Titlebook: Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication; Proceedings of MDCWC E. S. Gopi Conference proce

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LSTM Network for Hotspot Prediction in?Traffic Density of Cellular Network log likelihood ratio (LLR) method and cumulative distribution function (CDF) method to compute the hotspot parameters. On comparing the performances of the two methods, it can be concluded that the CDF method is more efficient and less computationally complex than the LLR method.

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Auto-encoder—LSTM-Based Outlier Detection Method for WSNslus discriminative feature representations, and reconstruction error of among input–output of smooth auto-encoder is utilized as an activation signal for outlier detection. Moreover, we employed LSTM-bidirectional RNN for maturity voting for collective outlier detection.

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Energy-Efficient Neighbor Discovery Using Bacterial Foraging Optimization (BFO) Algorithm for Directate active nodes with higher energy levels are selected from the neighbors during data transmission. The obtained results have shown that the recommended model minimizes power conservation and delay and enhances the lifetime of time of network activity.

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1876-1100 l Institute of Technology Tiruchirappalli, India.Serves as aThis book is a collection of best selected research papers presented at the Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2020) held during October 22nd to 24th 2020, at the D

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Dimensionality Reduction of KDD-99 Using Self-perpetuating Algorithmlarge. In this paper, a self-perpetuating algorithm on the individually analyzed feature selection techniques is proposed. The proposed algorithm came up with reduced feature subset of up to 14 features with reduced time, increased accuracy by 0.369%, and number of features decreased by 66.66% with J48 algorithm.

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