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Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualise complex databases for marketing segmentation. This boom in applications covers a wide range of business interests - from finance management, through forecasting, to production. The combination of statistical, neural and fuzzy methods now enables direct quantitative studies to be carried out without the need for rocket-science expertise. This is a review of the state-of-the-art in applications of neural-network methods in three important areas of business analysis. It includes a tutorial chapter to introduce new users to the potential and pitfalls of this new technology.
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.
Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. Neural Networks in Business Forecasting provides researchers and practitioners with some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.
This book is designed to enable the reader to design and run a neural network-based project. It presents everything the reader will need to know to ensure the success of such a project. The book contains a free disk with C and C++ programs, which implement many of the techniques discussed in the book.
Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
As technology continues to become more sophisticated, a computer’s ability to understand, interpret, and manipulate natural language is also accelerating. Persistent research in the field of natural language processing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror natural language processes that have existed for centuries. Natural Language Processing: Concepts, Methodologies, Tools, and Applications is a vital reference source on the latest concepts, processes, and techniques for communication between computers and humans. Highlighting a range of topics such as machine learning, computational linguistics, and semantic analysis, this multi-volume book is ideally designed for computer engineers, computer and software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of natural language processing.
Artificial neural networks have been recognized as a powerful tool to learn and reproduce systems in various fields of applications. Neural net works are inspired by the brain behavior and consist of one or several layers of neurons, or computing units, connected by links. Each artificial neuron receives an input value from the input layer or the neurons in the previ ous layer. Then it computes a scalar output from a linear combination of the received inputs using a given scalar function (the activation function), which is assumed the same for all neurons. One of the main properties of neural networks is their ability to learn from data. There are two types of learning: structural and parametric. Structural learning consists of learning the topology of the network, that is, the number of layers, the number of neurons in each layer, and what neurons are connected. This process is done by trial and error until a good fit to the data is obtained. Parametric learning consists of learning the weight values for a given topology of the network. Since the neural functions are given, this learning process is achieved by estimating the connection weights based on the given information. To this aim, an error function is minimized using several well known learning methods, such as the backpropagation algorithm. Unfortunately, for these methods: (a) The function resulting from the learning process has no physical or engineering interpretation. Thus, neural networks are seen as black boxes.
The Easy Introduction to Machine Learning (Ml) for Nontechnical People--In Business and Beyond Artificial Intelligence for Business is your plain-English guide to Artificial Intelligence (AI) and Machine Learning (ML): how they work, what they can and cannot do, and how to start profiting from them. Writing for nontechnical executives and professionals, Doug Rose demystifies AI/ML technology with intuitive analogies and explanations honed through years of teaching and consulting. Rose explains everything from early “expert systems” to advanced deep learning networks. First, Rose explains how AI and ML emerged, exploring pivotal early ideas that continue to influence the field. Next, he deepens your understanding of key ML concepts, showing how machines can create strategies and learn from mistakes. Then, Rose introduces current powerful neural networks: systems inspired by the structure and function of the human brain. He concludes by introducing leading AI applications, from automated customer interactions to event prediction. Throughout, Rose stays focused on business: applying these technologies to leverage new opportunities and solve real problems. Compare the ways a machine can learn, and explore current leading ML algorithms Start with the right problems, and avoid common AI/ML project mistakes Use neural networks to automate decision-making and identify unexpected patterns Help neural networks learn more quickly and effectively Harness AI chatbots, virtual assistants, virtual agents, and conversational AI applications
This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.