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Submit a ProposalExplainable Machine Learning Models and Architectures
 | Real-Time System Implementation Edited by Suman Lata Tripathi and Mufti Mahmud Series: Engineering Systems Design for Sustainable Development Copyright: 2024 | Status: Published ISBN: 9781394185849 | Hardcover | 268 pages Price: $225 USD  |
One Line Description
This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications.
Audience
Engineers, designers, researchers, and undergraduate, postgraduate and research students and faculty working in the areas of artificial intelligence, machine learning models, architectures and their applications
Engineers, designers, researchers, and undergraduate, postgraduate and research students and faculty working in the areas of artificial intelligence, machine learning models, architectures and their applications
Description
Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine learning models. Many real-time applications like processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems to have a lot of scope for improvements in terms of accuracy, speed, computational powers and overall power consumption.
This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must have for any library.
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Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine learning models. Many real-time applications like processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems to have a lot of scope for improvements in terms of accuracy, speed, computational powers and overall power consumption.
This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must have for any library.
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Author / Editor Details
Suman Lata Tripathi, PhD, is a professor at Lovely Professional University with more than 21 years of experience in academics. She has published more than 103 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published three books and currently has multiple volumes scheduled for publication from Wiley-Scrivener.
Suman Lata Tripathi, PhD, is a professor at Lovely Professional University with more than 21 years of experience in academics. She has published more than 103 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published three books and currently has multiple volumes scheduled for publication from Wiley-Scrivener.
Mufti Mahmud, PhD, is an associate professor of cognitive computing at the Department of Computer Science of Nottingham Trent University, UK. He is the Coordinator of the Computer Science and Informatics Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group and the Cognitive Computing & Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and the Medical Technologies Innovation Facility. He is a member of numerous societies and research committees.
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Table of Contents
Preface
Acknowledgements
1. A Comprehensive Review of Various Machine Learning Techniques
Pooja Pathak and Parul Choudhary
1.1 Introduction
1.1.1 Random Forest
1.1.2 Decision Tree
1.1.3 Support Vector Machine
1.1.4 Naive Bayes
1.1.5 K-Means Clustering
1.1.6 Principal Component Analysis
1.1.7 Linear Regression
1.1.8 Logistic Regression
1.1.9 Semi-Supervised Learning
1.1.10 Transductive SVM
1.1.11 Generative Models
1.1.12 Self-Training
1.1.13 Relearning
1.2 Conclusions
References
2. Artificial Intelligence and Image Recognition Algorithms
Siddharth, Anuranjana and Sanmukh Kaur
2.1 Introduction
2.2 Traditional Image Recognition Algorithms
2.2.1 Harris Corner Detector (1988)
2.2.2 SIFT (2004)
2.2.3 ASIFT
2.2.4 SURF (2006)
2.3 Neural Network–Based Algorithms
2.4 Convolutional Neural Network Architecture
2.5 Various CNN Architectures
2.5.1 LeNet-5 (1998)
2.5.2 AlexNet (2012)
2.5.3 VGGNet (2014)
2.5.4 GoogleNet (2015)
Conclusion
References
3. Efficient Architectures and Trade-Offs for FPGA-Based Real-Time Systems
L.M.I. Leo Joseph, J. Ajayan, Sandip Bhattacharya and Sreedhar Kollem
3.1 Overview of FPGA-Based Real-Time System
3.1.1 Key Elements of Real-Time System
3.1.2 Real-Time System and its Computation
3.1.3 FPGA Functionality and Applications
3.1.4 FPGA Applications
3.1.5 FPGA Architecture
3.1.6 Reconfigurable Architectures
3.2 Hybrid FPGA Configurations and its Algorithms
