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Mathematics
Special Issues
Mathematical Methods and Models Applied in Information Technology
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Mathematical Methods and Models Applied in Information Technology

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 1326

Special Issue Editors

Prof. Dr. Hector Florez

E-Mail Website
Guest Editor
ITI Research Group, Universidad Distrital Francisco Jose de Caldas, Bogota 110231, Colombia
Interests: artificial intelligence; model driven engineering; information systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hernan Astudillo

E-Mail Website
Guest Editor
Institute for Health Technology and Innovation—UNAB, Viña del Mar 2530959, Chile
Interests: software architecture; software engineering; e-Health; digital government; heritage computing

Special Issue Information

Dear Colleagues,

Participants of the 7th International Conference on Applied Informatics (https://icai.itiud.org/) are encouraged to submit a full paper to the dedicated Special Issue "Mathematical Methods and Models Applied in Information Technology" and will receive a 20% discount, which can be used together with an IOAP (https://www.mdpi.com/ioap) discount, on the Article Processing Charge (APC). Submissions to the Mathematics journal are conducted independently of the conference proceedings and will adhere to the standard procedures of the journal. This includes undergoing peer-review and APC payment upon acceptance.

This Special Issue aims to bring together researchers and practitioners interested in all topics related to mathematical methods and models that appear in diverse fields, as well as analyzing and using the vast quantities of big data collected by complex information systems. The topics include, but are not limited to, the following:

  • Artificial Intelligence;
  • Bioinformatics;
  • Data Analysis;
  • Decision Systems;
  • High-Performance Computing;
  • Learning Management Systems.

All accepted papers should be presented by an author, who must be registered to ICAI (https://icai.itiud.org/index.php?pid=registration).

Prof. Dr. Hector Florez
Prof. Dr. Hernan Astudillo
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. Mathematics 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 2600 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

  • artificial intelligence
  • data analysis
  • decision systems
  • natural language processing

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

Published Papers (1 paper)

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Research

29 pages, 8824 KiB  
Article
Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery
by César Luis Moreno González, Germán A. Montoya and Carlos Lozano Garzón
Mathematics 2025, 13(7), 1041; https://doi.org/10.3390/math13071041 - 23 Mar 2025
Viewed by 455
Abstract
Natural disasters continuously threaten populations worldwide, with hydrometeorological events standing out due to their unpredictability, rapid onset, and significant destructive capacity. However, developing countries often face severe budgetary constraints and rely heavily on international support, limiting their ability to implement optimal disaster response [...] Read more.
Natural disasters continuously threaten populations worldwide, with hydrometeorological events standing out due to their unpredictability, rapid onset, and significant destructive capacity. However, developing countries often face severe budgetary constraints and rely heavily on international support, limiting their ability to implement optimal disaster response strategies. This study addresses these challenges by developing and implementing YOLOv8-based deep learning models trained on high-resolution satellite imagery from the Maxar GeoEye-1 satellite. Unlike prior studies, we introduce a manually labeled dataset, consisting of 1400 undamaged and 1200 damaged buildings, derived from pre- and post-Hurricane Maria imagery. This dataset has been publicly released, providing a benchmark for future disaster assessment research. Additionally, we conduct a systematic evaluation of optimization strategies, comparing SGD with momentum, RMSProp, Adam, AdaMax, NAdam, and AdamW. Our results demonstrate that SGD with momentum outperforms Adam-based optimizers in training stability, convergence speed, and reliability across higher confidence thresholds, leading to more robust and consistent disaster damage predictions. To enhance usability, we propose deploying the trained model via a REST API, enabling real-time damage assessment with minimal computational resources, making it a low-cost, scalable tool for government agencies and humanitarian organizations. These findings contribute to machine learning-based disaster response, offering an efficient, cost-effective framework for large-scale damage assessment and reinforcing the importance of model selection, hyperparameter tuning, and optimization functions in critical real-world applications. Full article
(This article belongs to the Special Issue Mathematical Methods and Models Applied in Information Technology)
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