Three E-books on Artificial Intelligence

Explore bioinformatics, neural networks, and visual attention

16 June 2016

To view a sample of IEEE e-books that focus on artificial intelligence, log in to the IEEE Xplore Digital Library and click on “Books & eBooks.” You can then browse or search by title and download PDFs of selected chapter excerpts. To order books, visit Wiley.com.

  • Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

    BY YI PAN, MIN LI, AND JIANXIN WANG (2014)

    This is an essential reference for bioinformatics specialists or for anyone who wants to better understand the field. The book covers recent developments and includes methods for the analysis of protein data, with a focus on the analysis of sequences, structures, and interaction networks. Data preparation, simulation, evaluation methods, and applications are also examined. Case studies and visuals illustrate how to apply the methods.

  • Complex-Valued Neural Networks: Advances and Applications

    BY AKIRA HIROSE (2013)

    Complex-valued neural networks use complicated arithmetic, exhibiting specific characteristics in their learning, self-organizing, and processing dynamics. This guide is for ad­vanced computational intelligence and electro­magnetism theorists and mathematicians interested in artificial intelligence, machine-learning theories, and algorithms.

    The networks are suitable for processing complex amplitudes—involving amplitude and phase—which is a core concept in physical systems to deal with electromagnetic, light, sonic, and ultrasonic waves, as well as electron and superconducting waves.

    This resource deals with advanced theories in a wide variety of applications including brain-computer interfaces and communications and image-processing systems. Topics also include conventional complex-valued, quaternionic, and Clifford-algebraic neural networks.

  • Selective Visual Attention: Computational Models and Applications

    BY LIMING ZHANG AND WEISI LIN (2013)

    Visual attention combines a number of disciplines: artificial neural networks, artificial intelligence, vision science, and psychology. The aim of the technology is to build computational models similar to human vision to solve problems in applications including object recognition, unmanned vehicle navigation, and image coding and processing. The book covers the significance of vision research, psychology, and computer vision; existing computational visual attention models; and applications in image-processing tasks.

This article is part of our June 2016 special issue on artificial intelligence.

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