{"product_id":"explainable-machine-learning-models-and-architectures-9781394185849","title":"Explainable Machine Learning Models and Architectures","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eEXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications. 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 the processing of biomedical data in healthcare, smart transportation, satellite imag\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgements xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 A Comprehensive Review of Various Machine Learning Techniques 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePooja Pathak and Parul Choudhary\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.1.1 Random Forest 2\u003c\/p\u003e \u003cp\u003e1.1.2 Decision Tree 3\u003c\/p\u003e \u003cp\u003e1.1.3 Support Vector Machine 4\u003c\/p\u003e \u003cp\u003e1.1.4 Naive Bayes 5\u003c\/p\u003e \u003cp\u003e1.1.5 K-Means Clustering 6\u003c\/p\u003e \u003cp\u003e1.1.6 Principal Component Analysis 6\u003c\/p\u003e \u003cp\u003e1.1.7 Linear Regression 6\u003c\/p\u003e \u003cp\u003e1.1.8 Logistic Regression 7\u003c\/p\u003e \u003cp\u003e1.1.9 Semi-Supervised Learning 8\u003c\/p\u003e \u003cp\u003e1.1.10 Transductive SVM 9\u003c\/p\u003e \u003cp\u003e1.1.11 Generative Models 9\u003c\/p\u003e \u003cp\u003e1.1.12 Self-Training 9\u003c\/p\u003e \u003cp\u003e1.1.13 Relearning 9\u003c\/p\u003e \u003cp\u003e1.2 Conclusions 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Artificial Intelligence and Image Recognition Algorithms 11\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSiddharth, Anuranjana and Sanmukh Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 12\u003c\/p\u003e \u003cp\u003e2.2 Traditional Image Recognition Algorithms 13\u003c\/p\u003e \u003cp\u003e2.2.1 Harris Corner Detector (1988) 13\u003c\/p\u003e \u003cp\u003e2.2.2 SIFT (2004) 15\u003c\/p\u003e \u003cp\u003e2.2.3 ASIFT 16\u003c\/p\u003e \u003cp\u003e2.2.4 SURF (2006) 17\u003c\/p\u003e \u003cp\u003e2.3 Neural Network-Based Algorithms 21\u003c\/p\u003e \u003cp\u003e2.4 Convolutional Neural Network Architecture 22\u003c\/p\u003e \u003cp\u003e2.5 Various CNN Architectures 23\u003c\/p\u003e \u003cp\u003e2.5.1 LeNet-5 (1998) 23\u003c\/p\u003e \u003cp\u003e2.5.2 AlexNet (2012) 24\u003c\/p\u003e \u003cp\u003e2.5.3 VGGNet (2014) 24\u003c\/p\u003e \u003cp\u003e2.5.4 GoogleNet (2015) 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Efficient Architectures and Trade-Offs for FPGA-Based Real-Time Systems 31\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eL.M.I. Leo Joseph, J. Ajayan, Sandip Bhattacharya and Sreedhar Kollem\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Overview of FPGA-Based Real-Time System 31\u003c\/p\u003e \u003cp\u003e3.1.1 Key Elements of Real-Time System 32\u003c\/p\u003e \u003cp\u003e3.1.2 Real-Time System and its Computation 32\u003c\/p\u003e \u003cp\u003e3.1.3 FPGA Functionality and Applications 33\u003c\/p\u003e \u003cp\u003e3.1.4 FPGA Applications 33\u003c\/p\u003e \u003cp\u003e3.1.5 FPGA Architecture 34\u003c\/p\u003e \u003cp\u003e3.1.6 Reconfigurable Architectures 35\u003c\/p\u003e \u003cp\u003e3.2 Hybrid