Название: New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms
Автор: Раtriсiа Меlin, Оsсаr Саstillо
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2024
Страниц: 204
Язык: английский
Формат: pdf (true), epub
Размер: 35.6 MB

We describe in this book new directions on the theoretical developments of fuzzy logic, neural networks, and meta-heuristic algorithms, as well as their hybrid combinations, and their application in areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and optimization of complex problems. There are papers with the main topics from type-1 to type-3 fuzzy logic, which basically consists of a group of papers that propose new concepts and algorithms based on type-1, type-2, and type-3 fuzzy logic and their applications. There are also papers that present the theory and practice of meta-heuristics in diverse application areas. There are interesting papers on different applications of fuzzy logic, neural networks, and hybrid intelligent systems in medical problems. In addition, we can find papers describing applications of fuzzy logic, neural networks, and meta-heuristics in robotics problems. Another set of papers presents the theory and practice of neural networks in diverse application areas, including convolutional and deep learning neural networks. There are also a group of papers that present the theory and practice of optimization and evolutionary algorithms in different areas of application. Finally, we can find a group of papers describing the applications of fuzzy logic, neural networks, and meta-heuristics in pattern recognition problems. In conclusion, the edited book comprises contributions on diverse aspects of fuzzy logic, neural networks, and optimization of meta-heuristic algorithms for designing optimal hybrid intelligent systems and their application in areas such as intelligent control and robotics, pattern recognition, decision-making, time series prediction, and optimization of complex problems.

Название: Metaheuristics for Machine Learning: Algorithms and Applications
Автор: Каnаk Каlitа, Nаrауаnаn Gаnеsh
Издательство: Wiley-Scrivener
Серия: Artificial Intelligence and Soft Computing for Industrial Transformation
Год: 2024
Страниц: 327
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

The book unlocks the power of nature-inspired optimization in Machine Learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in Machine Learning and Artificial Intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing Machine Learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into Machine Learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and Machine Learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Genetic algorithms (GAs) fit to a cluster of metaheuristic optimization techniques that draw inspiration from natural selection and genetics. In order to find the optimal solution for a particular issue, the core idea underlying the GA is to mimic the evolutionary process.

Название: Optimization Algorithms (MEAP v12)
Автор: Аlаа Кhаmis
Издательство: Manning Publications
Год: 2024
Страниц: 739
Язык: английский
Формат: epub
Размер: 77.6 MB

Solve design, planning, and control problems using modern machine learning and AI techniques. Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. Inside you’ll find a wide range of optimization methods, from deterministic and stochastic derivative-free optimization to nature-inspired search algorithms and machine learning methods. Don’t worry—there’s no complex mathematical notation. You’ll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world.

Название: Modeling Visual Aesthetics, Emotion, and Artistic Style
Автор: Jаmеs Z. Wаng, Rеginаld В. Аdаms, Jr.
Издательство: Springer
Год: 2024
Страниц: 408
Язык: английский
Формат: pdf (true), epub
Размер: 42.7 MB

Modeling Visual Aesthetics, Emotion, and Artistic Style offers a comprehensive exploration of the increasingly significant topic of the complex interplay between human perception and digital technology. It embodies the cumulative knowledge and efforts of a wide array of active researchers and practitioners from diverse fields including Computer Vision, affective computing, robotics, psychology, data mining, Machine Learning, art history, and movement analysis. This volume seeks to address the profound and challenging research questions related to the computational modeling and analysis of visual aesthetics, emotions, and artistic style, vital components of the human experience that are increasingly relevant in our digitally connected world. The book's vast scope encompasses a broad range of topics. The initial chapters lay a strong foundation with background knowledge on emotion models and Machine Learning, which then transitions into exploring social visual perception in humans and its technological applications. Readers will uncover the psychological and neurological foundations of social and emotional perception from faces and bodies. Subsequent sections broaden this understanding to include technology's role in detecting discrete and subtle emotional expressions, examining facial neutrality, and including research contexts that involve children as well as adults. Furthermore, the book illuminates the dynamic intersection of art and technology, the language of photography, the relationship between breath-driven robotic performances and human dance, and the application of Machine Learning in analyzing artistic styles.

Название: Foundations of Vector Retrieval
Автор: Sеbаstiаn Вruсh
Издательство: Springer
Год: 2024
Страниц: 196
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book presents the fundamentals of vector retrieval. To this end, it delves into important data structures and algorithms that have been successfully used to solve the vector retrieval problem efficiently and effectively. We are witness to a few years of remarkable developments in Artificial Intelligence (AI) with the use of advanced machine learning algorithms, and in particular, Deep Learning. Gargantuan, complex neural networks that can learn through self-supervision—and quickly so with the aid of specialized hardware—transformed the research landscape so dramatically that, overnight it seems, many fields experienced not the usual, incremental progress, but rather a leap forward. Machine translation, natural language understanding, information retrieval, recommender systems, and Computer Vision are but a few examples of research areas that have had to grapple with the shock. Countless other disciplines beyond Computer Science such as robotics, biology, and chemistry too have benefited from Deep Learning. These neural networks and their training algorithms may be complex, and the scope of their impact broad and wide, but nonetheless they are simply functions in a high-dimensional space. A trained neural network takes a vector as input, crunches and transforms it in various ways, and produces another vector, often in some other space.

Название: Recommender Systems: Frontiers and Practices
Автор: Dоngshеng Li, Jiаnхun Liаn, Lе Zhаng, Каn Rеn
Издательство: Springer/House of Electronics Industry
Год: 2024
Страниц: 292
Язык: английский
Формат: pdf (true), epub
Размер: 25.0 MB

This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of Deep Learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch. The emergence of Deep Learning has greatly changed the development of recommendation technology, and it is necessary for researchers and technicians in the field of recommender systems to have a deep understanding of deep learning-based recommendation technology.

