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Proceedings of the 5th International Conference on Computing and Information held in Sudbury, Ontario, Canada, in May 1993. Among the topics: algorithms and complexity, distributed computing, concurrency and parallelism, and artificial intelligence. No index. Annotation copyright Book News, Inc. Por
The conference, held in Toronto in May of 1992, covered topics that include expert systems, theory of programming, concurrency, CASE methodology, and distributed computing. No index. Annotation copyright Book News, Inc. Portland, Or.
This volume constitutes the proceedings of the First International Conference on Temporal Logic (ICTL '94), held at Bonn, Germany in July 1994. Since its conception as a discipline thirty years ago, temporal logic is studied by many researchers of numerous backgrounds; presently it is in a stage of accelerated dynamic growth. This book, as the proceedings of the first international conference particularly dedicated to temporal logic, gives a thorough state-of-the-art report on all aspects of temporal logic research relevant for computer science and AI. It contains 27 technical contributions carefully selected for presentation at ICTL '94 as well as three surveys and position papers.
This book constitutes the refereed proceedings of the 5th International Congress on Parallel Computing Technologies, PaCT-99, held in St. Petersburg, Russia in September 1999. The 47 revised papers presented were carefully reviewed and selected from more than 100 submissions. The papers address all current issues in parallel processing ranging from theory, algorithms, programming, and software to implementation, architectures, hardware, and applications.
This book presents the refereed proceedings of the 7th Portuguese Conference on Artificial Intelligence, EPIA'95, held in Funchal, Madeira Island, Portugal, in October 1995. The 30 revised full papers and the 15 poster presentations included were selected during a highly competitive selection process from a total of 167 submissions from all over the world. Among the topics covered are automated reasoning and theorem proving, belief revision, constraint-based reasoning, distributed artificial intelligence, genetic algorithms, machine learning, neural networks, non-monotonic reasoning, planning and case-based reasoning, qualitative reasoning, robotics and control, and theory of computation.
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)