No abstract

Air quality index (AQI) is a number used by government agencies to communicate to the public how polluted the air currently. It is based on several factors like SO2, NO2, O3, RSPM/PM10, and PM2.5. Several methods were developed in the past by various researchers/environmental agencies for the determination of AQI. Still, there is no universally accepted method that exists, which is appropriate for all situations. We have developed a prediction model that is confined to standard classification or regression models. These prediction models have ignored the co-relation between sub-models in different time slots. The paper focusses on a refined model for inferring air pollutants based on historical and current meteorological datasets. Also, the model is designed to forecast AQI for the coming months, quarters or years where the emphasis is on how to improve its accuracy and performance. The algorithms are used on Air Pollution Geocodes Dataset (2016-2018), and results calculated for 196 cities of India on various classifiers. Accuracy of 94%-96% achieved from Linear Robust Regression, which increases to 97.92% after application of KNN and 97.91% after SVM and 97.47 after 5th epoch of ANN. Decision Tree Classifier has given the best accuracy of 99.7%, which increases by 0.02% on the application of the Random Forest Classifier. Forecasting achieved by Moving Average Smoothing using R-ARIMA, which offers daily values for the coming 45days or monthly data of AQI for the next year.

Drug discovery involves identifying novel drug-target (DT) interactions. Most proposed computer models for predicting drug-target interactions have emphasized binary classification, but the aim is to determine whether two drug targets interact.However, it is more practical but more challenging to anticipate the binding affinity, which evaluates the strength of a DT pair's association. The drug may not work if the binding affinity is not strong enough. Due to this reason, we need an expert system for predicting the affinity score between the drug and target protein. Advanced deep learning techniques can predict binding affinities because there are more new public affinity data in databases related to DT. This paper uses a comparative analysis of different drug and protein-encoding techniques to predict DT binding affinities based on similarities between drugs and proteins. The validation results on the standard dataset show that the proposed model is an excellent way to predict how well DT binds and can be very helpful in the process of new drugs. Hence, the model on the DAVIS dataset achieved a higher concordance index, that is, 0.897, and the lowest mean square error, that is, 0.226; for the KIBA dataset, the concordance index score achieved is 0.867, and the mean square error is 0.191. The findings are compared to baseline methods using some evaluation parameters, including the mean squared error and the concordance index.

No abstract