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Background: Preventing adverse drug reactions (ADRs) is imperative for the safety of the people. The problem of under-reporting the ADRs has been prevalent across the world, making it difficult to develop the prediction models, which are unbiased. As a result, most of the models are skewed to the negative samples leading to high accuracy but poor performance in other metrics such as precision, recall, F1 score, and AUROC score. Objective: In this work, we have proposed a novel way of predicting the ADRs by balancing the dataset. Method: The whole data set has been partitioned into balanced smaller data sets. SVMs with optimal kernel have been learned using each of the balanced data sets and the prediction of given ADR for the given drug has been obtained by voting from the ensembled optimal SVMs learned. Results: We have found that results are encouraging and comparable with the competing methods in the literature and obtained the average sensitivity of 0.97 for all the ADRs. The model has been interpreted and explained with SHAP values by various plots.

Determination of quality of an image is a very challenging task and is very important for modern image processing applications. One of the most common distortions in images is blurring. For a human visual system excessive blurring in an image is not visually pleasing and creates difficulty in identifying objects. In this paper we propose a quality measure which is calculated in spatial domain to determine the quality of blurred images. 

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