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Summary The development of a cloud‐based Parkinson's disease identification system is identified as one of the most challenging issues for predictive telediagnosis and telemonitoring in the emerging smart healthcare applications. The traditional way of Parkinson's disease identification by clinical parameters is more costly and uncomfortable for rural people to avail the testing and diagnosis from the remote place. So, the cloud‐assisted Parkinson's disease identification system (CAPDIS) is proposed based on non‐clinical parameters with patient‐centric and cost‐effective features for helping the poor patients living in rural as well as urban areas. In addition, the proposed system diagnoses the remote patient by examining the symptoms like dysphonia which is identified as the most severe neurodegenerative disorder in the world. Further, the proposed system has experimented with the benchmark voice dataset collected from the University of California‐Irvine (UCI) repository. It shows that the proposed CAPDIS system with adaptive linear kernel support vector machines (k‐SVM) classifier has significant improvements on detection accuracy, specificity, sensitivity, and Matthews's correlation coefficient scores while comparing to the existing classifiers. Therefore, the proposed adaptive linear k‐SVM classifiers provide 10% improvements on prediction accuracy and F1‐Score over the existing polynomial, radial basis, and sigmoidal‐based k‐SVM classifiers.

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Reviewing and buying the right goods from online websites is growing day by day in today's fast internet environment. Numerous goods in the same label are available to consumers. It is thus a difficult job for consumers to pick up the correct commodity at a decent price under different market conditions. Therefore, it is important for owners of online shopping websites to better understand their customers' needs and offer better services. For these reasons, the access log documented a vast amount of data related to user interactions with the websites. This access log therefore plays a key role in predicting user access trends and in recommending the best product to consumers. This research work therefore focuses on one such methodology for evaluating the pattern and behavioral analysis of users in e-commerce websites.