The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID-19 since its outbreak.The Internet of Things(IoT)along with other technologies like Machine Learning can re...The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID-19 since its outbreak.The Internet of Things(IoT)along with other technologies like Machine Learning can revolutionize the traditional healthcare system.Instead of reactive healthcare systems,IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services.In this study,a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection.The proposed framework is based on edge computing to provide personalized healthcare facilities with minimal latency,short response time,and optimal energy consumption.In this paper,the COVID-19 primary novel dataset has been used for experimental purposes employing various classification-based machine learning models.The proposed models were validated using kcross-validation to ensure the consistency of models.Based on the experimental results,our proposed models have recorded good accuracies with highest of 97.767%by Support Vector Machine.According to the findings of experiments,the proposed conceptual model will aid in the early detection and prediction of COVID-19 suspects,as well as continuous monitoring of the patient in order to provide emergency care in case of medical volatile situation.展开更多
This research explores the increasing importance of Artificial Intelligence(AI)and Machine Learning(ML)with relation to smart cities.It discusses the AI and ML’s ability to revolutionize various aspects of urban envi...This research explores the increasing importance of Artificial Intelligence(AI)and Machine Learning(ML)with relation to smart cities.It discusses the AI and ML’s ability to revolutionize various aspects of urban environments,including infrastructure,governance,public safety,and sustainability.The research presents the definition and characteristics of smart cities,highlighting the key components and technologies driving initiatives for smart cities.The methodology employed in this study involved a comprehensive review of relevant literature,research papers,and reports on the subject of AI and ML in smart cities.Various sources were consulted to gather information on the integration of AI and ML technologies in various aspects of smart cities,including infrastructure optimization,public safety enhancement,and citizen services improvement.The findings suggest that AI and ML technologies enable data-driven decision-making,predictive analytics,and optimization in smart city development.They are vital to the development of transport infrastructure,optimizing energy distribution,improving public safety,streamlining governance,and transforming healthcare services.However,ethical and privacy considerations,as well as technical challenges,need to be solved to guarantee the ethical and responsible usage of AI and ML in smart cities.The study concludes by discussing the challenges and future directions of AI and ML in shaping urban environments,highlighting the importance of collaborative efforts and responsible implementation.The findings highlight the transformative potential of AI and ML in optimizing resource utilization,enhancing citizen services,and creating more sustainable and resilient smart cities.Future studies should concentrate on addressing technical limitations,creating robust policy frameworks,and fostering fairness,accountability,and openness in the use of AI and ML technologies in smart cities.展开更多
This study utilizes ML classifiers to estimate canopy density based on three decades of data (1990-2021). The Support Vector Machine (SVM) classifier outperformed other classifiers, such as Random Tree and Maximum Lik...This study utilizes ML classifiers to estimate canopy density based on three decades of data (1990-2021). The Support Vector Machine (SVM) classifier outperformed other classifiers, such as Random Tree and Maximum Likelihood. Satellite data from Landsat and Sentinel 2 was classified using a developed python model, providing an economical and time-saving approach. The accuracy of the classification was evaluated through a confusion matrix and area computation. The findings indicate a negative trend in the overall decadal change, with significant tree loss attributed to jhum cultivation, mining, and quarry activities. However, positive changes were observed in recent years due to the ban on illegal mining. The study highlights the dynamic nature of tree cover and emphasizes the need for biennial assessments using at least five time-series data. Micro-level analysis in Shallang, West Khasi hills, revealed a concerning trend of shortening jhum cycles. Automation in canopy change analysis is crucial for effective forest monitoring, providing timely information for law enforcement proposals and involving forest managers, stakeholders, and watchdog organizations.展开更多
基金The authors would like to thank the SKIMS(Sher-i-Kashmir Institute of Medical Sciences)for permitting us to collect the COVID-19 data from various departments.
文摘The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID-19 since its outbreak.The Internet of Things(IoT)along with other technologies like Machine Learning can revolutionize the traditional healthcare system.Instead of reactive healthcare systems,IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services.In this study,a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection.The proposed framework is based on edge computing to provide personalized healthcare facilities with minimal latency,short response time,and optimal energy consumption.In this paper,the COVID-19 primary novel dataset has been used for experimental purposes employing various classification-based machine learning models.The proposed models were validated using kcross-validation to ensure the consistency of models.Based on the experimental results,our proposed models have recorded good accuracies with highest of 97.767%by Support Vector Machine.According to the findings of experiments,the proposed conceptual model will aid in the early detection and prediction of COVID-19 suspects,as well as continuous monitoring of the patient in order to provide emergency care in case of medical volatile situation.
文摘This research explores the increasing importance of Artificial Intelligence(AI)and Machine Learning(ML)with relation to smart cities.It discusses the AI and ML’s ability to revolutionize various aspects of urban environments,including infrastructure,governance,public safety,and sustainability.The research presents the definition and characteristics of smart cities,highlighting the key components and technologies driving initiatives for smart cities.The methodology employed in this study involved a comprehensive review of relevant literature,research papers,and reports on the subject of AI and ML in smart cities.Various sources were consulted to gather information on the integration of AI and ML technologies in various aspects of smart cities,including infrastructure optimization,public safety enhancement,and citizen services improvement.The findings suggest that AI and ML technologies enable data-driven decision-making,predictive analytics,and optimization in smart city development.They are vital to the development of transport infrastructure,optimizing energy distribution,improving public safety,streamlining governance,and transforming healthcare services.However,ethical and privacy considerations,as well as technical challenges,need to be solved to guarantee the ethical and responsible usage of AI and ML in smart cities.The study concludes by discussing the challenges and future directions of AI and ML in shaping urban environments,highlighting the importance of collaborative efforts and responsible implementation.The findings highlight the transformative potential of AI and ML in optimizing resource utilization,enhancing citizen services,and creating more sustainable and resilient smart cities.Future studies should concentrate on addressing technical limitations,creating robust policy frameworks,and fostering fairness,accountability,and openness in the use of AI and ML technologies in smart cities.
文摘This study utilizes ML classifiers to estimate canopy density based on three decades of data (1990-2021). The Support Vector Machine (SVM) classifier outperformed other classifiers, such as Random Tree and Maximum Likelihood. Satellite data from Landsat and Sentinel 2 was classified using a developed python model, providing an economical and time-saving approach. The accuracy of the classification was evaluated through a confusion matrix and area computation. The findings indicate a negative trend in the overall decadal change, with significant tree loss attributed to jhum cultivation, mining, and quarry activities. However, positive changes were observed in recent years due to the ban on illegal mining. The study highlights the dynamic nature of tree cover and emphasizes the need for biennial assessments using at least five time-series data. Micro-level analysis in Shallang, West Khasi hills, revealed a concerning trend of shortening jhum cycles. Automation in canopy change analysis is crucial for effective forest monitoring, providing timely information for law enforcement proposals and involving forest managers, stakeholders, and watchdog organizations.
