Cognitive Computing: Theory and Applications

Cognitive Computing: Theory and Applications

1st Edition - September 1, 2016

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  • Authors: Vijay Raghavan, Venkat Gudivada, Venu Govindaraju, C.R. Rao
  • Hardcover ISBN: 9780444637444
  • eBook ISBN: 9780444637512

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Cognitive Computing: Theory and Applications, written by internationally renowned experts, focuses on cognitive computing and its theory and applications, including the use of cognitive computing to manage renewable energy, the environment, and other scarce resources, machine learning models and algorithms, biometrics, Kernel Based Models for transductive learning, neural networks, graph analytics in cyber security, neural networks, data driven speech recognition, and analytical platforms to study the brain-computer interface.

Key Features

  • Comprehensively presents the various aspects of statistical methodology
  • Discusses a wide variety of diverse applications and recent developments
  • Contributors are internationally renowned experts in their respective areas


Statisticians and scientists in various disciplines who use statistical methodology in their work

Table of Contents

  • Section A: Fundamentals and Principles

    Chapter 1: Cognitive Computing: Concepts, Architectures, Systems, and Applications

    • Abstract
    • 1 Introduction
    • 2 Interdisciplinary Nature of Cognitive Science
    • 3 Cognitive Computing Systems
    • 4 Representations for Information and Knowledge
    • 5 Principal Technology Enablers of Cognitive Computing
    • 6 Cognitive Computing Architectures and Approaches
    • 7 Cognitive Computing Systems and Applications
    • 8 Trends and Future Research Directions
    • 9 Cognitive Computing Resources

    Chapter 2: Cognitive Computing and Neural Networks: Reverse Engineering the Brain

    • Abstract
    • 1 Introduction
    • 2 Brain Scalability
    • 3 Neocortical Brain Organization
    • 4 The Concept of a Basic Circuit
    • 5 Abstractions of Cortical Basic Circuits
    • 6 Large-Scale Cortical Simulations
    • 7 Hardware Support for Brain Simulation
    • 8 Deep Learning Networks
    • 9 Summary and Conclusion

    Section B: Complex Analytics and Machine Learning

    Chapter 3: Visual Analytic Decision-Making Environments for Large-Scale Time-Evolving Graphs

    • Abstract
    • 1 Introduction
    • 2 Visual Analytics as an Approach to Cognitive Computing
    • 3 Time-Evolving Graphs
    • 4 Visual Analytics as a Framework for Time-Evolving Graphs
    • 5 Visual Analytics Sandbox: An Implementation Architecture
    • 6 Conclusion and Future Research
    • Acknowledgments

    Chapter 4: CyGraph: Graph-Based Analytics and Visualization for Cybersecurity

    • Abstract
    • 1 Introduction
    • 2 Related Work
    • 3 Description of CyGraph
    • 4 Example Applications
    • 5 Summary
    • Acknowledgments

    Chapter 5: Cognitive Analytics: Going Beyond Big Data Analytics and Machine Learning

    • Abstract
    • 1 Introduction
    • 2 Evolution of Analytics and Core Themes
    • 3 Types of Learning
    • 4 Machine Learning Algorithms
    • 5 Cognitive Analytics: A Coveted Goal
    • 6 Cognitive Analytics Applications
    • 7 Current Trends and Research Issues
    • 8 Conclusions

    Chapter 6: A Cognitive Random Forest: An Intra- and Intercognitive Computing for Big Data Classification Under Cune Condition

    • Abstract
    • 1 Introduction
    • 2 Terminologies
    • 3 Random Forest Classifiers
    • 4 The STE-M Model
    • 5 Cognitive Random Forest
    • 6 Cognitive Computing System
    • 7 Experimental Validation
    • 8 Conclusions

    Chapter 7: Bayesian Additive Regression Tree for Seemingly Unrelated Regression with Automatic Tree Selection

    • Abstract
    • 1 Introduction
    • 2 BART for SUR with Automatic Tree Selection
    • 3 Fitting BART-SUR Model Through MCMC
    • 4 Simulation Studies
    • 5 Data Analysis
    • 6 Conclusion
    • Appendices

