Articles by "Artificial Intelligence"

Showing posts with label Artificial Intelligence. Show all posts

A. Kaveh ... 426 pages - Publisher: Springer; 2nd edition (November, 2016) ... Language: English - AmazonSIN: B01MSJNVOO.

This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally be utilized for optimal design of other systems such as hydraulic and electrical networks. In the second edition seven new chapters are added consisting of the new developments in the field of optimization. These chapters consist of the Enhanced Colliding Bodies Optimization, Global Sensitivity Analysis, Tug of War Optimization, Water Evaporation Optimization, Vibrating Particle System Optimization and Cyclical Parthenogenesis Optimization algorithms. A chapter is also devoted to optimal design of large scale structures.

Laura Graesser, Wah Loon Keng ... 416 pages - ISBN-13: 978-0135172384 - ISBN-10: 0135172381 ... Publisher: Addison-Wesley Professional; (December, 2019) - Language: English.


The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice: Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games–such as Go, Atari games, and DotA 2–to robotics.

Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python: Understand each key aspect of a deep RL problem + Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) + Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) + Understand how algorithms can be parallelized synchronously and asynchronously + Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work + Explore algorithm benchmark results with tuned hyperparameters + Understand how deep RL environments are designed.

Danny Kopec, Christopher Pileggi, David Ungar, Shweta Shetty ... 350 pages - ISBN-10: 194453458X - ISBN-13: 978-1944534585 ... Publisher : Mercury Learning (June, 2016) - Language: : English.

This book lends insight into solving some well-known AI problems using the most efficient problem-solving methods by humans and computers. The book discusses the importance of developing critical-thinking methods and skills, and develops a consistent approach toward each problem. This book assembles in one place a set of interesting and challenging AI–type problems that students regularly encounter in computer science, mathematics, and AI courses. These problems are not new, and students from all backgrounds can benefit from the kind of deductive thinking that goes into solving them. The book is especially useful as a companion to any course in computer science or mathematics where there are interesting problems to solve.

Features: •Addresses AI and problem-solving from different perspectives •Covers classic AI problems such as Sudoku, Map Coloring, Twelve Coins, Red Donkey, Cryptarithms, Monte Carlo Methods, Rubik’s Cube, Missionaries/Cannibals, Knight’s Tour, Monty Hall, and more •Includes a companion disc with source code, solutions, figures, and more •Offers playability sites where students can exercise the process of developing their solutions •Describes problem-solving methods that might be applied to a variety of situations.

Yorick Wilks ... 176 pages - Publisher: Icon Books Ltd.; (June, 2019) ... Language: English - AmazonSIN: B07RL4MCXK.

Artificial intelligence has long been a mainstay of science fiction and increasingly it feels as if AI is entering our everyday lives, with technology like Apple’s Siri now prominent, and self-driving cars almost upon us. But what do we actually mean when we talk about ‘AI’? Are the sentient machines of 2001 or The Matrix a real possibility or will real-world artificial intelligence look and feel very different? What has it done for us so far? And what technologies could it yield in the future?. AI expert Yorick Wilks takes a journey through the history of artificial intelligence up to the present day, examining its origins, controversies and achievements, as well as looking into just how it works. He also considers the future, assessing whether these technologies could menace our way of life, but also how we are all likely to benefit from AI applications in the years to come. Entertaining, enlightening, and keenly argued, this is the essential one-stop guide to the AI debate.

Noah Gift ... 272 pages - Publisher: Addison-Wesley Professional; (September, 2018) ... Language: English - ISBN-10: 0134863860 - ISBN-13: 978-0134863863.

Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning: Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value.

Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need + Quickly review all the Python you need to start building machine learning applications + Master the AI and ML tool chain and project lifecycle + Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn + Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems + Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services + Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more + Work with Microsoft Azure AI APIs + Walk through building six real-world AI applications, from start to finish.

Ralf T. Kreutzer, Marie Sirrenberg ... 313 pages - Publisher: Springer; (September, 2019) ... Language: English - ISBN-10: 3030252701 - ISBN-13: 978-3030252700.

Artificial Intelligence (AI) will change the lives of people and businesses more fundamentally than many people can even imagine today. This book illustrates the importance of AI in an era of digitalization. It introduces the foundations of AI and explains its benefits and challenges for companies and entire industries. In this regard, AI is approached not just as yet another technology, but as a fundamental innovation, which will spread into all areas of the economy and life, and will disrupt business processes and business models in the years to come. In turn, the book assesses the potential that AI holds, and clarifies the framework that is necessary for pursuing a responsible approach to AI. In a series of best-practice cases, the book subsequently highlights a broad range of sectors and industries, from production to services; from customer service to marketing and sales; and in industries like retail, health care, energy, transportation and many more. In closing, a dedicated chapter outlines a roadmap for a specific corporate AI journey.

Jeff Heaton ... 244 pages - Publisher: Heaton Research, Inc.; (August, 2014) ... Language: English - AmazonSIN: B00MYLNLSQ.

