Machine Intelligence, Big Data Analytics, and IoT in Image Processing
Beschreibung
Discusses both theoretical and practical aspects of how to harness advanced technologies to develop practical applications such as drone-based surveillance, smart transportation, healthcare, farming solutions, and robotics used in automation.
The concepts of machine intelligence, big data analytics, and the Internet of Things (IoT) continue to improve our lives through various cutting-edge applications such as disease detection in real-time, crop yield prediction, smart parking, and so forth. The transformative effects of these technologies are life-changing because they play an important role in demystifying smart healthcare, plant pathology, and smart city/village planning, design and development. This book presents a cross-disciplinary perspective on the practical applications of machine intelligence, big data analytics, and IoT by compiling cutting-edge research and insights from researchers, academicians, and practitioners worldwide. It identifies and discusses various advanced technologies, such as artificial intelligence, machine learning, IoT, image processing, network security, cloud computing, and sensors, to provide effective solutions to the lifestyle challenges faced by humankind.
Machine Intelligence, Big Data Analytics, and IoT in Image Processing is a significant addition to the body of knowledge on practical applications emerging from machine intelligence, big data analytics, and IoT. The chapters deal with specific areas of applications of these technologies. This deliberate choice of covering a diversity of fields was to emphasize the applications of these technologies in almost every contemporary aspect of real life to assist working in different sectors by understanding and exploiting the strategic opportunities offered by these technologies.
Audience
The book will be of interest to a range of researchers and scientists in artificial intelligence who work on practical applications using machine learning, big data analytics, natural language processing, pattern recognition, and IoT by analyzing images. Software developers, industry specialists, and policymakers in medicine, agriculture, smart cities development, transportation, etc. will find this book exceedingly useful.
Weitere Details
Weitere Ausgaben
Personen
Ashok Kumar, PhD, is an assistant professor at Lovely Professional University, Phagwara, Punjab, India. He has 15+ years of teaching and research experience, filed 3 patents, and published many articles in international journals and conferences. His current areas of research interest include cloud computing, the Internet of Things, and mist computing.
Megha Bhushan, PhD, is an assistant professor at the School of Computing, DIT University, Dehradun, Uttarakhand, India. She has filed 4 patents and published many research articles in international journals and conferences. Her research interest includes software quality, software reuse, ontologies, artificial intelligence, and expert systems.
Jose Galindo, PhD, is currently in the Department of Computer Languages and Systems, University of Seville, Spain. He has developed many tools such as FaMa, FaMaDEB, FaMaOVM, TESALIA, and VIVID, and his research interests include recommender systems, software visualization, variability-intensive systems, and software product lines.
Lalit Garg, PhD, is a Senior Lecturer in the Department of Computer Information Systems, University of Malta, and an honorary lecturer at the University of Liverpool, UK. He has edited four books and published over 110 papers in refereed journals, conferences, and books. He has 12 patents and delivered more than twenty keynote speeches in different countries, and organized/chaired/co-chaired many international conferences.
Yu-Chen Hu, PhD, is a distinguished professor in the Department of Computer Science and Information Management, Providence University, Taichung City, Taiwan. His research interests include image and signal processing, data compression, information hiding, information security, computer network, and artificial network.
