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Modeling and Optimization of Signals Using Machine Learning Techniques

Edited by Chandra Singh, Rathishchandra R. Gatti, K.V.S.S.S.S. Sairam, Manjunatha Badiger, Naveen Kumar S., and Varun Saxena
Copyright: 2024   |   Status: Published
ISBN: 9781119847687  |  Hardcover  |  
416 pages
Price: $225 USD
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One Line Description
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.

Audience
Students, teachers, researchers, industry personnel, researchers, academicians, and other industry professionals, scientists, and engineers

Description
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.

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Author / Editor Details
Chandra Singh is an assistant professor in the Department of Electronics
and Communication Engineering at the Sahyadri College of Engineering and
Management. He is pursuing his PhD from VTU Belagavi, India. He has four
patents, published over 25 peer-reviewed publications, and is the editor
of seven books.

Rathishchandra R. Gatti, PhD, is a professor and Head of the Department of Mechanical Engineering and Robotics and Automation at the Sahyadri College of Engineering and Management, India. He has four patents, published more than 40 papers in peer-reviewed journals, and has edited seven books. He is also the editor of one journal, and he has over 20 years of industry experience.

K.V.S.S.S.S. SAIRAM, PhD, is a professor and Head of the Electronics and Communication Engineering Department at the NMAM Institute of Technology, Nitte, India. He has 25 years of experience in teaching and research, and has published over 50 papers in international journals and conferences. He is also a reviewer for several journals.

Manjunatha Badiger, PhD, is an assistant professor at Sahyadri College of Engineering and Management, Adyar, Mangalore, Karnataka, India. He has over 12 years of experience in academics, research, and administration. He earned his PhD in machine learning in 2024 at Visvesvaraya Technological University.

Naveen Kumar S., MTech, is an assistant professor at the Sahyadri College of Engineering and Management. Previously he was an assistant professor at JSS Academy of Technical Education, Noida, India. He obtained his MTech in automotive electronics from Sri Jayachamarajendra College of Engineering, Mysore, India.

