Practical resource the importance of Machine Learning and Deep Learning applications in various technologies and important real-world situations
New-Age Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and more, delivers real-world applications in healthcare to identify diseases and diagnosis, such as by creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning.
Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The editors and contributors describe the importance of each subject area and detail why they are so important to us from a societal and human perspective.
Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, New-Age Machine Learning Applications includes information on:
- Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing
- Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules
- Ai and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change
- Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records
With its practical approach to the subject, New-Age Machine Learning Applications is an ideal resource for professionals working with smart technologies such as machine and deep learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more.
Table of Contents:
- Statistical similarity in machine learning
- Development of machine learning-based methodologies for adaptive intelligent e-learning systems and time series analysis techniques
- Time-series forecasting for stock market using convolutional neural networks.
- Comparative study for applicability of color histograms for CBIR used for crop leaf disease detection.
- Stock Index Forecasting Using RNN-Long Short Term Memory
- Study and analysis of machine learning models for detection of phishing URLs
- Real-world applications of blockchain technology in internet of things
- Advanced persistent threat: Korean cyber security knack model impost and applicability
- Integration of Blockchain Technology and Internet of Things: Challenges and its Solutions
10. Machine learning techniques for swot analysis of online education system
11. Crop yield and soil moisture prediction using machine learning algorithms.
12. Multi-rate signal processing in WSN for channel capacity and energy efficiency using machine learning.
13. Introduction to mechanical design of AI-based robotic system
About the Author :
Indranath Chatterjee is a Professor in the Department of Computer Engineering at Tongmyong University, South Korea. He received his PhD from University of Delhi, India and has authored several books and numerous research papers. His areas of research are AI, computer vision, computation neuroscience and medical imaging.
Sheetal Zalte is an Assistant Professor in the Department of Computer Science at Shivaji University, India. She earned her PhD from Shivaji University, India, and to date, she has published many research papers. Her research area is mobile adhoc network.
