Articles by "Artificial Intelligence"

Showing posts with label Artificial Intelligence. Show all posts

Alma Y. Alanis, Nancy Arana-Daniel, Carlos Lopez-Franco ... 176 pages - Publisher: Academic Press; (February, 2019) ... Language: English - ASIN: B07NGPX9MF by Amazon.

Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various problems, including complex nonlinear systems, cellular computational networks, waste water treatment, attack detection on cyber-physical systems, control of UAVs, biomechanical and biomedical systems, time series forecasting, biofuels, and more. Besides the real-time implementations, the book contains all the theory required to use the proposed methodologies for different applications.Presents the current trends for the solution of complex engineering problems that cannot be solved through conventional methods + Includes real-life scenarios where a wide range of artificial neural network architectures can be used to solve the problems encountered in engineering + Contains all the theory required to use the proposed methodologies for different applications

Radek Silhavy ... 501 pages - Publisher: Springer; (May, 2018) ... Language: English - ASIN: B07DBK5RFZ by Amazon.

This book presents the latest trends and approaches in artificial intelligence research and its application to intelligent systems. It discusses hybridization of algorithms, new trends in neural networks, optimisation algorithms and real-life issues related to the application of artificial methods. The book constitutes the second volume of the refereed proceedings of the Artificial Intelligence and Algorithms in Intelligent Systems of the 7th Computer Science On-line Conference 2018 (CSOC 2018), held online in April 2018.

Tom Taulli ... 200 pages - Publisher: Apress; (August, 2019) ... Language: English - ISBN-10: 1484250273 - ISBN-13: 978-1484250273.

Artificial intelligence touches nearly every part of your day. While you may initially assume that technology such as smart speakers and digital assistants are the extent of it, AI has in fact rapidly become a general-purpose technology, reverberating across industries including transportation, healthcare, financial services, and many more. In our modern era, an understanding of AI and its possibilities for your organization is essential for growth and success. Artificial Intelligence Basics has arrived to equip you with a fundamental, timely grasp of AI and its impact. Author Tom Taulli provides an engaging, non-technical introduction to important concepts such as machine learning, deep learning, natural language processing (NLP), robotics, and more. In addition to guiding you through real-world case studies and practical implementation steps, Taulli uses his expertise to expand on the bigger questions that surround AI. These include societal trends, ethics, and future impact AI will have on world governments, company structures, and daily life. Google, Amazon, Facebook, and similar tech giants are far from the only organizations on which artificial intelligence has had―and will continue to have―an incredibly significant result. AI is the present and the future of your business as well as your home life. Strengthening your prowess on the subject will prove invaluable to your preparation for the future of tech, and Artificial Intelligence Basics is the indispensable guide that you’ve been seeking.

Seyedali Mirjalili, Jin Song Dong ... 72 pages - Publisher: Springer; (July, 2019) ... Language: English - ASIN: B07VLFKLHW by Amazon.

This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

Yadong Cui ... 224 pages - Publisher: Springer; (September, 2019) ... Language: English - ASIN: B07Y4ZW2BZ by Amazon.

This book comprehensively describes the status quo of artificial intelligence technology applications in the judicial field in China. Written by Cui Yadong, the former President of Shanghai Senior People's Court, it is divided into three parts: the first part focuses mainly on the theoretical issues related to artificial intelligence and judicial applications. The second part highlights practical aspects, discussing the research and development process, the implementation of the"206 system" and the major breakthroughs. The third part then addresses lessons learned and the thinking, particularly the thinking on "building the future rule of law of artificial intelligence", a new topic that responds to people's concerns about the risks and challenges of the development of artificial intelligence. In this context, the book argues that the judicial task is twofold: On the one hand, it should actively promote the integration and application of AI in the judiciary, judicial intelligence, and judicial modernization. On the other hand, it should encourage the construction of a future rule of law system of artificial intelligence, highlight the role of the judiciary in dealing with future risks and challenges, bring the development of artificial intelligence into line with the rule of law, and use the rule of law to promote, standardize and guarantee the safe, reliable and controllable development of artificial intelligence.

Jacob Turner ... 400 pages - Publisher: Palgrave Macmillan; (October, 2018) ... Language: English - ISBN-10: 3319962345 - ISBN-13: 978-3319962344.

This book explains why AI is unique, what legal and ethical problems it could cause, and how we can address them. It argues that AI is unlike any other previous technology, owing to its ability to take decisions independently and unpredictably. This gives rise to three issues: responsibility--who is liable if AI causes harm; rights--the disputed moral and pragmatic grounds for granting AI legal personality; and the ethics surrounding the decision-making of AI. The book suggests that in order to address these questions we need to develop new institutions and regulations on a cross-industry and international level. Incorporating clear explanations of complex topics, Robot Rules will appeal to a multi-disciplinary audience, from those with an interest in law, politics and philosophy, to computer programming, engineering and neuroscience.

Abdulhamit Subasi ... 456 pages - Publisher: Academic Press; (March, 2019) ... Language: English - ISBN-10: 0128174447 - ISBN-13: 978-0128174449.

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.

G. A. Vijayalakshmi Pai ... 316 pages - Publisher: Wiley-ISTE; (December 27, 2017) ... Language: English - ASIN: B078X252PR by Amazon.

The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.

Marcos Lopez de Prado ... 400 pages - Publisher: Wiley; (February, 2018) ... Language: English - ASIN: B079KLDW21 by Amazon.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Ke-Lin Du, M. N. S. Swamy ... 824 pages - Publisher: Springer; (December, 2013) ... Language: English - ISBN-10: 144715570X - ISBN-13: 978-1447155706.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence and data mining.

Zacharias Voulgaris, Yunus Emrah Bulut ... 312 pages - Publisher: Technics Publications; (September, 2018) ... Language: English - ASIN: B07HJDNZL2 by Amazon.

Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world.  

The first two chapters introduce the field, with Chapter 1 surveying Deep Learning models and Chapter 2 providing an overview of algorithms beyond Deep Learning, including Optimization, Fuzzy Logic, and Artificial Creativity. The next chapters focus on AI frameworks; they contain data and Python and Julia code in a provided Docker, so you can practice. Chapter 3 covers Apache's MXNet, Chapter 4 covers TensorFlow, and Chapter 5 investigates Keras. After covering these Deep Learning frameworks, we explore a series of optimization frameworks, with Chapter 6 covering Particle Swarm Optimization (PSO), Chapter 7 on Genetic Algorithms (GAs), and Chapter 8 discussing Simulated Annealing (SA). Chapter 9 begins our exploration of advanced AI methods, by covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Chapter 10 discusses optimization ensembles and how they can add value to the Data Science pipeline. 
Chapter 11 contains several alternative AI frameworks including Extreme Learning Machines (ELMs), Capsule Networks (CapsNets), and Fuzzy Inference Systems (FIS). Chapter 12 covers other considerations complementary to the AI topics covered, including Big Data concepts, Data Science specialization areas, and useful data resources to experiment on.

A comprehensive glossary is included, as well as a series of appendices covering Transfer Learning, Reinforcement Learning, Autoencoder Systems, and Generative Adversarial Networks. There is also an appendix on the business aspects of AI in data science projects, and an appendix on how to use the Docker image to access the book's data and code. The field of AI is vast, and can be overwhelming for the newcomer to approach. This book will arm you with a solid understanding of the field, plus inspire you to explore further.

Artificial Neural Networks: Understand The Basic Concepts [Size: 285 MB] ... Artificial Intelligence is becoming progressively more relevant in today’s world. The rise of Artificial intelligence has the potential to transform our future more than any other technology. By using the power of algorithms, you can develop applications which intelligently interact with the world around you, from building intelligent recommender systems to creating self-driving cars, robots and chatbots. Neural networks are a key element of artificial intelligence.

Neural networks are one of the most fascinating machine learning models and are used to solve wide range of problems in different areas of artificial intelligence and machine learning. Yet too few really understand how neural networks actually work. This course will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. The purpose of this course is to make neural networks accessible to as many students as possible.

In this course I’m going to explain the key aspects of neural networks and provide you with a foundation to get started with advanced topics. You will build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. You’ll understand how to solve complex computational problems efficiently.

Marco Dorigo, Thomas Stützle ... 319 pages - Publisher: Bradford Book; (June, 2004) ... Language: English - ISBN-10: 0262042193 - ISBN-13: 978-0262042192

The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

Artificial Intelligence Masterclass: Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI Models [Size: 6.10 GB] ... Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Sounds tempting right… Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch. In this course, we will teach you how to develop the most powerful Artificial intelligence model based on the most robust Hybrid Intelligent System. So far this model proves to be the best state of the art AI ever created beating its predecessors at all the AI competitions with incredibly high scores. This Hybrid Model is aptly named the Full World Model, and it combines all the state of the art models of the different AI branches, including Deep Learning, Deep Reinforcement Learning, Policy Gradient, and even, Deep NeuroEvolution. Learn how to combine the below models in order to achieve best performing artificial intelligence system: Fully-Connected Neural Networks + Convolutional Neural Networks + Recurrent Neural Networks + Variational AutoEncoders + Mixed Density Networks + Genetic Algorithms + Evolution Strategies + Covariance Matrix Adaptation Evolution Strategy (CMA-ES) + Parameter-Exploring Policy Gradients.

