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Machine intelligence and signal analysis

Tanveer, M.

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Machine intelligence and signal analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine intelligence and signal analysis/ edited by M. Tanveer, Ram Bilas Pachori.
其他作者:	Tanveer, M.
出版者:	Singapore :Springer Singapore : : 2019.,
面頁冊數:	xx, 767 p. :ill., digital ;24 cm.
內容註:	Chapter 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.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-981-13-0923-6
ISBN:	9789811309236

Machine intelligence and signal analysis
Machine intelligence and signal analysis[electronic resource] /edited by M. Tanveer, Ram Bilas Pachori. - Singapore :Springer Singapore :2019. - xx, 767 p. :ill., digital ;24 cm. - Advances in intelligent systems and computing,v.7482194-5357 ;. - Advances in intelligent systems and computing ;v.748..

Chapter 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.

The 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.

ISBN: 9789811309236

Standard No.: 10.1007/978-981-13-0923-6doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .M334 2019

Dewey Class. No.: 006.31

Machine intelligence and signal analysis
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520 $a The 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.
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