Inhalt
Preface xv
Part I: Demystifying Smart Healthcare 1
1 Deep Learning Techniques Using Transfer Learning for Classification of Alzheimer's Disease 3
Monika Sethi, Sachin Ahuja and Puneet Bawa
1.1 Introduction 4
1.2 Transfer Learning Techniques 6
1.3 AD Classification Using Conventional Training Methods 9
1.4 AD Classification Using Transfer Learning 12
1.5 Conclusion 16
References 16
2 Medical Image Analysis of Lung Cancer CT Scans Using Deep Learning with Swarm Optimization Techniques 23
Debnath Bhattacharyya, E. Stephen Neal Joshua and N. Thirupathi Rao
2.1 Introduction 24
2.2 The Major Contributions of the Proposed Model 26
2.3 Related Works 28
2.4 Problem Statement 32
2.5 Proposed Model 33
2.5.1 Swarm Optimization in Lung Cancer Medical Image Analysis 33
2.5.2 Deep Learning with PSO 34
2.5.3 Proposed CNN Architectures 35
2.6 Dataset Description 37
2.7 Results and Discussions 39
2.7.1 Parameters for Performance Evaluation 39
2.8 Conclusion 47
References 48
3 Liver Cancer Classification With Using Gray-Level Co-Occurrence Matrix Using Deep Learning Techniques 51
Debnath Bhattacharyya, E. Stephen Neal Joshua and N. Thirupathi Rao
3.1 Introduction 52
3.1.1 Liver Roles in Human Body 53
3.1.2 Liver Diseases 53
3.1.3 Types of Liver Tumors 55
3.1.3.1 Benign Tumors 55
3.1.3.2 Malignant Tumors 57
3.1.4 Characteristics of a Medical Imaging Procedure 58
3.1.5 Problems Related to Liver Cancer Classification 60
3.1.6 Purpose of the Systematic Study 61
3.2 Related Works 62
3.3 Proposed Methodology 66
3.3.1 Gaussian Mixture Model 68
3.3.2 Dataset Description 69
3.3.3 Performance Metrics 70
3.3.3.1 Accuracy Measures 70
3.3.3.2 Key Findings 74
3.3.3.3 Key Issues Addressed 75
3.4 Conclusion 77
References 77
4 Transforming the Technologies for Resilient and Digital Future During COVID-19 Pandemic 81
Garima Kohli and Kumar Gourav
4.1 Introduction 82
4.2 Digital Technologies Used 84
4.2.1 Artificial Intelligence 85
4.2.2 Internet of Things 85
4.2.3 Telehealth/Telemedicine 87
4.2.4 Cloud Computing 87
4.2.5 Blockchain 88
4.2.6 5g 89
4.3 Challenges in Transforming Digital Technology 90
4.3.1 Increasing Digitalization 91
4.3.2 Work From Home Culture 91
4.3.3 Workplace Monitoring and Techno Stress 91
4.3.4 Online Fraud 92
4.3.5 Accessing Internet 92
4.3.6 Internet Shutdowns 92
4.3.7 Digital Payments 92
4.3.8 Privacy and Surveillance 93
4.4 Implications for Research 93
4.5 Conclusion 94
References 95
Part II: Plant Pathology 101
5 Plant Pathology Detection Using Deep Learning 103
Sangeeta V., Appala S. Muttipati and Brahmaji Godi
5.1 Introduction 104
5.2 Plant Leaf Disease 105
5.3 Background Knowledge 109
5.4 Architecture of ResNet 512 V 2 111
5.4.1 Working of Residual Network 112
5.5 Methodology 113
5.5.1 Image Resizing 113
5.5.2 Data Augmentation 113
5.5.2.1 Types of Data Augmentation 114
5.5.3 Data Normalization 114
5.5.4 Data Splitting 116
5.6 Result Analysis 116
5.6.1 Data Collection 117
5.6.2 Feature Extractions 117
5.6.3 Plant Leaf Disease Detection 117
5.7 Conclusion 119
References 120
6 Smart Irrigation and Cultivation Recommendation System for Precision Agriculture Driven by IoT 123
N. Marline Joys Kumari, N. Thirupathi Rao and Debnath Bhattacharyya
6.1 Introduction 124
6.1.1 Background of the Problem 127
6.1.1.1 Need of Water Management 127
6.1.1.2 Importance of Precision Agriculture 127
6.1.1.3 Internet of Things 128
6.1.1.4 Application of IoT in Machine Learning and Deep Learning 129
6.2 Related Works 131
6.3 Challenges of IoT in Smart Irrigation 133
6.4 Farmers' Challenges in the Current Situation 135
6.5 Data Collection in Precision Agriculture 136
6.5.1 Algorithm 136
6.5.1.1 Environmental Consideration on Stage Production of Crop 140
6.5.2 Implementation Measures 141
6.5.2.1 Analysis of Relevant Vectors 141
6.5.2.2 Mean Square Error 141
6.5.2.3 Potential of IoT in Precision Agriculture 141
6.5.3 Architecture of the Proposed Model 143
6.6 Conclusion 147
References 147
7 Machine Learning-Based Hybrid Model for Wheat Yield Prediction 151
