An Efficient Classification of Tuberous Sclerosis Disease Using Nature Inspired PSO and ACO based Optimized Neural Network -- Mid-term Home Health Care Planning Problem with Flexible Departing Way for Caregivers -- Performance Analysis of NASNet on Unconstrained Ear Recognition -- Optimization of performance parameter for Vehicular Ad-hoc NETwork (VANET) using Swarm Intelligence -- Development of Fast and Reliable Nature-Inspired Computing for Supervised Learning in High-Dimensional Data -- Application of Genetic Algorithms for Unit Commitment and Economic Dispatch Problems in microgrids -- Application of Genetic Algorithms for Designing Micro-Hydro Power Plants in Rural Isolated Areas - a case study in San Miguelito, Honduras -- Performance Evaluation of Different Machine Learning Methods and Deep-Learning Based Convolutional Neural Network for Health Decision Making -- Clustering Bank Customer Complaints on Social Media for Analytical CRM via Multi-Objective Particle Swarm Optimization -- Benchmarking Gene Selection Techniques for Prediction of Distinct Carcinoma from Gene Expression Data: A Computational Study -- An Evolutionary Algorithm based Hybrid Parallel Framework for Asia Foreign Exchange Rate prediction.

This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.