This book provides a multidisciplinary view of smart infrastructure through a range of diverse introductory and advanced topics. The book features an array of subjects that include: smart cities and infrastructure, e-healthcare, emergency and disaster management, Internet of Vehicles, supply chain management, eGovernance, and high performance computing. The book is divided into five parts: Smart Transportation, Smart Healthcare, Miscellaneous Applications, Big Data and High Performance Computing, and Internet of Things (IoT). Contributions are from academics, researchers, and industry professionals around the world.

• Features a broad mix of topics related to smart infrastructure and smart applications, particularly high performance computing, big data, and artificial intelligence;
• Includes a strong emphasis on methodological aspects of infrastructure, technology and application development;

• Presents a substantial overview of research and development on key economic sectors including healthcare and transportation.

  Autorentext

Rashid Mehmood: Rashid Mehmood is the Research Professor of Big Data Systems and the Director of Research, Training, and Consultancy at the High Performance Computing Centre, King Abdulaziz University, Saudi Arabia. He has gained qualifications and academic work experience from universities in the UK including Cambridge and Oxford. Rashid has over 20 years of research experience in computational modelling and simulation systems coupled with his expertise in high performance computing. His broad research aim is to develop multi-disciplinary science and technology to enable a better quality of life and Smart Economy with a focus on real-time intelligence and dynamic system management. He has published over 150 research papers including 5 edited books. He has organised and chaired international conferences and workshops in his areas of expertise including EuropeComm 2009 and Nets4Cars 2010 - 2013. He has led and contributed to academia-industry collaborative projects funded by EPSRC, EU, UK regional funds, and Technology Strategy Board UK with the value over £50 million. He is a founding member of the Future Cities and Community Resilience (FCCR) Network. He is a member of ACM, OSA, Senior Member IEEE and former Vice-Chairman of IET Wales SW Network.

Simon See: Prof. Simon See is currently the Solution Architecture and Engineering Director and Chief Solution Architect for Nvidia AI Technology Center. He is also a Professor and Chief Scientific Computing Officer in Shanghai Jiao Tong University. Prof. See is also the Chief Scientific Computing Advisor for BGI (China) and has a position in NanyangTechnological University (Singapore) and King-Mong Kung University of Technology (Thailand). Prof See is currently involved in a number of smart city projects, especially in Singapore and China. His research interests are in the area of High Performance Computing, Big Data, Artificial Intelligence, machine learning, computational science, Applied Mathematics, and simulation methodology. Prof. See is also leading some of the AI initiatives in Asia Pacific. He has published over 200 papers in these areas and has won various awards. Prof. See is also a member of SIAM, IEEE, and IET. He also a committee member of more than 50 conferences. Prof. See graduated from University of Salford (UK) with a Ph.D. in electrical engineering and numerical analysis in 1993. Prior to joining NVIDIA, Prof. See worked for SGI, DSO National Lab. of Singapore, IBM, International Simulation Ltd (UK), Sun Microsystems, and Oracle. He is also providing consultancy to a number of national research and supercomputing centers.

Iyad Katib: Iyad Katib is an Associate Professor with the Computer Science Department and the current Vice Dean and the College Council Secretary of the Faculty of Computing and Information Technology (FCIT) in King Abdulaziz University (KAU). He is also the Director of KAU High Performance Computing Center. Iyad received his Ph.D. and MS degrees in Computer Science from University of Missouri-Kansas City in 2011 and 2004, respectively. He received his BS degree in Statistics/Computer Science from King Abdul Aziz University in 1999. His current research interest is on Computer Networking and High Performance Computing.

Imrich Chlamtac: Dr. Chlamtac is the President of EAI, the European Alliance for Innovation, where he pioneered EAI as a global initiative for promoting growth of ICT based economy and digital society. Based on community cooperation principles, EAI supports research and innovation with events, publications, and its innovation platform through the cooperation of over hundred and fifty thousand subscribers worldwide. Dr. Chlamtac is also the Founding President of CREATE-NET and Bruno Kessler Professor at the University of Trento, top ranked ICT institutions in Italy.

Prior to coming to Europe ****Dr. Chlamtac served as Associate Provost for Research and Distinguished Chair in Telecommunications at the University of Texas in Dallas, the number one USA ranked young university in the Times of Higher Education ranking 2017. Prior to that he was a Professor at Boston University, University of Massachusetts and Technion.

Dr. Chlamtac holds multiple academic and honorary appointments including the University of Trento, the Tel Aviv University, the Beijing University of Posts and Telecommunications, and the Budapest University of Technology and Economics.

Dr. Chlamtac scientific recognitions include IEEE and ACM Fellowship, the ACM Award for Outstanding Contributions to Research on Mobility, the IEEE Award for Outstanding Technical Contributions to Wireless Personal Communications, New Talents in Simu...

Inhalt
Chapter1: Enterprise Systems for Networked Smart Cities.- Chapter2: Sentiment Analysis of Arabic Tweets for Road Traffic Congestion and Event Detection.- Chapter3: Automatic Detection and Validation of Smart City Events Using HPC and Apache Spark Platforms.- Chapter4: In-Memory Deep Learning Computations on GPUs for Prediction of Road Traffic Incidents using Big Data Fusion.- Chapter5: Hybrid Statistical and Machine Learning Methods for Road Traffic Prediction: A Review and Tutorial.- Chapter6: Comparison of Decision Trees and Deep Learning for Object Classification in Autonomous Driving.- Chapter7: A Smart Disaster Management System for Future Cities using Deep Learning, GPUs, and In-Memory Computing.- Chapter8: Parallel Shortest Path Big Data Graph Computations of US Road Network using Apache Spark: Survey, Architecture, and Evaluation.- Chapter9: A Survey of Methods and Tools for Large-Scale DNA Mixture Profiling.- Chapter10: An Architecture to Improve Security of Cloud Computing in Healthcare Sectors.- Chapter11: The Role of Big Data and Twitter Data Analytics in Healthcare Supply Chain Management.- Chapter12: A Mobile Cloud Framework for Context-aware and Portable Recommender System for Smart Markets.- Chapter13: Association Rule Mining in Higher Education: A Case Study of Computer Science Students.- Chapter14: SelecWeb: A Software Tool for Automatic Selection of Web Frameworks.- Chapter15: On Performance of Commodity Single Board Computer based Clusters: A Big Data perspective.- Chapter16: Parallel Iterative Solution of Large Sparse Linear Equation Systems on the Intel MIC Architecture.- Chapter17: Performance Characteristics for Sparse Matrix Vector Multiplication on GPUs.- Chapter18: HPC-Smart Infrastructures: A Review and Outlook on Performance Analysis Methods and Tools.- Chapter19: Big Data Tools, Technologies and Applications: A Survey.- Chapter20: Big Data for Smart Infrastructures Design: Opportunities and Challenges.- Chapter21: Software Quality in the era of Big Data,…