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Modeling and optimization of signals...

Singh, Chandra.

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Modeling and optimization of signals using machine learning techniques

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling and optimization of signals using machine learning techniques/ edited by Chandra Singh ... [et al.]
其他作者:	Singh, Chandra.
出版者:	Hoboken, NJ :John Wiley & Sons ; : 2024.,
面頁冊數:	1 online resource (419 p.)
內容註:	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 > -- Inclusion Criteria -- 2.4.3.2 B > -- 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.
內容註:	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.
內容註:	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.
內容註:	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.
內容註:	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).
內容註:	12.4.3 MLX90614 IR (Infrared) Sensor for Temperature Measurement.
標題:	Signal processing - Mathematical models. -
電子資源:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119847717
ISBN:	9781119847717

Modeling and optimization of signals using machine learning techniques
Modeling and optimization of signals using machine learning techniques[electronic resource] /edited by Chandra Singh ... [et al.] - 1st ed. - Hoboken, NJ :John Wiley & Sons ;2024. - 1 online resource (419 p.)

Includes bibliographical references and index.

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 > -- Inclusion Criteria -- 2.4.3.2 B > -- 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.

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.

ISBN: 9781119847717Subjects--Topical Terms:

678506
Signal processing
--Mathematical models.

LC Class. No.: Q325.5 / .M634 2024

Dewey Class. No.: 006.31

Modeling and optimization of signals using machine learning techniques
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245 0 0 $a Modeling and optimization of signals using machine learning techniques $h [electronic resource] / $c edited by Chandra Singh ... [et al.]
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260 $a Hoboken, NJ : $b John Wiley & Sons ; $a Beverly, MA : $b Scrivener, $c 2024.
300 $a 1 online resource (419 p.)
504 $a Includes bibliographical references and index.
505 0 $a 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 > -- Inclusion Criteria -- 2.4.3.2 B > -- 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 $a 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 $a 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 $a 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 $a 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).
505 8 $a 12.4.3 MLX90614 IR (Infrared) Sensor for Temperature Measurement.
520 $a 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 $a Description based on publisher supplied metadata and other sources.
650 0 $a Signal processing $x Mathematical models. $3 678506
650 0 $a Machine learning. $3 533906
700 1 $a Singh, Chandra. $3 3675653
856 4 0 $u https://onlinelibrary.wiley.com/doi/book/10.1002/9781119847717