3.2.1 Hybrid FPGA
3.2.2 Hybrid FPGA Architecture
3.2.3 Hybrid FPGA Configuration
3.3 Hybrid FPGA Algorithms
3.3.1 Relevance of Hardware-Accelerated Architecture to FPGA Software Implementation
3.4 CNN Hardware Accelerator Architecture Overview
3.5 Summary
References
4. A Low-Power Audio Processing Using Machine Learning Module on FPGA and Applications
Suman Lata Tripathi, Dasari Lakshmi Prasanna and Mufti Mahmud
4.1 Introduction
4.2 Existing Machine Learning Modules and Audio Classifiers
4.3 Audio Processing Module Using Machine Learning
4.4 Application of Proposed FPGA-Based ML Models
4.5 Implementation of a Microphone on FPGA
4.6 Conclusion
4.7 Future Scope
References
5. Synthesis and Time Analysis of FPGA-Based DIT-FFT Module for Efficient VLSI Signal Processing Applications
Siba Kumar Panda, Konasagar Achyut and Dhruba Charan Panda
5.1 Introduction
5.2 Implementation of DIT-FFT Algorithm
5.2.1 A Quick Overview of DIT-FFT
5.2.2 Algorithmic Representation with Example
5.2.3 Simulated Output Waveform
5.3 Synthesis of Designed Circuit
5.4 Static Timing Analysis of Designed Circuit
5.5 Result and Discussion
5.6 Conclusion
References
6. Artificial Intelligence–Based Active Virtual Voice Assistant
Swathi Gowroju, G. Mounika, D. Bhavana, Shaik Abdul Latheef and A. Abhilash
6.1 Introduction
6.2 Literature Survey
6.3 System Functions
6.4 Model Training
6.5 Discussion
6.5.1 Furnishing Movie Recommendations
6.5.2 KNN Algorithm Book Recommendation
6.6 Results
6.7 Conclusion
References
7. Image Forgery Detection: An Approach with Machine Learning
Madhusmita Mishra, Silvia Tittotto and Santos Kumar Das
7.1 Introduction
7.2 Historical Background
7.3 CNN Architecture
7.4 Analysis of Error Level of Image
7.5 Proposed Model of Image Forgery Detection, Results and Discussion
7.6 Conclusion
7.7 Future Research Directions
References
8. Applications of Artificial Neural Networks in Optical Performance Monitoring
Isra Imtiyaz, Anuranjana, Sanmukh Kaur and Anubhav Gautam
8.1 Introduction
8.2 Algorithms Employed for Performance Monitoring
8.2.1 Artificial Neural Networks
8.2.2 Deep Neural Networks
8.2.3 Convolutional Neural Networks
8.2.3.1 Convolutional Layer
8.2.3.2 Non-Linear Layer
8.2.3.3 Pooling Layer
8.2.3.4 Fully Connected Layer
8.2.4 Support Vector Regression (SVR)
8.2.5 Support Vector Machine (SVM)
8.2.6 Kernel Ridge Regression (KRR)
8.2.7 Long Short-Term Memory (LSTM)
8.3 Artificial Intelligence (AI) Methods, Performance Monitoring and Applications in Optical Networks
8.3.1 Performance Monitoring
8.3.2 Applications of AI in Optical Networking
8.4 Optical Impairments and Fault Management
8.4.1 Noise
8.4.2 Distortion
8.4.3 Timing
8.4.4 Component Faults
8.4.5 Transmission Impairments
8.4.6 Fault Management in Optical Network
8.5 Conclusion
References
9. Website Development with Django Web Framework
Sanmukh Kaur Anuranjana and Yashasvi Roy
9.1 Introduction
9.2 Salient Features of Django
9.2.1 Complete
9.2.2 Versatile
9.2.3 Secure
9.2.4 Scalable
9.2.5 Maintainable
9.2.6 Portable
9.3 UI Design
9.3.1 HTML
9.3.2 CSS
9.3.3 Bootstrap
9.4 Methodology
9.5 UI Design
9.6 Backend Development
9.6.1 Login Page
9.6.2 Registration Page
9.6.3 User Tracking
9.7 Ouputs
9.8 Conclusion
References
10. Revenue Forecasting Using Machine Learning Models
Yashasvi Roy and Sanmukh Kaur
10.1 Introduction
10.2 Types of Forecasting
10.2.1 Qualitative Forecasting
10.2.1.1 Industries That Use Qualitative Forecasting
10.2.1.2 Qualitative Forecasting Methods
10.2.2 Quantitative Forecasting
10.2.2.1 Quantitative Forecasting Methods
10.2.3 Artificial Intelligence Forecasting
10.2.3.1 Artificial Neural Network (ANN)
10.2.3.2 Support Vector Machine (SVM)
10.3 Types of ML Models Used in Finance
10.3.1 Linear Regression
10.3.1.1 Simple Linear Regression
10.3.1.2 Multiple Linear Regression
10.3.2 Ridge Regression
10.3.3 Decision Tree
10.3.3.1 Prediction of Continuous Variables
10.3.3.2 Prediction of Categorical Variables
10.3.4 Random Forest Regressor
10.3.5 Gradient Boosting Regression
10.3.5.1 Advantages of Gradient Boosting
10.4 Model Performance
10.4.1 R-Squared Method
10.4.2 Mean Squared Error (MSE)
10.4.3 Root Mean Square Error (RMSE)
10.5 Conclusion
References
11. Application of Machine Learning Optimization Techniques in Wind Resource Assessment
Udhayakumar K. and Krishnamoorthy R.
