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Machine Learning Paradigms

Advances in Data Analytics

  • Book
  • © 2019

Overview

Editors:
  1. George A. Tsihrintzis

    1. University of Piraeus , Piraeus, Greece

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    You can also search for this editor in PubMed   Google Scholar

  2. Dionisios N. Sotiropoulos

    1. University of Piraeus , Piraeus, Greece

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    You can also search for this editor in PubMed   Google Scholar

  3. Lakhmi C. Jain

    1. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, Australia

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    You can also search for this editor in PubMed   Google Scholar

  • Includes chapters from leading global experts on recent theoretical and applied advances in the use of machine learning in data analytics
  • Presents recent research in pattern recognition and data analytics
  • Is intended for both experts/researchers in the fields of pattern recognition, machine learning and data analytics as well as for readers working in the general field of computer science who wish to learn more about these emerging disciplines and their applications

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 149)

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Machine Learning Paradigms: Advances in Data Analytics

    • George A. Tsihrintzis, Dionisios N. Sotiropoulos, Lakhmi C. Jain
    Pages 1-4

  3. Data Analytics in the Medical, Biological and Signal Sciences

    1. Front Matter

      Pages 5-5
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    2. A Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics

      • Danilo Dessì, Diego Reforgiato Recupero, Gianni Fenu, Sergio Consoli
      Pages 7-30

    3. Classification Methods in Image Analysis with a Special Focus on Medical Analytics

      • Lucio Amelio, Alessia Amelio
      Pages 31-69

    4. Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field

      • Carine Bou Rjeily, Georges Badr, Amir Hajjarm El Hassani, Emmanuel Andres
      Pages 71-99

    5. Machine Learning Methods for the Protein Fold Recognition Problem

      • Katarzyna Stapor, Irena Roterman-Konieczna, Piotr Fabian
      Pages 101-127

    6. Speech Analytics Based on Machine Learning

      • Grazina Korvel, Adam Kurowski, Bozena Kostek, Andrzej Czyzewski
      Pages 129-157

  4. Data Analytics in Social Studies and Social Interactions

    1. Front Matter

      Pages 159-159
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    2. Trends on Sentiment Analysis over Social Networks: Pre-processing Ramifications, Stand-Alone Classifiers and Ensemble Averaging

      • Christos Troussas, Akrivi Krouska, Maria Virvou
      Pages 161-186

    3. Finding a Healthy Equilibrium of Geo-demographic Segments for a Telecom Business: Who Are Malicious Hot-Spotters?

      • J. Sidorova, O. Rosander, L. Skold, H. Grahn, L. Lundberg
      Pages 187-196

  5. Data Analytics in Traffic, Computer and Power Networks

    1. Front Matter

      Pages 197-197
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    2. Advanced Parametric Methods for Short-Term Traffic Forecasting in the Era of Big Data

      • George A. Gravvanis, Athanasios I. Salamanis, Christos K. Filelis-Papadopoulos
      Pages 199-231

    3. Network Traffic Analytics for Internet Service Providers—Application in Early Prediction of DDoS Attacks

      • Apostolos P. Leros, Antonios S. Andreatos
      Pages 233-267

    4. Intelligent Data Analysis in Electric Power Engineering Applications

      • V. P. Androvitsaneas, K. Boulas, G. D. Dounias
      Pages 269-313

  6. Data Analytics for Digital Forensics

    1. Front Matter

      Pages 315-315
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    2. Combining Genetic Algorithms and Neural Networks for File Forgery Detection

      • Konstantinos Κarampidis, Ioannis Deligiannis, Giorgos Papadourakis
      Pages 317-335

  7. Theoretical Advances and Tools for Data Analytics

    1. Front Matter

      Pages 337-337
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    2. Deep Learning Analytics

      • Nikolaos Passalis, Anastasios Tefas
      Pages 339-370
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Keywords

About this book

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities.

The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences.

Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics.

This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.

Reviews

“It contains interesting work on machine learning in the medical domain. … it is an interesting collection of machine learning applications across multiple domains. It may be of interest to readers working in one of the discussed areas.” (K. Waldhör, Computing Reviews, January, 2019)

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Editors and Affiliations

  • University of Piraeus , Piraeus, Greece

    George A. Tsihrintzis, Dionisios N. Sotiropoulos

  • Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, Australia

    Lakhmi C. Jain

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