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Mathematics
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The Recent Advances in Computational Intelligence
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The Recent Advances in Computational Intelligence

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 2965

Special Issue Editors

Dr. Changjing Shang

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Guest Editor
Department of Computer Science, Aberystwyth University, Aberystwyth, UK
Interests: intelligent robotics; computational intelligence; machine learning
Dr. George Panoutsos

E-Mail Website
Guest Editor
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S10 2TN, UK
Interests: computational intelligence; human-level machine intelligence; artificial intelligence; interpretable machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Neil Mac Parthaláin

E-Mail Website
Guest Editor
Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, Ceredigion, UK
Interests: computational intelligence; artificial intelligence; feature selection
Dr. Mahdi Mahfouf

E-Mail Website
Guest Editor
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
Interests: interpretable machine learning; computational intelligence; artificial intelligence
Dr. Lyudmila Mihaylova

E-Mail Website
Guest Editor
Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S10 2TN, UK
Interests: machine learning; computational intelligence; statistical signal processing; robot SLAM; navigation and autonomous systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 21st Annual Workshop on Computational Intelligence (UKCI2022) was hosted by Sheffield University during September 7th– 9th, 2022. The UKCI workshop series is a premier European event for the presentation of leading research and development in all areas of computational intelligence (CI). The aim of UKCI2022 was to provide a forum for the academic community and the industry at large to share experiences of advancing and applying computational intelligence techniques, to discuss new trends, and also to exchange views and ideas. UKCI2022 was oranised with the support of the workshop’s organising and programme committees, a network of reviewers and volunteers, and crucially the contribution of authors and keynote speakers.

As a research field, CI attracts great interest from scientists, engineers and practitioners working primarily in the areas of neural networks, fuzzy systems and evolutionary computation. This Special Issue offers an opportunity to showcase the contributions of UKCI2022. The 21 authors of the top-ranked papers, based on the review reports returned by the international program committee and reviewers, are invited to submit substantially extended versions to be considered for publication in this Special Issue of Mathematics. It is dedicated to recent advances in computational intelligence algorithms and applications. The submissions are expected to jointly reflect both the most recent advances in computational intelligence and their applications as they progress from the initial scientific contributions, as well as any relevant trends. We hope that this Special Issue will bring a great selection of research contributions to readers, marking progress and promoting scientific excellence in computational intelligence and its applications. 

Best wishes, 

Dr. Changjing Shang
Dr. George Panoutsos
Dr. Neil Mac Parthaláin
Dr. Mahdi Mahfouf
Dr. Lyudmila Mihaylova
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 submissions that pass pre-check are 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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

  • fuzzy systems
  • neural networks
  • evolutionary computation
  • evolving systems
  • machine learning
  • data mining
  • cognitive computing
  • intelligent robotics
  • hybrid methods
  • deep learning
  • applications of computational intelligence

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Published Papers (2 papers)

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28 pages, 685 KiB  
Article
Towards Refined Autism Screening: A Fuzzy Logic Approach with a Focus on Subtle Diagnostic Challenges
by Philip Smith and Sarah Greenfield
Mathematics 2024, 12(13), 2012; https://doi.org/10.3390/math12132012 - 28 Jun 2024
Viewed by 826
Abstract
This study explores the creation and testing of a Fuzzy Inferencing System for automating preliminary referrals for autism diagnosis, utilizing membership functions aligned with the Autism Quotient 10-item questionnaire. Validated across three distinct datasets, the system demonstrated perfect accuracy in deterministic settings and [...] Read more.
This study explores the creation and testing of a Fuzzy Inferencing System for automating preliminary referrals for autism diagnosis, utilizing membership functions aligned with the Autism Quotient 10-item questionnaire. Validated across three distinct datasets, the system demonstrated perfect accuracy in deterministic settings and an overall accuracy of 92.91% in a broad fuzzy dataset. The use of Fuzzy Logic reflects the complex and variable nature of autism diagnosis, suggesting its potential applicability in this field. While the system effectively categorized clear referral and non-referral scenarios, it faced challenges in accurately identifying cases requiring a second opinion. These results indicate the need for further refinement to enhance the efficiency and accuracy of preliminary autism screenings, pointing to future avenues for improving the system’s performance. The motivation behind this study is to address the diagnostic gap for high-functioning adults whose symptoms present in a more neurotypical manner. Many current deep learning approaches for diagnosing autism focus on quantitative datasets like fMRI and facial expressions, often overlooking behavioral traits. However, autism diagnosis still heavily relies on long histories and multi-stakeholder information from parents, teachers, doctors and behavioral experts. This research addresses the challenge of creating an automated system that can handle the nuances and variability inherent in ASD symptoms. The theoretical innovation lies in the novel application of Fuzzy Logic to interpret these subtle diagnostic indicators, providing a more systematic approach compared to traditional methods. By bridging the gap between subjective clinical evaluations and objective computational techniques, this study aims to enhance the preliminary screening process for ASD. Full article
(This article belongs to the Special Issue The Recent Advances in Computational Intelligence)
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22 pages, 9386 KiB  
Article
Transport Object Detection in Street View Imagery Using Decomposed Convolutional Neural Networks
by Yunpeng Bai, Changjing Shang, Ying Li, Liang Shen, Shangzhu Jin and Qiang Shen
Mathematics 2023, 11(18), 3839; https://doi.org/10.3390/math11183839 - 7 Sep 2023
Cited by 3 | Viewed by 1493
Abstract
Deep learning has achieved great successes in performing many visual recognition tasks, including object detection. Nevertheless, existing deep networks are computationally expensive and memory intensive, hindering their deployment in resource-constrained environments, such as mobile or embedded devices that are widely used by city [...] Read more.
Deep learning has achieved great successes in performing many visual recognition tasks, including object detection. Nevertheless, existing deep networks are computationally expensive and memory intensive, hindering their deployment in resource-constrained environments, such as mobile or embedded devices that are widely used by city travellers. Recently, estimating city-level travel patterns using street imagery has been shown to be a potentially valid way according to a case study with Google Street View (GSV), addressing a critical challenge in transport object detection. This paper presents a compressed deep network using tensor decomposition to detect transport objects in GSV images, which is sustainable and eco-friendly. In particular, a new dataset named Transport Mode Share-Tokyo (TMS-Tokyo) is created to serve the public for transport object detection. This is based on the selection and filtering of 32,555 acquired images that involve 50,827 visible transport objects (including cars, pedestrians, buses, trucks, motors, vans, cyclists and parked bicycles) from the GSV imagery of Tokyo. Then a compressed convolutional neural network (termed SVDet) is proposed for street view object detection via tensor train decomposition on a given baseline detector. The method proposed herein yields a mean average precision (mAP) of 77.6% on the newly introduced dataset, TMS-Tokyo, necessitating just 17.29 M parameters and a computational capacity of 16.52 G FLOPs. As such, it markedly surpasses the performance of existing state-of-the-art methods documented in the literature. Full article
(This article belongs to the Special Issue The Recent Advances in Computational Intelligence)
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