Multimedia Data Processing and Computing

This book focuses on different applications of multimedia with supervised and unsupervised data engineering in the modern world. It includes AI-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, manufacturing, Data Science, automation in electronics industries, and many more relevant fields. Multimedia Data Processing and Computing provides a complete introduction to Machine Learning (ML) concepts, as well as practical guidance on how to use Machine Learning tools and techniques in real-world data engineering situations. It is divided into three sections. In this book on multimedia data engineering and Machine Learning, the reader will learn how to prepare inputs, interpret outputs, appraise discoveries, and employ algorithmic strategies that are at the heart of successful data mining. The chapters focus on the use of various Machine Learning algorithms, neural net- work algorithms, evolutionary techniques, fuzzy logic techniques, and Deep Learning techniques through projects, so that the reader can easily understand not only the concept of different algorithms but also the real-world implementation of the algorithms using IoT devices. The authors bring together concepts, ideas, paradigms, tools, methodologies, and strategies that span both supervised and unsupervised engineering, with a particular emphasis on multimedia data engineering. The authors also emphasize the need for developing a foundation of Machine Learning expertise in order to deal with a variety of real-world case studies in a variety of sectors such as biological communication systems, healthcare, security, finance, and economics, among others. Finally, the book also presents real-world case studies from Machine Learning ecosystems to demonstrate the necessary Machine Learning skills to become a successful practitioner. Using advanced techniques of Machine Learning such as deep neural networks, ChatGPT is able to comprehend natural language questions and generate responses that are human-like in nature. Deep neural networks are a specific type of Machine Learning algorithm that are designed to simulate the functioning of the human brain. This is accomplished by creating multiple connected levels of artificial neurons that are able to process data in a hierarchical fashion. The use of deep neural networks has enabled ChatGPT to learn from a large corpus of text data and generate responses that are contextually appropriate and linguistically sophisticated. Therefore, ChatGPT represents an impressive application of AI technology. By using Machine Learning algorithms to process natural language inputs, ChatGPT is capable of mimicking human-like conversations and generating responses that are relevant and coherent. The primary users for the book include undergraduate and postgraduate students, researchers, academicians, specialists, and practitioners in Computer Science and engineering. Cover Half Title Series Page Title Page Copyright Page Contents Preface Editor Biographies List of Contributors Chapter 1: A Review on Despeckling of the Earth’s Surface Visuals Captured by Synthetic Aperture Radar 1.1. Introduction 1.2. Synthetic Aperture Radar (SAR) 1.2.1. SAR Geometry 1.2.2. Frequency Bands and Characteristics 1.2.3. Frequency Polarization, Penetration, and Scattering 1.3. Applications of Sar Visuals 1.4. Inevitable Challenges in Sar Imagery 1.5. Formulation of Sar Despeckling Problem 1.6. Sar Despeckling Methodologies 1.6.1. Filtration-based Techniques 1.6.2. Optimization-based Techniques 1.6.3. Hybrid Techniques 1.6.4. Deep Network–based Techniques 1.7. Comparative Analysis 1.8. Conclusion and Future Scope References Chapter 2: Emotion Recognition Using Multimodal Fusion Models: A Review 2.1. Introduction 2.2. Emotion Theories and Models 2.3. Emotion Recognition and Deep Learning 2.3.1. Facial Expression Recognition 2.3.2. Speech Emotion Recognition 2.3.3. Multimodel Emotion Recognition 2.4. Multimodal Emotion Recognition 2.4.1. Multimodal Emotion Recognition Combining Audio and Text 2.4.2. Multimodal Emotion Recognition Combining Image and Text 2.4.3. Multimodal Emotion Recognition Combining Facial and Body Physiology 2.4.4. Other Multimodal Emotion Recognition Models 2.5. Databases 2.5.1. Database Descriptions 2.6. Conclusion and Future Work References Chapter 3: Comparison of CNN-Based Features with Gradient Features for Tomato Plant Leaf Disease Detection 3.1. Introduction 3.2. Proposed System for Tomato Disease Detection 3.2.1. Local Directional Pattern 3.2.2. Histogram of Oriented Gradient (HOG) 3.2.3. Convolutional Neural Network-based Features 3.2.4. Support Vector Machine–based Classification 3.3. Experimental Analysis 3.4. Conclusion References Chapter 4: Delay-sensitive and Energy-efficient Approach for Improving