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COVID - 19 affected severely worldwide. The pandemic has caused many causalities in a very short span. The IoT-cloud-based healthcare model requirement is utmost in this situation to provide a better decision in the covid-19 pandemic. In this paper, an attempt has been made to perform predictive analytics regarding the disease using a machine learning classifier. This research proposed an enhanced KNN (k NearestNeighbor) algorithm eKNN, which did not randomly choose the value of k. However, it used a mathematical function of the dataset’s sample size while determining the k value. The enhanced KNN algorithm eKNN has experimented on 7 benchmark COVID-19 datasets of different size, which has been gathered from standard data cloud of different countries (Brazil, Mexico, etc.). It appeared that the enhanced KNN classifier performs significantly better than ordinary KNN. The second research question augmented the enhanced KNN algorithm with feature selection using ACO (Ant Colony Optimization). Results indicated that the enhanced KNN classifier along with the feature selection mechanism performed way better than enhanced KNN without feature selection. This paper involves proposing an improved KNN attempting to find an optimal value of k and studying IoT-cloud-based COVID - 19 detection.

Regression testing is an expensive activity and Test Case Prioritization (TCP) acts as an improvement mechanism for it. TCP techniques for object oriented programs need attention and in our study, we explored prioritization of JUnit test cases. Ten benchmark Java programs with their several mutated versions were studied. As collecting coverage information is a costly effort, we bypassed these steps and used optimization heuristics for ordering JUnit test cases at test method level. Our approach formulated a novel fitness objective which depends on the number of modified lines executed per unit of execution time. As regression testing is performed after some modification is done on an existing program, maximizing the execution of number of modified lines is highly lucrative. The test case prioritization problem was replicated in context of 0/1 Knapsack problem and then it was solved using Genetic Algorithm (GA). Our exploration also included application of Simulated Annealing and Ant Colony Optimization method for determining the best execution ordering of test cases. We examined the usage of Multi-objective GA by building another new fitness metric which aims to maximize the number of inheritance edges covered by a test case. Results indicate the superiority of optimization heuristics over other existing approaches. It appeared that multiobjective GA yielded better result than single objective prioritization. Among the single objective techniques, ACO performed best. To the best of our knowledge, this is the first study which explored all the above mentioned optimization heuristics for ordering JUnit test cases with the newly coined fitness intents.INDEX TERMS Fault detected, fitness objective, optimization heuristics, regression testing, test method.

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