Computational Intelligence For Signal And Image Processing Volume Ii
Download Computational Intelligence For Signal And Image Processing Volume Ii PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Computational Intelligence For Signal And Image Processing Volume Ii book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Computational Intelligence for Signal and Image Processing, volume II
Author: Deepika Koundal
language: en
Publisher: Frontiers Media SA
Release Date: 2025-03-24
The first volume of this collection comprised 10 research articles that focused on the applications of Computational Intelligence for Signal and Image Processing, such as education, healthcare, and security. The findings presented in this Research Topic showcased the active development and research within the field of Computational Intelligence methods for the times ahead. Due to the success of that first volume and to facilitate its progression, this second volume embarks on an intriguing exploration at the intersection of neuroscience and cutting-edge technology. This edition focuses on algorithms inspired by the intricacies of the brain, delving into how these algorithms act as catalysts for the evolution of methodologies in image/video and signal processing, IoT applications, and beyond. It highlights the profound potential of brain-inspired algorithms to revolutionize various domains, paving the way for innovation and efficiency.
Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems
This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations.
Computational Intelligence - Volume II
Author: Hisao Ishibuchi
language: en
Publisher: EOLSS Publications
Release Date: 2015-12-30
Computational intelligence is a component of Encyclopedia of Technology, Information, and Systems Management Resources in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. Computational intelligence is a rapidly growing research field including a wide variety of problem-solving techniques inspired by nature. Traditionally computational intelligence consists of three major research areas: Neural Networks, Fuzzy Systems, and Evolutionary Computation. Neural networks are mathematical models inspired by brains. Neural networks have massively parallel network structures with many neurons and weighted connections. Whereas each neuron has a simple input-output relation, a neural network with many neurons can realize a highly non-linear complicated mapping. Connection weights between neurons can be adjusted in an automated manner by a learning algorithm to realize a non-linear mapping required in a particular application task. Fuzzy systems are mathematical models proposed to handle inherent fuzziness in natural language. For example, it is very difficult to mathematically define the meaning of “cold” in everyday conversations such as “It is cold today” and “Can I have cold water”. The meaning of “cold” may be different in a different situation. Even in the same situation, a different person may have a different meaning. Fuzzy systems offer a mathematical mechanism to handle inherent fuzziness in natural language. As a result, fuzzy systems have been successfully applied to real-world problems by extracting linguistic knowledge from human experts in the form of fuzzy IF-THEN rules. Evolutionary computation includes various population-based search algorithms inspired by evolution in nature. Those algorithms usually have the following three mechanisms: fitness evaluation to measure the quality of each solution, selection to choose good solutions from the current population, and variation operators to generate offspring from parents. Evolutionary computation has high applicability to a wide range of optimization problems with different characteristics since it does not need any explicit mathematical formulations of objective functions. For example, simulation-based fitness evaluation is often used in evolutionary design. Subjective fitness evaluation by a human user is also often used in evolutionary art and music. These volumes are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers.