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Special Issues
Machine Learning and Image Processing: Applications and Challenges
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Machine Learning and Image Processing: Applications and Challenges

A Special Issue of Big Data and Cognitive Computing (ISSN 2504-2289) belonging to the section "Data Mining and Machine Learning".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 4500

Editor

Dr. Balazs Harangi

E-Mail Website
Guest Editor
Faculty of Informatics, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary
Interests: machine learning; image processing; medical imaging; artificial intelligence; healthcare informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning and image processing are among the most rapidly evolving fields in computer science, driving innovation across a wide range of application domains. This Special Issue aims to bring together recent advances and novel methodologies at the intersection of these disciplines, highlighting both theoretical developments and practical implementations. Particular emphasis will be placed on applications in medical imaging, agricultural analysis, and engineering, where machine learning-powered image processing techniques are provoking a transformative impact. This Special Issue seeks contributions addressing new algorithms, deep learning models, interpretability, and explainability, as well as domain-specific challenges such as data quality, annotation, and real-time analysis. By presenting a diverse set of research studies and case examples, this Special Issue intends to offer a comprehensive overview of the current trends, challenges, and future directions in machine learning and image processing, as well as fostering cross-disciplinary collaboration. We especially encourage submissions that address real-world problems, propose innovative solutions, or review the state of the art in these rapidly growing areas.

Dr. Balazs Harangi
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing 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 1800 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
  • image processing
  • deep learning
  • medical imaging
  • agricultural image analysis
  • engineering applications
  • computer vision
  • data annotation
  • pattern recognition
  • artificial intelligence

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Published Papers (3 papers)

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Research

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24 pages, 7898 KB  
Article
Unifying Aesthetic Evaluation via Multimodal Annotation and Fine-Grained Sentiment Analysis
by Kai Liu, Hangyu Xiong, Jinyi Zhang and Min Peng
Big Data Cogn. Comput. 2026, 10(1), 37; https://doi.org/10.3390/bdcc10010037 - 22 Jan 2026
Viewed by 1460
Abstract
With the rapid growth of visual content, automated aesthetic evaluation has become increasingly important. However, existing research faces three key challenges: (1) the absence of datasets combining Image Aesthetic Assessment (IAA) scores and Image Aesthetic Captioning (IAC) descriptions; (2) limited integration of quantitative [...] Read more.
With the rapid growth of visual content, automated aesthetic evaluation has become increasingly important. However, existing research faces three key challenges: (1) the absence of datasets combining Image Aesthetic Assessment (IAA) scores and Image Aesthetic Captioning (IAC) descriptions; (2) limited integration of quantitative scores and qualitative text, hindering comprehensive modeling; (3) the subjective nature of aesthetics, which complicates consistent fine-grained evaluation. To tackle these issues, we propose a unified multimodal framework. To address the lack of data, we develop the Textual Aesthetic Sentiment Labeling Pipeline (TASLP) for automatic annotation and construct the Reddit Multimodal Sentiment Dataset (RMSD) with paired IAA and IAC labels. To improve annotation integration, we introduce the Aesthetic Category Sentiment Analysis (ACSA) task, which models fine-grained aesthetic attributes across modalities. To handle subjectivity, we design two models—LAGA for IAA and ACSFM for IAC—that leverage ACSA features to enhance consistency and interpretability. Experiments on RMSD and public benchmarks show that our approach alleviates data limitations and delivers competitive performance, highlighting the effectiveness of fine-grained sentiment modeling and multimodal learning in aesthetic evaluation. Full article
(This article belongs to the Special Issue Machine Learning and Image Processing: Applications and Challenges)
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18 pages, 4122 KB  
Article
AI-Enabled Diagnosis Using YOLOv9: Leveraging X-Ray Image Analysis in Dentistry
by Dhiaa Musleh, Atta Rahman, Haya Almossaeed, Fay Balhareth, Ghadah Alqahtani, Norah Alobaidan, Jana Altalag, May Issa Aldossary and Fahd Alhaidari
Big Data Cogn. Comput. 2026, 10(1), 16; https://doi.org/10.3390/bdcc10010016 - 2 Jan 2026
Cited by 3 | Viewed by 2071
Abstract
Artificial Intelligence (AI)-enabled diagnosis has emerged as a promising avenue for revolutionizing medical image analysis, such as X-ray analysis, across a wide range of healthcare disciplines, including dentistry, consequently offering swift, efficient, and accurate solutions for identifying various dental conditions. In this study, [...] Read more.
Artificial Intelligence (AI)-enabled diagnosis has emerged as a promising avenue for revolutionizing medical image analysis, such as X-ray analysis, across a wide range of healthcare disciplines, including dentistry, consequently offering swift, efficient, and accurate solutions for identifying various dental conditions. In this study, we investigated the application of the YOLOv9 model, a cutting-edge object detection algorithm, to automate the diagnosis of dental diseases from X-ray images. The proposed methodology encompasses a comprehensive analysis of dental datasets, as well as preprocessing and model training. Through rigorous experimentation, remarkable accuracy, precision, recall, mAP@50, and an F1-score of 84.89%, 89.2%, 86.9%, 89.2%, and 88%, respectively, are achieved. With significant improvements over the baseline model of 17.9%, 15.8%, 18.5%, and 16.81% in precision, recall, mAP@50, and F1-score, respectively, with 7.9 ms inference time. This demonstrates the effectiveness of the proposed approach in accurately identifying dental conditions. Additionally, we discuss the challenges in automated diagnosis of dental diseases and outline future research directions to address knowledge gaps in this domain. This study contributes to the growing body of literature on AI in dentistry, providing valuable insights for researchers and practitioners. Full article
(This article belongs to the Special Issue Machine Learning and Image Processing: Applications and Challenges)
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Review

