Soft Computing for Problem Solving: Proceedings of the SocProS 2022

This book provides an insight into the 11th International Conference on Soft Computing for Problem Solving (SocProS 2022). This international conference is a joint technical collaboration of the Soft Computing Research Society and the Indian Institute of Technology Mandi. This book presents the latest achievements and innovations in the interdisciplinary areas of Soft Computing, Machine Learning, and Data Science. It brings together the researchers, engineers, and practitioners to discuss thought-provoking developments and challenges, in order to select potential future directions. It covers original research papers in the areas including but not limited to algorithms (artificial neural network, deep learning, statistical methods, genetic algorithm, and particle swarm optimization) and applications (data mining and clustering, computer vision, medical and healthcare, finance, data envelopment analysis, business, and forecasting applications). This book is beneficial for young as well as experienced researchers dealing across complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.

Table of Contents:
Chapter 1. Benchmarking state-of-the-art methodologies for optic disc segmentation.- Chapter 2. Automated Student Emotion Analysis During Online Classes using Convolutional Neural Network.- Chapter 3. Transfer Learning based Malware Classification.- Chapter 4. A Study on Metric-Based and Initialization-Based Methods for Few-Shot Image Classification.- Chapter 5. A Fast and Efficient Methods for Eye pre-processing and DR Level Detection.- Chapter 6. A deep neural model CNN-LSTM network for automated sleep staging based on a single-channel EEG signal.- Chapter 7. An Ensemble Model for Gait Classification in Children and Adolescent with Cerebral Palsy: A Low-Cost Approach.- Chapter 8. Imbalanced Learning of Regular Grammar for DFA Extraction from LSTM Architecture.- Chapter 9. Medical Prescription Label Reading using Computer Vision and Deep Learning.- Chapter 10. Autoencoder-based Deep Neural Architecture for Epileptic Seizures Classification.- Chapter 11. Stock Market Prediction using Deep Learning Techniques for Short and Long Horizon.- Chapter 12. Improved CNN Model for Breast Cancer Classification.- Chapter 13. Performance Assessment of Normalization in CNN with Retinal Image Segmentation.- Chapter 14. A novel multi-day ahead index price forecast using multi-output based deep learning system.- Chapter 15. Automatic Retinal Vessel Segmentation using BTLBO. etc.