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Amit Chhabra
Researcher at Guru Nanak Dev University
Publications - 49
Citations - 669
Amit Chhabra is an academic researcher from Guru Nanak Dev University. The author has contributed to research in topics: Computer science & Scheduling (computing). The author has an hindex of 10, co-authored 33 publications receiving 436 citations.
Papers
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Journal ArticleDOI
Improved J48 Classification Algorithm for the Prediction of Diabetes
Gaganjot Kaur,Amit Chhabra +1 more
TL;DR: The modified J48 classifier is used to increase the accuracy rate of the data mining procedure and Experimental results showed a significant improvement over the existing J-48 algorithm.
Proceedings ArticleDOI
High Availability of Clouds: Failover Strategies for Cloud Computing Using Integrated Checkpointing Algorithms
TL;DR: The purposed fail over strategy will work on application layer and provide highly availability for Platform as a Service (PaaS) feature of cloud computing.
Book ChapterDOI
Weight Optimization in Artificial Neural Network Training by Improved Monarch Butterfly Algorithm
TL;DR: An improved version of swarm intelligence and monarch butterfly optimization algorithm for training the feed-forward artificial neural network is devised and outperforms other state-of-the-art algorithms that are shown in the recent outstanding computer science literature.
Journal ArticleDOI
Novel Improved Salp Swarm Algorithm: An Application for Feature Selection
Miodrag Zivkovic,Catalin Stoean,Amit Chhabra,Nebojsa Budimirovic,Aleksandar Petrovic,Nebojsa Bacanin +5 more
TL;DR: A modified version of the salp swarm algorithm for feature selection is proposed and the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution.
Journal ArticleDOI
Feature Selection by Hybrid Brain Storm Optimization Algorithm for COVID-19 Classification
TL;DR: A binary hybrid metaheuristic-based algorithm for selecting the optimal feature subset that efficiently reduces and selects the feature subset and at the same time results in higher classification accuracy than other methods in the literature.