Intelligent Systems and Machine Learning for Industry: Advancements, Challenges, and Practices
- Length: 348 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2022-12-21
- ISBN-10: 1032261447
- ISBN-13: 9781032261447
- Sales Rank: #0 (See Top 100 Books)
The book explores the concepts and challenges in developing novel approaches using the Internet of Things, intelligent systems, machine intelligence systems, and data analytics in various industrial sectors such as manufacturing, smart agriculture, smart cities, food processing, environment, defense, stock market and healthcare. Further, it discusses the latest improvements in the industrial sectors using machine intelligence learning and intelligent systems techniques, especially robotics.
Features:
- Highlights case studies and solutions to industrial problems using machine learning and intelligent systems.
- Covers applications in smart agriculture, smart healthcare, intelligent machines for disaster management, and smart manufacturing.
- Provides the latest methodologies using machine intelligence systems in the early forecasting of weather.
- Examines the research challenges and identifies the gaps in data collection and data analysis, especially imagery, signal, and speech.
- Provides applications of digitization and smart processing using the Internet of Things and effective intelligent agent systems in manufacturing.
- Discusses a systematic and exhaustive analysis of intelligent software effort estimation models.
It will serve as an ideal reference text for graduate students, post-graduate students, IT Professionals, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.
Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface About the Editors Contributors Chapter 1 A framework for a virtual reality-based medical support system 1.1 Introduction 1.2 Background 1.2.1 What is VR? 1.2.2 VR in healthcare education 1.2.3 Utilization of VR in the medical sector 1.3 Stakeholders of the system 1.3.1 Desirements of the stakeholders 1.4 System purpose and concept of operations 1.4.1 Concept of operations 1.4.1.1 Current and planned system 1.4.1.2 Functions of the system 1.4.1.3 Critical system requirements 1.4.2 Context diagram 1.4.2.1 First terminator 1.4.2.2 Second terminator 1.4.2.3 Third terminator 1.4.2.4 Fourth terminator 1.5 Systems requirements and use cases 1.5.1 Use cases 1.5.2 Block definition diagrams 1.6 Technical performance measure (TPM) 1.7 Conclusion References Chapter 2 ConvMax: Classification of COVID-19, pneumonia, and normal lungs from X-ray images using CNN with modified max-pooling layer 2.1 Introduction 2.2 Literature review 2.3 Proposed work 2.3.1 Proposed methodology 2.3.2 Dataset collection 2.3.3 Novel contributions of this study 2.3.4 System flow and related concepts 2.3.5 Proposed CNN architecture 2.4 Results and discussion 2.5 Conclusion References Chapter 3 Biorthogonal filter-based algorithm for denoising and segmentation of fundus images 3.1 Introduction 3.1.1 Motivation 3.1.2 Major contribution 3.1.3 Outcomes 3.1.4 Chapter organization 3.2 Literature review 3.2.1 Denoising of medical images 3.2.2 Segmentation techniques for medical images 3.3 Performance evaluation 3.3.1 Denoising performance metrics 3.3.2 Segmentation performance metrics 3.4 Biorthogonal wavelet transforms and filters 3.5 Investigational results 3.5.1 Noise suppression in images using the wavelet transform 3.5.2 Contrast enhancement of the images for improved segmentation 3.5.3 Findings 3.6 Discussion and conclusions References Chapter 4 Deep learning-based automatic detection of breast lesions on ultrasound images 4.1 Introduction 4.2 Methodology 4.2.1 Block diagram of proposed method 4.2.2 Breast ultrasound dataset 4.2.3 Preprocessing 4.2.3.1 Overview 4.2.3.2 Block diagram of the proposed speckle reduction method 4.2.3.3 Speckle reduction by circular hybrid median filter technique 4.2.3.4 Algorithm 4.2.3.5 Performance indices 4.2.4 Segmentation 4.2.4.1 Need for image segmentation 4.2.4.2 CNN-based image segmentation 4.2.4.3 Residual network 4.2.4.4 Implementation 4.2.4.5 Analyzing the network 4.2.4.6 Performance indices 4.3 Results and discussions 4.3.1 Preprocessing results 4.3.2 Segmentation results 4.4 Conclusion References Chapter 5 Heart disease prediction using enhanced machine learning techniques 5.1 Introduction 5.2 Overview of machine learning techniques 5.3 Heart disease datasets 5.4 Notable heart