Modeling and optimization of signals using machine learning techniques / (Record no. 96087)

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control field 14166966
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260711110753.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION
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fixed length control field 240829t20242024nju o 000 0 eng d
015 ## - NATIONAL BIBLIOGRAPHY NUMBER
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Record control number 300303937
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119847687
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119847717
Qualifying information electronic book
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International Standard Book Number 1119847710
Qualifying information electronic book
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119847699
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119847694
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Cancelled/invalid ISBN 9781119847687
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 1119847680
Qualifying information hardcover
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code CIPO000134689
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1453337992
035 #9 - SYSTEM CONTROL NUMBER
System control number (OCLCCM-Owned)1453337992
037 ## - SOURCE OF ACQUISITION
Stock number 9781119847687
Source of stock number/acquisition O'Reilly Media
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
049 ## - LOCAL HOLDINGS (OCLC)
Holding library MAIN
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TK5102.9
Item number .M63 2024
245 00 - TITLE STATEMENT
Title Modeling and optimization of signals using machine learning techniques /
Statement of responsibility, etc edited by Chandra Singh... [and 5 others].
264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Hoboken, NJ :
Name of publisher, distributor, etc John Wiley & Sons, Inc. ;
Place of publication, distribution, etc Beverly, MA :
Name of publisher, distributor, etc Scrivener Publishing LLC,
Date of publication, distribution, etc 2024.
264 #4 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Date of publication, distribution, etc ©2024
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
505 0# - CONTENTS
Formatted contents note Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Land Use and Land Cover Mapping of Remotely Sensed Data Using Fuzzy Set Theory-Related Algorithm -- 1.1 Introduction -- 1.1.1 Overview on Landsat 8 -- 1.2 Image Classification -- 1.3 Unsupervised Classification -- 1.4 Supervised Classification -- 1.5 Overview of Fuzzy Sets -- 1.5.1 Fuzzy C-Means Clustering -- 1.5.2 Algorithm of Fuzzy C-Means -- 1.6 Methodology -- 1.6.1 Modified Fuzzy C-Means Technique -- 1.6.2 Construction of a Fuzzy Inference System -- 1.6.3 K-Means Algorithm -- 1.7 Results and Discussion -- 1.7.1 FCM Technique Results -- 1.7.2 Modified FCM Technique Results -- 1.7.3 K-Means Technique Results -- 1.8 Conclusion -- References -- Chapter 2 Role of AI in Mortality Prediction in Intensive Care Unit Patients -- 2.1 Introduction -- 2.2 Background -- 2.3 Objectives -- 2.4 Machine Learning and Mortality Prediction -- 2.4.1 Model Selection -- 2.4.2 Mortality Prediction for ICU Patients -- 2.4.3 Datasets Generation and Preprocessing -- 2.4.3.1 A &gt -- Inclusion Criteria -- 2.4.3.2 B &gt -- Exclusion Criteria -- 2.4.4 Structure of Datasets -- 2.5 Discussions -- 2.6 Conclusion -- 2.7 Future Work -- 2.8 Acknowledgments -- 2.9 Funding -- 2.10 Competing Interest -- References -- Chapter 3 A Survey on Malware Detection Using Machine Learning -- 3.1 Background -- 3.2 Introduction -- 3.3 Literature Survey -- 3.4 Discussion -- 3.5 Conclusion -- References -- Chapter 4 EEG Data Analysis for IQ Test Using Machine Learning Approaches: A Survey -- Introduction -- 4.1 Related Work -- 4.1.1 Signal Pre-Processing, Filtering, and Feature Extraction -- 4.2 Equations -- 4.2.1 Alternating a Diffusion Map-Based Combination of Two FCN Datasets -- 4.2.2 Information Examination -- 4.2.3 Gaussian Kernel Function -- 4.3 Classification -- 4.4 Data Set -- 4.4.1 Pre-Preparing.
