This volume provides students with the necessary tools to better understand the fields of neurobiological modeling, cluster analysis of proteins and genes.
Sensor networks have many interesting applications with great utility; however, their actually deployment and realization rely on continuous innovations and solutions to many challenging problems.
Volume III continues the author's quest for developing a pedagogical, self-contained, yet rigorous analytical theory of 1-D cellular automata via a nonlinear dynamics perspective.
This book provides a new forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN).
This book comprehensively studies fuzzy temporal and spatial information, starting from the basics on fuzzy set theory and temporal/spatial reasoning, the development of a new model to represent fuzzy temporal/spatial information, the study of efficient and complete reasoning algorithms, and their application in an information retrieval context.
This volume collects together most of the papers presented at the Twelfth Neural Computation and Psychology Workshop (NCPW12) held in 2010 at Birkbeck College (England).
The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.
Towards Neuromorphic Machine Intelligence: Spike-Based Representation, Learning, and Applications provides readers with in-depth understanding of Spiking Neural Networks (SNNs), which is a burgeoning research branch of Artificial Neural Networks (ANNs), AI, and Machine Learning that sits at the heart of the integration between Computer Science and Neural Engineering.
Revolutionizing Communication: The Role of Artificial Intelligence explores the wide-ranging effects of artificial intelligence (AI) on how we connect and communicate, changing social interactions, relationships, and the very structure of our society.
An Introduction to Universal Artificial Intelligence provides the formal underpinning of what it means for an agent to act intelligently in an unknown environment.
This book is a comprehensive guide that explores the latest developments in anomaly detection techniques across a range of fields, including cybersecurity, finance, image processing, sensor networks, social network analysis, health systems, and IoT systems.
Image processing plays a crucial role in various fields, including digital multimedia, automated vision detection and inspection, and pattern recognition.
The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks.
The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks.
Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolutionKey FeaturesImplement neuroevolution algorithms to improve the performance of neural network architecturesUnderstand evolutionary algorithms and neuroevolution methods with real-world examplesLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulationsBook DescriptionNeuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes.
As multicriteria decision-making (MCDM) continues to grow and evolve, machine learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems.