Jan Romportl, Eva Zackova, Jozef Kelemen ... 219 pages - Publisher: Springer; (August, 2014) ... Language: English - ISBN-10: 3319096672 - ISBN-13: 978-3319096674

This book is an edited collection of chapters based on the papers presented at the conference “Beyond AI: Artificial Dreams” held in Pilsen in November 2012. The aim of the conference was to question deep-rooted ideas of artificial intelligence and cast critical reflection on methods standing at its foundations. Artificial Dreams epitomize our controversial quest for non-biological intelligence and therefore the contributors of this book tried to fully exploit such a controversy in their respective chapters, which resulted in an interdisciplinary dialogue between experts from engineering, natural sciences and humanities. While pursuing the Artificial Dreams, it has become clear that it is still more and more difficult to draw a clear divide between human and machine. And therefore this book tries to portrait such an image of what lies beyond artificial intelligence: we can see the disappearing human-machine divide, a very important phenomenon of nowadays technological society, the phenomenon which is often uncritically praised, or hypocritically condemned. And so this phenomenon found its place in the subtitle of the whole volume as well as in the title of the chapter of Kevin Warwick, one of the keynote speakers at “Beyond AI: Artificial Dreams”.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras + Sentiment analysis + Image recognition and classification + Regression analysis + K-Means Clustering + Principal Component Analysis + Train/Test and cross validation + Bayesian Methods + Decision Trees and Random Forests + Multivariate Regression + Multi-Level Models + Support Vector Machines + Reinforcement Learning + Collaborative Filtering + K-Nearest Neighbor + Bias/Variance Tradeoff + Ensemble Learning + Term Frequency / Inverse Document Frequency + Experimental Design and A/B Tests.

Mauro Castelli, L. Sekanina, M. Zhang, S. Cagnoni, P. García-Sánchez ... 323 pages - Publisher: Springer; (April, 2018) ... Language: English - ISBN-10: 3319775529 - ISBN-13: 978-3319775524 ...

This book constitutes the refereed proceedings of the 21st European Conference on Genetic Programming, EuroGP 2018, held in Parma, Italy, in April 2018, co-located with the Evo* 2018 events, EvoCOP, EvoMUSART, and EvoApplications. The 11 revised full papers presented together with 8 poster papers were carefully reviewed and selected from 36 submissions. The wide range of topics in this volume reflects the current state of research in the field. Thus, we see topics and applications including analysis of feature importance for metabolomics, semantic methods, evolution of boolean networks, generation of redundant features, ensembles of GP models, automatic design of grammatical representations, GP and neuroevolution, visual reinforcement learning, evolution of deep neural networks, evolution of graphs, and scheduling in heterogeneous networks.

Giuseppe Ciaburro, Balaji Venkateswaran ... 270 pages - Publisher: Packt Publishing; (September, 2017) ... Language: English - ISBN-10: 1788397878 - ISBN-13: 978-1788397872 ...

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.

S. Chakraverty, Susmita Mall ... 157 pages - Publisher: CRC Press; (July, 2017) ... Language: English - ASIN: B0744Q3RW2 by Amazon.

Differential equations play a vital role in the fields of engineering and science. Problems in engineering and science can be modeled using ordinary or partial differential equations. Analytical solutions of differential equations may not be obtained easily, so numerical methods have been developed to handle them. Machine intelligence methods, such as Artificial Neural Networks (ANN), are being used to solve differential equations, and these methods are presented in Artificial Neural Networks for Engineers and Scientists: Solving Ordinary Differential Equations. This book shows how computation of differential equation becomes faster once the ANN model is properly developed and applied.

Li Deng, Dong Yu ... 212 pages - Publisher: Now Publishers; (June, 2014) ... Language: English - ISBN-10: 1601988141 - ISBN-13: 978-1601988140 ...

Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been benefitting from recent research efforts, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning. Deep Learning: Methods and Applications is a timely and important book for researchers and students with an interest in deep learning methodology and its applications in signal and information processing.

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