505 8# - CONTENTS
Formatted contents note 4.4.2 EEG Data Producer -- 4.5 Information Obtained by EEG Signals -- 4.5.1 System Structure -- 4.5.2 Numerical Examination -- 4.5.3 EEG Circumference -- 4.6 Discussion -- 4.6.1 Comparison Between IQ Levels With Different Methods -- 4.7 Conclusion -- References -- Chapter 5 Machine Learning Methods in Radio Frequency and Microwave Domain -- 5.1 Introduction -- 5.2 Background on Machine Learning -- 5.2.1 Clustering -- 5.2.2 Principal Component Analysis -- 5.2.3 Naïve Bayes Algorithms -- 5.2.4 Support Vector Machines -- 5.2.5 Artificial Neural Networks -- 5.3 ML in RF Circuit Modeling and Synthesis -- 5.4 Conclusion -- References -- Chapter 6 A Survey: Emotion Detection Using Facial Reorganization Using Convolutional Neural Network (CNN) and Viola-Jones Algorithm -- 6.1 Introduction -- 6.1.1 Purpose -- 6.1.2 Process Flow -- 6.2 Review of Literature -- 6.3 Report on Present Investigation -- 6.3.1 Analysis of the Model -- 6.3.1.1 Emotion Recognition -- 6.4 Algorithms -- 6.4.1 CNN -- 6.4.2 Advantages -- 6.4.3 Disadvantages -- 6.5 Viola-Jones Algorithm -- 6.5.1 Training -- 6.5.2 Detection -- 6.6 Diagram -- 6.6.1 Working Diagram for Systems -- 6.6.2 The Application's Use Case Diagram -- 6.7 Results and Discussion -- 6.8 Limitations and Future Scope -- 6.9 Summary and Conclusion -- References -- Chapter 7 Power Quality Events Classification Using Digital Signal Processing and Machine Learning Techniques -- 7.1 Introduction -- 7.2 Methodology for the Identification of PQ Events -- 7.3 Power Quality Problems Arising in the Modern Power System -- 7.3.1 Sag -- 7.3.2 Swell -- 7.3.3 Overvoltage -- 7.3.4 Undervoltage -- 7.3.5 Impulsive Transient -- 7.3.6 Oscillatory Transient -- 7.3.7 Harmonics -- 7.4 Digital Signal Processing-Based Feature Extraction of PQ Events -- 7.4.1 Wavelet Transform-Based Feature Extraction -- 7.4.2 Multiresolution Analysis.
505 8# - CONTENTS
Formatted contents note 7.4.3 Future Generation and Extraction -- 7.4.4 Wavelet Energy -- 7.5 Feature Selection and Optimization -- 7.5.1 Genetic Algorithm -- 7.6 Machine Learning-Based Classification of PQ Disturbances -- 7.6.1 Support Vector Machine Classifier -- 7.6.2 Artificial Neural Network Classifier -- 7.6.2.1 Back-Propagation Neural Network -- 7.6.2.2 Probabilistic Neural Network -- 7.6.3 Performance Prediction of the ML Classifiers -- 7.7 Summary and Conclusion -- References -- Chapter 8 Hybridization of Artificial Neural Network with Spotted Hyena Optimization (SHO) Algorithm for Heart Disease Detection -- 8.1 Introduction -- 8.1.1 Objective of the Work -- 8.1.2 Scope of the Project -- 8.2 Literature Survey -- 8.2.1 Problem Identification -- 8.3 Proposed Methodology -- 8.3.1 Different Kinds of Machine Learning Approaches -- 8.3.1.1 Supervised Learning -- 8.3.1.2 Unsupervised Learning -- 8.3.1.3 Semi-Supervised Learning -- 8.3.1.4 Reinforcement Learning -- 8.4 Artificial Neural Network -- 8.4.1 ANN Classification -- 8.4.1.1 Input Layer -- 8.4.1.2 Hidden Layer -- 8.4.1.3 Output Layer -- 8.4.2 Spotted Hyena Optimization -- 8.4.2.1 Searching Behavior -- 8.4.2.2 Encircling Behavior -- 8.4.2.3 Hunting Behavior -- 8.4.2.4 Attacking Behavior -- 8.4.3 SHO-Based ANN -- 8.4.4 Benefits of SHO in ANN -- 8.5 Software Implementation Requirements -- 8.5.1 Results and Discussion -- 8.6 Conclusion -- References -- Chapter 9 The Role of Artificial Intelligence, Machine Learning, and Deep Learning to Combat the Socio-Economic Impact of the Global COVID-19 Pandemic -- 9.1 Introduction -- 9.2 Discussions on the Coronavirus -- 9.2.1 Coronavirus -- 9.2.2 COVID-19 -- 9.2.3 Origin of COVID-19 and Its Symptoms -- 9.2.4 Mode of Spreading -- 9.2.5 Steps Taken by the Government to Prevent the Spread of COVID-19 -- 9.3 Bad Impacts of the Coronavirus -- 9.3.1 Social Impact.
