Nilanjan Dey

Himansu Das

Bighnaraj Naik

H S Behera

Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data. Examines the methodology and requirements for development of big data architecture, big data modeling, big data as a service, big data analytics, and more Discusses big data applications for intelligent healthcare management, such as revenue management and pricing, predictive analytics/forecasting, big data integration for medical data, algorithms and techniques, etc. Covers the development of big data tools, such as data, web and text mining, data mining, optimization, machine learning, cloud in big data with Hadoop, big data in IoT, and more

Big Data Analytics for Intelligent Healthcare Management

Durga Prasad Mohapatra

Ajay Kumar Jena

This book covers both theory and applications in the automation of software testing tools and techniques for various types of software (e.g. object-oriented, aspect-oriented, and web-based software). When software fails, it is most often due to lack of proper and thorough testing, an aspect that is even more acute for object-oriented, aspect-oriented, and web-based software. Further, since it is more difficult to test distributed and service-oriented architecture-based applications, there is a pressing need to discuss the latest developments in automated software testing. This book discusses the most relevant issues, models, tools, challenges, and applications in automated software testing. Further, it brings together academic researchers, scientists, and engineers from a wide range of industrial application areas, who present their latest findings and identify future challenges in this fledging research area.

Foundations, Applications and Challenges

Automated Software Testing

Janmenjoy Nayak

Prasant Kumar Pattnaik

Siddharth Swarup Rautaray

The book focuses to foster new and original research ideas and results in three broad areas: computing, analytics, and networking with its prospective applications in the various interdisciplinary domains of engineering. This is an exciting and emerging interdisciplinary area in which a wide range of theory and methodologies are being investigated and developed to tackle complex and challenging real world problems. It also provides insights into the International Conference on Computing Analytics and Networking (ICCAN 2017) which is a premier international open forum for scientists, researchers and technocrats in academia as well as in industries from different parts of the world to present, interact, and exchange the state of art of concepts, prototypes, innovative research ideas in several diversified fields. The book includes invited keynote papers and paper presentations from both academia and industry to initiate and ignite our young minds in the meadow of momentous research and thereby enrich their existing knowledge. The book aims at postgraduate students and researchers working in the discipline of Computer Science & Engineering. It will be also useful for the researchers working in the domain of electronics as it contains some hardware technologies and forthcoming communication technologies.

Proceedings of ICCAN 2017

Progress in Computing, Analytics and Networking

Bhabani Shankar Prasad Mishra

Bijaya Ketan Panigrahi

Bijan Bihari Mishra

Satchidanand Dehuri

Ajit Kumar Nayak

This book discusses applications of computational intelligence in sensor networks. Consisting of twenty chapters, it addresses topics ranging from small-scale data processing to big data processing realized through sensor nodes with the help of computational approaches. Advances in sensor technology and computer networks have enabled sensor networks to evolve from small systems of large sensors to large nets of miniature sensors, from wired communications to wireless communications, and from static to dynamic network topology. In spite of these technological advances, sensor networks still face the challenges of communicating and processing large amounts of imprecise and partial data in resource-constrained environments. Further, optimal deployment of sensors in an environment is also seen as an intractable problem. On the other hand, computational intelligence techniques like neural networks, evolutionary computation, swarm intelligence, and fuzzy systems are gaining popularity in solving intractable problems in various disciplines including sensor networks. The contributions combine the best attributes of these two distinct fields, offering readers a comprehensive overview of the emerging research areas and presenting first-hand experience of a variety of computational intelligence approaches in sensor networks.

Computational Intelligence in Sensor Networks

Satchidananda Dehuri

Alok Kumar Jagadev

This book discusses harnessing the real power of cloud computing in optimization problems, presenting state-of-the-art computing paradigms, advances in applications, and challenges concerning both the theories and applications of cloud computing in optimization with a focus on diverse fields like the Internet of Things, fog-assisted cloud computing, and big data. In real life, many problems – ranging from social science to engineering sciences – can be identified as complex optimization problems. Very often these are intractable, and as a result researchers from industry as well as the academic community are concentrating their efforts on developing methods of addressing them. Further, the cloud computing paradigm plays a vital role in many areas of interest, like resource allocation, scheduling, energy management, virtualization, and security, and these areas are intertwined with many optimization problems. Using illustrations and figures, this book offers students and researchers a clear overview of the concepts and practices of cloud computing and its use in numerous complex optimization problems.

Cloud Computing for Optimization: Foundations, Applications, and Challenges

Valentina Emilia Balas

Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more. Examines IoT applications, the design of real-time intelligent systems, and how to manage the rapid growth of the large volume of sensor data Discusses intelligent management systems for applications such as healthcare, robotics and environment modeling Provides a focused approach towards the design and implementation of real-time intelligent systems for the management of sensor data in large-scale environments

Real-Time Data Analytics for Large Scale Sensor Data

Chittaranjan Pradhan

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning

Foundations, Biomedical Applications, and Challenges

Deep Learning for Data Analytics

Himansu Das

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