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 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
偏振调制激光测距方法是一种利用激光的偏振特性测量目标物体距离的高精度、高分辨率测距技术。但是,由于多种干扰因素的存在,例如多次反射、散射和杂散光,其测量精度和可靠性存在限制。提出了一种基于移动最小二乘(moving least square...偏振调制激光测距方法是一种利用激光的偏振特性测量目标物体距离的高精度、高分辨率测距技术。但是,由于多种干扰因素的存在,例如多次反射、散射和杂散光,其测量精度和可靠性存在限制。提出了一种基于移动最小二乘(moving least square,MLS)算法的偏振调制激光测距方法,可以将离散点云数据转换为连续的曲面,并实现数据的平滑和去噪,从而提高了测量精度和可靠性。实验结果表明,该方法在测距精度和抗干扰性方面具有优异的性能,可以满足实际应用的要求。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 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.
文摘偏振调制激光测距方法是一种利用激光的偏振特性测量目标物体距离的高精度、高分辨率测距技术。但是,由于多种干扰因素的存在,例如多次反射、散射和杂散光,其测量精度和可靠性存在限制。提出了一种基于移动最小二乘(moving least square,MLS)算法的偏振调制激光测距方法,可以将离散点云数据转换为连续的曲面,并实现数据的平滑和去噪,从而提高了测量精度和可靠性。实验结果表明,该方法在测距精度和抗干扰性方面具有优异的性能,可以满足实际应用的要求。
