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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: 20 September 2025 | Viewed by 535

Special Issue Editors


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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

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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

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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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Published Papers (1 paper)

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Research

26 pages, 9010 KiB  
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 275
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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