FPGA Configurations and its Algorithms 38\u003c\/p\u003e \u003cp\u003e3.2.1 Hybrid FPGA 38\u003c\/p\u003e \u003cp\u003e3.2.2 Hybrid FPGA Architecture 39\u003c\/p\u003e \u003cp\u003e3.2.3 Hybrid FPGA Configuration 40\u003c\/p\u003e \u003cp\u003e3.3 Hybrid FPGA Algorithms 42\u003c\/p\u003e \u003cp\u003e3.3.1 Relevance of Hardware-Accelerated Architecture to FPGA Software Implementation 44\u003c\/p\u003e \u003cp\u003e3.4 CNN Hardware Accelerator Architecture Overview 46\u003c\/p\u003e \u003cp\u003e3.5 Summary 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 A Low-Power Audio Processing Using Machine Learning Module on FPGA and Applications 49\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSuman Lata Tripathi, Dasari Lakshmi Prasanna and Mufti Mahmud\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 49\u003c\/p\u003e \u003cp\u003e4.2 Existing Machine Learning Modules and Audio Classifiers 50\u003c\/p\u003e \u003cp\u003e4.3 Audio Processing Module Using Machine Learning 56\u003c\/p\u003e \u003cp\u003e4.4 Application of Proposed FPGA-Based ML Models 57\u003c\/p\u003e \u003cp\u003e4.5 Implementation of a Microphone on FPGA 59\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 60\u003c\/p\u003e \u003cp\u003e4.7 Future Scope 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Synthesis and Time Analysis of FPGA-Based DIT-FFT Module for Efficient VLSI Signal Processing Applications 65\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSiba Kumar Panda, Konasagar Achyut and Dhruba Charan Panda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 66\u003c\/p\u003e \u003cp\u003e5.2 Implementation of DIT-FFT Algorithm 67\u003c\/p\u003e \u003cp\u003e5.2.1 A Quick Overview of DIT-FFT 67\u003c\/p\u003e \u003cp\u003e5.2.2 Algorithmic Representation with Example 69\u003c\/p\u003e \u003cp\u003e5.2.3 Simulated Output Waveform 69\u003c\/p\u003e \u003cp\u003e5.3 Synthesis of Designed Circuit 71\u003c\/p\u003e \u003cp\u003e5.4 Static Timing Analysis of Designed Circuit 73\u003c\/p\u003e \u003cp\u003e5.5 Result and Discussion 77\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Artificial Intelligence-Based Active Virtual Voice Assistant 81\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSwathi Gowroju, G. Mounika, D. Bhavana, Shaik Abdul Latheef and A. Abhilash\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 82\u003c\/p\u003e \u003cp\u003e6.2 Literature Survey 83\u003c\/p\u003e \u003cp\u003e6.3 System Functions 87\u003c\/p\u003e \u003cp\u003e6.4 Model Training 88\u003c\/p\u003e \u003cp\u003e6.5 Discussion 90\u003c\/p\u003e \u003cp\u003e6.5.1 Furnishing Movie Recommendations 91\u003c\/p\u003e \u003cp\u003e6.5.2 KNN Algorithm Book Recommendation 92\u003c\/p\u003e \u003cp\u003e6.6 Results 93\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Image Forgery Detection: An Approach with Machine Learning 105\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMadhusmita Mishra, Silvia Tittotto and Santos Kumar Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 105\u003c\/p\u003e \u003cp\u003e7.2 Historical Background 107\u003c\/p\u003e \u003cp\u003e7.3 CNN Architecture 109\u003c\/p\u003e \u003cp\u003e7.4 Analysis