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020 _a9789811309236
024 7 _a10.1007/978-981-13-0923-6
_2doi
040 _bspa
_dES-MaUEC
_cES-MaUEC
050 4 _aQ325.5
_b2019 EB
245 0 0 _aMachine intelligence and signal analysis
_cedited by M. Tanveer, Ram Bilas Pachori
264 1 _aSingapore
_bSpringer Singapore :
_bImprint: Springer
_c2019
300 _a1 recurso en línea (XX, 767 páginas)
_b301 ilustraciones, 224 ilustraciones en color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
_2rda
490 0 _aAdvances in Intelligent Systems and Computing
_x2194-5357
_v748
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aChapter 1: Detecting R-peaks in Electrocardiogram signal using Hilbert envelope -- Chapter 2: Lung Nodule Identification and Classification from Distorted CT Images for Diagnosis and Detection of Lung Cancer -- Chapter 3: Baseline wander and power-line interference removal from ECG signals using Fourier decomposition method -- Chapter 4: Baseline wander and power-line interference removal from ECG signals using Fourier decomposition method -- Chapter 5: An Empirical Analysis of Instance-based Transfer Learning Approach on Protease Substrate Cleavage Sites Prediction -- Chapter 6: Comparison analysis: single and multichannel EMD based filtering with application to BCI -- Chapter 7: A 2-norm Squared Fuzzy-based Least Squares Twin Parametric-margin Support Vector Machine -- Chapter 8: Redesign of a Railway Coach for Safe and Independent Travel of Elderly.
520 3 _aThe book covers the most recent developments in machine learning, signal analysis, and their applications. It covers the topics of machine intelligence such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and electromyogram (EMG) as well as other signals such as speech signals, communication signals, vibration signals, image, and video. Further, it analyzes normal and abnormal categories of real-world signals, for example normal and epileptic EEG signals using numerous classification techniques. The book is envisioned for researchers and graduate students in Computer Science and Engineering, Electrical Engineering, Applied Mathematics, and Biomedical Signal Processing.
988 _aPrimersemestre_2019_Robotics
650 7 _2embne
_aAprendizaje automático
_9166090
650 7 _2embne
_aInteligencia artificial
_9413115
650 7 _2embne
_aProceso de señales
_9150608
700 1 _aTanveer, M.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aPachori, Ram Bilas.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
776 0 8 _iPrinted edition:
_z9789811309229
776 0 8 _iPrinted edition:
_z9789811309243
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-13-0923-6
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _aSI
_cm
_dz
_feng
_ggw
_h0
_b09/2019
_eel
_zSI