Artificial Intelligence and Pattern Recognition Algorithm-Based Multimodal Data Analytics for Real-World Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2565

Special Issue Editors

School of Software, Jiangxi Normal University, Nanchang 330022, China
Interests: artificial intelligence; computer vision; machine learning; medical imaging processing
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Guest Editor
School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Interests: databases; artificial intelligence

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Guest Editor
College of Information Science and Technology, Northeast Normal University, Changchun 130117, China
Interests: artificial intelligence; digital image processing; pattern recognition; machine learning; biometrics; information security

Special Issue Information

Dear Colleagues, 

Recent years have witnessed the revolutionary development of multidisciplinary technologies for acquiring a massive amount of multimodal data. Accelerated by the tremendous increase in multimodal data, multimodal data research has been successfully applied in many domains, such as robotics, internet of things, engineering, natural language processing, and medical applications.

Multimodal data analytics is an effective way to integrate and analyze data from different sources to obtain a more holistic understanding of the multimodal learning process, which has attracted a lot of attention in both academia and industry in recent years.

However, multimodal data analytics still face the following challenges: manipulating, managing, mining, understanding, and visualizing different types of data. The recent advances of artificial intelligence and pattern recognition (AI&PR) algorithms can help researchers to discover complex architectures in high-dimensional multimodal data to better understand practical implications for various applications.

Inspired by the advantages of AI&PR algorithms, in this Special Issue, we invite original research and review articles on research and development in all areas of multimodal data analytics. This Special Issue aims to bring together innovative research around the world in any of the below application domains or in other areas that we have yet to see. 

Potential topics include but are not limited to the following: 

  1. AI- and PR-based multimodal fusion algorithms for healthcare;
  2. AI- and PR-based cross-modal retrieval algorithm;
  3. AI- and PR-based multimodal inferencing;
  4. AI- and PR-based multimodal information safety;
  5. AI- and PR-based multimodal data transmission algorithms;
  6. AI- and PR-based multimodal abnormal detection algorithms;
  7. AI- and PR-based multimodal representation learning algorithms;
  8. AI- and PR-based knowledge discovery algorithm for multimodal data;
  9. AI- and PR-based multimodal social media analysis algorithms.

Dr. Yugen Yi
Prof. Dr. Shaojie Qiao
Prof. Dr. Jun Kong
Guest Editors

Manuscript Submission Information

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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. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • artificial intelligence
  • pattern recognition
  • multimodal fusion algorithms
  • multimodal learning algorithms
  • multimodal analysis algorithms

Published Papers (1 paper)

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Research

12 pages, 862 KiB  
Article
An Improved Heteroscedastic Modeling Method for Chest X-ray Image Classification with Noisy Labels
by Qingji Guan, Qinrun Chen and Yaping Huang
Algorithms 2023, 16(5), 239; https://doi.org/10.3390/a16050239 - 04 May 2023
Cited by 1 | Viewed by 1237
Abstract
Chest X-ray image classification suffers from the high inter-similarity in appearance that is vulnerable to noisy labels. The data-dependent and heteroscedastic characteristic label noise make chest X-ray image classification more challenging. To address this problem, in this paper, we first revisit the heteroscedastic [...] Read more.
Chest X-ray image classification suffers from the high inter-similarity in appearance that is vulnerable to noisy labels. The data-dependent and heteroscedastic characteristic label noise make chest X-ray image classification more challenging. To address this problem, in this paper, we first revisit the heteroscedastic modeling (HM) for image classification with noise labels. Rather than modeling all images in one fell swoop as in HM, we instead propose a novel framework that considers the noisy and clean samples separately for chest X-ray image classification. The proposed framework consists of a Gaussian Mixture Model-based noise detector and a Heteroscedastic Modeling-based noise-aware classification network, named GMM-HM. The noise detector is constructed to judge whether one sample is clean or noisy. The noise-aware classification network models the noisy and clean samples with heteroscedastic and homoscedastic hypotheses, respectively. Through building the correlations between the corrupted noisy samples, the GMM-HM is much more robust than HM, which uses only the homoscedastic hypothesis. Compared with HM, we show consistent improvements on the ChestX-ray2017 dataset with different levels of symmetric and asymmetric noise. Furthermore, we also conduct experiments on a real asymmetric noisy dataset, ChestX-ray14. The experimental results on ChestX-ray14 show the superiority of the proposed method. Full article
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