11.1 Introduction
11.2 Wind Data Analysis Methods
11.2.1 Wind Characteristics Parameters
11.2.2 Wind Speed Distribution Methods
11.2.3 Weibull Method
11.2.4 Goodness of Fit
11.3 Wind Site and Measurement Details
11.3.1 Seasonal Wind Periods
11.3.2 Machine Learning and Optimization Techniques
11.3.2.1 Moth Flame Optimization (MFO) Method
11.4 Results and Discussions
11.4.1 Wind Characteristics
11.4.1.1 Kayathar Station (Onshore)
11.4.1.2 Gulf of Khambhat (Gujarat Offshore) Station
11.4.1.3 Jafrabad (Gujarat-Nearshore)
11.4.2 Wind Distribution Fitting
11.4.2.1 Kayathar Station (Onshore)
11.4.2.2 Bimodal Behaviour
11.4.2.3 Gulf of Khambhat (Offshore) Wind Distribution
11.4.2.4 Jafrabad Station (Nearshore) Distribution Fitting
11.4.3 Optimization Methods for Parameter Estimation
11.4.3.1 Optimization Parameters Comparison
11.4.4 Wind Power Density Analysis (WPD)
11.4.4.1 Comparison of Wind Power Density
11.5 Research Summary
11.6 Conclusions
References
12. IoT to Scale-Up Smart Infrastructure in Indian Cities: A New Paradigm
Indu Bala, Simarpreet Kaur, Lavpreet Kaur and Pavan Thimmavajjala
12.1 Introduction
12.2 Technological Progress: A Brief History
12.3 What is the Internet of Things (IoT)?
12.4 Economic Effects of Internet of Things
12.5 Infrastructure and Smart Infrastructure: The Difference
12.5.1 What is Smart Infrastructure?
12.5.2 What are the Principles of Smart Infrastructure?
12.5.3 Components of IoT-Based Smart City Project
12.6 Architecture for Smart Cities
12.6.1 Networking Technologies
12.6.2 Network Topologies
12.6.3 Network Architectures
12.6.3.1 Home Area Networks (HANs)
12.6.3.2 Field/Neighborhood Area Networks (FANs/NANs)
12.6.3.3 Wide Area Networks (WANs)
12.6.3.4 Network Protocols
12.7 IoT Technology in India’s Smart Cities: The Current Scenario
12.8 Challenges in IoT-Based Smart City Projects
12.8.1 Technological Challenges
12.8.1.1 Privacy and Security
12.8.1.2 Smart Sensors and Infrastructure Essentials
12.8.1.3 Networking in IoT Systems
12.8.1.4 Big Data Analytics
12.8.2 Financial - Economic Challenges
12.9 Role of Explainable AI
12.10 Conclusion and Future Scope
References
Index
Back to Top
Preface
Acknowledgements
1. A Comprehensive Review of Various Machine Learning Techniques
Pooja Pathak and Parul Choudhary
1.1 Introduction
1.1.1 Random Forest
1.1.2 Decision Tree
1.1.3 Support Vector Machine
1.1.4 Naive Bayes
1.1.5 K-Means Clustering
1.1.6 Principal Component Analysis
1.1.7 Linear Regression
1.1.8 Logistic Regression
1.1.9 Semi-Supervised Learning
1.1.10 Transductive SVM
1.1.11 Generative Models
1.1.12 Self-Training
1.1.13 Relearning
1.2 Conclusions
References
2. Artificial Intelligence and Image Recognition Algorithms
Siddharth, Anuranjana and Sanmukh Kaur
2.1 Introduction
2.2 Traditional Image Recognition Algorithms
2.2.1 Harris Corner Detector (1988)
2.2.2 SIFT (2004)
2.2.3 ASIFT
2.2.4 SURF (2006)
2.3 Neural Network–Based Algorithms
2.4 Convolutional Neural Network Architecture
2.5 Various CNN Architectures
2.5.1 LeNet-5 (1998)
2.5.2 AlexNet (2012)
2.5.3 VGGNet (2014)
2.5.4 GoogleNet (2015)
Conclusion
References
3. Efficient Architectures and Trade-Offs for FPGA-Based Real-Time Systems
L.M.I. Leo Joseph, J. Ajayan, Sandip Bhattacharya and Sreedhar Kollem
3.1 Overview of FPGA-Based Real-Time System
3.1.1 Key Elements of Real-Time System
3.1.2 Real-Time System and its Computation