Longevity of Wireless Sensor Networks 4.1. Introduction 4.2. The Internet of Things 4.3. Routing Protocol for Low-Power and Lossy Networks 4.4. Related Work 4.5. Energy and Time Efficiency Network Model 4.5.1. Energy Efficiency Network Model 4.5.2. Time Efficiency Network Model 4.6. Results and Analysis 4.7. Conclusion and Future Scope References Chapter 5: Detecting Lumpy Skin Disease Using Deep Learning Techniques 5.1. Introduction 5.2. Material and Methods 5.2.1. Dataset 5.2.2. Research Methodology 5.2.3. Parameter Tuning 5.2.4. Proposed Architecture 5.3. Model Evaluation and Results 5.3.1. Environment of Implementation 5.3.2. Description of the Model 5.3.3. Results and Evaluation 5.4. Conclusion and Future Work Acknowledgments References Chapter 6: Forest Fire Detection Using a Nine-Layer Deep Convolutional Neural Network 6.1. Introduction 6.2. Literature Survey 6.3. Materials and Methods 6.3.1. About the Dataset 6.3.2. Proposed Methodology 6.4. Results and Discussion 6.5. Conclusion References Chapter 7: Identification of the Features of a Vehicle Using CNN 7.1. Introduction 7.2. Literature Review 7.2.1. Image Capture 7.2.2. Identification and Detection of Vehicle 7.2.3. Automatic License Plate Recognition 7.2.4. Vehicle Logo Recognition 7.2.5. Vehicle Model Recognition 7.2.6. Re-identification of a Vehicle 7.3. Conclusion 7.4. Open Research Areas References Chapter 8: Plant Leaf Disease Detection Using Supervised Machine Learning Algorithm 8.1. Introduction 8.2. Literature Survey 8.3. Proposed System 8.3.1. Leaf Disease Image Database 8.3.2. Image Preprocessing 8.3.3. Feature Extraction 8.3.4. Classification 8.4. Results 8.4.1. Analysis of the Qualitative Data 8.5. Conclusion References Chapter 9: Smart Scholarship Registration Platform Using RPA Technology 9.1. Introduction 9.2. Robotic Process Automation 9.3. Benefits of RPA 9.4. Background 9.5. Issues in Robotization of a Process 9.6. Tools and Technologies 9.6.1. RPA As a Rising Technology 9.6.2. Automation 360 9.7. Methodology 9.7.1. Implications in Existing System 9.7.2. Proposed System 9.7.3. Data Collection and Source File Creation 9.7.4. Creation and Structure of the Task Bot 9.8. Implementation 9.8.1. Setting User Credentials 9.8.2. Designing the Task Bot 9.8.3. Running the Task Bot 9.8.4. Implementing the Task Bot 9.8.5. Opening the CSV File 9.8.6. Launching the Scholarship Form 9.8.7. Populating the Web Form 9.8.8. Sending the E-mail 9.9. Results Analysis 9.10. Conclusion References Chapter 10: Data Processing Methodologies and a Serverless Approach to Solar Data Analytics 10.1. Introduction 10.1.1. Solar Thermal Energy 10.2. Literature Review 10.3. Data Processing Methodologies 10.3.1. Artificial Intelligence 10.3.2. Machine Learning 10.3.3. Deep Learning 10.4. Serverless Solar Data Analytics 10.4.1. Kinesis Data Streams 10.4.2. Kinesis Data Firehose 10.4.3. Amazon S3 10.4.4. Amazon Athena 10.4.5. Quicksight 10.4.6. F. Lambda 10.4.7. Amazon Simple Queue Service (SQS) 10.5. Conclusion References Chapter 11: A Discussion with Illustrations on World Changing ChatGPT – An Open AI Tool 11.1. Introduction 11.2. Literature Review 11.3. AI and ChatGPT 11.3.1. Code, Chat and Career: Pros and Cons of Using AI Language Models for Coding Industry 11.3.2. Jobs of Future: Will AI Displace or Augment Human Workers? 11.4. Impact of ChatGPT 11.4.1. Impact that ChatGPT Creates on Students 11.4.2. Impact that ChatGPT Creates on Teachers/Academicians 11.4.3. Impact that ChatGPT Creates on Parents 11.5. Applications of ChatGPT 11.6. Advantages of ChatGPT 11.7. Disadvantages of ChatGPT 11.8. Algorithms Used In ChatGPT 11.8.1. Illustration 1 11.8.2. Illustration 2 11.8.3. Illustration 3 11.9. Future of ChatGPT 11.10. Conclusion References Chapter 12: The Use of Social Media Data and Natural Language Processing for Early Detection of Parkinson’s Disease Symptoms and Public Awareness 12.1. Introduction 12.2. Literature Review 12.2.1. Parkinson’s Disease Detection and Diagnosis 12.2.2. Social Media and Health Research 12.2.3. Natural Language Processing in Health Research 12.2.4. Early Detection of Health Issues Using Social Media and NLP 12.2.5. Public Awareness and Health Communication 12.3. Methodology 12.3.1. Data Collection 12.3.2. Data Preprocessing 12.3.3. Feature Extraction 12.3.4. Machine Learning Models 12.4. Results 12.4.1. Data Collection and Preprocessing 12.4.2. Feature Extraction 12.4.3. Machine Learning Models 12.4.4. Evaluation Metrics 12.4.5. Performance Results 12.4.6. Feature Importance 12.5. Discussion 12.6. Conclusion References Chapter 13: Advancing Early Cancer Detection with Machine Learning: A Comprehensive Review of Methods and Applications 13.1. Introduction 13.2. Literature Review 13.3. Methodology 13.4. Results 13.5. Application of Research 13.6. Conclusion References Index