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Applications of Deep Learning and Generative AI Models: Challenges and...
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Applications of Deep Learning and Generative AI Models: Challenges and Opportunities

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 July 2026 | Viewed by 1693

Special Issue Editors

Prof. Dr. David Gil

E-Mail Website
Guest Editor
Department of Computer Science Technology and Computation, University of Alicante, 03690 Alicante, Spain
Interests: big data applications; machine learning; data mining; IoT; human activity recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Julian Szymański

E-Mail Website
Guest Editor
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland
Interests: artificial intelligence; natural language processing; text categorisation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning and generative artificial intelligence (AI) are reshaping numerous technical domains through their powerful capabilities in data processing, pattern recognition, and synthetic content generation. This Special Issue aims to highlight both the disruptive potential and the practical challenges of deploying these technologies across diverse real-world applications. We invite submissions that include applied research, developments, and evaluations of deep learning and generative AI models in areas including, but not limited to, healthcare, manufacturing, environmental monitoring, industrial automation, smart infrastructure, digital media, and smart cities. Particular interest will be given to papers that explore novel use cases, implementation barriers, model performance, and system-level implications. 

This Special Issue offers a timely contribution to the growing interest in AI-driven systems. While deep learning has matured in many technical applications, generative AI introduces new paradigms in automation, simulation, and interaction, impacting areas such as human–machine collaboration, design, security, and reliability. Applied Sciences, with its interdisciplinary scope and its focus on engineering and applied sciences, provides an ideal venue to showcase such advances across sectors with high technological relevance.

Topics of interest include, but are not limited to, the following:  

  • Applications of large language models and generative AI in specific industries (e.g., smart manufacturing, healthcare, logistics);
  • Deep learning-based decision-support systems;
  • Multimodal models and their practical deployment (e.g., vision–language models);
  • Case studies using generative models for content creation, data augmentation, or simulation;
  • The opportunities and limitations of generative AI in regulated environments (e.g., medical diagnostics, critical infrastructure);
  • Challenges related to bias mitigation, transparency, explainability, and reliability in AI systems;
  • Comparative evaluations of traditional machine learning vs. generative approaches in applied scenarios;
  • Frameworks and architectures that enable scalable and trustworthy AI applications.

Prof. Dr. David Gil
Dr. Julian Szymański
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

  • deep learning
  • generative AI
  • large language models
  • multimodal models
  • applied machine learning
  • computer vision
  • intelligent systems
  • simulation and data augmentation
  • trustworthy AI
  • industrial AI applications

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

Published Papers (2 papers)

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32 pages, 515 KB  
Article
Knowledge-Driven Generative Design of Role-Playing Game Scenarios
by Wojciech Owczarek, Julia Wróbel and Damian Pęszor
Appl. Sci. 2026, 16(6), 2966; https://doi.org/10.3390/app16062966 - 19 Mar 2026
Viewed by 548
Abstract
The paper addresses the problem of the generative creation of role-playing game scenarios based on a knowledge compendium. The purpose of this exploratory research was to determine the impact of elements of the generation process on the quality of the scenario. The research [...] Read more.
The paper addresses the problem of the generative creation of role-playing game scenarios based on a knowledge compendium. The purpose of this exploratory research was to determine the impact of elements of the generation process on the quality of the scenario. The research was conducted in a generative pipeline using large language models and a compendium that describes the world of the game. The scope of the study includes the creation of a modular system that enables ablation studies and the analysis of the influence of individual factors on the quality of the results. The experiments involved comparisons between language models, variants of knowledge compendia, and the count of user prompt steps. In addition, an ablation study, a self-bias study and a small-scale study with human respondents were conducted. The main purpose of these additional studies was to examine the methods used and identify potential problems regarding them. The ablation study supported the significance of creating a scenario skeleton in a non-random way. No indesputible self-bias was found. The human-based study showed that the LLM evaluators are, on average, less critical than the human ones, but share some similar scoring patterns. The study demonstrated statistically significant differences resulting from the choice of language model in Relevance, Coherence, Informativeness, Interactivity and Structure criteria, as well as the influence of the size of the compendium and the count of user prompt steps on the quality of the results. It was discovered that in the process of generating role-playing game scenarios, it might be beneficial to use short, non-randomly filled structures as the basis for the output scenario generation. It was found that large language models tend to score the generated scenarios higher than human respondents. There is, however, an overlap in preferences regarding the generation model between the human and the machine evaluators. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Generative AI Models: Challenges and Opportunities)
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30 pages, 3486 KB  
Article
AI Creation of Facial Expression Database for Advanced Emotion Recognition Using Diffusion Model and Pre-Trained CNN Models
by Jia Jun Ho, Wee How Khoh, Ying Han Pang, Hui Yen Yap and Fang Chuen Lim Alvin
Appl. Sci. 2026, 16(6), 2769; https://doi.org/10.3390/app16062769 - 13 Mar 2026
Viewed by 479
Abstract
With applications in psychology, security, and human–computer interaction, facial expression recognition (FER) has become an essential tool for non-verbal communication. Current research often categorizes expressions into micro- and macro-types, yet existing datasets suffer from inconsistent labelling for classes, limited diversity of the databases, [...] Read more.
With applications in psychology, security, and human–computer interaction, facial expression recognition (FER) has become an essential tool for non-verbal communication. Current research often categorizes expressions into micro- and macro-types, yet existing datasets suffer from inconsistent labelling for classes, limited diversity of the databases, and insufficient scale for the currently available datasets. To address these gaps, this work proposes a novel framework combining the diffusion model with pre-trained CNNs. Leveraging original images from established datasets, CASME II, we generate synthetic facial expressions to augment training data, mitigating bias and inconsistency. The synthetic dataset is evaluated using ResNet 50, VGG16 and Inception V3 architectures. Inception V3 trained on the proposed AI-generated dataset and tested using CASME II, VGG-16 with data augmentation applied is trained on CASME II and tested on the proposed AI-generated dataset, and Inception V3 with 30% freezing layers method is trained on the proposed AI-generated dataset and tested using CASME II. These all successfully achieved state-of-the-art performance. The data augmentation and freezing layers approaches significantly improved the performance of the models. Our proposed approaches achieved state-of-the-art performance and outperformed most of the existing state-of-the-art approaches benchmarked in this study. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Generative AI Models: Challenges and Opportunities)
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