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Applied Sciences
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Deep Learning and Data Mining: Latest Advances and Applications
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Deep Learning and Data Mining: Latest Advances and Applications

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1852

Special Issue Editors

Prof. Dr. Mehmed M. Kantardzic

E-Mail Website
Guest Editor
J. B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
Interests: data mining; crowdsourcing; concept drift; streaming data; social networks analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Mehmet Akif Gulum

E-Mail Website
Guest Editor Assistant
The Percy L. Julian Science and Mathematics Center, Depauw University, Greencastle, IN 46135, USA
Interests: data mining; deep learning; computer vision; AI; XAI; AI in healthcare
Dr. Hanqing Hu

E-Mail Website
Guest Editor Assistant
Math & Computer Science Department, Hobart & William Smith Colleges, Lansing Hall 303, Geneva, NY 14456, USA
Interests: data mining; deep learning; large language models; concept drift; XAI

Special Issue Information

Dear Colleagues,

Deep learning and data mining are revolutionizing the way in which we process and analyze diverse and complex datasets, including text, images, sound, video, and structured data. These technologies enable the extraction of meaningful insights and allow critical problems to be solved across disciplines. From decoding brain activity in neuroscience to optimizing transportation systems and advancing precision medicine, their applications are as diverse as the data they process.

This Special Issue focuses on the latest advances in deep learning and data mining, highlighting theoretical innovations, impactful real-world applications, and interdisciplinary collaborations. We also aim to address key ethical considerations, including fairness, privacy, and societal impact.

We welcome submissions that showcase innovative research, theoretical contributions, and practical applications in the following areas:

  • Advances in Data Mining and Learning Algorithms–Innovative algorithms for supervised, unsupervised, recursive and active learning, focusing on dynamic, streaming, and graph-structured data;
  • Foundational Advances in Deep Learning–Developments in deep learning architectures for text, image, video, and multimodal data, including hybrid models and self-supervised learning;
  • Neuroscience and Cognitive Science–Deep learning applications in brain activity analysis, neural decoding, and mental health diagnostics;
  • Transportation and Smart Mobility–Optimizing traffic flow, autonomous systems, demand prediction, and route planning with data-driven methods;
  • Biomedical Healthcare Innovations–Deep learning and data mining in genomics, medical imaging, drug discovery, and wearable health technologies;
  • Climate and Environmental Science–Applications in climate modeling, weather prediction, and sustainable development;
  • Industrial and Financial Systems–The use of advanced techniques in fraud detection, risk analysis, and predictive maintenance;
  • Social and Behavioral Sciences–Insights into societal trends, human behavior, and decision-making through data analysis;
  • Data Visualization and Explainability–Techniques for enhancing the interpretability of models and visualizing complex datasets;
  • Ethical and Responsible Practices–Research on fairness, privacy, bias detection, and the societal impacts of data-driven systems.

Prof. Dr. Mehmed M. Kantardzic
Guest Editor

Dr. Mehmet Akif Gulum
Dr. Hanqing Hu
Guest Editor Assistants

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

  • data mining
  • deep learning
  • learning algorithms
  • multimodal data

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

Published Papers (4 papers)

