Modeling and Optimization of Signals Using Machine Learning Techniques

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 f...

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Detalles Bibliográficos
Otros Autores:	Singh, Chandra, editor (editor)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Hoboken, NJ : John Wiley & Sons, Inc [2024]
Edición:	First edition
Materias:	Signal processing > Mathematical models. Machine learning.
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009852334606719

Tabla de Contenidos:

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.
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.
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.
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.
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).
12.4.3 MLX90614 IR (Infrared) Sensor for Temperature Measurement.

Modeling and Optimization of Signals Using Machine Learning Techniques

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