Download or read book Predictive Analytics with Microsoft Azure Machine Learning written by Valentine Fontama and published by Apress. This book was released on 2014-11-25 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis. The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter. The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft.

Download or read book Fundamentals of Machine Learning for Predictive Data Analytics second edition written by John D. Kelleher and published by MIT Press. This book was released on 2020-10-20 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Download or read book An Introduction to Predictive Maintenance written by R. Keith Mobley and published by Elsevier. This book was released on 2002-10-24 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of An Introduction to Predictive Maintenance helps plant, process, maintenance and reliability managers and engineers to develop and implement a comprehensive maintenance management program, providing proven strategies for regularly monitoring critical process equipment and systems, predicting machine failures, and scheduling maintenance accordingly. Since the publication of the first edition in 1990, there have been many changes in both technology and methodology, including financial implications, the role of a maintenance organization, predictive maintenance techniques, various analyses, and maintenance of the program itself. This revision includes a complete update of the applicable chapters from the first edition as well as six additional chapters outlining the most recent information available. Having already been implemented and maintained successfully in hundreds of manufacturing and process plants worldwide, the practices detailed in this second edition of An Introduction to Predictive Maintenance will save plants and corporations, as well as U.S. industry as a whole, billions of dollars by minimizing unexpected equipment failures and its resultant high maintenance cost while increasing productivity. - A comprehensive introduction to a system of monitoring critical industrial equipment - Optimize the availability of process machinery and greatly reduce the cost of maintenance - Provides the means to improve product quality, productivity and profitability of manufacturing and production plants

Download or read book Predictive Maintenance in Dynamic Systems written by Edwin Lughofer and published by Springer. This book was released on 2019-02-28 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.

Download or read book Knowledge Management in Organizations written by Lorna Uden and published by Springer. This book was released on 2018-07-30 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the refereed proceedings of the 13th International Conference on Knowledge Management in Organizations, KMO 2018, held in Žilina, Slovakia, in August 2018. The theme of the conference was "Emerging Research for Knowledge Management in Organizations." The 59 papers accepted for KMO 2018 were selected from 141 submissions and are organized in topical sections on: Knowledge management models and analysis; knowledge sharing; knowledge transfer and learning; knowledge and service innovation; knowledge creation; knowledge and organization; information systems and information science; knowledge and technology management; data mining and intelligent science; business and customer relationship management; big data and IoT; and new trends in IT.

Download or read book Machine Learning for Cyber Physical Systems written by Jürgen Beyerer and published by Springer. This book was released on 2018-12-17 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

Download or read book Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing written by Amit Kumar Tyagi and published by CRC Press. This book was released on 2024-10-23 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries. Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning. Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields.

Download or read book Database Systems for Advanced Applications written by Shamkant B. Navathe and published by Springer. This book was released on 2016-03-24 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set LNCS 9642 and LNCS 9643 constitutes the refereed proceedings of the 21st International Conference on Database Systems for Advanced Applications, DASFAA 2016, held in Dallas, TX, USA, in April 2016. The 61 full papers presented were carefully reviewed and selected from a total of 183 submissions. The papers cover the following topics: crowdsourcing, data quality, entity identification, data mining and machine learning, recommendation, semantics computing and knowledge base, textual data, social networks, complex queries, similarity computing, graph databases, and miscellaneous, advanced applications.

Download or read book IoT Streams for Data Driven Predictive Maintenance and IoT Edge and Mobile for Embedded Machine Learning written by Joao Gama and published by Springer Nature. This book was released on 2021-01-09 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.

Download or read book Machine Learning and Data Science in the Oil and Gas Industry written by Patrick Bangert and published by Gulf Professional Publishing. This book was released on 2021-03-04 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. - Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful - Gain practical understanding of machine learning used in oil and gas operations through contributed case studies - Learn change management skills that will help gain confidence in pursuing the technology - Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Download or read book Advanced Manufacturing and Automation VII written by Kesheng Wang and published by Springer. This book was released on 2018-02-10 with total page 627 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings brings together a selection of papers from the 7th International Workshop of Advanced Manufacturing and Automation (IWAMA 2017), held in Changshu Institute of Technology, Changshu, China on September 11–12, 2017. Most of the topics are focusing on novel techniques for manufacturing and automation in Industry 4.0. These contributions are vital for maintaining and improving economic development and quality of life. The proceeding will assist academic researchers and industrial engineers to implement the concepts and theories of Industry 4.0 in industrial practice, in order to effectively respond to the challenges posed by the 4th industrial revolution and smart factories.

Download or read book 14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019 written by Francisco Martínez Álvarez and published by Springer. This book was released on 2019-04-30 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes 57 papers presented at the SOCO 2019 conference held in the historic city of Seville (Spain), in May 2019. Soft computing represents a set of computational techniques in machine learning, computer science and various engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. The selection of papers was extremely rigorous in order to maintain the high quality of the conference, which featured a number of special sessions, including sessions on: Soft Computing Methods in Manufacturing and Management Systems; Soft Computing Applications in the Field of Industrial and Environmental Enterprises; Optimization, Modeling and Control by Soft Computing Techniques; and Soft Computing in Aerospace, Mechanical and Civil Engineering: New methods and Industrial Applications.

