Head (CH), which will then be assigned to forward packets to Base Station (BS). In this research, we propose other clustering techniques, which utilize the Social Network Analysis approach theory of Betweeness Centrality (BC) which will then be implemented in the Setup phase. While in the Steady-State phase, one of the heuristic searching algorithms, Modified Bi-Directional A* (MBDA *) is implemented. The experiment was performed deploy 100 nodes statically in the 100x100 area, with one Base Station at coordinates (50,50). To find out the reliability of the system, the experiment to do in 5000 rounds. The performance of the designed routing protocol strategy will be tested based on network lifetime, throughput, and residual energy. The results show that BC-MBDA * is better than LEACH. This is influenced by the ways of working LEACH in determining the CH that is dynamic, which is always changing in every data transmission process. This will result in the use of energy, because they always doing any computation to determine CH in every transmission process. In contrast to BC-MBDA *, CH is statically determined, so it can decrease energy usage. ]]>
Energy is a key component in the Wireless Sensor Network (WSN)[1]. The system will not be able to run according to its function without the availability of adequate power units. One of the characteristics of wireless sensor network is Limitation energy[2]. A lot of research has been done to develop strategies to overcome this problem. One of them is clustering technique. The popular clustering technique is Low Energy Adaptive Clustering Hierarchy (LEACH)[3]. In LEACH, clustering techniques are used to determine Cluster Head (CH), which will then be assigned to forward packets to Base Station (BS). In this research, we propose other clustering techniques, which utilize the Social Network Analysis approach theory of Betweeness Centrality (BC) which will then be implemented in the Setup phase. While in the Steady-State phase, one of the heuristic searching algorithms, Modified Bi-Directional A* (MBDA *) is implemented. The experiment was performed deploy 100 nodes statically in the 100x100 area, with one Base Station at coordinates (50,50). To find out the reliability of the system, the experiment to do in 5000 rounds. The performance of the designed routing protocol strategy will be tested based on network lifetime, throughput, and residual energy. The results show that BC-MBDA * is better than LEACH. This is influenced by the ways of working LEACH in determining the CH that is dynamic, which is always changing in every data transmission process. This will result in the use of energy, because they always doing any computation to determine CH in every transmission process. In contrast to BC-MBDA *, CH is statically determined, so it can decrease energy usage. ]]> Sat, 06 Aug 2022 08:42:48 GMT /slideshow/energyaware-routing-in-wireless-sensor-network-using-modified-bidirectional-a-252447580/252447580 journalsats@slideshare.net(journalsats) Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A* journalsats Energy is a key component in the Wireless Sensor Network (WSN)[1]. The system will not be able to run according to its function without the availability of adequate power units. One of the characteristics of wireless sensor network is Limitation energy[2]. A lot of research has been done to develop strategies to overcome this problem. One of them is clustering technique. The popular clustering technique is Low Energy Adaptive Clustering Hierarchy (LEACH)[3]. In LEACH, clustering techniques are used to determine Cluster Head (CH), which will then be assigned to forward packets to Base Station (BS). In this research, we propose other clustering techniques, which utilize the Social Network Analysis approach theory of Betweeness Centrality (BC) which will then be implemented in the Setup phase. While in the Steady-State phase, one of the heuristic searching algorithms, Modified Bi-Directional A* (MBDA *) is implemented. The experiment was performed deploy 100 nodes statically in the 100x100 area, with one Base Station at coordinates (50,50). To find out the reliability of the system, the experiment to do in 5000 rounds. The performance of the designed routing protocol strategy will be tested based on network lifetime, throughput, and residual energy. The results show that BC-MBDA * is better than LEACH. This is influenced by the ways of working LEACH in determining the CH that is dynamic, which is always changing in every data transmission process. This will result in the use of energy, because they always doing any computation to determine CH in every transmission process. In contrast to BC-MBDA *, CH is statically determined, so it can decrease energy usage. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ijcatr07081003-220806084248-60681557-thumbnail.jpg?width=120&height=120&fit=bounds" /> Energy is a key component in the Wireless Sensor Network (WSN)[1]. The system will not be able to run according to its function without the availability of adequate power units. One of the characteristics of wireless sensor network is Limitation energy[2]. A lot of research has been done to develop strategies to overcome this problem. One of them is clustering technique. The popular clustering technique is Low Energy Adaptive Clustering Hierarchy (LEACH)[3]. In LEACH, clustering techniques are used to determine Cluster Head (CH), which will then be assigned to forward packets to Base Station (BS). In this research, we propose other clustering techniques, which utilize the Social Network Analysis approach theory of Betweeness Centrality (BC) which will then be implemented in the Setup phase. While in the Steady-State phase, one of the heuristic searching algorithms, Modified Bi-Directional A* (MBDA *) is implemented. The experiment was performed deploy 100 nodes statically in the 100x100 area, with one Base Station at coordinates (50,50). To find out the reliability of the system, the experiment to do in 5000 rounds. The performance of the designed routing protocol strategy will be tested based on network lifetime, throughput, and residual energy. The results show that BC-MBDA * is better than LEACH. This is influenced by the ways of working LEACH in determining the CH that is dynamic, which is always changing in every data transmission process. This will result in the use of energy, because they always doing any computation to determine CH in every transmission process. In contrast to BC-MBDA *, CH is statically determined, so it can decrease energy usage.

Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A* from Editor IJCATR

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