Machine Learning and IoT for Intelligent Systems and Smart Applications
- 228 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Machine Learning and IoT for Intelligent Systems and Smart Applications
About This Book
The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. This book discusses algorithmic applications in the field of machine learning and IoT with pertinent applications. It further discusses challenges and future directions in the machine learning area and develops understanding of its role in technology, in terms of IoT security issues. Pertinent applications described include speech recognition, medical diagnosis, optimizations, predictions, and security aspects.
Features:
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- Focuses on algorithmic and practical parts of the artificial intelligence approaches in IoT applications.
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- Discusses supervised and unsupervised machine learning for IoT data and devices.
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- Presents an overview of the different algorithms related to Machine learning and IoT.
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- Covers practical case studies on industrial and smart home automation.
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- Includes implementation of AI from case studies in personal and industrial IoT.
This book aims at Researchers and Graduate students in Computer Engineering, Networking Communications, Information Science Engineering, and Electrical Engineering.
Frequently asked questions
Information
1 A Study on Feature Extraction and Classification Techniques for Melanoma Detection
- Contents
- 1.1 Introduction
- 1.2 Feature Extraction
- 1.2.1 Fourier Transform (FT)
- 1.2.2 Short Time Fourier Transform (STFT)
- 1.2.3 Wavelet Transform
- 1.2.3.1 Discrete Wavelet Transform
- 1.2.3.2 Discrete Curvelet Transform
- 1.2.3.3 Discrete Contourlet Transform
- 1.2.3.4 Discrete Shearlet Transform
- 1.2.3.5 Bendlet Transform
- 1.3 Classification
- 1.3.1 Logistic Regression
- 1.3.2 K-Nearest Neighbor
- 1.3.3 Decision Trees
- 1.3.4 Support Vector Machine
- 1.4 Skin Cancer Diagnostic System for Melanoma Detection
- 1.5 Conclusion
- References
1.1 Introduction
Estimated New Cases in 2025 | 340,271 |
Estimated New Cases in 2040 | 466,914 |
Table of contents
- Cover
- Half Title
- Series Page
- Title Page
- Copyright Page
- Contents
- Preface
- Editorsā Biographies
- contributors
- Chapter 1 A Study on Feature Extraction and Classification Techniques for Melanoma Detection
- Chapter 2 Machine Learning Based Microstrip Antenna Design in Wireless Communications
- Chapter 3 LCL-T Filter Based Analysis of Two Stage Single Phase Grid Connected Module with Intelligent FANN Controllers
- Chapter 4 Motion Vector Analysis Using Machine Learning Models to Identify Lung Damages for COVID-19 Patients
- Chapter 5 Enhanced Effective Generative Adversarial Networks Based LRSD and SP Learned Dictionaries with Amplifying CS
- Chapter 6 Deep Learning Based Parkinson's Disease Prediction System
- Chapter 7 Non-uniform Data Reduction Technique with Edge Preservation to Improve Diagnostic Visualization of Medical Images
- Chapter 8 A Critical Study on Genetically Engineered Bioweapons and Computer-Based Techniques as Counter Measure
- Chapter 9 An Automated Hybrid Transfer Learning System for Detection and Segmentation of Tumor in MRI Brain Images with UNet and VGG-19 Network
- Chapter 10 Deep Learning-Computer Aided Melanoma Detection Using Transfer Learning
- Chapter 11 Development of an Agent-Based Interactive Tutoring System for Online Teaching in School Using Classter
- Chapter 12 Fusion of Datamining and Artificial Intelligence in Prediction of Hazardous Road Accidents
- Index