3.1.3 FPGA Functionality and Applications
3.1.4 FPGA Applications
3.1.5 FPGA Architecture
3.1.6 Reconfigurable Architectures
3.2 Hybrid FPGA Configurations and its Algorithms
3.2.1 Hybrid FPGA
3.2.2 Hybrid FPGA Architecture
3.2.3 Hybrid FPGA Configuration
3.3 Hybrid FPGA Algorithms
3.3.1 Relevance of Hardware-Accelerated Architecture to FPGA Software Implementation
3.4 CNN Hardware Accelerator Architecture Overview
3.5 Summary
References
4. A Low-Power Audio Processing Using Machine Learning Module on FPGA and Applications
Suman Lata Tripathi, Dasari Lakshmi Prasanna and Mufti Mahmud
4.1 Introduction
4.2 Existing Machine Learning Modules and Audio Classifiers
4.3 Audio Processing Module Using Machine Learning
4.4 Application of Proposed FPGA-Based ML Models
4.5 Implementation of a Microphone on FPGA
4.6 Conclusion
4.7 Future Scope
References
5. Synthesis and Time Analysis of FPGA-Based DIT-FFT Module for Efficient VLSI Signal Processing Applications
Siba Kumar Panda, Konasagar Achyut and Dhruba Charan Panda
5.1 Introduction
5.2 Implementation of DIT-FFT Algorithm
5.2.1 A Quick Overview of DIT-FFT
5.2.2 Algorithmic Representation with Example
5.2.3 Simulated Output Waveform
5.3 Synthesis of Designed Circuit
5.4 Static Timing Analysis of Designed Circuit
5.5 Result and Discussion
5.6 Conclusion
References
6. Artificial Intelligence–Based Active Virtual Voice Assistant
Swathi Gowroju, G. Mounika, D. Bhavana, Shaik Abdul Latheef and A. Abhilash
6.1 Introduction
6.2 Literature Survey
6.3 System Functions
6.4 Model Training
6.5 Discussion
6.5.1 Furnishing Movie Recommendations
6.5.2 KNN Algorithm Book Recommendation
6.6 Results
6.7 Conclusion
References
7. Image Forgery Detection: An Approach with Machine Learning
Madhusmita Mishra, Silvia Tittotto and Santos Kumar Das
7.1 Introduction
7.2 Historical Background
7.3 CNN Architecture
7.4 Analysis of Error Level of Image
7.5 Proposed Model of Image Forgery Detection, Results and Discussion
7.6 Conclusion
7.7 Future Research Directions
References
8. Applications of Artificial Neural Networks in Optical Performance Monitoring
Isra Imtiyaz, Anuranjana, Sanmukh Kaur and Anubhav Gautam
8.1 Introduction
8.2 Algorithms Employed for Performance Monitoring
8.2.1 Artificial Neural Networks
8.2.2 Deep Neural Networks
8.2.3 Convolutional Neural Networks
8.2.3.1 Convolutional Layer
8.2.3.2 Non-Linear Layer
8.2.3.3 Pooling Layer
8.2.3.4 Fully Connected Layer
8.2.4 Support Vector Regression (SVR)
8.2.5 Support Vector Machine (SVM)
8.2.6 Kernel Ridge Regression (KRR)
8.2.7 Long Short-Term Memory (LSTM)
8.3 Artificial Intelligence (AI) Methods, Performance Monitoring and Applications in Optical Networks
8.3.1 Performance Monitoring
8.3.2 Applications of AI in Optical Networking
8.4 Optical Impairments and Fault Management
8.4.1 Noise
8.4.2 Distortion
8.4.3 Timing
8.4.4 Component Faults
8.4.5 Transmission Impairments
8.4.6 Fault Management in Optical Network
8.5 Conclusion
References
9. Website Development with Django Web Framework
Sanmukh Kaur Anuranjana and Yashasvi Roy
9.1 Introduction
9.2 Salient Features of Django
9.2.1 Complete
9.2.2 Versatile
9.2.3 Secure
9.2.4 Scalable
9.2.5 Maintainable
9.2.6 Portable
9.3 UI Design
9.3.1 HTML
9.3.2 CSS
9.3.3 Bootstrap
9.4 Methodology
9.5 UI Design
9.6 Backend Development
9.6.1 Login Page
9.6.2 Registration Page
