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Applied Sciences
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Machine Vision and Machine Learning in Interdisciplinary Research
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Machine Vision and Machine Learning in Interdisciplinary Research

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 1226

Special Issue Editor

Dr. Yang Long

E-Mail Website
Guest Editor
Department of Computer Science, Durham University, Lower Mountjoy, South Road, Durham DH1 3LE, UK
Interests: computer vision and machine learning

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the burgeoning field of Machine Vision and Machine Learning (MV&ML) and their applications in interdisciplinary research. This Issue will delve into the innovative integration of MV&ML across various domains, including but not limited to healthcare, environmental monitoring, industrial automation, and smart cities. The scope and themes include the following:

  • Industry 4.0 and Manufacturing Applications: Here, we invite papers that discuss the integration of MV&ML in industrial automation, quality control, predictive maintenance, and supply chain optimization.
  • Technological Innovations in Machine Vision and Machine Learning: This theme will focus on the latest advancements in algorithms, techniques, and methodologies in MV&ML. Papers may cover areas such as deep learning, neural networks, pattern recognition, and image processing, emphasizing their novel application or theoretical innovation.
  • Healthcare and Life Sciences: We welcome studies demonstrating novel applications in medical imaging, predictive analytics, and BCI analysis.
  • Environmental Monitoring and Smart Cities: This theme seeks research that applies MV&ML to environmental challenges and enhancing urban living, such as climate change analysis, wildlife monitoring, pollution tracking, infrastructure monitoring, and showcasing how these technologies contribute to smarter, more efficient cities.
  • Ethical, Legal, and Social Implications: Papers under this theme will address the broader implications of MV&ML in society. Topics may include data privacy, ethical AI, the societal impact of automation, and the legal frameworks governing these technologies.

Dr. Yang Long
Guest Editor

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. Applied Sciences 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

  • machine learning
  • computer vision
  • manufacturing intelligence
  • health intelligence
  • environment intelligence
  • AI&ethics

Published Papers (3 papers)

