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3 results on 'LN cat08778a'

1. Artificial intelligence for fashion : how AI is revolutionizing the fashion industry.

Subjects
Artificial Intelligence, Neural Networks, Data Mining, Robotics & Manufacturing
Abstract

Summary: Learn how Artificial Intelligence (AI) is being applied in the fashion industry. With an application focused approach, this book provides real-world examples, breaks down technical jargon for non-technical readers, and provides an educational resource for fashion professionals. The book investigates the ways in which AI is impacting every part of the fashion value chain starting with product discovery and working backwards to manufacturing. Artificial Intelligence for Fashion walks you through concepts, such as connected retail, data mining, and artificially intelligent robotics. Each chapter contains an example of how AI is being applied in the fashion industry illustrated by one major technological theme. There are no equations, algorithms, or code. The technological explanations are cumulative so you'll discover more information about the inner workings of artificial intelligence in practical stages as the book progresses.

Published
2018

2. Artificial Intelligence and Cognitive Computing. Methods, Technologies, Systems, Applications and Policy Making.

Author

Lytras, Miltiadis, Visvizi, Anna, and Lytras, Miltiadis

Subjects
Information technology industries, data mining, decision-making system, rough set, mixed integer linear programming, assembly clearance, diesel engine quality, Internet of things, Wireless nodes, Hybrid clustering, Multi-hop routing, Network lifetime, Artificial intelligence, data envelopment analysis, decision making, artificial intelligence, performance, visual analytics, system, air quality, spatiotemporal, multivariate, dimension reduction, clustering, regular patterns, anomalies, speech recognition, Long Short Term Memory (LSTM), speech output correction, most-matching, empirical correlations, rheological properties, real-time, water-based drill-in fluid, artificial neural network, elastic parameters, Poisson's ratio, sandstone, self-adaptive differential evolution, total organic carbon, barnett shale, devonian shale, fishbone multilateral wells, predictive models, well productivity, international research, knowledge map visualization, policy documents quantification, research hotspot, policy keyword, minimum miscibility pressure (MMP), CO2 flooding, new models, face recognition, security, spoofing, histogram of oriented gradients, smart cities, deep learning, LSTM, neural networks, location prediction, trajectories, smart tourism, static Young's modulus, sandstone formations, machine learning, n/a
Abstract

Summary: Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today's world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that.

3. Machine Learning in Tribology.

Author

Tremmel, Stephan, Marian, Max, and Tremmel, Stephan

Subjects
Technology: general issues, History of engineering & technology, artificial intelligence, machine learning, artificial neural networks, tribology, condition monitoring, semi-supervised learning, random forest classifier, self-lubricating journal bearings, reduced order modelling, dynamic friction, rubber seal applications, tensor decomposition, laser surface texturing, texturing during moulding, digital twin, PINN, reynolds equation, triboinformatics, databases, data mining, meta-modeling, monitoring, analysis, prediction, optimization, fault data generation, Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), bearing fault diagnosis, unbalanced datasets, tribo-testing, tribo-informatics, natural language processing, tribAIn, BERT, amorphous carbon coatings, UHWMPE, total knee replacement, Gaussian processes, rolling bearing dynamics, cage instability, regression, neural networks, random forest, gradient boosting, evolutionary algorithms, rolling bearings, remaining useful life, feature engineering, structure-borne sound, n/a
Abstract

Summary: Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

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