disease prediction studies 5.5 Proposed heart disease prediction approach 5.5.1 Decision tree 5.5.2 Logistic regression 5.5.3 Support vector machine (SVM) 5.5.4 Random forest 5.5.5 XGBoost 5.5.6 Adaptive boosting 5.6 Experimental results and discussion 5.7 Conclusion References Chapter 6 Immersive technologies in healthcare education 6.1 Introduction 6.2 Background 6.2.1 Research questions 6.2.2 Reviews of VR/AR/MR/XR applications in the healthcare domain 6.3 Immersive technologies 6.3.1 Virtual reality (VR) 6.3.2 Augmented reality (AR) 6.3.3 Mixed reality (MR) 6.3.4 Extended reality (XR) 6.4 Immersive technologies in education 6.4.1 Research gaps 6.5 Conclusion References Chapter 7 Implications of technological trends toward smart farming 7.1 Introduction 7.1.1 Background 7.1.2 Motivation 7.1.3 Major contribution 7.1.4 Paper organization 7.2 What smart agriculture can do 7.3 IoT in smart agriculture 7.3.1 Major equipment and technologies 7.4 Intelligent systems in smart agriculture 7.4.1 Subsets of intelligent systems 7.4.1.1 Why artificial neural networks? 7.4.1.2 What distinguishes deep learning from machine learning techniques? 7.5 Smart measurements 7.5.1 Spectroscopic techniques 7.5.2 Image processing 7.5.3 Electronic nose in food quality 7.5.3.1 How electronic nose (e-nose) works 7.5.3.2 e-Nose sensing system 7.5.3.3 Pattern recognition algorithms 7.6 IoT integration with intelligent systems in smart agriculture 7.7 Summary References Chapter 8 A smart sensing technology for monitoring marine environment conditions 8.1 Introduction 8.2 Overview of IoT 8.2.1 Perception and execution 8.2.2 Data transmission 8.2.3 Preprocessing data 8.2.4 Application 8.2.5 The organizational layer 8.3 Characteristics of sensors 8.4 Platform for sensors 8.4.1 Marine observation system with static point 8.4.2 Subsurface floats 8.4.3 Remotely autonomous and operated vehicles (ROV) 8.4.4 Satellite 8.4.5 Remotely piloted aircraft (RPA) 8.5 Type of sensors for data collection 8.5.1 Acoustics 8.5.2 Cameras 8.5.3 Satellite sensors 8.5.4 Biosensors 8.6 Challenges in application 8.7 Conclusion References Chapter 9 Managing agriculture pollution using life-cycle assessment and artificial intelligence methods 9.1 Introduction 9.2 Benefits of artificial intelligence in agriculture 9.2.1 IoT drives data analytics 9.2.2 Drone-assistant technology 9.2.3 Weather prediction 9.2.4 Monitoring soil and crop health 9.2.5 Artificial intelligence can assist with labor shortages 9.2.6 Farm data analysis and pest monitoring 9.3 Managing environmental pollution in the agricultural sector 9.4 Advantages of LCA–AI integration 9.5 Conclusion References Chapter 10 Ensemble techniques for effective prediction of crop selection in the Coastal Andhra deltaic region 10.1 Introduction 10.1.1 Agriculture in India 10.1.2 Agriculture in Andhra Pradesh 10.1.3 Essential nutrients for production of crops 10.1.4 Challenges in agriculture 10.1.5 Data mining techniques in agriculture 10.1.6 Organization of the chapter 10.2 Related work 10.3 Learning methods 10.3.1 Machine learning 10.3.1.1 Classification tree 10.3.1.2 K-nearest neighbor algorithm 10.3.2 Ensemble methods 10.3.2.1 Random space 10.3.2.2 Bagging 10.3.2.3 AdaBoosting 10.4 Experimental analysis 10.4.1 Dataset 10.4.2 Process flow 10.4.2.1 Step 1: Selection of suitable dataset for crop prediction 10.4.2.2 Step 2: Identification of training data and testing data 10.4.2.3 Step 3: Build the crop advisor models 10.4.2.4 Step 4: Test the models 10.4.2.5 Step 5: Comparison of models 10.4.3 Performance metrics 10.4.3.1 Accuracy 10.4.3.2 Precision 10.4.3.3 Recall 10.4.3.4 F1-score 10.4.4 Experimentation 10.5 Results and discussion 10.6 Conclusion References Chapter 11 Artificial intelligence-based quality inference for food processing industry applications 11.1 Introduction 11.2 Food quality and safety 11.2.1 Artificial intelligence in food quality and safety 11.3 Non-destructive techniques 11.3.1 Near infrared 11.3.2 Hyperspectral imaging 11.3.3 Thermal imaging 11.3.4 e-Nose and e-tongue 11.4 Conclusion References Chapter 12 A study on intelligent systems and their influence on smarter defense service 12.1 Introduction 12.2 Artificial intelligence and its current status 12.2.1 AI as a growing technology 12.2.2 AI-built technologies 12.2.3 Fear of AI 12.3 Artificial intelligence and its usage in defense 12.3.1 Training 12.3.2 Surveillance 12.3.3 Artillery 12.3.4 