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Table of Contents
Preface
1. Land Use and Land Cover Mapping of Remotely Sensed Data Using Fuzzy Set Theory-Related Algorithm
Adithya Kumar and Shivakumar B.R.
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
2. Role of AI in Mortality Prediction in Intensive Care Unit Patients
Prabhudutta Ray, Sachin Sharma, Raj Rawal and Dharmesh Shah
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
3. A Survey on Malware Detection Using Machine Learning
Devika S. P., Pooja M. R. and Arpitha M. S.
3.1 Background
3.2 Introduction
3.3 Literature Survey
3.4 Discussion
3.5 Conclusion
References
4. EEG Data Analysis for IQ Test Using Machine Learning Approaches: A Survey
Bhoomika Patel H. C., Ravikumar V. and Pavan Kumar S. P.
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
5. Machine Learning Methods in Radio Frequency and Microwave Domain
Shanthi P. and Adish K.
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
6. A Survey: Emotion Detection Using Facial Reorganization Using Convolutional Neural Network (CNN) and Viola–Jones Algorithm
Vaibhav C. Gandhi, Dwij Kishor Siyal, Shivam Pankajkumar Patel and Arya Vipesh Shah
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
7. Power Quality Events Classification Using Digital Signal Processing and Machine Learning Techniques
E. Fantin Irudaya Raj and M. Balaji
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
8. Hybridization of Artificial Neural Network with Spotted Hyena Optimization (SHO) Algorithm for Heart Disease Detection
Shwetha N., Gangadhar N., Mahesh B. Neelagar, Sangeetha N. and Virupaxi Dalal
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
9. The Role of Artificial Intelligence, Machine Learning, and Deep Learning to Combat the Socio-Economic Impact of the Global COVID-19 Pandemic
Biswa Ranjan Senapati, Sipra Swain and Pabitra Mohan Khilar
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
10. A Review on Smart Bin Management Systems
Bhoomika Patel H. C., Soundarya B. C. and Pooja M. R.
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
11. Unlocking Machine Learning: 10 Innovative Avenues to Grasp Complex Concepts
K. Vidhyalakshmi and S. Thanga Ramya
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
12. Recognition Attendance System Ensuring COVID-19 Security
Praveen Kumar M., Ramya Poojary, Saksha S. Bhandary and Sushmitha M. Kulal
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
12.4.4 Pi Camera
12.4.5 Raspberry Pi
12.4.6 OLED Display
12.5 Methodology
12.6 Building the Database
12.7 Pi Camera for Extracting Face Features
12.8 Real-Time Testing on Raspberry Pi
12.9 Contactless Body Temperature Monitoring
12.9.1 MLX90614 Interfaced with the Raspberry Pi
12.10 Raspberry-Pi Setting Up an SMTP Email
12.11 Uploading to the Database
12.12 Updating the Website
12.13 Report Generation
12.14 Result
12.15 Discussion
12.16 Conclusion
References
13. Real-Time Industrial Noise Cancellation for the Extraction of Human Voice
Vinayprasad M. S., Chandrashekar Murthy B. N. and Yashwanth S. D.
13.1 Introduction
13.2 Literature Survey
13.3 Methodology
13.3.1 Design of Processing System
13.3.2 The NLMS Algorithm
13.3.3 Design of the System at the Machine End
13.3.4 Design of the System at the User End
13.4 Experimental Results
13.4.1 Time Domain Analysis of the Signals
13.4.2 Frequency Domain Analysis of the Signals
13.4.3 Performance of the Algorithm on Hardware
13.5 Conclusion
References
14. Machine Learning-Based Water Monitoring System Using IoT
T. Kesavan, E. Kaliappan, K. Nagendran and M. Murugesan
14.1 Introduction
14.2 Smart Water Monitoring System
14.3 Sensors and Hardware
14.3.1 Machine Learning Algorithm
14.4 PowerBI Reports
14.4.1 Reading of Data from the Sensors
14.4.2 Handling of Data by the Controller
14.4.3 Storage and Processing of Data in the Cloud
14.4.4 Training of Machine Data Models
14.4.5 Water Flow Controller Based on the Machine Learning Output
14.4.6 Analysis of the Water Data Reports
14.5 Conclusion
References
15. Design and Modelling of an Automated Driving Inspector Powered by Arduino and Raspberry Pi
Raghunandan K. R., Dilip Kumar K., Krishnaraj Rao N.S. Krishnaprasad Rao and Bhavya K.
15.1 Introduction
15.2 Literature Survey
15.2.1 Objectives
15.2.2 Preliminaries Used
15.2.3 Method Proposed
15.3 Results
15.4 Conclusion
References
16. Kalman Filter-Based Seizure Prediction Using Concatenated Serial-Parallel Block Technique
Purnima P. S. and Suresh M.
16.1 Introduction
16.2 Prior Work
16.3 Proposed Method
16.3.1 Phase 1
16.3.2 Phase 2
16.3.3 Phase 3
16.4 Serial-Parallel Block Concatenation Approach
16.4.1 PDF Method
16.5 Algorithm
16.6 Kalman Filter
16.7 Results and Discussion
16.8 Conclusion
References
17. Current Advancements in Steganography: A Review
Mallika Garg, Jagpal Singh Ubhi and Ashwani Kumar Aggarwal
17.1 Introduction
17.2 Evaluation Parameters
17.3 Types of Steganography
17.3.1 Host
17.3.2 Domain
17.4 Traditional Steganographic Techniques
17.4.1 Least Significant Bit (LSB) Steganography
17.4.2 Pixel-Value Differencing (PVD)
17.4.3 Edge-Based Embedding (EBE)
17.4.4 Random Pixel Embedding (RPE)
17.4.5 Pixel Mapping Method (PMM)
17.5 CNN-Based Steganographic Techniques
17.6 GAN-Based Steganographic Techniques
17.7 Steganalysis
17.8 Applications
17.9 Dataset Used for Steganography
17.9.1 BOSS
17.9.2 Pascal VOC
17.9.3 ImageNet
17.9.4 COCO
17.9.5 MNIST
17.10 Conclusion
References
18. Human Emotion Recognizing Intelligence System Using Machine Learning
Bhakthi P. Alva, Krishma Bopanna N., Prajwal S., Varun A. Naik and Lahari Vaidya
18.1 Introduction
18.2 Literature Review
18.3 Problem Statement
18.4 Methodology
18.5 Results
18.6 Applications
18.7 Conclusion
18.8 Future Work
References
19. Computing in Cognitive Science Using Ensemble Learning
Om Prakash Singh
19.1 Introduction
19.2 Recognition of Human Activities
19.3 Methodology
19.3.1 Dataset Organization
19.3.2 Handling the Multiclass Imbalanced Dataset with a Skewed Data Distribution
19.4 Applying the Boosting-Based Ensemble Learning
19.4.1 Ensemble Learning
19.4.1.1 Development of Ensemble Learning
19.4.1.2 Computational Justification of Ensemble Learning
19.4.2 Boosting Methods
19.4.2.1 Justification for the Use of the Boosting Method
19.4.2.2 Boosting Algorithms
19.4.2.3 Boosting and Ensemble Learning
19.5 Human Activity Features Computability
19.5.1 Activity Recognition and Behaviors Analysis
19.5.1.1 Boosting and Activity Recognition
19.5.1.2 Ensemble Learning and Human Behaviors
19.5.2 Data Processing and Feature Mapping
19.5.2.1 Imbalanced Skewed Distributed Data Processing
19.5.2.2 Feature Vector Mapping
19.6 Conclusion
References
About the Editors
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