of Error Level of Image 113\u003c\/p\u003e \u003cp\u003e7.5 Proposed Model of Image Forgery Detection, Results and Discussion 115\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 118\u003c\/p\u003e \u003cp\u003e7.7 Future Research Directions 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Applications of Artificial Neural Networks in Optical Performance Monitoring 123\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eIsra Imtiyaz, Anuranjana, Sanmukh Kaur and Anubhav Gautam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 123\u003c\/p\u003e \u003cp\u003e8.2 Algorithms Employed for Performance Monitoring 129\u003c\/p\u003e \u003cp\u003e8.2.1 Artificial Neural Networks 129\u003c\/p\u003e \u003cp\u003e8.2.2 Deep Neural Networks 130\u003c\/p\u003e \u003cp\u003e8.2.3 Convolutional Neural Networks 131\u003c\/p\u003e \u003cp\u003e8.2.3.1 Convolutional Layer 131\u003c\/p\u003e \u003cp\u003e8.2.3.2 Non-Linear Layer 132\u003c\/p\u003e \u003cp\u003e8.2.3.3 Pooling Layer 132\u003c\/p\u003e \u003cp\u003e8.2.3.4 Fully Connected Layer 132\u003c\/p\u003e \u003cp\u003e8.2.4 Support Vector Regression (SVR) 133\u003c\/p\u003e \u003cp\u003e8.2.5 Support Vector Machine (SVM) 133\u003c\/p\u003e \u003cp\u003e8.2.6 Kernel Ridge Regression (KRR) 133\u003c\/p\u003e \u003cp\u003e8.2.7 Long Short-Term Memory (LSTM) 133\u003c\/p\u003e \u003cp\u003e8.3 Artificial Intelligence (AI) Methods, Performance Monitoring and Applications in Optical Networks 134\u003c\/p\u003e \u003cp\u003e8.3.1 Performance Monitoring 134\u003c\/p\u003e \u003cp\u003e8.3.2 Applications of AI in Optical Networking 135\u003c\/p\u003e \u003cp\u003e8.4 Optical Impairments and Fault Management 135\u003c\/p\u003e \u003cp\u003e8.4.1 Noise 135\u003c\/p\u003e \u003cp\u003e8.4.2 Distortion 135\u003c\/p\u003e \u003cp\u003e8.4.3 Timing 136\u003c\/p\u003e \u003cp\u003e8.4.4 Component Faults 136\u003c\/p\u003e \u003cp\u003e8.4.5 Transmission Impairments 137\u003c\/p\u003e \u003cp\u003e8.4.6 Fault Management in Optical Network 137\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Website Development with Django Web Framework 141\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSanmukh Kaur, Anuranjana and Yashasvi Roy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 141\u003c\/p\u003e \u003cp\u003e9.2 Salient Features of Django 142\u003c\/p\u003e \u003cp\u003e9.2.1 Complete 142\u003c\/p\u003e \u003cp\u003e9.2.2 Versatile 142\u003c\/p\u003e \u003cp\u003e9.2.3 Secure 142\u003c\/p\u003e \u003cp\u003e9.2.4 Scalable 143\u003c\/p\u003e \u003cp\u003e9.2.5 Maintainable 143\u003c\/p\u003e \u003cp\u003e9.2.6 Portable 143\u003c\/p\u003e \u003cp\u003e9.3 UI Design 143\u003c\/p\u003e \u003cp\u003e9.3.1 HTML 143\u003c\/p\u003e \u003cp\u003e9.3.2 CSS 144\u003c\/p\u003e \u003cp\u003e9.3.3 Bootstrap 144\u003c\/p\u003e \u003cp\u003e9.4 Methodology 144\u003c\/p\u003e \u003cp\u003e9.5 UI Design 144\u003c\/p\u003e \u003cp\u003e9.6 Backend Development 148\u003c\/p\u003e \u003cp\u003e9.6.1 Login Page 148\u003c\/p\u003e \u003cp\u003e9.6.2 Registration Page 149\u003c\/p\u003e \u003cp\u003e9.6.3 User Tracking 149\u003c\/p\u003e \u003cp\u003e9.7 Ouputs 150\u003c\/p\u003e \u003cp\u003e9.8 