505 8# - CONTENTS
Formatted contents note 9.3.1.1 Mental Health and Psychological Impacts Due to COVID-19 -- 9.3.1.2 Impact on Internet Data Consumption Due to COVID-19 -- 9.3.1.3 Impact on Sports and Entertainment Due to COVID-19 -- 9.3.2 Economic Impact Due to COVID-19 -- 9.3.2.1 Impact on Transportation Due to COVID-19 -- 9.3.2.2 Impact on the Economy Due to COVID-19 -- 9 3.2.3 Impact on Agriculture Due to COVID-19 -- 9.4 Benefits Due to the Impact of COVID-19 -- 9.4.1 Health Benefits -- 9.4.1.1 Cleaner Air -- 9.4.1.2 Limited Smoking -- 9.4.1.3 Drinking Alcohol is Down for a Few -- 9.4.1.4 Time for Personal Healthcare -- 9.4.2 Other Benefits Due to the Lockdown -- 9.5 Role of Technology to Combat the Global Pandemic COVID-19 -- 9.5.1 Use of Different Technologies -- 9.5.1.1 Computer Vision -- 9.5.1.2 Three-Dimensional Printing -- 9.5.1.3 Vehicular Ad Hoc Network (VANET) -- 9.5.1.4 Blockchain -- 9.5.1.5 Telehealth Technology -- 9.5.2 Technological Devices -- 9.5.2.1 Drones -- 9.5.2.2 Robots -- 9.5.3 Technological Applications -- 9.5.3.1 Open-Source Technology -- 9.5.3.2 Mobile Apps -- 9.5.3.3 Video Conferencing -- 9.6 The Role of Artificial Intelligence, Machine Learning, and Deep Learning in COVID-19 -- 9.6.1 Symbolic Rule-Based Method -- 9.6.2 Probabilistic Method -- 9.6.3 Evolutionary Computation Method -- 9.6.4 Machine Learning Approach -- 9.6.5 Deep Learning Approach -- 9.7 Related Studies -- 9.8 Conclusion -- References -- Chapter 10 A Review on Smart Bin Management Systems -- 10.1 Introduction -- 10.1.1 Internet of Things (IoT) -- 10.2 Related Work -- 10.3 Challenges, Solution, and Issues -- 10.4 Advantages -- Conclusion -- References -- Chapter 11 Unlocking Machine Learning: 10 Innovative Avenues to Grasp Complex Concepts -- 11.1 Regression -- 11.1.1 General Approach -- 11.1.2 Different Regression Models -- 11.2 Classification -- 11.2.1 Definition -- 11.2.2 Example.
