Advanced machine intelligence and signal processing / Deepak Gupta, Koj Sambyo, Mukesh Prasad, Sonali Agarwal, editors.
2022
Q325.5
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Details
Title
Advanced machine intelligence and signal processing / Deepak Gupta, Koj Sambyo, Mukesh Prasad, Sonali Agarwal, editors.
ISBN
9789811908408 (electronic bk.)
9811908400 (electronic bk.)
9789811908392
9811908397
9811908400 (electronic bk.)
9789811908392
9811908397
Published
Singapore : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource (xiv, 876 pages) : illustrations (chiefly color).
Item Number
10.1007/978-981-19-0840-8 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing, and their applications in real world. The topics covered in machine learning involve feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modeling from video, 3D object recognition, localization and tracking, medical image analysis, and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multitask, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), and electromyogram (EMG).
Note
Selected papers from the 3rd International Conference on Machine Intelligence and Signal Processing (MISP-2021), held September 23-25 2021, National Institute of Technology Arunachal Pradesh, Jote, India.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed June 29, 2022).
Added Author
Gupta, Deepak, editor.
Sambyo, Koj, editor.
Prasad, Mukesh, editor.
Agarwal, Sonali, editor.
Sambyo, Koj, editor.
Prasad, Mukesh, editor.
Agarwal, Sonali, editor.
Added Meeting Name
International Conference on Machine Intelligence and Signal Processing (3rd : 2021 : Jote, India)/
Series
Lecture notes in electrical engineering ; v. 858. 1876-1119
Available in Other Form
Print version: 9789811908392
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Table of Contents
Leukocyte Subtyping using Convolutional Neural Networks for Enhanced Disease Prediction
Comparative analysis of novel approaches to automated COVID-19 detection using radiography images
OXGBoost: An Optimized eXtreme Gradient Boosting Algorithm for Classification of Breast Cancer
An Empirical Study on Graph-based Clustering Algorithms using Schizophrenia Genes
Traffic Rule Violation Detection System: Deep Learning Approach
A Web Application for Early Prediction of Diabetes Using Artificial Neural Network
Web based disease prediction system via machine learning approach.
Comparative analysis of novel approaches to automated COVID-19 detection using radiography images
OXGBoost: An Optimized eXtreme Gradient Boosting Algorithm for Classification of Breast Cancer
An Empirical Study on Graph-based Clustering Algorithms using Schizophrenia Genes
Traffic Rule Violation Detection System: Deep Learning Approach
A Web Application for Early Prediction of Diabetes Using Artificial Neural Network
Web based disease prediction system via machine learning approach.