Описание: Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures introduces AI-based Lagrange optimization techniques that can enable more rational engineering decisions for concrete structures while conforming to codes of practice. It shows how objective functions including cost, CO2 emissions, and structural weight of concrete structures are optimized either separately or simultaneously while satisfying constraining design conditions using an ANN-based Lagrange algorithm. Any design target can be adopted as an objective function.
Many optimized design examples are verified by both conventional structural calculations and big datasets. Uniquely applies the new powerful tools of AI to concrete structural design and optimizationMulti-objective functions of concrete structures optimized either separately or simultaneouslyDesign requirements imposed by codes are automatically satisfied by constraining conditionsHeavily illustrated in color with practical design examplesThe book suits undergraduate and graduate students who have an understanding of collegelevel calculus and will be especially beneficial to engineers and contractors who seek to optimize concrete structures.
Автор: Goldberg Yoav Название: Neural Network Methods in Natural Language Processing ISBN: 1627052984 ISBN-13(EAN): 9781627052986 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 11504.00 р. Наличие на складе: Нет в наличии.
Описание: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
Описание: Covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas, including medicine, finance, operations research, business, social media, security, and more. The book covers everything from the applications and uses of artificial neural networks to deep learning and non-linear problems.
Описание: The aim of this book is to handle different application problems of science and engineering using expert Artificial Neural Network (ANN). As such, the book starts with basics of ANN along with different mathematical preliminaries with respect to algebraic equations. Then it addresses ANN based methods for solving different algebraic equations viz. polynomial equations, diophantine equations, transcendental equations, system of linear and nonlinear equations, eigenvalue problems etc. which are the basic equations to handle the application problems mentioned in the content of the book. Although there exist various methods to handle these problems, but sometimes those may be problem dependent and may fail to give a converge solution with particular discretization. Accordingly, ANN based methods have been addressed here to solve these problems. Detail ANN architecture with step by step procedure and algorithm have been included. Different example problems are solved with respect to various application and mathematical problems. Convergence plots and/or convergence tables of the solutions are depicted to show the efficacy of these methods. It is worth mentioning that various application problems viz. Bakery problem, Power electronics applications, Pole placement, Electrical Network Analysis, Structural engineering problem etc. have been solved using the ANN based methods.
This book provides a starting point for software professionals to apply artificial neural networks for software reliability prediction without having analyst capability and expertise in various ANN architectures and their optimization.
Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process are presented. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.
Автор: Dehuri Satchidananda Et Al Название: Integration Of Swarm Intelligence And Artificial Neural Network ISBN: 9814280143 ISBN-13(EAN): 9789814280143 Издательство: World Scientific Publishing Рейтинг: Цена: 16790.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides a forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). This title accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning.
Are you thinking that as much as we want to look for logical frameworks for intelligence, there is no certainty or scientific proof that intelligence is as structured as we believe it to be?
As in the evolutionary process, where chaos and order wisely coexist, I see a research gap related to our brain and mind, typically related to focusing on models based solely on order.
But if we are researching Artificial Intelligence, why are we so attached to the order and models that are supposed to be those of our brain?
Or, what binds us so much to what we see only, without opening spaces to what we don't see, if only to consider them small pieces of chaos?
In this openness and vision, when it comes to intelligence, I propose a new concept: that of unstructured intelligence, which I will try to explain in this book.
In this book, you will learn:
Automatic Learning
Machine Learning Paradigms
Inductive Learning
Induction Of Decision Trees
The relevance of attributes
Algorithms
Cluster
And Much more...
I think one of the main reasons for AI's long winter was that we went deep into it, creating architectures focused on existing paradigms, with little investment in new technologies and standards, such as machine learning itself.
But are we aren't repeating the same mistake in this new wave of AI?
If so, I consider the main mistake too much focus on artificial neural network architectures, as if this was the solution to solving complex learning problems in the human pattern or even the main door to generic artificial intelligence with semantic analysis capabilities.
And a possible solution to avoid the same history of past failure, perhaps, is to tackle high complexity real-world learning problems collectively and collaboratively, such as creating AI systems that can teach them to learn for themselves, like us humans.
So the architecture that seems to be the most logical for such problems is precisely the hybrid, where we have the most varied types of learning. In fact, before we are born, we are already learning in a hybrid way, with labeled and unlabeled data, by its very nature, and all its mechanisms of evolution.
You may think that you don't remember any important labeled data when you were a baby or child, but your mind and brain did a swell job to solve the puzzles that required some labeling to move on, as unsupervised learning systems follow.
So we can think of a similar machine architecture where the basis for all inferences is supervised learning, but capable of labeling any data that is not done by humans or other machines. And even criticize existing labels.
We are actually talking about machine learning - unsupervised - to generate labels for machine learning.
And creativity, in my view, is one of the essential links to evolve in understanding and formalizing new machine learning models.
Do you really want to easily learn and understand Machine Learning?
If so, get started today: scroll to the top, and click "BUY NOW"
Автор: Quang Hung Do Название: Artificial Neural Network Applications in Business and Engineering ISBN: 1799832384 ISBN-13(EAN): 9781799832386 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 39085.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In today's modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method.
Artificial Neural Network Applications in Business and Engineering is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.
Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao Название: Network Embedding: Theories, Methods, and Applications ISBN: 1636390447 ISBN-13(EAN): 9781636390444 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12751.00 р. Наличие на складе: Нет в наличии.
Описание:
Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.
This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.
Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao Название: Network Embedding: Theories, Methods, and Applications ISBN: 1636390463 ISBN-13(EAN): 9781636390468 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 15939.00 р. Наличие на складе: Нет в наличии.
Описание:
Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.
This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.
Автор: Li Hong-xing Название: Fuzzy Systems To Quantum Mechanics ISBN: 9811211183 ISBN-13(EAN): 9789811211188 Издательство: World Scientific Publishing Рейтинг: Цена: 22968.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This unique compendium represents important action of fuzzy systems to quantum mechanics. From fuzzy sets to fuzzy systems, it also gives clear descriptions on the development on fuzzy logic, where the most important result is the probability presentation of fuzzy systems.
The important conclusions on fuzzy systems are used in the study of quantum mechanics, which is a very new idea. Eight important conclusions are obtained. The author has proved that mass-point motions in classical mechanics must have waves, which means that any mass-point motion in classical mechanics has wave mass-point dualism as well as any microscopic particle motion must have wave-particle dualism. Based on this conclusion, it has been proven that classical mechanics and quantum mechanics are unified.
Автор: by Shashi Narayan, Claire Gardent Название: Deep Learning Approaches to Text Production ISBN: 1681737604 ISBN-13(EAN): 9781681737607 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 14276.00 р. Наличие на складе: Нет в наличии.
Описание: Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in text-to-text transformations such as sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, and text summarisation. This book offers an overview of the fundamentals of neural models for text production. In particular, we elaborate on three main aspects of neural approaches to text production: how sequential decoders learn to generate adequate text, how encoders learn to produce better input representations, and how neural generators account for task-specific objectives. Indeed, each text-production task raises a slightly different challenge (e.g, how to take the dialogue context into account when producing a dialogue turn, how to detect and merge relevant information when summarising a text, or how to produce a well-formed text that correctly captures the information contained in some input data in the case of data-to-text generation). We outline the constraints specific to some of these tasks and examine how existing neural models account for them. More generally, this book considers text-to-text, meaning-to-text, and data-to-text transformations. It aims to provide the audience with a basic knowledge of neural approaches to text production and a roadmap to get them started with the related work. The book is mainly targeted at researchers, graduate students, and industrials interested in text production from different forms of inputs.
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