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Research

13 pages, 1111 KB  
Article
Enhancing Pediatric Asthma Homecare Management: The Potential of Deep Learning Associated with Spirometry-Labelled Data
by Heidi Cleverley-Leblanc, Johan N. Siebert, Jonathan Doenz, Mary-Anne Hartley, Alain Gervaix, Constance Barazzone-Argiroffo, Laurence Lacroix and Isabelle Ruchonnet-Metrailler
Appl. Sci. 2025, 15(19), 10662; https://doi.org/10.3390/app151910662 - 2 Oct 2025
Abstract
A critical factor contributing to the burden of childhood asthma is the lack of effective self-management in homecare settings. Artificial intelligence (AI) and lung sound monitoring could help address this gap. Yet, existing AI-driven auscultation tools focus on wheeze detection and often rely [...] Read more.
A critical factor contributing to the burden of childhood asthma is the lack of effective self-management in homecare settings. Artificial intelligence (AI) and lung sound monitoring could help address this gap. Yet, existing AI-driven auscultation tools focus on wheeze detection and often rely on subjective human labels. To improve the early detection of asthma worsening in children in homecare setting, we trained and evaluated a Deep Learning model based on spirometry-labelled lung sounds recordings to detect asthma exacerbation. A single-center prospective observational study was conducted between November 2020 and September 2022 at a tertiary pediatric pulmonology department. Electronic stethoscopes were used to record lung sounds before and after bronchodilator administration in outpatients. In the same session, children also underwent spirometry, which served as the reference standard for labelling the lung sound data. Model performance was assessed on an internal validation set using receiver operating characteristic (ROC) curves. A total of 16.8 h of lung sound recordings from 151 asthmatic pediatric outpatients were collected. The model showed promising discrimination performance, achieving an AUROC of 0.763 in the training set, but performance in the validation set was limited (AUROC = 0.398). This negative result demonstrates that acoustic features alone may not provide sufficient diagnostic information for the early detection of asthma attacks, especially in mostly asymptomatic outpatients typical of homecare settings. It also underlines the challenges introduced by differences in how digital stethoscopes process sounds and highlights the need to define the severity threshold at which acoustic monitoring becomes informative, and clinically relevant for home management. Full article
(This article belongs to the Special Issue Deep Learning and Data Mining: Latest Advances and Applications)
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13 pages, 3494 KB  
Article
Deep Learning-Based Detection of Intracranial Hemorrhages in Postmortem Computed Tomography: Comparative Study of 15 Transfer-Learned Models
by Rentaro Matsumoto, Hidetoshi Matsuo, Marie Sugimoto, Takaaki Matsunaga, Mizuho Nishio, Atsushi K. Kono, Gentaro Yamasaki, Motonori Takahashi, Takeshi Kondo, Yasuhiro Ueno, Ryuichi Katada and Takamichi Murakami
Appl. Sci. 2025, 15(19), 10513; https://doi.org/10.3390/app151910513 - 28 Sep 2025
Abstract
With the increasing use of postmortem imaging, deep learning (DL)-based automated analysis may assist in the detection of intracranial hemorrhages. However, limited postmortem data complicate model training. This study aims to assess the accuracy of DL models in detecting intracranial hemorrhages in postmortem [...] Read more.
With the increasing use of postmortem imaging, deep learning (DL)-based automated analysis may assist in the detection of intracranial hemorrhages. However, limited postmortem data complicate model training. This study aims to assess the accuracy of DL models in detecting intracranial hemorrhages in postmortem head computed tomography (CT) scans using transfer learning. A total of 75,000 labeled head CT images from the Radiological Society of North America Intracranial Hemorrhage Detection Challenge serve as the training data for the 15 DL models. Each model is fine-tuned via transfer learning. A total of 134 postmortem cases with hemorrhage status confirmed by autopsy serve as the external test set. Model performance is evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, training time, inference time, and number of parameters. Spearman’s rank correlation coefficients are calculated for these metrics. DenseNet201 achieves the highest AUC (0.907), with the AUCs of the 15 models ranging from 0.862 to 0.907. A longer inference time moderately correlates with higher AUC (Spearman’s ρ = 0.586, p = 0.022), whereas the number of parameters is not positively correlated with performance (ρ = −0.472, p = 0.076). The sensitivity and specificity are 0.828 and 0.871, respectively. Transfer learning using a large non-postmortem dataset enables accurate intracranial hemorrhage detection using postmortem CT, potentially reducing the autopsy workload. The results demonstrate that models with fewer parameters often perform comparably to more complex models, emphasizing the need to balance accuracy with computational efficiency. Full article
(This article belongs to the Special Issue Deep Learning and Data Mining: Latest Advances and Applications)
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19 pages, 9171 KB  
Article
Long-Term Cotton Node Count Prediction Using Feature Selection, Data Augmentation, and Multivariate Time-Series Forecasting
by Vaishnavi Thesma, Glen C. Rains and Javad Mohammadpour Velni
Appl. Sci. 2025, 15(16), 9159; https://doi.org/10.3390/app15169159 - 20 Aug 2025
Viewed by 383
Abstract
In this paper, we present an approach to performing long-term cotton node count prediction using feature selection, data augmentation, and forecasting using a multivariate long short-term memory (LSTM) model. Specifically, we used in situ measurement data that was collected from a cotton research [...] Read more.
In this paper, we present an approach to performing long-term cotton node count prediction using feature selection, data augmentation, and forecasting using a multivariate long short-term memory (LSTM) model. Specifically, we used in situ measurement data that was collected from a cotton research field from Tifton, GA, USA, to perform feature selection to select the most important input measurements and enable random data generation using Gaussian distribution to increase the size of our dataset. We concatenated the generated data to create longer, usable time-series and trained a multivariate LSTM model to predict average cotton node count. Our model’s prediction results on both the training and testing data had a low RMSE of less than 3, low MAE of less than 2.5, and high R2 score of at least 0.85. Our model also showed promise in accurately forecasting cotton node count in subsequent seasons via transfer learning. In particular, our transfer learned model maintained a low RMSE of less than 7.5 and MAE of less than 3.5, despite the subsequent season data having a shorter temporal scale. Moreover, we validated our results by performing hypothesis testing against a similar time-series forecasting model, namely, the gated recurrent unit (GRU) model. Full article
(This article belongs to the Special Issue Deep Learning and Data Mining: Latest Advances and Applications)
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26 pages, 9010 KB  
Article
Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
by Amadej Krepek, Iztok Fister and Iztok Fister, Jr.
Appl. Sci. 2025, 15(12), 6793; https://doi.org/10.3390/app15126793 - 17 Jun 2025
Viewed by 582
Abstract
Nowadays, technological progress has promoted the integration of artificial intelligence into modern human lives rapidly. On the other hand, extreme weather events in recent years have started to influence human well-being. As a result, these events have been addressed by artificial intelligence methods [...] Read more.
Nowadays, technological progress has promoted the integration of artificial intelligence into modern human lives rapidly. On the other hand, extreme weather events in recent years have started to influence human well-being. As a result, these events have been addressed by artificial intelligence methods more and more frequently. In line with this, the paper focuses on searching for predicting the air temperature in a particular Slovenian micro-location by using a weather prediction model Maximus based on a long-short term memory neural network learned by the long-term, lower-resolution dataset CERRA. During this huge experimental study, the Maximus prediction model was tested with the ICON-D2 general-purpose weather prediction model and validated with real data from the mobile weather station positioned at a specific micro-location. The weather station employs Internet of Things sensors for measuring temperature, humidity, wind speed and direction, and rain, while it is powered by solar cells. The results of comparing the Maximus proposed prediction model for predicting the air temperature in micro-locations with the general-purpose weather prediction model ICON-D2 has encouraged the authors to continue searching for an air temperature prediction model at the micro-location in the future. Full article
(This article belongs to the Special Issue Deep Learning and Data Mining: Latest Advances and Applications)
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