9.6.3 User Tracking
9.7 Ouputs
9.8 Conclusion
References
10. Revenue Forecasting Using Machine Learning Models
Yashasvi Roy and Sanmukh Kaur
10.1 Introduction
10.2 Types of Forecasting
10.2.1 Qualitative Forecasting
10.2.1.1 Industries That Use Qualitative Forecasting
10.2.1.2 Qualitative Forecasting Methods
10.2.2 Quantitative Forecasting
10.2.2.1 Quantitative Forecasting Methods
10.2.3 Artificial Intelligence Forecasting
10.2.3.1 Artificial Neural Network (ANN)
10.2.3.2 Support Vector Machine (SVM)
10.3 Types of ML Models Used in Finance
10.3.1 Linear Regression
10.3.1.1 Simple Linear Regression
10.3.1.2 Multiple Linear Regression
10.3.2 Ridge Regression
10.3.3 Decision Tree
10.3.3.1 Prediction of Continuous Variables
10.3.3.2 Prediction of Categorical Variables
10.3.4 Random Forest Regressor
10.3.5 Gradient Boosting Regression
10.3.5.1 Advantages of Gradient Boosting
10.4 Model Performance
10.4.1 R-Squared Method
10.4.2 Mean Squared Error (MSE)
10.4.3 Root Mean Square Error (RMSE)
10.5 Conclusion
References
11. Application of Machine Learning Optimization Techniques in Wind Resource Assessment
Udhayakumar K. and Krishnamoorthy R.
11.1 Introduction
11.2 Wind Data Analysis Methods
11.2.1 Wind Characteristics Parameters
11.2.2 Wind Speed Distribution Methods
11.2.3 Weibull Method
11.2.4 Goodness of Fit
11.3 Wind Site and Measurement Details
11.3.1 Seasonal Wind Periods
11.3.2 Machine Learning and Optimization Techniques
11.3.2.1 Moth Flame Optimization (MFO) Method
11.4 Results and Discussions
11.4.1 Wind Characteristics
11.4.1.1 Kayathar Station (Onshore)
11.4.1.2 Gulf of Khambhat (Gujarat Offshore) Station
11.4.1.3 Jafrabad (Gujarat-Nearshore)
11.4.2 Wind Distribution Fitting
11.4.2.1 Kayathar Station (Onshore)
11.4.2.2 Bimodal Behaviour
11.4.2.3 Gulf of Khambhat (Offshore) Wind Distribution
11.4.2.4 Jafrabad Station (Nearshore) Distribution Fitting
11.4.3 Optimization Methods for Parameter Estimation
11.4.3.1 Optimization Parameters Comparison
11.4.4 Wind Power Density Analysis (WPD)
11.4.4.1 Comparison of Wind Power Density
11.5 Research Summary
11.6 Conclusions
References
12. IoT to Scale-Up Smart Infrastructure in Indian Cities: A New Paradigm
Indu Bala, Simarpreet Kaur, Lavpreet Kaur and Pavan Thimmavajjala
12.1 Introduction
12.2 Technological Progress: A Brief History
12.3 What is the Internet of Things (IoT)?
12.4 Economic Effects of Internet of Things
12.5 Infrastructure and Smart Infrastructure: The Difference
12.5.1 What is Smart Infrastructure?
12.5.2 What are the Principles of Smart Infrastructure?
12.5.3 Components of IoT-Based Smart City Project
12.6 Architecture for Smart Cities
12.6.1 Networking Technologies
12.6.2 Network Topologies
12.6.3 Network Architectures
12.6.3.1 Home Area Networks (HANs)
12.6.3.2 Field/Neighborhood Area Networks (FANs/NANs)
12.6.3.3 Wide Area Networks (WANs)
12.6.3.4 Network Protocols
12.7 IoT Technology in India’s Smart Cities: The Current Scenario
12.8 Challenges in IoT-Based Smart City Projects
12.8.1 Technological Challenges
12.8.1.1 Privacy and Security
12.8.1.2 Smart Sensors and Infrastructure Essentials
12.8.1.3 Networking in IoT Systems
12.8.1.4 Big Data Analytics
12.8.2 Financial - Economic Challenges
12.9 Role of Explainable AI
12.10 Conclusion and Future Scope
References
Index
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