Special Issue "Evolving Machine Learning and Deep Learning Models for Computer Vision (ECV)"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editors

Dr. Li Zhang
Guest Editor
Department of Computer and Information Sciences, Faculty of Engineering and Environment, Northumbria University, Newcastle NE1 8ST, UK
Interests: deep learning; machine learning; computer vision and evolutionary computation
Special Issues and Collections in MDPI journals
Prof. Dr. Chee Peng Lim
Guest Editor
Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC 3216, Australia
Interests: data analytics; condition monitoring; optimisation and decision support
Special Issues and Collections in MDPI journals
Prof. Dr. Chengyu Liu
Guest Editor
State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: intelligent monitoring; machine learning for cardiovascular signals

Special Issue Information

Dear Colleagues,

Evolutionary algorithms have demonstrated superior global search capabilities and have been applied to diverse real-life single-, multi-, and many-objective optimisation problems. Examples include the use of evolutionary algorithms for optimal parameter selection and discriminative feature selection pertaining to diverse classification and regression models, as well as hybrid evolutionary and clustering algorithms for image segmentation and visual saliency detection.

In parallel, deep learning models have demonstrated great success in dealing with complex computer vision tasks. Examples include the use of deep convolutional neural networks combined with recurrent models for image caption generation and visual question generation. Deep learning combined with transfer learning has also been employed to deal with various computer vision tasks. Nevertheless, the design of new and effective deep learning models and identification of the optimal hyper-parameters of the resulting models require profound domain knowledge, which may not always be available to researchers. In this regard, the superior search capabilities of evolutionary algorithms can be exploited to tackle such optimisation problems, e.g., to formulate evolving deep neural networks that fit the tasks at hand.

This Special Issue aims to stimulate research pertaining to not only feature selection, optimal topology, and hyper-parameter identification for clustering and classification systems but also evolving deep learning architecture generation through evolutionary algorithm and related paradigms.

Potential topics include but are not limited to the following:

  • Image segmentation
  • Data stream clustering
  • Feature selection
  • Object detection and recognition
  • Image description generation
  • Visual question generation
  • Visual saliency detection
  • Image retrieval
  • Image classification
  • Human or object attribute prediction
  • Facial expression recognition and age estimation
  • Human action recognition
  • Bioinformatics (e.g., skin cancer, heart disease, and brain tumour classification)
  • Machine translation, language generation, and speech recognition
  • Evolving deep neural network generation for diverse computer vision, image processing, and signal
  • processing problems
  • Hybrid clustering techniques

Optimal topology and hyper-parameter identification for classification and ensemble learning models

Dr. Li Zhang
Prof. Dr. Chee Peng Lim
Prof. Dr. Chengyu Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • deep learning
  • machine learning
  • medical imaging
  • computer vision and evolutionary computation

Published Papers

This special issue is now open for submission.
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