The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model's feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality,

The uncommon illness known as monkeypox is brought on by the monkeypox virus. The Orthopoxvirus genus belongs to the family Poxviridae, which also contains the monkeypox virus. The variola virus, which causes smallpox; the vaccinia virus, which is used in the smallpox vaccine; and the cowpox virus are all members of the Orthopoxvirus genus. There is no relationship between chickenpox and monkeypox. After two outbreaks of a disorder resembling pox, monkeypox was first discovered in colonies of monkeys kept for research in 1958. The illness, also known as “monkeypox”, still has no known cause. However, non-human primates and African rodents can spread the disease to humans (such as monkeys). In 1970, a human was exposed to monkeypox for the first time. Several additional nations in central and western Africa currently have documented cases of monkeypox. Before the 2022 outbreak, almost all instances of monkeypox in people outside of Africa were connected to either imported animals or foreign travel to nations where the illness frequently occurs. In this work, the most recent monkeypox dataset was evaluated and the significant instances were visualized. Additionally, nine different forecasting models were also used, and the prophet model emerged as the most reliable one when compared with all nine models with an MSE value of 41,922.55, an R2 score of 0.49, a MAPE value of 16.82, an MAE value of 146.29, and an RMSE value of 204.75, which could be considerable assistance to clinicians treating monkeypox patients and government agencies monitoring the origination and current state of the disease.

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Humans experience a variety of emotions throughout the course of their daily lives, including happiness, sadness, and rage. As a result, an effective emotion identification system is essential for electroencephalography (EEG) data to accurately reflect emotion in real-time. Although recent studies on this problem can provide acceptable performance measures, it is still not adequate for the implementation of a complete emotion recognition system. In this research work, we propose a new approach for an emotion recognition system, using multichannel EEG calculation with our developed entropy known as multivariate multiscale modified-distribution entropy (MM-mDistEn) which is combined with a model based on an artificial neural network (ANN) to attain a better outcome over existing methods. The proposed system has been tested with two different datasets and achieved better accuracy than existing methods. For the GAMEEMO dataset, we achieved an average accuracy ± standard deviation of 95.73% ± 0.67 for valence and 96.78% ± 0.25 for arousal. Moreover, the average accuracy percentage for the DEAP dataset reached 92.57% ± 1.51 in valence and 80.23% ± 1.83 in arousal.