Название: Intelligent Quantum Information Processing
Автор: Siddhаrthа Вhаttасhаrууа, Ivаn Сruz-Асеvеs, Аrраn Dеуаsi
Издательство: CRC Press
Серия: Quantum Machine Intelligence
Год: 2024
Страниц: 255
Язык: английский
Формат: pdf (true)
Размер: 13.3 MB

The book discusses the foundations of intelligent quantum information processing applied to several real-life engineering problems, including intelligent quantum systems, intelligent quantum communication, intelligent process optimization, and intelligent quantum distributed networks. Quantum computer, as the name suggests, principally works on several quantum physical features. These could be used as an immense alternative to today’s apposite computers since they possess faster processing capability (even exponentially) than classical computers. The term “quantum computing” is fundamentally a synergistic combination of thoughts from quantum physics, classical information theory, and Computer Science. This volume aims to bring together recent advances and trends in the methodological approaches, theoretical studies, and mathematical and applied techniques related to intelligent quantum information processing and their applications to engineering problems. The scope of the book, in essence, is confined to but not bounded to introducing different novel hybrid quantum computational algorithms for addressing the limitations of the conventional information processing algorithms, including quantum machine learning, quantum key distribution, quantum information processing, quantum encryption algorithms, quantum networks, and quantum knowledge discovery in databases, to name a few.

Название: Societal Impacts of Artificial Intelligence and Machine Learning
Автор: Саrlо Liрizzi
Издательство: Springer
Серия: Synthesis Lectures on Computer Science
Год: 2024
Страниц: 160
Язык: английский
Формат: pdf (true), epub
Размер: 22.0 MB

This book goes beyond the current hype of expectations generated by the news on Artificial Intelligence and Machine Learning by analyzing realistic expectations for society, its limitations, and possible future scenarios for the use of this technology in our current society. Artificial Intelligence is one of the top topics today and is inflating expectations beyond what the technology can do in the foreseeable future. The future cannot be predicted, but the future of some elements of our society, such as technology, can be estimated. This book merges the modeling of human reasoning with the power of AI technology allowing readers to make more informed decisions about their personal or financial decisions or just being more educated on current technologies. Algorithms are the root science of AI. With minimal exceptions, all the new algorithms are around more “powerful” versions of what is called “artificial neural networks” (ANN). Artificial Neural Networks are a type of Machine Learning algorithm that is modeled after the structure and function of the human brain. They comprise interconnected nodes, called artificial neurons, that process information and make decisions. ANNs are used to analyze patterns and make predictions from large sets of data.

Название: Computational Imaging and Analytics in Biomedical Engineering: Algorithms and Applications
Автор: Т.R. Gаnеsh Ваbu, U. Sаrаvаnаkumаr, Ваlасhаndrа Раttаnаik
Издательство: Apple Academic Press/CRC Press
Серия: AAP Series on Digital Signal Processing, Computer Vision and Image Processing
Год: 2024
Страниц: 356
Язык: английский
Формат: pdf (true)
Размер: 27.9 MB

This new book focuses on mathematical and numerical methods for medical images and data. The book presents the various mathematical modeling techniques, numerical analysis, computing and computational techniques, and applications of machine learning for medical images and medical informatics. It also focuses on programming concepts using MATLAB and Python for medical image and signal analytics. The volume demonstrates the use of computational techniques and tools such as Machine Learning, deep neural networks, Artificial Intelligence (AI) and human-computer interaction ,fusion methods for CT and pet images, etc., for diagnosis of brain disorders, cervical cancer, lung disease, melanoma, atrial fibrillation and other circulatory issues, dental images, diabetes, and other medical issues. For further simplification and optimization, emerging techniques such as neural networks, Machine Learning, Deep Learning (DL), and Artificial Intelligence (AI) are adapted for feature extraction of medical images, medical image segmen­tation, and image-based bio-models. Image categorization, object identifica­tion, pattern recognition, and reasoning are just a few of the principles that are examined in medical imaging. By extracting meaningful patterns for a specific condition, they are employed in medical imaging to improve accu­racy. They also aid in the process of deciding on a course of action. Using Machine Learning to improve medical imaging is a trend that will continue in the future.

Название: Generative Art with javascript and SVG: Utilizing Scalable Vector Graphics and Algorithms for Creative Coding and Design
Автор: Dаvid Маtthеw
Издательство: Apress
Год: 2024
Страниц: 237
Язык: английский
Формат: pdf, epub
Размер: 11.3 MB

This book introduces you to the exciting world of generative art (artwork that are solely or partially created with the use of an autonomous system) through the medium of SVG and javascript. More specifically, it will teach the use of Graphery SVG (gySVG), a javascript library that closely mirrors the SVG spec and makes scripting SVG very intuitive and enjoyable. Armed with gySVG, the reader will be taught tried and trusted techniques in producing generative imagery. Each chapter will build upon the previous one, and those completely new to programming will be given a primer to help them find their feet. Beginning with a simple generative sketch that illustrates, and then explains, fundamental programming concepts such as variables, data types, operators, loops, conditionals and functions - the reader will go on to explore the power of Scalable Vector Graphics, how to play with randomness and regularity, how to use noise to create organic variance, and how to make sketches interactive and dynamic. More advanced topics will then be tackled, such as paths, filter effects, trigonometry, shape packing, flow fields and fractals. For web developers and designers and creative coders with an interest in digital and generative art as well as artists who are interested in learning to code with javascript.