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001			395237
003			ES-MaUEC
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008			220625s2022 si | s |||| 0|eng d
020			_a9789811908408
024	7		_a10.1007/978-981-19-0840-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
050		4	_aQ325.5 _b2022 EB
245	1	0	_aAdvanced Machine Intelligence and Signal Processing _cedited by Deepak Gupta, Koj Sambyo, Mukesh Prasad, Sonali Agarwal.
250			_a1st edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIV, 876 páginas) _b404 ilustraciones, 324 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aLecture Notes in Electrical Engineering _x1876-1119 _v858
505	0		_aLeukocyte 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.
520			_aThis 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).
776	0	8	_iPrinted edition: _z9789811908392
776	0	8	_iPrinted edition: _z9789811908415
776	0	8	_iPrinted edition: _z9789811908422
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-0840-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Robotics_2022
999			_c395237 _d395237