Haneet Kour, Vaishali Pandith, Jatinder Manhas and Vinod Sharma
7.1 Introduction 152
7.2 Related Work 153
7.3 Materials and Methods 155
7.3.1 Methodology for the Current Work 155
7.3.1.1 Data Collection for Wheat Crop 155
7.3.1.2 Data Pre-Processing 156
7.3.1.3 Implementation of the Proposed Hybrid Model 157
7.3.2 Techniques Used for Feature Selection 159
7.3.2.1 ReliefF Algorithm 159
7.3.2.2 Genetic Algorithm 161
7.3.3 Implementation of Machine Learning Techniques for Wheat Yield Prediction 162
7.3.3.1 K-Nearest Neighbor 162
7.3.3.2 Artificial Neural Network 163
7.3.3.3 Logistic Regression 164
7.3.3.4 Naïve Bayes 164
7.3.3.5 Support Vector Machine 165
7.3.3.6 Linear Discriminant Analysis 166
7.4 Experimental Result and Analysis 167
7.5 Conclusion 173
Acknowledgment 173
References 174
8 A Status Quo of Machine Learning Algorithms in Smart Agricultural Systems Employing IoT-Based WSN: Trends, Challenges and Futuristic Competences 177
Abhishek Bhola, Suraj Srivastava, Ajit Noonia, Bhisham Sharma and Sushil Kumar Narang
8.1 Introduction 178
8.2 Types of Wireless Sensor for Smart Agriculture 179
8.3 Application of Machine Learning Algorithms for Smart Decision Making in Smart Agriculture 179
8.4 ml and WSN-Based Techniques for Smart Agriculture 185
8.5 Future Scope in Smart Agriculture 188
8.6 Conclusion 190
References 190
Part III: Smart City and Villages 197
9 Impact of Data Pre-Processing in Information Retrieval for Data Analytics 199
Huma Naz, Sachin Ahuja, Rahul Nijhawan and Neelu Jyothi Ahuja
9.1 Introduction 200
9.1.1 Tasks Involved in Data Pre-Processing 200
9.2 Related Work 202
9.3 Experimental Setup and Methodology 205
9.3.1 Methodology 205
9.3.2 Application of Various Data Pre-Processing Tasks on Datasets 206
9.3.3 Applied Techniques 207
9.3.3.1 Decision Tree 207
9.3.3.2 Naive Bayes 207
9.3.3.3 Artificial Neural Network 208
9.3.4 Proposed Work 208
9.3.4.1 PIMA Diabetes Dataset (PID) 208
9.3.5 Cleveland Heart Disease Dataset 211
9.3.6 Framingham Heart Study 215
9.3.7 Diabetic Dataset 217
9.4 Experimental Result and Discussion 220
9.5 Conclusion and Future Work 222
References 222
10 Cloud Computing Security, Risk, and Challenges: A Detailed Analysis of Preventive Measures and Applications 225
Anurag Sinha, N. K. Singh, Ayushman Srivastava, Sagorika Sen and Samarth Sinha
10.1 Introduction 226
10.2 Background 228
10.2.1 History of Cloud Computing 228
10.2.1.1 Software-as-a-Service Model 230
10.2.1.2 Infrastructure-as-a-Service Model 230
10.2.1.3 Platform-as-a-Service Model 232
10.2.2 Types of Cloud Computing 232
10.2.3 Cloud Service Model 232
10.2.4 Characteristics of Cloud Computing 234
10.2.5 Advantages of Cloud Computing 234
10.2.6 Challenges in Cloud Computing 235
10.2.7 Cloud Security 236
10.2.7.1 Foundation Security 236
10.2.7.2 SaaS and PaaS Host Security 237
10.2.7.3 Virtual Server Security 237
10.2.7.4 Foundation Security: The Application Level 238
10.2.7.5 Supplier Data and Its Security 238
10.2.7.6 Need of Security in Cloud 239
10.2.8 Cloud Computing Applications 239
10.3 Literature Review 241
10.4 Cloud Computing Challenges and Its Solution 242
10.4.1 Solution and Practices for Cloud Challenges 246
10.5 Cloud Computing Security Issues and Its Preventive Measures 248
10.5.1 General Security Threats in Cloud 249
10.5.2 Preventive Measures 254
10.6 Cloud Data Protection and Security Using Steganography 258
10.6.1 Types of Steganography 259
10.6.2 Data Steganography in Cloud Environment 260
10.6.3 Pixel Value Differencing Method 261
10.7 Related Study 263
10.8 Conclusion 263
References 264
11 Internet of Drone Things: A New Age Invention 269
Prachi Dahiya
11.1 Introduction 269
11.2 Unmanned Aerial Vehicles 271
11.2.1 UAV Features and Working 274
11.2.2 IoDT Architecture 275
11.3 Application Areas 280
11.3.1 Other Application Areas 284
11.4 IoDT Attacks 285
11.4.1 Counter Measures 291
11.5 Fusion of IoDT With Other Technologies 296
11.6 Recent Advancements in IoDT 299
11.7 Conclusion 302
References 303
