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Electronics
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Artificial Intelligence in Vision Modelling
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Artificial Intelligence in Vision Modelling

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 3782

Special Issue Editors

Dr. Rudrasis Chakraborty

E-Mail Website
Guest Editor
Senior Staff Scientist, Lawrence Livermore National Lab, Livermore, CA 94550, USA
Interests: machine learning; computer vision; artificial intelligence; deep learning
Dr. Indrasis Chakraborty

E-Mail Website
Guest Editor
Lawrence Livermore National Laboratory, Livermore, Livermore, CA 94550, USA
Interests: time series; applied machine learning

Special Issue Information

Dear Colleagues,

In the era of deep and machine learning, multi-task and multi-modal learning have been explored separately. However, in most practical scenarios, we often require solving several tasks while the data consists of multiple modalities, e.g., image and text, image and temporal information, and many more. In this Special Issue, we will focus on theory and applications involving multi-modalities and multi-task applications. More specifically, we will encourage authors to submit manuscripts containing novel techniques for optimization in the setting of multi-task learning while the data sources span across different modalities. We will focus on articles demonstrating novel techniques to structure latent space in a multi-modal/multi-task setting. Often, when dealing with multi-modal data, we need to place additional geometric constraints on the latent space. Hence, articles dealing with novel geometric techniques to achieve the above are highly encouraged.

Dr. Rudrasis Chakraborty
Dr. Indrasis Chakraborty
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. Electronics 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 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

  • multi-task learning
  • multi-modalities
  • latent representation
  • geometric representation

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

Published Papers (3 papers)

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Research

12 pages, 3571 KiB  
Article
ResShift-4E: Improved Diffusion Model for Super-Resolution with Microscopy Images
by Depeng Gao, Ying Gong, Jingzhuo Cao, Bingshu Wang, Han Zhang, Jiangkai Dong and Jianlin Qiu
Electronics 2025, 14(3), 479; https://doi.org/10.3390/electronics14030479 - 24 Jan 2025
Cited by 1 | Viewed by 999
Abstract
Blind super-resolution algorithms based on diffusion models still face significant challenges at the current stage, including high computational cost, long inference time, and limited cross domain generalization ability. This paper aims to apply super-resolution algorithms to the field of optical microscopy imaging to [...] Read more.
Blind super-resolution algorithms based on diffusion models still face significant challenges at the current stage, including high computational cost, long inference time, and limited cross domain generalization ability. This paper aims to apply super-resolution algorithms to the field of optical microscopy imaging to reveal more microscopic structures and details. Firstly, we proposed a lightweight super-resolution model called ResShift-4E, which is an optimized model from two important aspects: reducing the diffusion steps in ResShift and strengthening the influence of the original residuals on model learning. Secondly, we constructed a dataset of Multimodal High-resolution Microscopy Images (MHMI) including a total of 1220 images, which is available on line. Moreover, we extended our model to application-oriented research on blind image super-resolution of optical microscopy imaging. The experimental results demonstrate that our ResShift-4E model outperforms other models on various microscopy images. Full article
(This article belongs to the Special Issue Artificial Intelligence in Vision Modelling)
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27 pages, 3711 KiB  
Article
An IoT Framework for Assessing the Correlation Between Sentiment-Analyzed Texts and Facial Emotional Expressions
by Sebastian-Ioan Petruc, Razvan Bogdan, Marian-Emanuel Ionascu, Sergiu Nimara and Marius Marcu
Electronics 2025, 14(1), 118; https://doi.org/10.3390/electronics14010118 - 30 Dec 2024
Viewed by 620
Abstract
Emotion monitoring technologies leveraging detection of facial expressions have gained important attention in psychological and social research due to their ability of providing objective emotional measurements. However, this paper addresses a gap in the literature consisting of the correlation between emotional facial response [...] Read more.
Emotion monitoring technologies leveraging detection of facial expressions have gained important attention in psychological and social research due to their ability of providing objective emotional measurements. However, this paper addresses a gap in the literature consisting of the correlation between emotional facial response and sentiment analysis of written texts, developing a system capable of recognizing real-time emotional responses. The system uses a Raspberry Pi 4 and a Pi Camera module in order to perform real-time video capturing and facial expression analysis with the DeepFace version 0.0.80 model, while sentiment analysis of texts was performed utilizing Afinn version 0.1.0. User secure authentication and real-time database were implemented with Firebase. Although suitable for assessing psycho-emotional health in test takers, the system also provides valuable insights into the strong compatibility of the sentiment analysis performed on texts and the monitored facial emotional response, computing for each testing session the “compatibility” parameter. The framework provides an example of a new methodology for performing comparisons between different machine learning models, contributing to the enhancement of machine learning models’ efficiency and accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence in Vision Modelling)
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28 pages, 5142 KiB  
Article
Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose Data
by Ali Akdag and Omer Kaan Baykan
Electronics 2024, 13(8), 1591; https://doi.org/10.3390/electronics13081591 - 22 Apr 2024
Cited by 9 | Viewed by 1514
Abstract
This study introduces an innovative multichannel approach that focuses on the features and configurations of fingers in isolated sign language recognition. The foundation of this approach is based on three different types of data, derived from finger pose data obtained using MediaPipe and [...] Read more.
This study introduces an innovative multichannel approach that focuses on the features and configurations of fingers in isolated sign language recognition. The foundation of this approach is based on three different types of data, derived from finger pose data obtained using MediaPipe and processed in separate channels. Using these multichannel data, we trained the proposed MultiChannel-MobileNetV2 model to provide a detailed analysis of finger movements. In our study, we first subject the features extracted from all trained models to dimensionality reduction using Principal Component Analysis. Subsequently, we combine these processed features for classification using a Support Vector Machine. Furthermore, our proposed method includes processing body and facial information using MobileNetV2. Our final proposed sign language recognition method has achieved remarkable accuracy rates of 97.15%, 95.13%, 99.78%, and 95.37% on the BosphorusSign22k-general, BosphorusSign22k, LSA64, and GSL datasets, respectively. These results underscore the generalizability and adaptability of the proposed method, proving its competitive edge over existing studies in the literature. Full article
(This article belongs to the Special Issue Artificial Intelligence in Vision Modelling)
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