國立虎尾科技大學 |

Machine intelligence and signal analysis

紀錄類型:	書目-語言資料,印刷品 : 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.;
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 ;173..

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:

561253
Machine learning.

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

Dewey Class. No.: 006.31

Machine intelligence and signal analysis
LDR:02855nam a2200337 a 4500 001 941268
003 DE-He213
005 20191230140759.0
006 m d
007 cr nn 008maaau
008 200417s2019 si s 0 eng d
020 $a 9789811309236 $q (electronic bk.)
020 $a 9789811309229 $q (paper)
024 7 $a 10.1007/978-981-13-0923-6 $2 doi
035 $a 978-981-13-0923-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .M334 2019
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M149 2019
245 0 0 $a Machine intelligence and signal analysis $h [electronic resource] / $c edited by M. Tanveer, Ram Bilas Pachori.
260 $a Singapore : $c 2019. $b Springer Singapore : $b Imprint: Springer,
300 $a xx, 767 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in intelligent systems and computing, $x 2194-5357 ; $v v.748
505 0 $a 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.
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.
650 0 $a Machine learning. $3 561253
650 0 $a Artificial intelligence. $3 559380
650 0 $a Signal processing. $3 561459
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Signal, Image and Speech Processing. $3 670837
650 2 4 $a Computer Communication Networks. $3 669310
650 2 4 $a Power Electronics, Electrical Machines and Networks. $3 768690
700 1 $a Tanveer, M. $3 1228389
700 1 $a Pachori, Ram Bilas. $3 1228390
710 2 $a SpringerLink (Online service) $3 593884
711 2 $a Workshop on the Preservation of Stability under Discretization $d (2001 : $c Fort Collins, Colo.) $3 527686
773 0 $t Springer eBooks
830 0 $a Advances in intelligent systems and computing ; $v 173. $3 884093
856 4 0 $u https://doi.org/10.1007/978-981-13-0923-6
950 $a Intelligent Technologies and Robotics (Springer-42732)