Evolutionary Computing and Mobile Sustainable Networks [electronic resource] : Proceedings of ICECMSN 2021 / edited by V. Suma, Xavier Fernando, Ke-Lin Du, Haoxiang Wang.

Improved Grey wolf Optimization based Feature selection and classification using CNN for Diabetic Retinopathy detection -- Feature Selection Using Modified Sine Cosine Algorithm with COVID-19 Dataset -- Blood Cell Image Denoising based on Tunicate Rat Swarm Optimization with Median Filter -- A Hybrid Approach for Deep Noise Suppression using Deep Neural Networks -- Human Health Care Systems Analysis for Cloud Data Structure of Biometric System using ECG Analysis -- Data mining for Solving Medical Diagnostics Problems -- Classification of Diabetic Retinopathy using Ensemble of Machine Learning Classifiers with IDRID Dataset -- Epileptic Seizure Prediction Using Geometrical Features Extracted From HRV Signal -- An Extensive Survey on Outlier Prediction using Mining and Learning Approaches -- Performance Comparison of Data Security Strategies in Fog Computing -- Design and Simulation of a Direct-PSK Based Telecommand Receiver for Small Satellite -- Analysis of Data Aggregation and Clustering Protocol in Wireless Sensor Networks using Machine Learning -- DetecSec : A Framework to Detect and Mitigate ARP Cache Poisoning Attacks -- PAPR Reduction in SDR based OFDM System.

This book mainly reflects the recent research works in evolutionary computation technologies and mobile sustainable networks with a specific focus on computational intelligence and communication technologies that widely ranges from theoretical foundations to practical applications in enhancing the sustainability of mobile networks. Today, network sustainability has become a significant research domain in both academia and industries present across the globe. Also, the network sustainability paradigm has generated a solution for existing optimization challenges in mobile communication networks. Recently, the research advances in evolutionary computing technologies including swarm intelligence algorithms and other evolutionary algorithm paradigms are considered as the widely accepted descriptors for mobile sustainable networks virtualization, optimization, and automation. To deal with the emerging impacts on mobile communication networks, this book discusses about the state-of-the research works on developing a sustainable design and their implementation in mobile networks. With the advent of evolutionary computation algorithms, this book contributes varied research chapters to develop a new perspective on mobile sustainable networks.