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Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining - and ways to overcome the related challenges - in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementations.
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Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment.
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Knowledge engineering and data mining are fundamental topics in the area of artificial intelligence and knowledge-based systems. This Special Issue covers the entire knowledge engineering pipeline: from data acquisition and data mining to knowledge extraction and exploitation. The reader will find topics including data mining methods, multidimensional data analysis, supervised and unsupervised learning methods, methods of knowledge-based management, language ontologies, ontology learning, and others.
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Real-time, web-based, and interactive visualisations are proven to be outstanding methodologies and tools in numerous fields when knowledge in sophisticated data science and visualisation techniques is available. The rationale for this is because modern data science analytical approaches like machine/deep learning or artificial intelligence, as well as digital twinning, promise to give data insights, enable informed decision-making, and facilitate rich interactions among stakeholders.The benefits of data visualisation, data science, and digital twinning technologies motivate this book, which exhibits and presents numerous developed and advanced data science and visualisation approaches. Chapters cover such topics as deep learning techniques, web and dashboard-based visualisations during the COVID pandemic, 3D modelling of trees for mobile communications, digital twinning in the mining industry, data science libraries, and potential areas of future data science development.
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Profile haben Konjunktur. Seit der Verbreitung von Social Networking Sites sind sie alltäglicher Ort der Selbstdarstellung. Doch die Praktiken und Techniken der Profilierung sind keineswegs neu. Schon lange beschreiben Profile potentielle StraftäterInnen. Nun bestimmen sie auch die potentielle Kreditwürdigkeit. Im Spannungsfeld zwischen Profil und Profilierung nehmen die Beiträge aus Medienwissenschaft, Soziologie, Geschichtswissenschaft und Informatik die vielschichtigen Dimensionen dieses zentralen Phänomens der digitalen Medienkultur in den Blick: Wie verändern sich Bedeutung und Bewertung des Profil-Begriffs? Wie stehen Profile in Zusammenhang mit Subjektivierung und Machtkonstellationen? Welche Wechselwirkungen zwischen Profilen und Privatheit sind gegenwärtig relevant?
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"A Microscopic, Telescopic, and Kaleidoscopic View of Data Science."
Data mining --- Big data
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The present reprint contains 33 articles accepted and published in the Special Issue entitled "Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2022" in the MDPI journal, Mathematics, which covers a wide range of topics connected to the theory and applications of feature representation learning for image processing, artificial intelligence, data mining and robotics. These topics include, among others, elements from image blurring, image aesthetic quality assessment, pedestrian detection, visual tracking, vehicle re-identification, face recognition, 3D reconstruction, the stability of switched systems, domain adaption, deep reinforcement, sentiment analysis, graph convolutional networks, knowledge graphs, geometric metric learning, etc. It is hoped that this reprint will be interesting and useful for those working in the area of image processing, computer vision, machine learning, natural language processing and robotics, as well as for those with backgrounds in machine learning who are willing to become familiar with recent advancements in artificial intelligence, which, today, is present in almost all aspects of human life and activities.
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In recent years, a considerable amount of effort has been devoted, both in industry and academia, to improving maintenance. Time is a critical factor in maintenance, and efforts are placed to monitor, analyze, and visualize machine or asset data in order to anticipate to any possible failure, prevent damage, and save costs. The MANTIS Book aims to highlight the underpinning fundamentals of Condition-Based Maintenance related conceptual ideas, an overall idea of preventive maintenance, the economic impact and technical solution. The core content of this book describes the outcome of the Cyber-Physical System based Proactive Collaborative Maintenance project, also known as MANTIS, and funded by EU ECSEL Joint Undertaking under Grant Agreement nº 662189. The ambition has been to support the creation of a maintenance-oriented reference architecture that support the maintenance data lifecycle, to enable the use of novel kinds of maintenance strategies for industrial machinery. The key enabler has been the fine blend of collecting data through Cyber-Physical Systems, and the usage of machine learning techniques and advanced visualization for the enhanced monitoring of the machines. Topics discussed include, in the context of maintenance: Cyber-Physical Systems, Communication Middleware, Machine Learning, Advanced Visualization, Business Models, Future Trends. An important focus of the book is the application of the techniques in real world context, and in fact all the work is driven by the pilots, all of them centered on real machines and factories. This book is suitable for industrial and maintenance managers that want to implement a new strategy for maintenance in their companies. It should give readers a basic idea on the first steps to implementing a maintenance-oriented platform or information system.
Cooperating objects (Computer systems) --- Data mining --- Energy
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Dieses Buch systematisiert Ziele, Einsatzszenarien, Vorgehensweisen, Methoden und Anwendungsfelder für eine automatisierte Datenanalyse in der Medizin und Medizintechnik. Es wendet sich hauptsächlich an Doktoranden, Diplom- und Masterstudenten der Ingenieurwissenschaften und Informatik. Im Mittelpunkt steht dabei das Spannungsfeld zwischen medizinischen Anwendern und ihren Zielstellungen sowie den Potenzialen vorhandener Data-Mining-Verfahren.
Computational Intelligence --- Medizintechnik --- Data Mining --- Statistik --- Medizin
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