    Section C: Applications

    Chapter 8: Cognitive Systems for the Food–Water–Energy Nexus

    • Abstract
    • 1 Introduction
    • 2 Invariance, Correlation, and Data
    • 3 Time-Series Data
    • 4 Images, Video, and Spatio-Temporal Data
    • 5 Autonomous Systems to Manage Complexity
    • 6 Conclusion

    Chapter 9: Cognitive Computing Applications in Education and Learning

    • Abstract
    • 1 Introduction
    • 2 EDM and LA
    • 3 Recent Research
    • 4 Conclusion

    Chapter 10: Large Scale Data Enabled Evolution of Spoken Language Research and Applications

    • Abstract
    • 1 Introduction
    • 2 Speech Signals
    • 3 Signal Preprocessing
    • 4 Segmental Feature Extraction
    • 5 Prosodic Feature Extraction
    • 6 Mathematical Models
    • 7 Speech Processing Core Tasks and Applications
    • 8 Resources for Speech Research
    • 9 Trends and Research Directions
    • 10 Conclusions

    Chapter 11: The Internet of Things and Cognitive Computing

    • Abstract
    • 1 Introduction
    • 2 The IoT—Definition and History
    • 3 The Role of Big Data
    • 4 Big Data Challenges and Opportunities for IoT and Cognitive Computing
    • 5 IoT Use Cases and Opportunity to Leverage Cognitive Computing
    • 6 Future Opportunities for IoT and Cognitive Computing

Product details

  • No. of pages: 404
  • Language: English
  • Copyright: © North Holland 2016
  • Published: September 1, 2016
  • Imprint: North Holland
  • Hardcover ISBN: 9780444637444
  • eBook ISBN: 9780444637512

About the Authors

Vijay Raghavan

Vijay V. Raghavan, Alfred and Helen Lamson Endowed Professor in Computer Science, The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, USA.

Prof Raghavan also serves as the Director of the NSF-sponsored Industry/ University Cooperative Research Center for Visual and Decision Informatics. In this role, he co-ordinates several multi-institutional, industry-driven research projects and manages a budget of over $500K/year. From 1997 to 2003, he led a $2.3M research and development project in close collaboration with the USGS National Wetlands Research Center and with the Department of Energy's Office of Science and Technical Information on creating a digital library with data mining capabilities incorporated.

His research interests are in Big Data, data mining, information retrieval, machine learning and Internet computing. He has published over 250 peer-reviewed research papers --appearing in top-level journals and proceedings - that cumulatively accord him an h-index of 31, based on citations.

He has served as major advisor for 24 doctoral students and has garnered $10 million in external funding. Besides substantial technical expertise, Dr. Raghavan has vast experience managing interdisciplinary and multi- institutional collaborative projects. He has also directed industry-sponsored research, for companies such as GE Healthcare and Araicom Life Sciences L.L.C., on projects pertaining to Neuro-imaging based dementia detection and literature-based biomedical hypotheses generation, respectively.

Affiliations and Expertise

The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, USA

Venkat Gudivada

Venkat N. Gudivada is a professor and chair of the Computer Science Department at East Carolina University. Prior to this, he was a professor and founding chair of the Weisberg Division of Computer Science at Marshall University. His industry tenure spans over six years as a vice president for Wall Street companies in the New York City area including Merrill Lynch (now Bank of America Merrill Lynch) and Financial Technologies International (now GoldenSource). Previous academic tenure includes work at the University of Michigan, University of Missouri, and Ohio University.

He has published over 90 peer-reviewed technical articles and rendered professional service in various roles including conference program chair, keynote speaker, program committee member, and guest editor of IEEE journals. Gudivada's research sponsors include National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), U.S. Department of Energy, U.S. Department of Navy, U.S. Army Research Office, MU Foundation, and WV Division of Science and Research. His current research interests encompass Big Data Management, High Performance Computing, Information Retrieval, Image and Natural Language Processing, and Personalized Learning. Gudivada received a PhD degree in Computer Science from the University of Louisiana at Lafayette.

