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Advanced machine intelligence and signal processing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced machine intelligence and signal processingedited by Deepak Gupta ... [et al.].
other author:	Gupta, Deepak.
Published:	Singapore :Springer Nature Singapore :2022.
Description:	xiv, 876 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learningCongresses.
Online resource:	https://doi.org/10.1007/978-981-19-0840-8
ISBN:	9789811908408$q(electronic bk.)

Advanced machine intelligence and signal processing
Advanced machine intelligence and signal processing[electronic resource] /edited by Deepak Gupta ... [et al.]. - Singapore :Springer Nature Singapore :2022. - xiv, 876 p. :ill. (chiefly color), digital ;24 cm. - Lecture notes in electrical engineering,v. 8581876-1119 ;. - Lecture notes in electrical engineering ;v.132..

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.

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)

ISBN: 9789811908408$q(electronic bk.)

Standard No.: 10.1007/978-981-19-0840-8doiSubjects--Topical Terms:

384498
Machine learning
--Congresses.

LC Class. No.: Q325.5 / .A38 2022

Dewey Class. No.: 006.31

Advanced machine intelligence and signal processing
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520 $a 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)
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