Thampi / Piramuthu / Li Machine Learning and Metaheuristics Algorithms, and Applications

Second Symposium, SoMMA 2020, Chennai, India, October 14–17, 2020, Revised Selected Papers
1. Auflage 2021
ISBN: 978-981-16-0419-5
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 1366, 247 Seiten, eBook

Reihe: Communications in Computer and Information Science

This book constitutes the refereed proceedings of the Second Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, SoMMA 2020, held in Chennai, India, in October 2020. Due to the COVID-19 pandemic the conference was held online. 

The 12 full papers and 7 short papers presented in this volume were thoroughly reviewed and selected from 40 qualified submissions. The papers cover such topics as machine learning, artificial intelligence, Internet of Things, modeling and simulation, disctibuted computing methodologies, computer graphics, etc.

Zielgruppe

Research

Autoren/Hrsg.

Thampi, Sabu M.

Piramuthu, Selwyn

Li, Kuan-Ching

Berretti, Stefano

Wozniak, Michal

Singh, Dhananjay

Inhaltsverzeichnis

Learning 3DMM Deformation Coefficients for Action Unit Detection.- Smart Security and Surveillance System in Laboratories Using Machine Learning.- Deep Neural Networks with Multi-Class SVM for Recognition of Cross-Spectral Iris Images.- Gaze Fusion-Deep Neural Network Model for Glaucoma Detection.- Emotion Recognition from Facial Expressions Using Siamese Network.- Activity Modeling of Individuals in Domestic Households Using Fuzzy Logic.- Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models.- An Improved Salp Swarm Algorithm Based on Adaptive β-Hill Climbing for Stock Market Prediction.- Data Driven Methods for Finding Pattern Anomalies in Food Safety.- Exam Seating Allocation to Prevent Malpractice Using Genetic Multi-Optimization Algorithm.- Big Data: Does BIG Matter for Your Business?.- Modelling Energy Consumption of Domestic Households via Supervised and Unsupervised Learning: A Case Study.- Machine Learning and Soft Computing Techniques for Combustion System Diagnostics and Monitoring: A Survey.- Traffic Sign Classification Using ODENet.- Analysis of UNSW-NB15 Dataset Using Machine Learning Classifiers.- Concept Drift Detection in Phishing Using Autoencoders.- Detection of Obfuscated Mobile Malware with Machine Learning and Deep Learning Models.- CybSecMLC: A Comparative Analysis on Cyber Security Intrusion Detection Using Machine Learning Classifiers.