Big data in engineering applications [electronic resource] / Edited by Sanjiban Sekhar Roy, Pijush Samui Ravinesh Deo, Stavros Ntalampiras.
Material type: TextSeries: Publication details: Singapore : Springer, 2018.Description: 1 online resourceISBN:- 9789811084768
Item type | Current library | Call number | Status | Date due | Barcode | |
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E-Resources | Main Library E-Resources | 006.3 B592 (Browse shelf(Opens below)) | Available | E005406 |
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006.3 Ar791 Artificial intelligence and ambient intelligence / | 006.3 Ar791 AI in learning designing the future / | 006.3 B241 New trends in intelligent information and database systems / | 006.3 B592 Big data in engineering applications | 006.3 C647 Cloud computing for geospatial big data analytics intelligent edge, fog and mist computing / | 006.3 C676 Cognitive architectures / | 006.3 C738 Computation in complex networks / |
Big Data Applications in Education and Health Care Analysis of Compressive strength of alkali activated cement using Big data analysis Application of cluster based AI methods on daily streamflows Bigdata applications to smart power systems Big Data in e-commerce Interaction of Independent Component Analysis (ICA) and Support Vector Machine (SVM) in exploration of Greenfield areas Big Data Analysis of decay Coefficient of Naval Propulsion Plant Information Extraction and Text Summarization in documents using Apache Spark Detecting Outliers from Big Data Streams Machine Learning in Big Data Applications.
This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.
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