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Advances and Applications of Generative AI: Bridging Theory and Practice
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Advances and Applications of Generative AI: Bridging Theory and Practice

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

Deadline for manuscript submissions: closed (30 November 2025) | Viewed by 7695

Special Issue Editor

Dr. Samuel Cheng

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019-1102, USA
Interests: information theory; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The realm of generative artificial intelligence (AI) represents one of the most dynamic and rapidly evolving frontiers in technology today. With its foundation in deep learning and neural networks, generative AI has transcended theoretical exploration, embedding itself in the fabric of multiple sectors. These technologies not only enhance existing applications, but also create unprecedented opportunities in domains such as digital content creation, personalized medicine, autonomous systems, and beyond. This Special Issue of Applied Sciences aims to showcase cutting-edge research, dissect ethical dilemmas, and demonstrate practical implementations of generative AI, fostering a deeper understanding of its capabilities and limitations.

The potential of generative AI to transform industries by automating creativity and decision-making processes invites both excitement and scrutiny. Innovations in this field are rapidly advancing, enabling machines to generate realistic images, compose music, simulate environments, and even write textual content with remarkable proficiency. However, as these capabilities grow, so too does the need for the rigorous examination of the social, ethical, and technical challenges they present. This Special Issue seeks contributions that not only advance the technological framework, but also engage critically with the broader implications of generative technologies, ensuring a responsible trajectory of development.

To cultivate a comprehensive narrative on generative AI, this Special Issue will gather insights from a diverse array of disciplines and sectors. We invite researchers, engineers, and industry professionals to contribute their findings and experiences, ranging from theoretical advancements to empirical studies and from ethical analyses to regulatory considerations. Through a collaborative and interdisciplinary approach, this publication aims to not only track the progress of generative AI, but also guide its future in a manner that maximizes the benefits while mitigating the risks. Contributions that explore the integration of generative AI with other areas of artificial intelligence, such as reinforcement learning and predictive analytics, are particularly encouraged. This Issue will serve as a platform for discussing how generative AI can be developed and utilized while maintaining a commitment to societal welfare and ethical standards.

Topics of Interest:

We welcome submissions concerning a variety of topics related to generative AI, including, but not limited to, the following:

  • Novel algorithms and architectures for generative models (e.g., GANs, VAEs, diffusion models);
  • Applications of generative AI in healthcare, automotive, robotics, entertainment, and other industries;
  • Ethical considerations, bias mitigation, and fairness in generative AI;
  • Integration of generative AI with other AI technologies like reinforcement learning and supervised learning;
  • Improvements in the efficiency, scalability, and robustness of generative models;
  • Advances in text, image, audio, and video generation;
  • Regulatory and policy frameworks relevant to generative AI;
  • Economic impacts and business models enabled by generative AI technologies;
  • Case studies and real-world implementations of generative AI systems.

Dr. Samuel Cheng
Guest Editor

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

  • generative AI
  • generative models
  • AI technologies
  • applications of generative AI
  • generative AI systems

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

Published Papers (3 papers)