Download or read book Predictive Maintenance for HVAC Systems Leveraging Machine Learning for Optimal Performance written by Charles Nehme and published by Charles Nehme. This book was released on with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world of heating, ventilation, and air conditioning (HVAC) systems has long been vital to ensuring comfort in residential, commercial, and industrial environments. However, as these systems grow in complexity and scale, so too does the challenge of maintaining them efficiently. Traditionally, HVAC systems have relied on reactive or time-based maintenance strategies that often result in unnecessary downtime, increased operational costs, and inefficient energy use. In an age where sustainability and cost-effectiveness are paramount, businesses and organizations are seeking smarter solutions. This is where machine learning (ML) enters the scene. Machine learning has revolutionized industries across the globe, from healthcare to finance. Its ability to analyze vast amounts of data and predict outcomes with precision offers HVAC systems the potential to leap from reactive maintenance strategies to predictive ones. Imagine a world where HVAC systems can detect a malfunction before it occurs, optimize their own performance, and ensure energy efficiency with minimal human intervention. This is the promise of predictive maintenance powered by machine learning. The goal of this book is to bridge the gap between two seemingly distinct worlds—HVAC maintenance and machine learning. By providing an in-depth exploration of how predictive models can be applied to HVAC systems, this book is designed for engineers, data scientists, HVAC professionals, and facility managers alike. Whether you're an experienced machine learning practitioner looking to understand the specific needs of HVAC systems, or an HVAC professional eager to learn how AI can revolutionize maintenance strategies, this book provides a roadmap for implementing predictive maintenance in real-world environments. In writing this book, I’ve drawn from a wide array of sources: industry best practices, academic research, and hands-on case studies of machine learning models applied in HVAC settings. You will learn not only the technical foundations of machine learning but also how to gather, clean, and preprocess HVAC data for predictive modeling, select appropriate algorithms, and deploy models in live systems. The book aims to demystify the technical aspects of predictive maintenance and show how it can be implemented at scale. With practical examples, industry use cases, and step-by-step guides, you’ll gain a deep understanding of the processes involved in transforming your HVAC maintenance strategy from reactive to predictive. As we venture into the era of smart buildings, energy efficiency, and self-optimizing systems, the integration of machine learning with HVAC systems will no longer be a luxury—it will be a necessity. By the end of this book, you’ll be equipped with the knowledge and tools to lead the charge in this transformation. Thank you for joining me on this journey into the future of HVAC maintenance. I hope this book empowers you to harness the potential of machine learning and usher in a new era of efficiency, reliability, and sustainability in HVAC systems.

Download or read book Congress on Intelligent Systems written by Harish Sharma and published by Springer. This book was released on 2021-05-28 with total page 815 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of selected papers presented at the First Congress on Intelligent Systems (CIS 2020), held in New Delhi, India during September 5 – 6, 2020. It includes novel and innovative work from experts, practitioners, scientists and decision-makers from academia and industry. It covers topics such as Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision making systems, intelligent information processing, intelligent transportation, artificial intelligence for machine vision, imaging sensors technology, image segmentation, convolutional neural network, image/video classification, soft computing for machine vision, pattern recognition, human computer interaction, robotic devices and systems, autonomous vehicles, intelligent control systems, human motor control, game playing, evolutionary algorithms, swarm optimization, neural network, deep learning, supervised learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro fuzzy systems.

Download or read book Artificial Intelligence Applications and Innovations written by Ilias Maglogiannis and published by Springer Nature. This book was released on 2022-06-16 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of five International Workshops held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, virtually and in Hersonissos, Crete, Greece, in June 2022: the 11th Mining Humanistic Data Workshop (MHDW 2022); the 7th 5G-Putting Intelligence to the Network Edge Workshop (5G-PINE 2022); the 1st workshop on AI in Energy, Building and Micro-Grids (AIBMG 2022); the 1st Workshop/Special Session on Machine Learning and Big Data in Health Care (ML@HC 2022); and the 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics (AIBEI 2022). The 35 full papers presented at these workshops were carefully reviewed and selected from 74 submissions.

Download or read book Intelligent Predictive Maintenance written by Min Liu and published by Springer Nature. This book was released on with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Empowering Sustainable Industrial 4 0 Systems With Machine Intelligence written by Ahmad, Muneer and published by IGI Global. This book was released on 2022-04-01 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent advancement of industrial computerization has significantly helped in resolving the challenges with conventional industrial systems. The Industry 4.0 quality standards demand smart and intelligent solutions to revolutionize industrial applications. The integration of machine intelligence and internet of things (IoT) technologies can further devise innovative solutions to recent industrial application issues. Empowering Sustainable Industrial 4.0 Systems With Machine Intelligence assesses the challenges, limitations, and potential solutions for creating more sustainable and agile industrial systems. This publication presents recent intelligent systems for a wide range of industrial applications and smart safety measures toward industrial systems. Covering topics such as geospatial technologies, remote sensing, and temporal analysis, this book is a dynamic resource for health professionals, pharmaceutical professionals, manufacturing professionals, policymakers, engineers, computer scientists, researchers, instructors, students, and academicians.