Water makes up about 70% of the earth’s surface and is one of the most important sources vital to sustaining life. Rapid urbanization and industrialization have led to a deterioration of water quality at an alarming rate, resulting in harrowing diseases. Water quality has been conventionally estimated through expensive and time-consuming lab and statistical analyses, which render the contemporary notion of real-time monitoring moot. The alarming consequences of poor water quality necessitate an alternative method, which is quicker and inexpensive. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality index (WQI), which is a singular index to describe the general quality of water, and the water quality class (WQC), which is a distinctive class defined on the basis of the WQI. The proposed methodology employs four input parameters, namely, temperature, turbidity, pH and total dissolved solids. Of all the employed algorithms, gradient boosting, with a learning rate of 0.1 and polynomial regression, with a degree of 2, predict the WQI most efficiently, having a mean absolute error (MAE) of 1.9642 and 2.7273, respectively. Whereas multi-layer perceptron (MLP), with a configuration of (3, 7), classifies the WQC most efficiently, with an accuracy of 0.8507. The proposed methodology achieves reasonable accuracy using a minimal number of parameters to validate the possibility of its use in real time water quality detection systems.

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Web-based question answering (QA) systems are effective in corroborating answers from multiple Web sources. However, Web also contains false, fabricated, and biased information that can have adverse effects on the accuracy of answers in Web-based QA systems. Existing, solutions focus primarily on finding relevant Web pages but either do not evaluate Web pages' credibility or evaluate two to three out of seven credibility categories. This research proposed a credibility assessment algorithm that uses seven categories, including correctness, authority, currency, professionalism, popularity, impartiality, quality, for scoring credibility, where each credibility category consists of multiple factors. The credibility assessment module is added on top of an existing QA system to score answers based on the credibility of Web pages. The system ranks answers based on the Web pages' credibility from where answers have been taken. The research conducted extensive quantitative tests on 211 factoid questions, taken from TREC QA data from 1999-2001. Our research findings show that credibility categories including correctness, professionalism, impartiality, and quality significantly improved the accuracy of answers. On the other hand, categories such as authority, currency, popularity played a minor role instead. This research hopes to allow researchers and experts in using the Web credibility assessment model to improve the accuracy of information systems. Credibility scores should assist Web users in selecting credible information, while also forcing content creators to focus more on publishing credible content.

The COVID-19 pandemic has emerged as the world's most serious health crisis, affecting millions of people all over the world. The majority of nations have imposed nationwide curfews and reduced economic activity to combat the spread of this infectious disease. Governments are monitoring the situation and making critical decisions based on the daily number of new cases and deaths reported. Therefore, this study aims to predict the daily new deaths using four tree-based ensemble models i.e., Gradient Tree Boosting (GB), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Voting Regressor (VR) for the three most affected countries, which are the United States, Brazil, and India. The results showed that VR outperformed other models in predicting daily new deaths for all three countries. The predictions of daily new deaths made using VR for Brazil and India are very close to the actual new deaths, whereas the prediction of daily new deaths for the United States still needs to be improved.<br>