Faculty Publications
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Item A single program multiple data algorithm for feature selection(Springer Verlag service@springer.de, 2020) Chanduka, B.; Gangavarapu, T.; Jaidhar, C.D.Feature selection is a critical component in data science and has been the topic of research for many years. Advances in hardware and the availability of better multiprocessing platforms have enabled parallel computing to reach very high levels of performance. Minimum Redundancy Maximum Relevance (mRMR) is a powerful feature selection technique used in many applications. In this paper, we present a novel optimized Single Program Multiple Data (SPMD) approach to implement the mRMR algorithm with synchronous computation, optimum load balancing and greater speedup than task-parallel approaches. The experimental results presented using multiple synthesized datasets prove the efficiency and scalability of the proposed technique over original mRMR. © Springer Nature Switzerland AG 2020.Item A novel bio-inspired hybrid metaheuristic for unsolicited bulk email detection(Springer Science and Business Media Deutschland GmbH, 2020) Gangavarapu, T.; Jaidhar, C.D.With the recent influx of technology, Unsolicited Bulk Emails (UBEs) have become a potential problem, leaving computer users and organizations at the risk of brand, data, and financial loss. In this paper, we present a novel bio-inspired hybrid parallel optimization algorithm (Cuckoo-Firefly-GR), which combines Genetic Replacement (GR) of low fitness individuals with a hybrid of Cuckoo Search (CS) and Firefly (FA) optimizations. Cuckoo-Firefly-GR not only employs the random walk in CS, but also uses mechanisms in FA to generate and select fitter individuals. The content- and behavior-based features of emails used in the existing works, along with Doc2Vec features of the email body are employed to extract the syntactic and semantic information in the emails. By establishing an optimal balance between intensification and diversification, and reaching global optimization using two metaheuristics, we argue that the proposed algorithm significantly improves the performance of UBE detection, by selecting the most discriminative feature subspace. This study presents significant observations from the extensive evaluations on UBE corpora of 3, 844 emails, that underline the efficiency and superiority of our proposed Cuckoo-Firefly-GR over the base optimizations (Cuckoo-GR and Firefly-GR), dense autoencoders, recurrent neural autoencoders, and several state-of-the-art methods. Furthermore, the instructive feature subset obtained using the proposed Cuckoo-Firefly-GR, when classified using a dense neural model, achieved an accuracy of $$99\%$$. © Springer Nature Switzerland AG 2020.