Nature inspired computing for data science
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
Studies in computational intelligence, v. 871, SCI v. 871
1 online resource (xii, 295 pages) : illustrations (some color)
9783030338206, 3030338207
1129143745
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