Jump to: Research

60 pages, 2148 KB  
Review
Atmospheric Turbulence Mitigation in the Deep Learning Era: A Critical Review from CNNs and GANs to Transformers, Diffusion, Mamba, and Physics-Informed Models
by Nurul Jannah, Teddy Surya Gunawan, Mira Kartiwi, Nadirah Abdul Rahim and Ali Sophian
Big Data Cogn. Comput. 2026, 10(8), 282; https://doi.org/10.3390/bdcc10080282 - 21 Aug 2026
Viewed by 264
Abstract
Anyone who has watched a distant scene shimmer above hot pavement has seen atmospheric turbulence destroy image detail. In long-range imaging, turbulence produces spatially varying blur, geometric warping, scintillation, and temporal instability. Recovering the underlying scene is therefore an ill-posed inverse problem, and [...] Read more.
Anyone who has watched a distant scene shimmer above hot pavement has seen atmospheric turbulence destroy image detail. In long-range imaging, turbulence produces spatially varying blur, geometric warping, scintillation, and temporal instability. Recovering the underlying scene is therefore an ill-posed inverse problem, and learned priors must compensate for distortions that simplified optical models capture only partially. We use turbulence mitigation as the umbrella term for all countermeasures and turbulence restoration for its computational core, the estimation of a clean image from degraded observations. From a cognitive-computing perspective, mitigation is not merely image enhancement. It is an uncertainty-constrained visual inference problem in which an intelligent system must reconstruct, interpret, and act on observations relayed through a stochastic physical channel. This critical review examines how the deep learning era has reshaped turbulence mitigation, with physics as the foundation for understanding degradation and designing inductive biases. It traces the architectural progression from convolutional and adversarial networks to Transformers, denoising diffusion models, Mamba and other state-space architectures, and physics-informed frameworks. For each family, we ask a common question: How does it treat the aleatoric uncertainty intrinsic to a random optical channel and the epistemic uncertainty introduced by scarce and simulator-dominated training data? The review also analyzes datasets, simulation strategies, loss functions, and evaluation metrics, and it separates the small body of shared-protocol benchmark evidence from the far larger body of self-reported results that cannot be compared across studies. Persistent obstacles include the synthetic-to-real domain gap, the scarcity of paired real turbulence data, the mismatch between fidelity metrics and downstream task performance, and the computational cost that limits operational deployment. We close with an AI-centered agenda in which uncertainty quantification stands alongside domain adaptation as a first-order priority. Full article
(This article belongs to the Special Issue Machine Learning and Image Processing: Applications and Challenges)
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