12 Computer Vision-Oriented Gesture Recognition System for Real-Time ISL Prediction 305
Mukul Joshi, Gayatri Valluri, Jyoti Rawat and Kriti
12.1 Introduction 305
12.2 Literature Review 307
12.3 System Architecture 309
12.3.1 Model Development Phase 309
12.3.2 Development Environment Phase 311
12.4 Methodology 312
12.4.1 Image Pre-Processing Phase 312
12.4.2 Model Building Phase 313
12.5 Implementation and Results 314
12.5.1 Performance 314
12.5.2 Confusion Matrix 318
12.6 Conclusion and Future Scope 318
References 319
13 Recent Advances in Intelligent Transportation Systems in India: Analysis, Applications, Challenges, and Future Work 323
Elamurugan Balasundaram, Cailassame Nedunchezhian, Mathiazhagan Arumugam and Vinoth Asaikannu
13.1 Introduction 324
13.2 A Primer on ITS 325
13.3 The ITS Stages 326
13.4 Functions of ITS 327
13.5 ITS Advantages 328
13.6 ITS Applications 329
13.7 ITS Across the World 331
13.8 India's Status of ITS 333
13.9 Suggestions for Improving India's ITS Position 334
13.10 Conclusion 335
References 335
14 Evolutionary Approaches in Navigation Systems for Road Transportation System 341
Noopur Tyagi, Jaiteg Singh and Saravjeet Singh
14.1 Introduction 342
14.1.1 Navigation System 343
14.1.2 Genetic Algorithm 347
14.1.3 Differential Evolution 348
14.2 Related Studies 349
14.2.1 Related Studies of Evolutionary Algorithms 351
14.3 Navigation Based on Evolutionary Algorithm 352
14.3.1 Operators and Terms Used in Evolutionary Algorithms 353
14.3.2 Operator and Terms Used in Evolutionary Algorithm 357
14.4 Meta-Heuristic Algorithms for Navigation 359
14.4.1 Drawbacks of DE 362
14.5 Conclusion 362
References 363
15 IoT-Based Smart Parking System for Indian Smart Cities 369
E. Fantin Irudaya Raj, M. Appadurai, M. Chithamabara Thanu and E. Francy Irudaya Rani
15.1 Introduction 370
15.2 Indian Smart Cities Mission 371
15.3 Vehicle Parking and Its Requirements in a Smart City Configuration 373
15.4 Technologies Incorporated in a Vehicle Parking System in Smart Cities 375
15.5 Sensors for Vehicle Parking System 383
15.5.1 Active Sensors 384
15.5.2 Passive Sensors 386
15.6 IoT-Based Vehicle Parking System for Indian Smart Cities 387
15.6.1 Guidance to the Customers Through Smart Devices 389
15.6.2 Smart Parking Reservation System 391
15.7 Advantages of IoT-Based Vehicle Parking System 392
15.8 Conclusion 392
References 393
16 Security of Smart Home Solution Based on Secure Piggybacked Key Exchange Mechanism 399
Jatin Arora and Saravjeet Singh
16.1 Introduction 400
16.2 IoT Challenges 404
16.3 IoT Vulnerabilities 405
16.4 Layer-Wise Threats in IoT Architecture 406
16.4.1 Sensing Layer Security Issues 407
16.4.2 Network Layer Security Issues 408
16.4.3 Middleware Layer Security Issues 409
16.4.4 Gateways Security Issues 410
16.4.5 Application Layer Security Issues 411
16.5 Attack Prevention Techniques 411
16.5.1 IoT Authentication 412
16.5.2 Session Establishment 413
16.6 Conclusion 414
References 414
17 Machine Learning Models in Prediction of Strength Parameters of FRP-Wrapped RC Beams 419
Aman Kumar, Harish Chandra Arora, Nishant Raj Kapoor and Ashok Kumar
17.1 Introduction 420
17.1.1 Defining Fiber-Reinforced Polymer 421
17.1.2 Types of FRP Composites 422
17.1.2.1 Carbon Fiber-Reinforced Polymer 422
17.1.2.2 Glass Fiber 423
17.1.2.3 Aramid Fiber 424
17.1.2.4 Basalt Fiber 424
17.2 Strengthening of RC Beams With FRP Systems 425
17.2.1 FRP-to-Concrete Bond 426
17.2.2 Flexural Strengthening of Beams With FRP Composite 427
17.2.3 Shear Strengthening of Beams With FRP Composite 427
17.3 Machine Learning Models 428
17.3.1 Prediction of Bond Strength 430
17.3.2 Estimation of Flexural Strength 434
17.3.3 Estimation of Shear Strength 434
17.4 Conclusion 441
References 441
18 Prediction of Indoor Air Quality Using Artificial Intelligence 447
Nishant Raj Kapoor, Ashok Kumar, Anuj Kumar, Aman Kumar and Harish Chandra Arora
18.1 Introduction 448
18.2 Indoor Air Quality Parameters 450
18.2.1 Physical Parameters 453
18.2.1.1 Humidity 453