505 8# - CONTENTS
Formatted contents note 11.2.3 Day-to-Day Example -- 11.2.3.1 Optical Character Recognition (OCR) -- 11.2.3.2 Face Recognition -- 11.2.3.3 Recognition of Speech -- 11.2.3.4 Medical Findings -- 11.2.3.5 Extraction of Acquaintance -- 11.2.3.6 Compression -- 11.2.3.7 Additional Examples -- 11.2.4 Discriminant -- 11.2.5 Algorithms -- 11.3 Clustering -- 11.3.1 Data Examples Using Natural Clusters -- 11.4 Clustering (k-means) -- 11.4.1 Outline -- 11.4.2 Example -- 11.4.2.1 Problem -- 11.4.2.2 Solution -- 11.4.3 Some Methods for Initialization -- 11.4.4 Disadvantages -- 11.4.5 Use Case: Image Compression and Segmentation -- 11.4.5.1 Segmentation of Images -- 11.4.5.2 Compression of Data -- 11.5 Reduction of Dimensionality -- 11.5.1 Introduction -- 11.5.1.1 Feature Selection -- 11.5.1.2 Feature Extraction -- 11.5.1.3 Error Measures -- 11.5.2 Benefits of Reducing Dimensionality -- 11.5.3 Subset Selection -- 11.5.3.1 Selecting Forward -- 11.5.3.2 Remarks -- 11.5.3.3 Selection in Reverse -- 11.6 The Ensemble Method -- 11.6.1 Random Forest -- 11.6.2 Algorithm -- 11.6.3 Benefits and Drawbacks -- 11.6.3.1 Benefits -- 11.6.3.2 Drawbacks -- 11.6.4 Deep Learning and Neural Networks -- 11.6.4.1 Definition -- 11.6.4.2 Remarks -- 11.6.5 Applications -- 11.6.6 Artificial Neural Network -- 11.6.6.1 Biological Motivation -- 11.7 Transfer of Learning -- 11.8 Learning Through Reinforcement -- 11.9 Processing of Natural Languages -- 11.10 Word Embeddings -- 11.11 Conclusion -- References -- Chapter 12 Recognition Attendance System Ensuring COVID-19 Security -- 12.1 Introduction -- 12.2 Literature Survey -- 12.3 Software Requirements -- 12.3.1 Operating System -- Windows 7 and Above -- 12.3.2 IDE-Visual Studio Code -- 12.3.3 Programming Languages: Python, HTML, CSS, JS, and PHP -- 12.4 Hardware Requirements -- 12.4.1 Three Processors and Above -- 12.4.2 RAM -- 2GB (Minimum Capacity).
520 ## - SUMMARY, ETC.
Summary, etc Explore the power of machine learning to revolutionize signal processing and optimization with cutting-edge techniques and practical insights in this outstanding new volume from Scrivener Publishing. Modeling and Optimization of Signals using Machine Learning Techniques is designed for researchers from academia, industries, and R&D organizations worldwide who are passionate about advancing machine learning methods, signal processing theory, data mining, artificial intelligence, and optimization. This book addresses the role of machine learning in transforming vast signal databases from sensor networks, internet services, and communication systems into actionable decision systems. It explores the development of computational solutions and novel models to handle complex real-world signals such as speech, music, biomedical data, and multimedia. Through comprehensive coverage of cutting-edge techniques, this book equips readers with the tools to automate signal processing and analysis, ultimately enhancing the retrieval of valuable information from extensive data storage systems. By providing both theoretical insights and practical guidance, the book serves as a comprehensive resource for researchers, engineers, and practitioners aiming to harness the power of machine learning in signal processing. Whether for the veteran engineer, scientist in the lab, student, or faculty, this groundbreaking new volume is a valuable resource for researchers and other industry professionals interested in the intersection of technology and agriculture.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note Description based on online resource; title from digital title page (viewed on August 29, 2024).
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Signal processing
General subdivision Mathematical models.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
Authority record control number http://id.loc.gov/authorities/subjects/sh85079324
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Traitement du signal
General subdivision Modèles mathématiques.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Apprentissage automatique.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Information Technology.
Source of heading or term bisacsh/2023
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element COMPUTERS.
Source of heading or term bisacsh/2023
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Information Technology.
Source of heading or term bisacsh/2024
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element COMPUTERS.
Source of heading or term bisacsh/2024
655 #0 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
International Standard Book Number 1119847680
-- 9781119847687
Record control number (OCoLC)1419058383
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Uniform Resource Identifier https://onlinelibrary.wiley.com/doi/book/10.1002/9781119847717
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