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Symmetry
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Symmetry in Artificial Intelligence and Machine Learning: Current Advances
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Symmetry in Artificial Intelligence and Machine Learning: Current Advances

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 584

Special Issue Editors

Dr. Haobing Liu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Ocean University of China, Qingdao 266005, China
Interests: data mining; machine learning; IoT application
Prof. Dr. Yanwei Yu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Ocean University of China, Qingdao 266005, China
Interests: data mining; machine learning; database systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yuxi Wang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Ocean University of China, Qingdao 266005, China
Interests: embodied intelligent perception; multimodal large models; 3D AIGC
Dr. Tianzi Zang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210024, China
Interests: artificial intelligence; data mining; deep learning

Special Issue Information

Dear Colleagues,

The rapid evolution of artificial intelligence (AI) and machine learning (ML) has reshaped industries, scientific research, and daily life. Symmetry has been widely embraced in AI and ML, with notable manifestations: for instance, the symmetric architecture of encoders and decoders in autoencoders enables effective feature reconstruction. Meanwhile, in multi-view learning, the symmetry among diverse data views facilitates the discovery of shared knowledge while preserving view-specific characteristics. However, challenges pertaining to model interpretability, open-world machine learning, and the need for robust, scalable solutions in dynamic environments persist.

This Special Issue aims to provide a platform for researchers to showcase their latest innovations, theoretical insights, and practical applications at the intersection of symmetry, AI, and ML. We are seeking high-quality original papers that address emerging challenges and present groundbreaking solutions in AI/ML. Topics of interest include (but are not limited to) the following:

  • Symmetry in new architectures and algorithms for machine learning.
  • Symmetric neural networks.
  • AI/ML-based symmetric data processing and analysis (e.g., mirror detection, water reflection detection).
  • AI/ML for data mining, computer vision, and natural language processing.
  • Responsible AI (e.g., fairness, interpretability, bias mitigation, and privacy-preserving learning).

Dr. Haobing Liu
Prof. Dr. Yanwei Yu
Dr. Yuxi Wang
Dr. Tianzi Zang
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 250 words) can be sent to the Editorial Office for assessment.

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. Symmetry 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 2400 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

  • artificial intelligence
  • machine learning
  • deep learning
  • symmetry
  • data mining
  • computer vision
  • natural language processing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

18 pages, 984 KB  
Article
Deep Multimodal Learning for Heart Sound Classification Using CNN, Transformer, and BiLSTM with Attention
by Ilyas Ait Ichou, Samir Elouaham, Boujemaa Nassiri and Jamal Isknan
Symmetry 2026, 18(4), 556; https://doi.org/10.3390/sym18040556 - 25 Mar 2026
Viewed by 390
Abstract
Phonocardiogram (PCG) signals offer a non-invasive, low-cost screening tool for cardiovascular diseases. However, their noisy and non-stationary nature makes automated classification challenging, and traditional methods often fail to capture complex spectral-temporal patterns. This study proposes a multimodal deep learning architecture for the binary [...] Read more.
Phonocardiogram (PCG) signals offer a non-invasive, low-cost screening tool for cardiovascular diseases. However, their noisy and non-stationary nature makes automated classification challenging, and traditional methods often fail to capture complex spectral-temporal patterns. This study proposes a multimodal deep learning architecture for the binary classification of heart sounds (Healthy vs. Unhealthy). The hybrid model integrates Convolutional Neural Networks (CNNs), Transformer encoders, and Bidirectional Long Short-Term Memory (BiLSTM) networks with an attention mechanism. It utilizes an early-fusion feature extraction pipeline combining MFCCs, Mel-spectrograms, and Chroma descriptors. To ensure robust evaluation and prevent data leakage, SMOTE is applied exclusively to the training folds within a strict zero-leakage, patient-wise 5-fold cross-validation protocol. The proposed framework demonstrates exceptional performance, achieving an average accuracy of 91.67%, a sensitivity of 80.95%, a specificity of 94.46%, and an AUC-ROC of 96.50%. An ablation study confirms that integrating Transformer and BiLSTM modules significantly enhances diagnostic stability over baseline CNNs. Furthermore, with exactly 858,434 parameters (3.27 MB) and interpretable attention maps, this highly optimized model provides a robust assistive solution suitable for deployment in digital stethoscopes and mobile telemedicine systems. Full article
(This article belongs to the Special Issue Symmetry in Artificial Intelligence and Machine Learning: Current Advances)
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