Big Data in Engineering Applications

ISBN-13: 9789811084751 / Angielski / Twarda / 2018 / 384 str.

Sanjiban Sekhar Roy; Pijush Samui; Ravinesh Deo

Opis

This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences.

Spis treści

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.

O autorze

Sanjiban Sekhar Roy is working as an associate professor in School of Computer Science and Engineering, VIT University. He joined this institute in  the year 2009 as an assistant professor. He holds a B.E degree in Information Technology from the University of North Bengal and an M.Tech degree in Computer Science and Engineering from VIT University. He qualified GATE examination, which is a national level engineering entrance test conducted by IITs and IISC. Sanjiban carried out his nine months M.Tech project as an intern student in Indian Institute of Technology (IIT), Kharagpur, India. In the year 2016 he completed his Ph.D. degree in Computer science and Engineering from VIT University. His research interests include machine learning, data mining, and pattern recognitions. He has to his credit around 33 articles published in international journals and international conferences and one edited book with Elsevier publisher. He is an editorial board member of “International Journal of Advanced Intelligent Paradigms”, Inderscience and reviewer for many international journals.

Pijush Samui is working as an associate professor in civil engineering department at NIT Patna, India. He graduated in 2000, with a B.Tech. in Civil Engineering from Indian Institute of Engineering Science and Technology, Shibpur, India. He received his M.Sc. in Geotechnical Earthquake Engineering from Indian Institute of Science, Bangalore, India (2004). He holds a Ph.D. in Geotechnical Earthquake Engineering
(2008) from Indian Institute of Science, Bangalore, India. He was a postdoctoral fellow at University of Pittsburgh (USA) (2008-2009) and Tampere University of Technology (Finland) (2009- 2010). At University of Pittsburgh, he worked on design of efficient tool for rock cutting and application of Support Vector Machine (SVM) in designing of geostructure. At Tampere University of Technology, he worked on design of railway embankment, slope reliability and site characterization. In 2010, Dr. Pijush joined in the Center for Disaster Mitigation and Management at VIT University as an Associate Professor. He was promoted to a Professor in 2012. Dr. Pijush’s research focuses on the application of Artificial Intelligence for designing civil engineering structure, design of foundation, stability of railway embankment, reliability analysis, site characterization, earthquake engineering and BIG data. Dr. Pijush is the recipient of the prestigious CIMO fellowship (2009) from Finland, for his integrated research on the design of railway embankment. He was awarded Shamsher Prakash Research Award (2011) by IIT Roorkee for his innovative research on the application of Artificial Intelligence in designing civil engineering structure. He was selected as the recipient of IGS Sardar Resham Singh Memorial Award – 2013 for his innovative research on infrastructure project. He was elected Fellow of International Congress of Disaster Management in 2010. He served as a guest in disaster advance journal.He also serves as an editorial board member in several international journals. Dr. Pijush is active in a variety of professional organizations including the Indian Geotechnical Society, Indian Science Congress, Institution of Engineers, World federation of Soft Computing, and Geotechnical Engineering for Disaster Mitigation and Rehabilitation. He has organized numerous workshops and conferences on the applications of artificial intelligence in civil engineering design. As per google scholar, his citation is 787 and H index=17. As per Scopus his citation is 522 and H index=14.

Dr Ravinesh Deo, an Environmental Modelling Expert, has a PhD from The University of Adelaide (Australia), Master of Science (University of Canterbury, New Zealand), Bachelor of Science (University of the South Pacific, Fiji) and Graduate Certificate in Tertiary Teaching and Learning (University of Southern Queensland, Australia). Currently Dr Deo works as Senior Lecturer at University of Southern Queensland and Associate Professor at Chinese Academy of Sciences (Cold and Arid Regions Environmental and Engineering Research Institute, Lanzhou). Dr Deo has also worked Senior Research Fellow (McGill University, Canada), Research Academic in Wind Engineering (The University of Sydney, Australia), Visiting Research Fellow (University of Adelaide, Australia) and Postdoctoral Research Fellow (The University of Queensland), Principal Research Scientist at Queensland Climate Change Centre of Excellence and as Associate Lecturer (University of the South Pacific). Dr Deo won high competitive fellowships and grants including internationally-competitive Chinese Academy of Science Presidential Fellowship and nationally-competitive Endeavour Executive Fellowship that funded Visiting Professor positions in China and Europe. He was awarded the University’s Publication Excellence Award, Head of Department Research Award and University Gold Medal for Academic Excellence. Dr Deo has served as Editorial Board Member of journals, chaired on "IEEE ICMLA Special Session on Machine Learning Algorithms for Environmental Applications, serves as Committee Member of IEEE and other international conference. Dr Deo works with international researchers from Australia, Algeria, Bangladesh, Canada, India, Turkey, Spain, Iran, Malaysia, China, Africa, Korea and United States. Dr Deo’s Google scholar profile has more than 870 citations with H Index of 14, and he has published more than 50 high-impact journal and conference papers. Dr Deo supervises a significant load of PhD and Masters Projects in machine learning applications, health, climate, agriculture, environment, engineering and renewable energy sectors.

Stavros Ntalampiras received the engineering and Ph.D. degrees from the Department of Electrical and Computer Engineering, University of Patras, Greece, in 2006 and 2010, respectively. Subsequently, he joined the System Architectures Group of the Department of Electronics and Information at Politecnico di Milano as a post-doc researcher. From May 2013 to March 2015, he was conducting research with the Joint Research Center of the European Commission. Since April 2015 he is a Politecnico di Milano senior research fellow carrying out didactic and research activities. He has authored over 50 publications in peer-reviewed journals and conferences with more than 450 citations. He is a member of the IEEE Computational Intelligent Society Task Force on Computational  Audio Processing, while his current research interests include content-based signal processing, fault diagnosis, audio pattern recognition, cyber physical systems, and critical infrastructure protection.

Blurb

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.