COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.
Pervasiveness rise of smart devices and sensor-based gadgets in building IoT systems is increasing unprecedented in technological innovations. The emergence of the Internet of Things technologies reshaped nearly every sector including agriculture. The agriculture sector contributes a significant figure to the country’s economy and it has a wide-ranging involvement in the advancement of human civilization. In the current scenario, the proper techniques of farming are needed as utmost propriety for better crop quality and quantity in a high-competition market. Crop disease prediction is key to shattering the problems of the farmer, reducing the usage of insecticides, and pesticides, and improving the financial conditions of the farmer. The Internet of Things and data analytics possess the ability to positively modernize the agricultural sector. However, Internet of Things-based applications needed to be deployed on a platform that offers real-time experience, reduced latency, and optimal bandwidth usage. Fog computing extends the computational power closer to the edge network where the devices reside and facilitate edge intelligence. In this paper, the fog importance, fog computing architecture along with the perceptions of data analysis in a fog environment, and emerging research challenges are discussed. Furthermore, in this paper an IoT-Fog based framework for the prediction of crop disease is proposed. The proposed framework consists of four phases, sensor layer, fog layer, cloud layer and End-user. The proposed framework may be beneficial in the farming domain for the analysis of crop disease prediction in the early stage and may reduce the chances of disease outbreaks in the field
Among various diseases one of the hazardous diseases that takes uncountable lives every year is heart disease. In todays world, it is believed that one person dies from heart disease every minute. By WHO report number of deaths that take place due to heart disease is 1.75 million, with the number expected to rise to 75 million by 2030. As a result, an accurate and fast heart disease prognosis is a critical necessity for lowering heart disease-related death rates. The Angiography is the easiest and effective clinical test but it has drawback of expensive in cost and many side effects. So, in order to deal with this difficult issue, a significant role in various disease prognosis is shown by ICT. Machine learning is an emerging technology that performs automation from previous available data and shows promising results, it is a subset of artificial intelligence. In this work different literatures related to heart disease and machine learning techniques used in those literatures are critically analyzed and a review of them is summarized in a systematic manner.
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