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New technologies and computing methodologies are now used to address the existing issues of urban traffic systems. The development of computational intelligence methods such as machine learning and deep learning, enables engineers to find innovative solutions to guide traffic in order to reduce transportation and mobility problems in urban areas.

This volume, Computational Intelligence for Sustainable Transportation and Mobility, presents several computing models for intelligent transportation systems, which may hold the key to achieving sustainable development goals by optimizing traffic flow and minimizing associated risks. The book begins with the basic computational Intelligence techniques for traffic systems and explains its applications in vehicular traffic prediction, model optimization, behavior analysis, traffic density estimation, and more. The main objectives of this book are to present novel techniques developed, new technologies and computational intelligence for sustainable mobility and transportation solutions, as well as giving an understanding of some Industry 4.0 trends.

Readers will learn how to apply computational intelligence techniques such as multiagent systems (MAS), whale optimization, artificial Intelligence (AI), deep neural networks (DNNs) so that they can to develop algorithms, models, and approaches for sustainable transportation operations.

Key Features:

- Provides an overview of machine learning models and their optimization for intelligent transportation systems in urban areas

- Covers classification of traffic behavior

- Demonstrates the application of artificial immune system algorithms for traffic prediction

- Covers traffic density estimation using deep learning models

- Covers Fog and edge computing for intelligent transportation systems

- Gives an IoT and Industry 4.0 perspective about intelligent transportation systems to readers

- Presents a current perspective on an urban hyperloop system for India

Bentham Science

Computational Intelligence For Data Analysis

Computational Intelligence for Sustainable Transportation and Mobility: Volume 1

This book presents use-cases of IoT, AI and Machine Learning (ML) for healthcare delivery and medical devices. It compiles 15 topics that discuss the applications, opportunities, and future trends of machine intelligence in the medical domain. The objective of the book is to demonstrate how these technologies can be used to keep patients safe and healthy and, at the same time, to empower physicians to deliver superior care.

Readers will be familiarized with core principles, algorithms, protocols, emerging trends, security problems, and the latest concepts in e-healthcare services. It also includes a quick overview of deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, practical methodology, and how they can be used to provide better solutions to healthcare related issues. The book is a timely update for basic and advanced readers in medicine, biomedical engineering, and computer science.

Key topics covered in the book:

- An introduction to the concept of the Internet of Medical Things (IoMT)

- Cloud-edge based IoMT architecture and performance optimization in the context of Medical Big Data

- A comprehensive survey on different IoMT interference mitigation techniques for Wireless Body Area Networks (WBANs)

- Artificial Intelligence and the Internet of Medical Things

- A review of new machine learning and AI solutions in different medical areas.

- A Deep Learning based solution to optimize obstacle recognition for visually impaired patients

- A survey of the latest breakthroughs in Brain-Computer Interfaces and their applications

- Deep Learning for brain tumor detection

- Blockchain and patient data management

Bentham Science Publishers

Machine Intelligence for Internet of Medical Things: Applications and Future Trends