Machine Learning and Metaheuristics Algorithms, and Applications
Second Symposium, SoMMA 2020, Chennai, India, October 14–17, 2020, Revised Selected Papers
Series: Communications in Computer and Information Science; 1366;
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Product details:
- Edition number 1st ed. 2021
- Publisher Springer Nature Singapore
- Date of Publication 6 February 2021
- Number of Volumes 1 pieces, Book
- ISBN 9789811604188
- Binding Paperback
- No. of pages247 pages
- Size 235x155 mm
- Weight 454 g
- Language English
- Illustrations X, 247 p. 23 illus. Illustrations, black & white 135
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Long description:
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.
Table of Contents:
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 Î2-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.
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