Data Science in Engineering Vol. 10
Author : Thomas Matarazzo
Publisher : Springer Nature
Page : 140 pages
File Size : 33,65 MB
Release :
Category :
ISBN : 3031681428
Data Science in Engineering, Volume 10
Author : Ramin Madarshahian
Publisher : Springer Nature
Page : 185 pages
File Size : 20,29 MB
Release : 2023-12-07
Category : Computers
ISBN : 3031349466
Book Description
Data Science in Engineering, Volume 10: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the tenth volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Novel Data-driven Analysis Methods Deep Learning Gaussian Process Analysis Real-time Video-based Analysis Applications to Nonlinear Dynamics and Damage Detection High-rate Structural Monitoring and Prognostics
Data-Driven Science and Engineering
Author : Steven L. Brunton
Publisher : Cambridge University Press
Page : 615 pages
File Size : 49,91 MB
Release : 2022-05-05
Category : Computers
ISBN : 1009098489
Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Foundations of Data Science for Engineering Problem Solving
Author : Parikshit Narendra Mahalle
Publisher : Springer Nature
Page : 125 pages
File Size : 17,98 MB
Release : 2021-08-21
Category : Technology & Engineering
ISBN : 9811651604
Book Description
This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.
Data Science And Knowledge Engineering For Sensing Decision Support - Proceedings Of The 13th International Flins Conference
Author : Jun Liu
Publisher : World Scientific
Page : 1625 pages
File Size : 31,63 MB
Release : 2018-07-30
Category : Computers
ISBN : 9813273240
Book Description
FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to include Computational Intelligence for applied research. The contributions of the FLINS conference cover state-of-the-art research, development, and technology for computational intelligence systems, with special focuses on data science and knowledge engineering for sensing decision support, both from the foundations and the applications points-of-view.
Big Data, Cloud Computing, Data Science & Engineering
Author : Roger Lee
Publisher : Springer
Page : 196 pages
File Size : 15,82 MB
Release : 2018-08-13
Category : Technology & Engineering
ISBN : 3319968033
Book Description
This book presents the outcomes of the 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2018), which was held on July 10–12, 2018 in Kanazawa. The aim of the conference was to bring together researchers and scientists, businesspeople and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, to share their experiences, and to exchange new ideas and information in a meaningful way. All aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here. The conference organizers selected the best papers from among those accepted for presentation. The papers were chosen on the basis of review scores submitted by members of the program committee and subsequently underwent further rigorous review. Following this second round of review, 13 of the conference’s most promising papers were selected for this Springer (SCI) book. We eagerly await the important contributions that we know these authors will make to the field of computer and information science.
Measurement and Data Analysis for Engineering and Science, Third Edition
Author : Patrick F. Dunn
Publisher : CRC Press
Page : 634 pages
File Size : 17,16 MB
Release : 2014-05-23
Category : Technology & Engineering
ISBN : 1466594969
Book Description
The third edition of Measurement and Data Analysis for Engineering and Science provides an up-to-date approach to presenting the methods of experimentation in science and engineering. Widely adopted by colleges and universities within the U.S. and abroad, this edition has been developed as a modular work to make it more adaptable to different approaches from various schools. This text details current methods and highlights the six fundamental tools required for implementation: planning an experiment, identifying measurement system components, assessing measurement system component performance, setting signal sampling conditions, analyzing experimental results, and reporting experimental results. What’s New in the Third Edition: This latest edition includes a new chapter order that presents a logical sequence of topics in experimentation, from the planning of an experiment to the reporting of the experimental results. It adds a new chapter on sensors and transducers that describes approximately 50 different sensors commonly used in engineering, presents uncertainty analysis in two separate chapters, and provides a problem topic summary in each chapter. New topics include smart measurement systems, focusing on the Arduino® microcontroller and its use in the wireless transmission of data, and MATLAB® and Simulink® programming for microcontrollers. Further topic additions are on the rejection of data outliers, light radiation, calibrations of sensors, comparison of first-order sensor responses, the voltage divider, determining an appropriate sample period, and planning a successful experiment. Measurement and Data Analysis for Engineering and Science also contains more than 100 solved example problems, over 400 homework problems, and provides over 75 MATLAB® Sidebars with accompanying MATLAB M-files, Arduino codes, and data files available for download.
Data Science and Big Data Analytics
Author : Durgesh Kumar Mishra
Publisher : Springer
Page : 418 pages
File Size : 13,41 MB
Release : 2018-08-01
Category : Technology & Engineering
ISBN : 9811076413
Book Description
This book presents conjectural advances in big data analysis, machine learning and computational intelligence, as well as their potential applications in scientific computing. It discusses major issues pertaining to big data analysis using computational intelligence techniques, and the conjectural elements are supported by simulation and modelling applications to help address real-world problems. An extensive bibliography is provided at the end of each chapter. Further, the main content is supplemented by a wealth of figures, graphs, and tables, offering a valuable guide for researchers in the field of big data analytics and computational intelligence.
Machine Learning and Data Science
Author : Prateek Agrawal
Publisher : John Wiley & Sons
Page : 276 pages
File Size : 11,42 MB
Release : 2022-07-25
Category : Computers
ISBN : 1119776473
Book Description
MACHINE LEARNING AND DATA SCIENCE Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia. Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms. These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.
Computational Intelligence in Data Science
Author : Sarath Chandran K R
Publisher : Springer Nature
Page : 341 pages
File Size : 45,80 MB
Release : 2023-07-21
Category : Computers
ISBN : 303138296X
Book Description
This book constitutes the proceedings of the 6th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2023, which took place in Kalavakkam, India, in February 2023. The 24 full papers presented in this volume were carefully reviewed and selected from 134 submissions. The major theme of the conference was intended to be computation intelligence and knowledge management. Various emerging areas like IoT, cyber security and data science need computation intelligence to align with the cutting-edge research. Machine learning delivers insights hidden in data for rapid, automated responses and improved decision making. Machine learning for IoT can be used to project future trends, detect anomalies, and augment intelligence by ingesting image, video, and audio.