18.2.1.2 Air Changes (Ventilation) 454
18.2.1.3 Air Velocity 454
18.2.1.4 Temperature 454
18.2.2 Particulate Matter 455
18.2.3 Chemical Parameters 456
18.2.3.1 Carbon Dioxide 456
18.2.3.2 Carbon Monoxide 456
18.2.3.3 Nitrogen Dioxide 456
18.2.3.4 Sulphur Dioxide 457
18.2.3.5 Ozone 457
18.2.3.6 Gaseous Ammonia 458
18.2.3.7 Volatile Organic Compounds 458
18.2.4 Biological Parameters 459
18.3 AI in Indoor Air Quality Prediction 459
18.4 Conclusion 464
References 465
Index 471
1
Deep Learning Techniques Using Transfer Learning for Classification of Alzheimer's Disease
Monika Sethi1, Sachin Ahuja2* and Puneet Bawa1
1 Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India
2 ED-Engineering at Chandigarh University, Punjab, India
Abstract
Alzheimer's disease (AD) is a severe disorder in which brain cells degenerate, increasing memory loss with treatment choices for AD symptoms varying based on the disease's stage, and as the disease progresses, individuals at certain phases undergo specific healthcare. The majority of existing studies make predictions based on a single data modality either they utilize magnetic resonance imaging (MRI)/positron emission tomography (PET)/diffusion tensor imaging (DTI) or the combination of these modalities. However, a thorough understanding of AD staging assessment can be achieved by integrating these data modalities and performance could be further enhanced using a combination of two or more modalities. However, deep learning techniques trained the network from scratch, which has the following drawbacks: (a) demands an enormous quantity of labeled training dataset that could be a problem for the medical field where physicians annotate the data, further it could be very expensive, (b) requires a huge amount of computational resources. (c) These models also require tedious and careful adjustments of numerous hyper-parameters, which results to under or overfitting and, in turn, to degraded performance. (d) With a limited medical training data set, the cost function might get stuck in a local-minima problem. In this chapter, a study is done based on the models used for AD diagnosis. Many researchers fine-tuned their networks instead of scratch training and utilized CaffeNet, GoogleNet, VGGNet-16, VGGNet-19, DenseNet with varying depths, Inception-V4, AlexNet, ResNet-18, ResNet-152, or even ensemble transfer-learning models pretrained on generalized images for AD classification performed better.
Keywords: Alzheimer disease, transfer learning, deep learning, parameter optimization
1.1. Introduction
In the United States, AD is the most widespread neurodegenerative condition and the sixth major cause of fatalities. The global disease burden of AD is expected to exceed $2 trillion by 2030, requiring preventative care [1]. Despite the tremendous study and advancements in clinical practice, nearly half of AD patients are correctly identified for anatomy and progression of the disease based on medical indicators. The existence of neu-rofibrillary tangles and amyloid plaques in histology is the most definitive evidence for AD. Consequently, the presence of plaque is not associated with the beginning of AD, but rather with sensory and neuron damage. Dr. Alois Alzheimer (a psychiatrist and neuropsychologist) was the origin for the naming of this disease, who studied the brain of a 51-year-old woman who died of severe cognitive impairment in 1906 [2]. Dr. Alois investigated her brain and discovered clumps, which were actually the accumulation of proteins in and around the neurons, resulting in their loss. The key characteristics for identifying or confirming the existence of the illness are shrinkage of the hippocampus and cerebral cortex, as well as growth of the ventricles. The hippocampus is essential in learning and memory, in addition to acting as a connection between the central nervous system of the body's organs. AD eventually destroys the portion of the brain that controls heart and respiratory activity, resulting in death [3].