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Research

20 pages, 13884 KB  
Article
Prototype-Guided Zero-Shot Medical Image Segmentation with Large Vision-Language Models
by Huong Pham and Samuel Cheng
Appl. Sci. 2025, 15(21), 11441; https://doi.org/10.3390/app152111441 - 26 Oct 2025
Viewed by 1036
Abstract
Building on advances in promptable segmentation models, this work introduces a framework that integrates Large Vision-Language Model (LVLM) bounding box priors with prototype-based region of interest (ROI) selection to improve zero-shot medical image segmentation. Unlike prior methods such as SaLIP, which often misidentify [...] Read more.
Building on advances in promptable segmentation models, this work introduces a framework that integrates Large Vision-Language Model (LVLM) bounding box priors with prototype-based region of interest (ROI) selection to improve zero-shot medical image segmentation. Unlike prior methods such as SaLIP, which often misidentify regions due to reliance on text–image CLIP similarity, the proposed approach leverages visual prototypes to mitigate language bias and enhance ROI ranking, resulting in more accurate segmentation. Bounding box estimation is further strengthened through systematic prompt engineering to optimize LVLM performance across diverse datasets and imaging modalities. Evaluation was conducted on three publicly available benchmark datasets—CC359 (brain MRI), HC18 (fetal head ultrasound), and CXRMAL (chest X-ray)—without any task-specific fine-tuning. The proposed method achieved substantial improvements over prior approaches. On CC359, it reached a Dice score of 0.95 ± 0.06 and a mean Intersection-over-Union (mIoU) of 0.91 ± 0.10. On HC18, it attained a Dice score of 0.82 ± 0.20 and mIoU of 0.74 ± 0.22. On CXRMAL, the model achieved a Dice score of 0.90 ± 0.08 and mIoU of 0.83 ± 0.12. These standard deviations reflect variability across test images within each dataset, indicating the robustness of the proposed zero-shot framework. These results demonstrate that integrating LVLM-derived bounding box priors with prototype-based selection substantially advances zero-shot medical image segmentation. Full article
(This article belongs to the Special Issue Advances and Applications of Generative AI: Bridging Theory and Practice)
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33 pages, 537 KB  
Article
Generative Artificial Intelligence and Machine Translators in Spanish Translation of Early Vulnerability Cybersecurity Alerts
by Javier Román Martínez, David Triana Robles, Mouhcine El Oualidi Charchmi, Ines Salamanca Estévez and Noemí DeCastro-García
Appl. Sci. 2025, 15(8), 4090; https://doi.org/10.3390/app15084090 - 8 Apr 2025
Cited by 1 | Viewed by 1771
Abstract
The increasing reliance on artificial intelligence in cybersecurity has broadened the role of generative artificial intelligence in tasks such as text generation and translation. This study assesses the effectiveness of generative artificial intelligence and conventional translation tools in translating early vulnerability alerts from [...] Read more.
The increasing reliance on artificial intelligence in cybersecurity has broadened the role of generative artificial intelligence in tasks such as text generation and translation. This study assesses the effectiveness of generative artificial intelligence and conventional translation tools in translating early vulnerability alerts from English to Spanish—a critical process for ensuring the timely dissemination of cybersecurity information. Utilizing a dataset provided by the Spanish National Cybersecurity Institute, translations were generated using various systems and evaluated through linguistic assessment metrics, including methods measuring lexical similarity and others capturing semantic meaning beyond direct word matching. Additionally, word embeddings were employed to enhance the accuracy of semantic similarity analysis. The results indicate that conventional translation tools generally exhibit greater accuracy and structural fidelity, whereas generative artificial intelligence produces more natural-sounding translations. However, this flexibility results in greater variability in translation quality. The findings suggest that while generative artificial intelligence may serve as a valuable complement to traditional tools, its inconsistencies may limit its suitability for highly technical content that demands precision. This study underscores the importance of integrating both approaches to improve the accuracy and accessibility of cybersecurity alerts across different languages. Full article
(This article belongs to the Special Issue Advances and Applications of Generative AI: Bridging Theory and Practice)
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9 pages, 344 KB  
Article
Enhancing Historical Extended Reality Experiences: Prompt Engineering Strategies for AI-Generated Dialogue
by Lazaros Rafail Kouzelis and Ourania Spantidi
Appl. Sci. 2024, 14(15), 6405; https://doi.org/10.3390/app14156405 - 23 Jul 2024
Cited by 6 | Viewed by 3284
Abstract
Extended reality offers unique ways to create mediated spaces that enhance and help popularize experiences across several domains, including entertainment, creativity, and culture. There are still issues that hinder the widespread adoption of the medium, such as the over-reliance on scripted sequences, generalized [...] Read more.
Extended reality offers unique ways to create mediated spaces that enhance and help popularize experiences across several domains, including entertainment, creativity, and culture. There are still issues that hinder the widespread adoption of the medium, such as the over-reliance on scripted sequences, generalized approaches, and curated asset production. Artificial intelligence can be used to, in part, alleviate these issues, but this comes with its own set of challenges, such as factual inaccuracy or hallucinations. We delve into prompt engineering methods for the GPT API, enhancing context understanding to enable more realistic performances in historical event recreations. Specifically, we experiment with the Great Fire of Smyrna in 1922 as our historical context, situating the AI agent in the middle of chaos as a resident that has been affected by the event. Our experiments demonstrate that refined prompt engineering techniques significantly reduce factual inaccuracies and enhance the emotional resonance of AI-generated dialogues, which can lead to more immersive and engaging XR experiences. Our experiments indicate that AI can effectively support historical recreations by providing dynamic and contextually appropriate interactions. Full article
(This article belongs to the Special Issue Advances and Applications of Generative AI: Bridging Theory and Practice)
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