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: Innovation in the University 4.0 System based on Smart Technologies
Автор: Shаshi Каnt Guрtа, Jоаnnа Rоsаk-Szуrосkа
Издательство: CRC Press
Год: 2024
Страниц: 241
Язык: английский
Формат: pdf (true)
Размер: 36.2 MB

This text presents a comprehensive analysis of mathematical formulations for proving the effectiveness of Artificial Intelligence in education and investigates the possibilities for integrating advanced Artificial Intelligence algorithms. Artificial Intelligence (AI) in education signifies a paradigm shift in teaching and learning. A major driving force behind AI is the concept of neural networks and Deep Learning. These powerful tools enable AI applications to deliver personalized, dynamic, and engaging learning experiences. To understand the role of these technologies in education, we must first comprehend the basics. Neural networks are AI systems modeled after the human brain, consisting of interconnected layers of nodes, or “neurons,” that process information. These layers constitute an input layer, one or more hidden layers, and an output layer. Each node processes the input it receives and passes on the result, simulating the process of human brain cells transmitting signals. Deep Learning, a subset of Machine Learning, involves using neural networks with multiple hidden layers. The text is primarily written for graduate students, postgraduate students, and academic researchers working in the fields of Computer Science and engineering, information technology and Machine Learning.

Название: Artificial Intelligence in Forecasting: Tools and Techniques
Автор: Sасhi Nаndаn Моhаntу, Рrееthi Nаnjundаn, Теjаswini Каr
Издательство: CRC Press
Год: 2024
Страниц: 365
Язык: английский
Формат: pdf (true)
Размер: 12.4 MB

Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use Machine Learning to improve accuracy. Artificial Intelligence (AI) has revolutionized the way forecasting is made in many industries, from finance to retail to healthcare. In today’s fast-paced world, accurate forecasting is essential for businesses to make informed decisions and stay ahead of the competition. The article looks at different tools and techniques used in AI for Forecasting and highlights their advantages and limitations. One of the most widely used AI tools for forecasting is Machine Learning (ML). ML is a subset of AI that allows computer systems to learn from data without being explicitly programmed. Forecasting trains ML algorithms on historical data to identify patterns and relationships that can be used to predict future outcomes. Many ML techniques are available, including regression analysis, decision trees, and neural networks.

Название: Generative AI in Practice: 100+ Amazing Ways Generative Artificial Intelligence is Changing Business and Society
Автор: Веrnаrd Маrr
Издательство: Wiley
Год: 2024
Страниц: 304
Язык: английский
Формат: pdf, azw3, epub (true), mobi
Размер: 10.1 MB

An indispensable look at the next frontier of technological advancement and its impact on our world. Generative AI is rewriting the rulebook with its seemingly endless capabilities, from crafting intricate industrial designs, writing computer code, and producing mesmerizing synthetic voices to composing enchanting music and innovating genetic breakthroughs. In Generative AI in Practice, renowned futurist Bernard Marr offers readers a deep dive into the captivating universe of GenAI. This comprehensive guide introduces you to the basics of this groundbreaking technology and outlines the profound impact that GenAI will have on business and society. Professionals, technophiles, and anyone with an interest in the future will need to understand how GenAI is set to redefine jobs, revolutionize business, and question the foundations everything we do. In this book, Marr sheds light on the most innovative real-world GenAI applications through practical examples, describing how they are moulding industries like retail, healthcare, education, finance, and beyond. You'll enjoy a captivating discussion of innovations in media and entertainment, seismic shifts in advertising, and the future trajectory of GenAI. Coding and Programming: The AI Revolution: This new wave of advanced GenAI large language models isn't just capable of writing text – they can write computer code. Which makes sense when you think that computer code is just another type of language. This means GenAI can aid the work of coders, programmers, and developers, and speed up the software development process. ChatGPT can write code in a number of programming languages, including javascript, Python, and C++. It can also act like a coding tutor, explaining how code works, and debug code.

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: Domain-Specific Computer Architectures for Emerging Applications: Machine Learning and Neural Networks
Автор: Сhао Wаng
Издательство: CRC Press
Год: 2024
Страниц: 417
Язык: английский
Формат: pdf (true)
Размер: 37.9 MB

With the end of Moore’s Law, domain-specific architecture (DSA) has become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency, and real-time responsiveness for the target application. DSAs often start from domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and communication, and proposing the heterogeneous accelerator architecture suitable for that particular application. In the emerging field of big data, machine learning, data mining, and artificial intelligence algorithms, as the core components of next‑generation applications, have attracted more attention from researchers. Utilizing existing hardware and software means to carry out the design of a new algorithmic architecture has become a hot research topic nowadays. Accelerating new algorithms in the era of big data is very different from the past. Machine Learning is concerned with using data to construct appropriate predictive models to make predictions about unknown data. According to the similarity of the presentation and implementation of Machine Learning algorithms, we can categorize the algorithms such as Bayesian‑based algorithms and neural network‑based algorithms. Of course, the scope of machine learning is so vast that some algorithms are difficult to categorize explicitly into a particular class, and for some classifications, algorithms of the same classification can target different types of problems.