Unfortunately, AD does not yet have a definitive cure [4]. Instead, the objective is to reduce the illness's development, treat suffering, manage learning disabilities, and enhance the quality of life. Clinical trials, on the other hand, can significantly slow down the progression of psychiatric disorders if the diagnosis is made early. Whereas more psychological therapies and, eventually, prevention or even a cure are essential (long-term) goals, early diagnosis may result in improved treatment outcomes benefits for diseased. Except in a few cases where genetic abnormalities may be identified, the precise cause of AD is still obscure.
The assessment of empirical biomarkers is necessary for the early treatment of disease [5]. A number of noninvasive neuroimaging approaches, including computed tomography (CT) scans, both structural and functional MRI and PET, have been explored for the prediction of AD. To produce cross sectional pictures of the bones, blood arteries, and soft tissues within the body, computer processing is used to integrate a succession of X-ray images recorded from different angle defined on your body. Plain X-rays do not give as much detail as CT scan imaging. An MRI scan employs a powerful magnet and radio waves to see at structures beneath the brain, according to the National Institute of Health. MRI scans are used by healthcare physicians to examine a variety of diseases, from damaged ligaments to cancer. To see and evaluate changes in cellular metabolism, PET is a functional imaging method, which thus employs radioactive additives termed as radiotracers.
Radiologists and clinicians, who are medical experts, analyze medical imaging data [6]. As a result of the probable tiredness of human specialists while evaluating images manually, a computer-assisted approach has proven to be beneficial for researchers as well as physicians. However, machine learning (ML) approaches are helping to improve the issue. Medical image analysis tasks need the use of ML to discover or learn useful features that characterize the correlations or patterns present in data. Since relevant or task-related characteristics are often created by human specialists on the basis of their domain expertise, it might be difficult for nonexperts to use ML techniques for their own research in the traditional manner. A number of projects are now working on the problem of learning sparse representations from training samples or pre-set dictionaries. Since then, there are attempts to generate sparse representations based on predefined dictionaries, which might be learned from training dataset. As a result of the concept of parsimony, sparse representation is used in many scientific fields. A sparsely inducing penalization and feature learning technique has been shown to be effective in medical image analysis when it comes to determining feature representation and selection [7]. Though data with a shallow architecture are still found to have meaningful patterns or regularities, techniques such as sparse representation or dictionary learning are still limited in their ability to represent them. Feature engineering has been incorporated into a learning phase in deep learning (DL), though, overcoming this issue [8]. Instead of manually extracting features, DL takes simply a collection of data with little preparation, if required, and then learns the valuable interpretations in an automatic method. Due to this shift in responsibility for feature extraction and selection, even non-experts in ML may now use DL effectively for their own research work, especially in the medical field for imaging analysis [9].
However, DL is afflicted by data dependency, one of the most signifi-cant problems. Comparatively to standard ML approaches, DL relies on a significant quantity of training data in order to discover hidden patterns in data. There is an interesting relationship between the size of the model in terms of the numbers of layers and the volume of information required.
Transfer learning (TL) eliminates the dependency of a huge amount of data requirement, which inspires us to utilize this to combat the problem of inadequate training data. This concept is driven by the idea that people may strategically utilize past knowledge to solve new problems or accomplish desirable results. The fundamental reasoning underlying this idea in ML was presented during a Neural Information Processing Systems (NIPS-95) symposium on "Learning to Learn," which emphasized the need of lifelong ML approaches that store and apply previously acquired information [10]. TL approaches have recently shown results in a variety of practical applications. In Verma et al. [11], researchers utilized TL methods to transfer text data across domains. For fixing natural language processing issues, structural correspondence learning was presented by an author in Nalavade et al. [12]. Researchers employed several Convolutional Neural Network (CNN)-based TL models to detect AD [13].
This chapter presents the results of several TL techniques employed by previous researchers to identify AD.
1.2. Transfer Learning Techniques
TL is an ML research subject that focuses on retaining information received while addressing the problem and adapting it to some other but similar issue. As an instance, knowledge acquired when learning to identify trucks may be used while aiming to classify other four-wheeler vehicles. In CNN, this may be implemented in one of two ways: either the weights of all CNN layers are coupled to some other CNN layer with classification Layer output, as well as just utilizing "off-the-shelf CNN features," whereby CNN serves like a generalized feature extractor to be analyzed later.
Several domains of knowledge engineering, such as classifier, prediction, and segmentation, have already experienced significant results using ML and data mining techniques [14]. Many ML...
