Less-supervised Segmentation with CNNs: Scenarios, Models and Optimization reviews recent progress in deep learning for image segmentation under scenarios with limited supervision, with a focus on medical imaging. The book presents main app...Daugiau...
This introduces artificial neural network-based Lagrange optimization techniques for structural design in prestressed concrete based on Eurocode 2 and composite structures based on American Institute of Steel Construction and American Concrete Insti...Daugiau...
Neural networks are at the heart of AIso ensure youre on the cutting edge with this guide! For true beginners, get a crash course in Python and the mathematical concepts youll need to understand and create neural networks. Or jump right into pr...Daugiau...
Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resou...Daugiau...
This book illustrates recent advances in Neural Artificial Intelligent Theories and Applications discussed by selected papers presented at 30th edition of the International Workshops on Neural Network (WIRN 2023). The book discusses novel technolo...Daugiau...
A hands-on guide to powerful graph-based deep learning models! Learn how to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more.Graph Neural Networks in Action teaches you to create...Daugiau...
This book explores the stability analysis of neural networks and evolving intelligent systems, focusing on their ability to adapt to changing environments. It differentiates between neural networks, which have a static structure and...Daugiau...
Turchetti (Universita Politecnica delle Marche) investigates the properties of neural networks as sources of random functions, and whether approximating properties similar to those valid for deterministic functions hold for random functions. After in...Daugiau...
Neural approaches have traditionally excelled at perceptual tasks like pattern recognition, whereas symbolic frameworks have offered powerful methods for knowledge representation, logical inference, and interpretability, but the current AI landscape...Daugiau...
If only it were possible to develop automated and trainable neural systems that could justify their behavior in a way that could be interpreted by humans like a symbolic system. The field of Neurosymbolic AI aims to combine two disparate approaches t...Daugiau...
The second edition of Neuromorphic Computing Principles and Organization delves deeply into neuromorphic computing, focusing on designing fault-tolerant, scalable hardware for spiking neural networks. Each chapter includes exercis...Daugiau...
This book provides a direct method based on system solutions to address the problems related to the analysis and control of delayed neural networks. The method proposed in this book is important for the following reasons: It does not involve the c...Daugiau...
This two volume set provides the complete proceedings of the 1990 International Joint Conference on Neural Networks held in Washington, D.C. Complete with subject, author, and title indices, it provides an invaluable reference to the current state-of...Daugiau...
This book presents the proceedings of the NeuroIS Retreat 2024, June 9 - 11, Vienna, Austria, reporting on topics at the intersection of information systems (IS) research, neurophysiology and the brain sciences. Readers will discover the latest findi...Daugiau...
This book presents advanced AI approaches and their practical applications in traffic and energy systems, expanding the limits of existing research and practice. It showcases the potential of neural networks and graph models to greatly improve the e...Daugiau...
Deep learning has achieved impressive results in image classification, computer vision and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resour...Daugiau...
The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using tradi...Daugiau...
(Išleidimo metai: 14-Jan-2025, Paperback / softback, Leidėjas: Morgan Kaufmann Publishers In, ISBN-13: 9780443292026)
Neural Network Algorithms and Their Engineering Applications presents the relevant techniques used to improve the global search ability of neural network algorithms in solving complex engineering problems with multimodal properties. The book p...Daugiau...
This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The autho...Daugiau...
This book provides a new data augmentation method based on the local stochastic distribution patterns in natural time series data of global and regional seismicity rates and their correlated seismicity rates. The augmentation procedure is called t...Daugiau...
This book proposes high-efficiency modeling methods and novel intelligent control strategies for several representative flexible systems developed by means of neural networks and can be regarded as an authoritative reference for the study of dynamic...Daugiau...
(Išleidimo metai: 20-Dec-2024, Hardback, Leidėjas: River Publishers, ISBN-13: 9788770228343)
Intelligence in Chips: Integrated Sensors and Memristive Computing is an authoritative resource that navigates the exciting landscape of in-memory computing, neuromorphic circuits, and memristive technologies. This book curates expert ins...Daugiau...
This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. It concentrates on cutting-edge research in DL and GAN which includes crea...Daugiau...
The text presents mathematical modeling techniques such as wavelet transform, differential calculus, and numerical techniques for multi-dimensional data. It will serve as an ideal reference text for graduate students and academic researchers in di...Daugiau...
This book is written in a clear and thorough way to cover both the traditional and modern uses of artificial intelligence and soft computing. It gives an in-depth look at mathematical models, algorithms, and real-world problems that are hard to so...Daugiau...
Gexiang Zhang, Sergey Verlan, Tingfang Wu, Francis George C. Cabarle, Jie Xue, David Orellana-Martķn, Jianping Dong, Luis Valencia-Cabrera, Mario J. Pérez-Jiménez
Spiking neural P systems represent a significant advancement in the field of membrane computing, drawing inspiration from the communication patterns observed in neurons. Since their inception in 2006, these distributed and parallel neural-like com...Daugiau...
This book deals with the application of ANNs in real-world problems requiring data analysis and signal processing. Artificial neural networks (ANNs) have emerged in society thanks to the large number of applications that have been used in an awe-i...Daugiau...
This book serves as source of introductory material and reference for medical application development and related technologies by providing the detail implementation of the cutting-edge deep learning methodologies. It targets the cloud based advance...Daugiau...
Recurrent dynamical systems have become fundamental models that describe complex behaviour in a wide range of fields. Such systems can be found in the study of oscillators, population dynamics, climate fluctuations, control theory, neural networks d...Daugiau...
Primer to Neuromorphic Computing highlights critical and ongoing research into the diverse applications of neuromorphic computing. It includes an overview of primary scientific concepts for the research topic of neuromorphic computing, such...Daugiau...
This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners. It introduces a deep learning tool set with deep learning concepts interwoven to enhance understan...Daugiau...
Mathematical chaos in neural networks is a powerful tool that reflects the worlds complexity and has the potential to uncover the mysteries of the brains intellectual activity. Through this monograph, the authors aim to contribute to modern chaos re...Daugiau...
Simulation of brain neurons in real-time using biophysically-meaningful models is a pre-requisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experime...Daugiau...
This book focuses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passi...Daugiau...
This accessible shortform book describes storm surge forecasting to enable port managers and practitioners to forecast these and mitigate their effects. This is particularly useful as global warming increases the severity of typhoons, particularly w...Daugiau...
In this book, the stability analysis and estimator design problems are discussed for delayed discrete-time memristive neural networks. In each chapter, the analysis problems are firstly considered, where the stability, synchronization and other pe...Daugiau...
This book introduces AI-based Lagrange optimization techniques which enable more optimised concrete structural design, while conforming to codes of practice. The principles are outlined and then applied to the design of RC columns and beams, offer...Daugiau...
