Biosignal Processing And Classification Using Computational Learning And Intelligence
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Biosignal Processing and Classification Using Computational Learning and Intelligence
Author: Alejandro A. Torres-García
language: en
Publisher: Academic Press
Release Date: 2021-09-18
Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms and Applications posits an approach for biosignal processing and classification using computational learning and intelligence, highlighting that the term biosignal refers to all kinds of signals that can be continuously measured and monitored in living beings. The book is composed of five relevant parts. Part One is an introduction to biosignals and Part Two describes the relevant techniques for biosignal processing, feature extraction and feature selection/dimensionality reduction. Part Three presents the fundamentals of computational learning (machine learning). Then, the main techniques of computational intelligence are described in Part Four. The authors focus primarily on the explanation of the most used methods in the last part of this book, which is the most extensive portion of the book. This part consists of a recapitulation of the newest applications and reviews in which these techniques have been successfully applied to the biosignals' domain, including EEG-based Brain-Computer Interfaces (BCI) focused on P300 and Imagined Speech, emotion recognition from voice and video, leukemia recognition, infant cry recognition, EEGbased ADHD identification among others. - Provides coverage of the fundamentals of signal processing, including sensing the heart, sending the brain, sensing human acoustic, and sensing other organs - Includes coverage biosignal pre-processing techniques such as filtering, artifiact removal, and feature extraction techniques such as Fourier transform, wavelet transform, and MFCC - Covers the latest techniques in machine learning and computational intelligence, including Supervised Learning, common classifiers, feature selection, dimensionality reduction, fuzzy logic, neural networks, Deep Learning, bio-inspired algorithms, and Hybrid Systems - Written by engineers to help engineers, computer scientists, researchers, and clinicians understand the technology and applications of computational learning to biosignal processing
Intelligent Systems and Machine Learning
Author: Sachi Nandan Mohanty
language: en
Publisher: Springer Nature
Release Date: 2023-07-09
This two-volume set constitutes the refereed proceedings of the First EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022, held in Hyderabad, India, in December 16-17,2022. The 75 full papers presented were carefully reviewed and selected from 209 submissions. The conference focuses on Intelligent Systems and Machine Learning Applications in Health care; Digital Forensic & Network Security; Intelligent Communication Wireless Networks; Internet of Things (IoT) Applications; Social Informatics; and Emerging Applications.
Computational Virology
Author: Vijaykumar Yogesh Muley
language: en
Publisher: Springer Nature
Release Date: 2025-06-02
This volume explores computational methods for the rapid analysis of viral infections and strategies for their mitigation, which have significantly advanced the understanding of viral pathogenesis and host responses. Beginning with methods for identifying viral genomes from metagenomic sequencing data, the book progresses to topics such as next-generation sequencing to study host responses against viral infections, virus-host protein interactions to identify therapeutic targets, viral taxonomy, zoonotic transmission, reverse zoonosis, and antivirals, including their mechanisms of action, focusing on virus entry and life cycle. Practical workflows for identifying potential drug-like compounds from resources such as PubChem are also covered. Written for the highly successful Methods in Molecular Biology series, chapters include detailed implementation advice to ensure successful experimental results. Authoritative and practical, Computational Virology provides researchers, students, and professionals in virology, bioinformatics, artificial intelligence, and systems biology with critical insights into the challenges posed by viral pathogens.