Overall lockdown limitations toward the start of the year 2020 are credited to the annihilation and fatalities worldwide because of COVID-19. Most of the nations revealed rapid growth of COVID-19 cases and subsequently declared lockdown in several stages. Because of these lockdowns, industries had to stop producing goods other than the actual merchandise needed to survive. The air quality and natural water quality witnessed a noticeable improvement from limited human activity. This paper presents an investigation demonstrating this improvement under various lockdown periods, specifically for the Indian subcontinent. The rivers and atmosphere of Indian settings have been utilized here as a contextual analysis associated with industrial pollution. This work aims to study the associations and interrelationships between lockdowns during COVID-19 and their effect on air and water quality. The paper presents then and now an analysis of the Indian atmosphere based on various particulate matters and river health based on the biological oxygen demand, chemical oxygen demand, and dissolved oxygen. The study indicated a significant dip in air and water pollution levels and a significant improvement in the atmosphere and rivers’ quality during this period. Significant water bodies witnessed the pH level of 7.5 amidst lockdown, which is a good indicator of improved water health since the pH level of drinkable water is 7. The analysis carried out in this paper can also be mapped to other countries and landscapes of the world.
In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
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