Hybrid Computational Intelligence for Pattern Analysis and Understanding

Aim & scope

Key features

• Provides insights into the latest trends in hybrid intelligent algorithms and architectures

• Focuses on the application of hybrid intelligent techniques for pattern recognition and analysis

• Volumes include source codes, cases studies, data sets and video demonstrations

• Written for researchers and students who want to understand the application of hybrid computational intelligence advances and explore the significance of hybrid computational intelligence for pattern analysis

Topical coverage includes but is not limited to:

Hybrid Computational Intelligence [Neuro-Fuzzy, Rough-neuro, Rough-fuzzy, Fuzz-Rough, Fuzz-Evolutionary, Neuro-Evolutionary, Neuro-Fuzz-Evolutionary, Quantum-Fuzzy, Quantum-Neuro, Quantum-Evolutionary] algorithms and architectures, and their application to a wide range of pattern analysis:

Image and pattern mining Pattern recognition and analysis Biomedical text mining Voice and speech recognition and analysis Chemometrics Deep metric learning for pattern recognition Multimodal pattern recognition of social signals in HCI Special hardware architectures for pattern recognition Logical combinatorial pattern recognition Gesture analysis for human-robot interaction Human mind analysis Real-time video processing and analysis Stereo-to-auto stereoscopic 3D video conversion Virtual and augmented reality Multi-modal image registration Content-Based Image Retrieval (CBIR) Interventional image analysis Pattern recognition in remote sensing Statistical techniques in pattern recognition Graph-based representations

New volume proposals

• Volumes can be Edited, Multi-Authored, or Authored Monographs

• New volume proposals should:

  • Include a well-structured Table of Contents

  • Be innovative, including original features, and any overlaps with published titles in the HCIPAU Series should be explained

  • Include a list of confirmed or tentative, geographically distributed, authors (for Edited volumes)

Indexing

All published volumes in this book series are submitted for indexing in:

• Scopus

• EI Indexing / Compendex

• Book Citation Index

• Google Scholar

Audience

Graduate students, researchers, and professionals interested in developing the application of hybrid computational intelligence advances and exploring the significance of hybrid computational intelligence for pattern analysis

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