Cyberattacks 12.3.5 Cognitive radio and cognitive electronic warfare 12.3.6 Computational military reasoning (tactical artificial intelligence) 12.3.7 Intelligent and autonomous unmanned weapon systems 12.3.8 Information processing, intelligent analysis, and data fusion using AI 12.4 Practical use of AI in military applications 12.4.1 Application of neural networks in object location 12.4.2 Location of underwater mines using deep convolution neural network 12.4.3 Application of neural networks in cybersecurity 12.5 Challenges of using AI in defense operations 12.6 Conclusion References Chapter 13 Steam turbine controller using fuzzy logic 13.1 Introduction 13.1.1 Description of technology at block level 13.1.2 Practical realization of fuzzy controller 13.2 Description of methodology used for implementation 13.2.1 Choosing fuzzy controller inputs and outputs 13.2.2 Linguistic descriptions 13.2.3 Rules 13.2.4 Rule-bases 13.2.5 Operations 13.2.6 Fuzzy quantification of knowledge 13.2.7 Defuzzification methods 13.3 Fuzzy logic controller 13.3.1 Problems encountered and their solutions 13.3.2 Deciding the input variables 13.3.3 Deciding membership functions 13.3.4 Deciding range 13.4 Deciding the type of function 13.5 Results and discussions 13.6 Concluding remarks Conflict of interest References Chapter 14 Speech recognition for Indian-accent English using a transformer model 14.1 Introduction 14.2 Problem statement 14.3 Proposed methodology 14.4 Data overview 14.4.1 Text files 14.4.2 Data preprocessing 14.5 Technologies used 14.5.1 Transformers 14.5.2 Attention 14.5.3 Elliptic curve cryptography (ECC) 14.5.4 Keys in ECC 14.5.5 Addition in ECC 14.5.6 Multiplication in ECC 14.6 Literature survey 14.6.1 “SpecAugment: a simple data augmentation method for automatic speech recognition” 14.6.1.1 Augmentation policy 14.6.1.2 Learning rate schedules 14.6.1.3 Results 14.6.2 “Speech recognition using deep neural networks: a systematic review” 14.6.2.1 Machine learning techniques 14.6.2.2 Generative models 14.6.2.3 Deep neural networks 14.6.2.4 Conclusion 14.6.3 “Deep learning: from speech recognition to language and multimodal processing” 14.6.3.1 Introduction 14.6.3.2 From deep generative models to DL models 14.6.3.3 Advanced architectures 14.6.3.4 Summary 14.6.4 “Speech commands: a dataset for limited vocabulary-speech recognition” 14.6.4.1 Abstract 14.6.4.2 Introduction 14.6.4.3 Conclusion 14.6.5 “A neural attention model for speech command recognition” 14.6.5.1 Abstract 14.6.5.2 Introduction 14.6.5.3 Neural network implementation 14.6.5.4 Conclusion 14.6.6 “Automatic speech recognition using different neural network architectures – a survey” 14.6.6.1 Convolutional neural network (CNN) 14.6.6.2 Recurrent neural network (RNN) 14.6.7 Towards end-to-end speech recognition with recurrent neural networks 14.7 Implementation 14.7.1 Input audio processing 14.7.2 Encoder 14.7.3 Target sentence encoding 14.7.4 Decoder 14.8 Results and conclusions References Chapter 15 Stock market prediction using sentiment analysis with LSTM and RFR 15.1 Introduction 15.2 Background study 15.2.1 Literature review 15.2.2 Time series 15.2.3 Deep learning – LSTM 15.2.4 Random forest 15.2.5 Sentiment analysis 15.3 Methodology 15.3.1 Data source 15.3.2 Evaluation criteria 15.4 Approach and implementation 15.4.1 Data preprocessing 15.4.2 Visualization 15.4.3 Time series analysis 15.4.4 Sentiment analysis 15.4.5 LSTM 15.4.6 RFR 15.5 Results 15.6 Conclusion References Chapter 16 A systematic and exhaustive analysis of intelligent software effort estimation models 16.1 Introduction 16.1.1 Algorithmic models 16.1.2 Expert systems 16.1.3 Soft computing techniques 16.1.4 Major contributions 16.1.5 Organization of the chapter 16.2 Literature survey 16.3 Research outcomes in software effort estimation 16.3.1 Scopus database search 16.3.2 Initial search outcomes 16.4 Systematic analysis 16.4.1 Statistical analysis 16.4.2 Network analysis 16.4.2.1 Citation analysis of documents 16.4.2.2 Citation analysis of sources 16.4.2.3 Citation analysis by authors 16.4.2.4 Analysis of citations by organization 16.4.2.5 Citation analysis by country 16.4.2.6 Co-citation analysis by cited references 16.4.2.7 Co-citation analysis by cited sources 16.4.2.8 Co-citation analysis by cited authors 16.5 Results and discussions 16.6 Research directions 16.7 Conclusion References Index
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