| Description |
1 online resource |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- About the Editors -- Contributors -- Chapter 1: Skin Cancer Classification Using Image Processing with Machine Learning Techniques -- 1.1 Introduction -- 1.2 Related Works -- 1.3 Materials and Methods -- 1.3.1 Dataset -- 1.3.2 Preprocessing Operations -- 1.3.3 LCNet Architecture -- 1.4 Results and Discussion -- 1.5 Conclusion -- References -- Chapter 2: Trusted Location Information Verification Using Blockchain in Internet of Vehicles -- 2.1 Introduction -- 2.2 Related Work |
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2.3 Trusted Location Information Verification Using Blockchain -- 2.3.1 Assumptions -- 2.3.2 System Model -- 2.3.3 Location Sharing -- 2.3.4 Location Verification -- 2.4 Results and Simulation -- 2.4.1 Location Leakage -- 2.4.2 Channel Capacity Utilization -- 2.4.3 Message Delivery Success Rate -- 2.4.4 Processing Time -- 2.4.5 Security Attack Resilience -- 2.5 Conclusion -- References -- Chapter 3: Comparative Analysis of Word-Embedding Techniques Using LSTM Model -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Methodology -- 3.3.1 Dataset -- 3.3.2 Word-Embedding Techniques -- 3.3.2.1 Word2vec |
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3.3.2.2 GloVe -- 3.3.2.3 FastText -- 3.3.2.4 BERT -- 3.3.3 LSTM Deep Learning Classifier -- 3.3.4 Evaluation Metric -- 3.4 Results and Discussion -- 3.5 Conclusion and Future Work -- References -- Chapter 4: A Deep Learning Approach for Mask-Based Face Detection -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Dataset -- 4.4 Proposed System -- 4.4.1 TensorFlow -- 4.4.2 Keras -- 4.4.3 OpenCV -- 4.4.4 Numpy -- 4.4.5 Convolution Neural Network (CNN) -- 4.5 System Flow Chart -- 4.6 Evaluating Performance Using Performance Matrix -- 4.6.1 Experiments and Result -- 4.7 Conclusion and Future Scope |
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6.3.2.1 Radial Basis Function Neural Network -- 6.3.2.2 Support Vector Machine -- 6.3.3 Homogeneous Ensemble Classifiers -- 6.3.3.1 Dagging -- 6.3.3.2 ECOC -- 6.3.3.3 Proposed Bagged RBF and SVM Classifiers -- 6.4 Performance Evaluation Measures -- 6.4.1 Cross-Validation Technique -- 6.4.2 Criteria for Evaluation -- 6.5 Experimental Results and Discussion -- 6.5.1 Vehicle Dataset Description -- 6.5.2 Experiments and Analysis -- 6.6 Conclusion -- Acknowledgment -- References -- Chapter 7: Design and Implementation of a Network Security Model within a Local Area Network -- 7.1 Introduction |
| Summary |
This book focuses on data analytics with machine learning using IoT and blockchain technology. Integrating these three fields by examining their interconnections, Intelligent Data Analytics, IoT, and Blockchain examines the opportunities and challenges of developing systems and applications exploiting these technologies. Written primarily for researchers who are working in this multi-disciplinary field, the book also benefits industry experts and technology executives who want to develop their organizations' decision-making capabilities. Highlights of the book include: Using image processing with machine learning techniques A deep learning approach for facial recognition A scalable system architecture for smart cities based on cognitive IoT Source authentication of videos shared on social media Survey of blockchain in healthcare Accident prediction by vehicle tracking Big data analytics in disaster management Applicability, limitations, and opportunities of blockchain technology The book presents novel ideas and insights on different aspects of data analytics, blockchain technology, and IoT. It views these technologies as interdisciplinary fields concerning processes and systems that extract knowledge and insights from data. Focusing on recent advances, the book offers a variety of solutions to real-life challenges with an emphasis on security |
| Notes |
"An Auerbach book." |
| Bibliography |
Includes bibliographical references and index |
| Notes |
7.1.1 Problem Statement |
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Bashir Alam is a professor at Jamia Millia Islamia, New Delhi, India, where he heads the Department of Computer Engineering and obtained a PhD degree. He has 22 years of teaching and research experience. His areas of research include Big Data analytics, Artificial Intelligence, parallel and distributed systems, Cloud Computing, machine learning, GPU computing, blockchain, and information security. Mansaf Alam is a professor in the Department of Computer Science, Faculty of Natural Sciences, Jamia Millia Islamia. A Young Faculty Research Fellow and the Editor-in-Chief of the Journal of Applied Information Science, he pursues research in Artificial Intelligence, Big Data analytics, machine learning, deep learning, Cloud Computing, and data mining |
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Description based on online resource; title from digital title page (viewed on April 03, 2024) |
| Subject |
Blockchains (Databases)
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Machine learning.
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Internet of things.
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Data mining.
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COMPUTERS / Data Processing / General
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COMPUTERS / Artificial Intelligence
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Machine learning
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| Form |
Electronic book
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| Author |
Alam, Bashir, editor.
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Alam, Mansaf, editor.
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| ISBN |
9781000962154 |
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1000962156 |
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9781003371380 |
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1003371388 |
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9781000962123 |
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1000962121 |
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