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Applications of Data Science and Artificial Intelligence, 2nd Edition
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Applications of Data Science and Artificial Intelligence, 2nd Edition

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 2026 | Viewed by 1379

Special Issue Editors

Dr. Carlos J. Costa

E-Mail Website
Guest Editor
ISEG—Lisbon School of Economics and Management, Universidade de Lisboa, 1200-781 Lisboa, Portugal
Interests: data science; data science and management; machine learning in finance; gamification; information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Manuela Aparicio

E-Mail Website
Guest Editor
NOVA IMS Information Management School, Universidade Nova de Lisboa Campus de Campolide, 1070-312 Lisboa, Portugal
Interests: data science; artificial intelligence; information systems; e-learning; digital transformation; gamification; e-commerce
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Significant advances in artificial intelligence (AI) have highlighted new challenges and opportunities in this field. Moreover, data science is a rapidly growing area of study and professional discipline. It is thus critical to investigate this new reality from a social and corporate standpoint. Abundant information about data science and AI and how they may be used to solve economic and societal problems exists. However, in order to realize the widespread use of these tools in business and everyday life, their efficacy must be objectively assessed. This Special Issue aims to gather contributions from academics investigating a variety of subjects and viewpoints, including AI-related management, social sciences, and engineering. Given the present level of AI and data science, three forms are of particular interest: machine learning, natural language processing, and robotics. Submissions addressing other relevant topics will also be considered.

Dr. Carlos J. Costa
Dr. Manuela Aparicio
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 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-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 science applications
  • AI applications
  • machine learning applications
  • NLP applications
  • AI trends
  • data science trends

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Related Special Issue

Published Papers (2 papers)

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Research

25 pages, 5394 KB  
Article
Towards the Development of Multiscale Digital Twins for Fiber-Reinforced Composite Materials Using Machine Learning
by Brandon L. Hearley, Evan J. Pineda, Brett A. Bednarcyk, Joseph R. Baker and Laura G. Wilson
Appl. Sci. 2026, 16(8), 3666; https://doi.org/10.3390/app16083666 - 9 Apr 2026
Viewed by 569
Abstract
Material considerations are often neglected when developing digital twins, particularly at the relevant length scales that drive material and structural performance. For reinforced composite materials, the microscale has the largest impact on nonlinear material behavior and progressive damage, and thus accurately representing the [...] Read more.
Material considerations are often neglected when developing digital twins, particularly at the relevant length scales that drive material and structural performance. For reinforced composite materials, the microscale has the largest impact on nonlinear material behavior and progressive damage, and thus accurately representing the disordered microstructure of a composite due to processing and manufacturing is critical to developing the material digital twin in the multiscale hierarchy. Automating microstructure characterization is typically done by either training convolutional neural network models using a pretrained encoder or using prompt-based segmentation tools. In this work, a toolset for developing segmentation models is presented, combining these two methods to enable rapid annotation, training, and deployment of microscopy segmentation models for automated material digital twin development without user knowledge of machine learning. Additionally, a Bayesian optimization framework is developed for generating statistically equivalent representative volume elements (SRVE) to a segmented microstructure using a random microstructure generator that implements soft body dynamics. Progressive failure analysis of random, statistically equivalent, and ordered microstructures is compared to the segmented microstructure subject to transverse loading to demonstrate the importance of accurately representing the driving material length scale of a composite digital twin. Ordered microstructures over-predicted crack initiation and ultimate strength and strain. Random and optimized RVE microstructures better agreed with the segmented simulation results, with no significant difference observed between the two methodologies. The improvement in predicted macroscale behavior for models that capture disordered microstructures due to manufacturing processes demonstrates the importance of capturing microstructure features in composites modeling and indicates that SRVEs that capture microstructural features of the physical material can be used in material digital twin development. Further, the toolsets provided in this work allow for rapid development of composite material digital twins without user expertise in machine learning. This has enabled the development of an integrated workflow to automatically characterize and idealize composite microstructures and generate representative geometric models for efficient micromechanics analysis. Full article
(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence, 2nd Edition)
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24 pages, 1254 KB  
Article
ConvNeXt Meets Vision Transformers: A Powerful Hybrid Framework for Facial Age Estimation
by Gaby Maroun, Salah Eddine Bekhouche and Fadi Dornaika
Appl. Sci. 2026, 16(7), 3281; https://doi.org/10.3390/app16073281 - 28 Mar 2026
Viewed by 433
Abstract
Age estimation based on facial images is a challenging task due to the complex and nonlinear nature of facial aging, which is influenced by both genetic and environmental factors. To address this challenge, we propose a hybrid ConvNeXt–Transformer framework that combines convolutional local [...] Read more.
Age estimation based on facial images is a challenging task due to the complex and nonlinear nature of facial aging, which is influenced by both genetic and environmental factors. To address this challenge, we propose a hybrid ConvNeXt–Transformer framework that combines convolutional local feature extraction with attention-based global contextual modeling within a unified age regression pipeline. The methodological contribution of this work lies in the sequential integration of these two complementary paradigms for facial age estimation, allowing the model to capture both fine-grained textural cues—such as wrinkles and skin spots—and long-range spatial dependencies. We evaluate the proposed framework on benchmark datasets including MORPH II, CACD, UTKFace, and AFAD. The results show competitive performance across these datasets and confirm the effectiveness of the proposed hybrid design through extensive ablation analyses. Experimental results demonstrate that our approach achieves state-of-the-art MAE on MORPH II (2.26), CACD (4.35), and AFAD (3.09) under the adopted benchmark settings while remaining competitive on UTKFace. To address computational efficiency, we employ ImageNet pre-trained backbones and explore different architectural configurations, including fusion strategies and varying depths of the Transformer module, as well as regularization techniques such as stochastic depth and label smoothing. Ablation studies confirm the contribution of each component, particularly the role of attention mechanisms, in enhancing the model’s sensitivity to age-relevant features. Overall, the proposed hybrid framework provides a robust and accurate solution for facial age estimation, effectively balancing performance and computational cost. Full article
(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence, 2nd Edition)
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