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Research

25 pages, 2411 KiB  
Article
No Pain, No Gain—Giving Real-Time Emotional Feedback in a Virtual Mirror Improves Collaboration in Virtual Teamwork
by Nicklas Schneider, Ignacio Vazquez and Peter A. Gloor
Appl. Sci. 2024, 14(13), 5659; https://doi.org/10.3390/app14135659 - 28 Jun 2024
Viewed by 172
Abstract
This study investigates the impact of real-time emotional feedback on the quality of teamwork conducted over videoconferencing. We developed a framework that provides real-time feedback through a virtual mirror based on facial and voice emotion recognition. In an experiment with 28 teams (84 [...] Read more.
This study investigates the impact of real-time emotional feedback on the quality of teamwork conducted over videoconferencing. We developed a framework that provides real-time feedback through a virtual mirror based on facial and voice emotion recognition. In an experiment with 28 teams (84 participants), teams collaborated over Zoom (version 5.16.6) to set up a virtual Mars station using custom simulation software (Mars Star City, version 4.0). Participants were divided into 14 experimental teams, which were shown the virtual mirror, and 14 control teams without it. Team performance was measured by the improvement in the Mars simulation output quality. Our analysis using correlation, multi-level regression, and machine learning revealed that fewer interruptions but an increasing number over time correlated with higher performance. Higher vocal arousal and happiness also enhanced performance. We confirmed that female presence in teams boosts performance. SHAP values indicated that high variability in happiness, head movement, and positive facial valence—an “emotional rollercoaster”—positively predicted team performance. The experimental group outperformed the control group, suggesting that virtual mirroring improves virtual teamwork and that interrupting each other more while speaking less, leads to better results. Full article
(This article belongs to the Special Issue Machine Vision and Machine Learning in Interdisciplinary Research)
13 pages, 2365 KiB  
Article
Advancements in Gaze Coordinate Prediction Using Deep Learning: A Novel Ensemble Loss Approach
by Seunghyun Kim, Seungkeon Lee and Eui Chul Lee
Appl. Sci. 2024, 14(12), 5334; https://doi.org/10.3390/app14125334 - 20 Jun 2024
Viewed by 253
Abstract
Recent advancements in deep learning have enabled gaze estimation from images of the face and eye areas without the need for precise geometric locations of the eyes and face. This approach eliminates the need for complex user-dependent calibration and the issues associated with [...] Read more.
Recent advancements in deep learning have enabled gaze estimation from images of the face and eye areas without the need for precise geometric locations of the eyes and face. This approach eliminates the need for complex user-dependent calibration and the issues associated with extracting and tracking geometric positions, making further exploration of gaze position performance enhancements challenging. Motivated by this, our study focuses on an ensemble loss function that can enhance the performance of existing 2D-based deep learning models for gaze coordinate (x, y) prediction. We propose a new function and demonstrate its effectiveness by applying it to models from prior studies. The results show significant performance improvements across all cases. When applied to ResNet and iTracker models, the average absolute error reduced significantly from 7.5 cm to 1.2 cm and from 7.67 cm to 1.3 cm, respectively. Notably, when implemented on the AFF-Net, which boasts state-of-the-art performance, the average absolute error was reduced from 4.21 cm to 0.81 cm, based on our MPIIFaceGaze dataset. Additionally, predictions for ranges never encountered during the training phase also displayed a very low error of 0.77 cm in terms of MAE without any personalization process. These findings suggest significant potential for accuracy improvements while maintaining computational complexity similar to the existing models without the need for creating additional or more complex models. Full article
(This article belongs to the Special Issue Machine Vision and Machine Learning in Interdisciplinary Research)
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10 pages, 2657 KiB  
Article
Usefulness Evaluation for Nonlocal Means Algorithm in Low-Dose Computed Tomography with Various Iterative Reconstruction Intensities and Kernels: A Pilot Study
by Chaehyeon Song, Yubin Jin, Jina Shim, Seong-Hyeon Kang and Youngjin Lee
Appl. Sci. 2024, 14(8), 3392; https://doi.org/10.3390/app14083392 - 17 Apr 2024
Viewed by 477
Abstract
The aim of this study was to evaluate the application feasibility of the nonlocal means (NLM) noise reduction algorithm in low-dose computed tomography (CT) images using an advanced modeled iterative reconstruction (ADMIRE) iterative reconstruction technique-based tin filter with various applied parameters. Low-dose CT [...] Read more.
The aim of this study was to evaluate the application feasibility of the nonlocal means (NLM) noise reduction algorithm in low-dose computed tomography (CT) images using an advanced modeled iterative reconstruction (ADMIRE) iterative reconstruction technique-based tin filter with various applied parameters. Low-dose CT images were based on high pitch and tin filters and acquired using slices of the aortic arch, the four chambers of the heart, and the end of the heart. Intensities A2 and A3 as well as kernels B40 and B59 were used as the parameters for the ADMIRE technique. The NLM denoising algorithm was modeled based on the principle of weighting between pixels; the contrast-to-noise ratio (CNR), edge rise distance (ERD), and blind/referenceless image spatial quality evaluator (BRISQUE) were used as image quality evaluation parameters. The CNR result was the highest, with an average of 43.51 in three slices when the proposed NLM denoising algorithm was applied to CT images acquired using the ADMIRE intensity 2 and B59 kernel. The ERD results were similar to those obtained using the ADMIRE intensity 2 and B59 kernel in the CT image acquired using the proposed method. In addition, BRISQUE, which can evaluate the overall image quality, showed a similar trend to the ERD results. In conclusion, the NLM noise reduction algorithm is expected to maximize image quality by preserving efficient edge information while improving noise characteristics in low-dose CT examinations. Full article
(This article belongs to the Special Issue Machine Vision and Machine Learning in Interdisciplinary Research)
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