Название: Linguistic Resources for Natural Language Processing: On the Necessity of Using Linguistic Methods to Develop NLP Software
Автор: Мах Silbеrztеin
Издательство: Springer
Год: 2024
Страниц: 230
Язык: английский
Формат: pdf (true), epub
Размер: 51.0 MB

Empirical ― data-driven, neural network-based, probabilistic, and statistical ― methods seem to be the modern trend. Recently, OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney chatbots have been garnering a lot of attention for their detailed answers across many knowledge domains. In consequence, most AI researchers are no longer interested in trying to understand what common intelligence is or how intelligent agents construct scenarios to solve various problems. Instead, they now develop systems that extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with empirical methods are trendy, as most researchers in NLP today use large training corpora, always to the detriment of the development of formalized dictionaries and grammars. Not questioning the intrinsic value of many software applications based on empirical methods, this volume aims at rehabilitating the linguistic approach to NLP. In an introduction, the editor uncovers several limitations and flaws of using training corpora to develop NLP applications, even the simplest ones, such as automatic taggers.

Название: Computer Science in Sport: Modeling, Simulation, Data Analysis and Visualization of Sports-Related Data
Автор: Dаniеl Меmmеrt
Издательство: Springer
Год: 2024
Страниц: 247
Язык: английский
Формат: pdf (true), epub
Размер: 17.7 MB

In recent years, computer science in sport has grown extremely, mainly because more and more new data has become available. Computer Science tools in sports, whether used for opponent preparation, competition, or scientific analysis, have become indispensable across various levels of expertise nowadays. A completely new market has emerged through the utilization of these tools in the four major fields of application: clubs and associations, business, science, and the media. This market is progressively gaining importance within university research and educational activities. This textbook aims to live up to the now broad diversity of Computer Science in sport by having more than 30 authors report from their special field and concisely summarise the latest findings. The book is divided into four main sections: data sets, modelling, simulation and data analysis. In addition to background information on programming languages (R and Python) and visualisation, the textbook is framed by history and an outlook. Python is highly popular in the community of data scientists in general and sports analysts in particular because it is a open-source, dynamic, object-oriented, high-level programming language, which provides highly flexible and up-to-date functionalities due to its available modules and libraries.

Название: Analysis and Visualization of Discrete Data Using Neural Networks
Автор: Коji Коуаmаdа
Издательство: World Scientific Publishing
Год: 2024
Страниц: 230
Язык: английский
Формат: pdf (true)
Размер: 53.9 MB

This book serves as a comprehensive step-by-step guide on data analysis and statistical analysis. It covers fundamental operations in Excel, such as table components, formula bar, and ribbon, and introduces visualization techniques and PDE derivation using Excel. It also provides an overview of Google Colab, including code and text cells, and explores visualization and Deep Learning applications. Key features of the book include topics like statistical analysis, regression analysis, optimization, correlation analysis, and neural networks. It adopts a practical approach by providing examples and step-by-step instructions for learners to apply the techniques to real-world problems. The book also highlights the strengths and features of both Excel and Google Colab, allowing learners to leverage the capabilities of each platform. The clear explanations of concepts, visual aids, and code snippets aid comprehension help learners understand the principles of data analysis and statistical analysis. Overall, this book serves as a valuable resource for professionals, researchers, and students seeking to develop skills in data analysis, regression statistics, optimization, and advanced modeling techniques using Excel, Colab, and neural networks. Google Colaboratory (Colab for short) is a free cloud service provided by Google, where you can use the Jupyter notebook and Python to analyze data and build a model for Machine Learning.

Название: An Introduction to Image Classification: From Designed Models to End-to-End Learning
Автор: Кlаus D. Тоеnniеs
Издательство: Springer
Год: 2024
Страниц: 297
Язык: английский
Формат: pdf (true), epub
Размер: 69.6 MB

Image classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involve feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with Deep Neural Networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural network. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight. The book starts from introducing methods for model-driven feature extraction and classification, including basic Computer Vision techniques for extracting high-level semantics from images. The topic of image classification is presented as a thoroughly curated sequence of steps that gradually increase understanding of the working of a fully trainable classifier. Practical exercises in Python/Keras/Tensorflow have been designed to allow for experimental exploration of these concepts. In each chapter, suitable functions from Python modules are briefly introduced to provide students with the necessary tools to conduct these experiments.