Conclusion 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Revenue Forecasting Using Machine Learning Models 155\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYashasvi Roy and Sanmukh Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 155\u003c\/p\u003e \u003cp\u003e10.2 Types of Forecasting 156\u003c\/p\u003e \u003cp\u003e10.2.1 Qualitative Forecasting 156\u003c\/p\u003e \u003cp\u003e10.2.1.1 Industries That Use Qualitative Forecasting 157\u003c\/p\u003e \u003cp\u003e10.2.1.2 Qualitative Forecasting Methods 158\u003c\/p\u003e \u003cp\u003e10.2.2 Quantitative Forecasting 158\u003c\/p\u003e \u003cp\u003e10.2.2.1 Quantitative Forecasting Methods 159\u003c\/p\u003e \u003cp\u003e10.2.3 Artificial Intelligence Forecasting 160\u003c\/p\u003e \u003cp\u003e10.2.3.1 Artificial Neural Network (ANN) 160\u003c\/p\u003e \u003cp\u003e10.2.3.2 Support Vector Machine (SVM) 161\u003c\/p\u003e \u003cp\u003e10.3 Types of ML Models Used in Finance 162\u003c\/p\u003e \u003cp\u003e10.3.1 Linear Regression 162\u003c\/p\u003e \u003cp\u003e10.3.1.1 Simple Linear Regression 162\u003c\/p\u003e \u003cp\u003e10.3.1.2 Multiple Linear Regression 162\u003c\/p\u003e \u003cp\u003e10.3.2 Ridge Regression 163\u003c\/p\u003e \u003cp\u003e10.3.3 Decision Tree 164\u003c\/p\u003e \u003cp\u003e10.3.3.1 Prediction of Continuous Variables 164\u003c\/p\u003e \u003cp\u003e10.3.3.2 Prediction of Categorical Variables 165\u003c\/p\u003e \u003cp\u003e10.3.4 Random Forest Regressor 165\u003c\/p\u003e \u003cp\u003e10.3.5 Gradient Boosting Regression 166\u003c\/p\u003e \u003cp\u003e10.3.5.1 Advantages of Gradient Boosting 167\u003c\/p\u003e \u003cp\u003e10.4 Model Performance 167\u003c\/p\u003e \u003cp\u003e10.4.1 R-Squared Method 167\u003c\/p\u003e \u003cp\u003e10.4.2 Mean Squared Error (MSE) 167\u003c\/p\u003e \u003cp\u003e10.4.3 Root Mean Square Error (RMSE) 168\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Application of Machine Learning Optimization Techniques in Wind Resource Assessment 171\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eUdhayakumar K. and Krishnamoorthy R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 172\u003c\/p\u003e \u003cp\u003e11.2 Wind Data Analysis Methods 173\u003c\/p\u003e \u003cp\u003e11.2.1 Wind Characteristics Parameters 173\u003c\/p\u003e \u003cp\u003e11.2.2 Wind Speed Distribution Methods 173\u003c\/p\u003e \u003cp\u003e11.2.3 Weibull Method 174\u003c\/p\u003e \u003cp\u003e11.2.4 Goodness of Fit 175\u003c\/p\u003e \u003cp\u003e11.3 Wind Site and Measurement Details 175\u003c\/p\u003e \u003cp\u003e11.3.1 Seasonal Wind Periods 176\u003c\/p\u003e \u003cp\u003e11.3.2 Machine Learning and Optimization Techniques 176\u003c\/p\u003e \u003cp\u003e11.3.2.1 Moth Flame Optimization (MFO) Method 176\u003c\/p\u003e \u003cp\u003e11.4 Results and Discussions 180\u003c\/p\u003e \u003cp\u003e11.4.1 Wind Characteristics 182\u003c\/p\u003e \u003cp\u003e11.4.1.1 Kayathar Station (Onshore) 182\u003c\/p\u003e \u003cp\u003e11.4.1.2 Gulf of Khambhat (Gujarat Offshore) Station 187\u003c\/p\u003e \u003cp\u003e11.4.1.3 Jafrabad (Gujarat-Nearshore) 192\u003c\/p\u003e \u003cp\u003e11.4.2 Wind Distribution Fitting 195\u003c\/p\u003e \u003cp\u003e11.4.2.1 Kayathar Station (Onshore) 196\u003c\/p\u003e \u003cp\u003e11.4.2.2 Bimodal Behaviour 196\u003c\/p\u003e \u003cp\u003e11.4.2.3 Gulf of Khambhat (Offshore) Wind Distribution 202\u003c\/p\u003e \u003cp\u003e11.4.2.4 Jafrabad Station (Nearshore) Distribution Fitting 203\u003c\/p\u003e \u003cp\u003e11.4.3 Optimization Methods for Parameter Estimation 212\u003c\/p\u003e \u003cp\u003e11.4.3.1 Optimization Parameters Comparison 212\u003c\/p\u003e \u003cp\u003e11.4.4 Wind Power Density Analysis (WPD) 214\u003c\/p\u003e \u003cp\u003e11.4.4.1 Comparison of Wind Power Density 215\u003c\/p\u003e \u003cp\u003e11.5 Research Summary 221\u003c\/p\u003e \u003cp\u003e11.6 Conclusions 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 IoT to Scale-Up Smart Infrastructure in Indian Cities: A New Paradigm 227\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eIndu Bala, Simarpreet Kaur, Lavpreet Kaur and Pavan Thimmavajjala\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 228\u003c\/p\u003e \u003cp\u003e12.2 Technological Progress: A Brief History 229\u003c\/p\u003e \u003cp\u003e12.3 What is the Internet of Things (IoT)? 230\u003c\/p\u003e \u003cp\u003e12.4 Economic Effects of Internet of Things 230\u003c\/p\u003e \u003cp\u003e12.5 Infrastructure and Smart Infrastructure: The Difference 232\u003c\/p\u003e \u003cp\u003e12.5.1 What is Smart Infrastructure? 233\u003c\/p\u003e \u003cp\u003e12.5.2 What are the Principles of Smart Infrastructure? 234\u003c\/p\u003e \u003cp\u003e12.5.3 Components of IoT-Based Smart City Project 235\u003c\/p\u003e \u003cp\u003e12.6 Architecture for Smart Cities 236\u003c\/p\u003e \u003cp\u003e12.6.1 Networking Technologies 237\u003c\/p\u003e \u003cp\u003e12.6.2 Network Topologies 237\u003c\/p\u003e \u003cp\u003e12.6.3 Network Architectures 238\u003c\/p\u003e \u003cp\u003e12.6.3.1 Home Area Networks (HANs) 238\u003c\/p\u003e \u003cp\u003e12.6.3.2 Field\/Neighborhood Area Networks (FANs\/NANs) 238\u003c\/p\u003e \u003cp\u003e12.6.3.3 Wide Area Networks (WANs) 238\u003c\/p\u003e \u003cp\u003e12.6.3.4 Network Protocols 238\u003c\/p\u003e \u003cp\u003e12.7 IoT Technology in India’s Smart Cities: The Current Scenario 239\u003c\/p\u003e \u003cp\u003e12.8 Challenges in IoT-Based Smart City Projects 243\u003c\/p\u003e \u003cp\u003e12.8.1 Technological Challenges 243\u003c\/p\u003e \u003cp\u003e12.8.1.1 Privacy and Security 243\u003c\/p\u003e \u003cp\u003e12.8.1.2 Smart Sensors and Infrastructure Essentials 243\u003c\/p\u003e \u003cp\u003e12.8.1.3 Networking in IoT Systems 244\u003c\/p\u003e \u003cp\u003e12.8.1.4 Big Data Analytics 244\u003c\/p\u003e \u003cp\u003e12.8.2 Financial - Economic Challenges 244\u003c\/p\u003e \u003cp\u003e12.9 Role of Explainable AI 245\u003c\/p\u003e \u003cp\u003e12.10 Conclusion and Future Scope 246\u003c\/p\u003e \u003cp\u003eReferences 246\u003c\/p\u003e \u003cp\u003eIndex 251\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":53515692605783,"sku":"9781394185849","price":140.4,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/explainable-machine-learning-models-and-architectures-9781394185849","provider":"Book Curl","version":"1.0","type":"link"}