Affiliations and Expertise

Professor, East Carolina University, NC, USA

Venu Govindaraju

Venu Govindaraju, SUNY Distinguished Professor of Computer Science and Engineering, is the founding director of the Center for Unified Biometrics and Sensors. He received his Ph.D. from the University at Buffalo, State University of New York. His research focus is on machine learning and pattern recognition in the domains of Document Image Analysis and Biometrics.

Prof. Govindaraju has co-authored over 400 refereed scientific papers. His seminal work in handwriting recognition was at the core of the first handwritten address interpretation system used by the US Postal Service. He has been a Principal or Co-Investigator of sponsored projects funded for about $65 million dollars. Prof. Govindaraju has supervised the dissertations of 30 doctoral students. He has served on the editorial boards of premier journals.

Prof. Govindaraju is a Fellow of the ACM (Association of Computing Machinery), IEEE (Institute of Electrical and Electronics Engineers), AAAS (American Association for the Advancement of Science), the IAPR (International Association of Pattern Recognition), and the SPIE (International Society of Optics and Photonics). He is recipient of the 2004 MIT Global Indus Technovator award and the 2010 IEEE Technical Achievement award.

Affiliations and Expertise

University at Buffalo, Amherst, NY, USA

C.R. Rao

C. R. Rao is a world famous statistician who earned a place in the history of statistics as one of those “who developed statistics from its adhoc origins into a firmly grounded mathematical science.”

He was employed at the Indian Statistical Institute (ISI) in 1943 as a research scholar after obtaining an MA degree in mathematics with a first class and first rank from Andhra University in1941 and MA degree in statistics from Calcutta University in 1943 with a first class, first rank, gold medal and record marks which remain unbroken during the last 73 years.

“At the age of 28 he was made a full professor at ISI in recognition of his creativity.” While at ISI, Rao went to Cambridge University (CU) in 1946 on an invitation to work on an anthropometric project using the methodology developed at ISI. Rao worked in the museum of archeology and anthropology in Duckworth laboratory of CU during 1946-1948 as a paid visiting scholar. The results were reported in the book “Ancient Inhabitants of Jebel Moya” published by the Cambridge Press under the joint authorship of Rao and two anthropologists. On the basis of work done at CU during the two year period, 1946-1948, Rao earned a Ph.D. degree and a few years later Sc.D. degree of CU and the rare honor of life fellowship of Kings College, Cambridge.

He retired from ISI in 1980 at the mandatory age of 60 after working for 40 years during which period he developed ISI as an international center for statistical education and research. He also took an active part in establishing state statistical bureaus to collect local statistics and transmitting them to Central Statistical Organization in New Delhi. Rao played a pivitol role in launching undergraduate and postgraduate courses at ISI. He is the author of 475 research publications and several breakthrough papers contributing to statistical theory and methodology for applications to problems in all areas of human endeavor. There are a number of classical statistical terms named after him, the most popular of which are Cramer-Rao inequality, Rao-Blackwellization, Rao’s Orthogonal arrays used in quality control, Rao’s score test, Rao’s Quadratic Entropy used in ecological work, Rao’s metric and distance which are incorporated in most statistical books.

He is the author of 10 books, of which two important books are, Linear Statistical Inference which is translated into German, Russian, Czec, Polish and Japanese languages,and Statistics and Truth which is translated into, French, German, Japanese, Mainland Chinese, Taiwan Chinese, Turkish and Korean languages.

He directed the research work of 50 students for the Ph.D. degrees who in turn produced 500 Ph.D.’s. Rao received 38 hon. Doctorate degree from universities in 19 countries spanning 6 continents. He received the highest awards in statistics in USA,UK and India: National Medal of Science awarded by the president of USA, Indian National Medal of Science awarded by the Prime Minister of India and the Guy Medal in Gold awarded by the Royal Statistical Society, UK. Rao was a recipient of the first batch of Bhatnagar awards in 1959 for mathematical sciences and and numerous medals in India and abroad from Science Academies. He is a Fellow of Royal Society (FRS),UK, and member of National Academy of Sciences, USA, Lithuania and Europe. In his honor a research Institute named as CRRAO ADVANCED INSTITUTE OF MATHEMATICS, STATISTICS AND COMPUTER SCIENCE was established in the campus of Hyderabad University.

Affiliations and Expertise

University of Hyderabad Campus, India

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