Nature can be a great source of inspiration for artificial intelligence algorithms because its technology is considerably more advanced than our own. Among its wonders are strong AI, nanotechnology, and advanced robotics. Nature can therefore serve as a guide for real-life problem solving. In this book, you will encounter algorithms influenced by ants, bees, genomes, birds, and cells that provide practical methods for many types of AI situations. Although nature is the muse behind the methods, we are not duplicating its exact processes. The complex behaviors in nature merely provide inspiration in our quest to gain new insights about data.

Artificial Intelligence for Humans is a book series meant to teach AI to those readers who lack an extensive mathematical background. The reader only needs knowledge of basic college algebra and computer programming. Additional topics are thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, and Python. Other languages are planned. No knowledge of biology is needed to read this book.

Edward B. Magrab, Shapour Azarm ... 848 pages - Publisher: Prentice Hall; 3rd edition (January, 2010) ... Language: English - ISBN-10: 0131991108 - ISBN-13: 978-0131991101.

An Engineer's Guide to MATLAB, is an authoritative guide to generating readable, compact, and verifiably correct MATLAB programs. It is ideal for undergraduate engineering courses in Mechanical, Aeronautical, Civil, and Electrical engineering that require/use MATLAB. This highly respected guide helps students develop a strong working knowledge of MATLAB that can be used to solve a wide range of engineering problems. Since solving these problems usually involves writing relatively short, one-time-use programs, the authors demonstrate how to effectively develop programs that are compact yet readable, easy to debug, and quick to execute. Emphasis is on using MATLAB to obtain solutions to several classes of engineering problems, so technical material is presented in summary form only.

Jeff Heaton ... 375 pages - Publisher: Heaton Research, Inc.; (November, 2015) ... Language: English - AmazonSIN: B0184WRDEQ.

Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.

Allen Downey ... 200 pages - Publisher: O'Reilly Media; 2nd edition (July, 2018) ... Language: English - ASIN: B07FFC87K8 by Amazon.

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations. In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform + Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines + Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata + Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism. Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

Ivan Nunes da Silva, Danilo Hernane Spatti ... 307 pages - Publisher: Springer; (August, 2016) - Language: English - ISBN-10: 3319431617 - ISBN-13: 978-3319431611 ...

This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

E. S. Gopi ... 256 pages - Publisher: Springer; (October, 2019) ... Language: English - ASIN: B07Z9MS1BZ by Amazon.

This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way, using Matlab to illustrate topics and concepts. The author covers computational intelligence tools like particle swarm optimization, bacterial foraging, simulated annealing, genetic algorithm, and artificial neural networks. The Matlab based illustrations along with the code are given for every topic. Readers get a quick basic understanding of various pattern recognition techniques using only the required depth in math. The Matlab program and algorithm are given along with the running text, providing clarity and usefulness of the various techniques.

Presents pattern recognition and the computational intelligence using Matlab; Includes mixtures of theory, math, and algorithms, letting readers understand the concepts quickly; Outlines an array of classifiers, various regression models, statistical tests and the techniques for pattern recognition using computational intelligence.

Yen-Wei Chen, Lakhmi C. Jain ... 218 pages - Publisher: Springer; (November, 2019) ... Language: English - ASIN: B081R8DDN6 by Amazon.

This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data.

Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.

Yinyan Zhang, Shuai Li, Xuefeng Zhou ... 225 pages - Publisher: Springer; (November, 2019) ... Language: English - ISBN-10: 3030333833 - ISBN-13: 978-3030333836.

This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.

Gul Muhammad Khan ... 139 pages - Publisher: Springer; (October, 2017) - Language: English - ASIN: B076YWYQ38 by Amazon.

This book presents recent research on the evolution of artificial neural development, and searches for learning genes. It is fascinating to see how all biological cells share virtually the same traits, but humans have a decided edge over other species when it comes to intelligence. Although DNA decides the form each particular species takes, does it also account for intelligent behaviour in living beings.

The authors explore the factors that are perceived as intelligent behaviour in living beings and the incorporation of these factors in machines using genetic programming, which ultimately provides a platform for exploring the possibility of machines that can learn by themselves, i.e. that can “learn how to learn”. The book will be of interest not only to the specialized scientific community pursuing machine intelligence, but also general readers who would like to know more about the incorporation of intelligent behaviour in machines, inspired by the human brain.

Robert Kozma, Cesare Alippi, Yoonsuck Choe, Francesco Carlo Morabito ... 352 pages - Publisher: Academic Press; (November, 2018) ... Language: English - ISBN-10: 0128154802 - ISBN-13: 978-0128154809.

Artificial Intelligence in the Age of Neural Networks and Brain Computing demonstrates that existing disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity and smart autonomous search engines. The book covers the major basic ideas of brain-like computing behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as future alternatives. The success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel and Amazon can be interpreted using this book.

Nikola Kirilov Kasabov ... 738 pages - Publisher: Springer; (August, 2018) ... Language: English - ASIN: B07GXCN2QN by Amazon.

Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.

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.

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