Special Issues
Table of Content

Visual and Large Language Models for Generalized Applications

Submission Deadline: 15 March 2026 View: 685 Submit to Special Issue

Guest Editors

Dr. Mukhriddin Mukhiddinov

Email: mmuhriddinm@gmai.com

Affiliation: Department of Computer Systems, Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent, 100084, Uzbekistan

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Research Interests: deep learning, natural language processing, large language models, computer vision, neural network, transformers, visually impaired people, embedded systems

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Dr. Oybek Djuraev

Email: odjuraev@gmail.com

Affiliation: Department of Communication and Digital Technologies, University of Management and Future Technologies, Tashkent, 100208, Uzbekistan

Homepage:

Research Interests: natural language processing, deep learning, neural network, computer vision

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Summary

Recent advances in artificial intelligence have been largely driven by the development of large-scale models, particularly visual models and large language models (LLMs), which have shown remarkable capabilities across diverse tasks. Their ability to generalize across modalities and domains marks a paradigm shift in how intelligent systems are built and deployed.

This Special Issue aims to explore the integration and application of visual models and LLMs for solving complex, real-world problems across multiple domains. We welcome original research articles, reviews, and case studies that address novel methodologies, frameworks, and applications involving vision-language models, multimodal learning, and generalized AI systems. The issue will provide a platform for highlighting both theoretical advancements and practical implementations that push the boundaries of AI's adaptability and robustness.

Suggested Themes Include:
· Multimodal learning with vision and language models
· Generalized AI architectures and frameworks
· Applications of LLMs in robotics, healthcare, and smart systems
· Cross-modal retrieval and image captioning
· Zero-shot and few-shot learning with foundation models
· Safety, ethics, and interpretability of large-scale models
· Efficient training and deployment of vision-language models


Keywords

Large Language Models, Visual Models, Multimodal Learning, Foundation Models, Generalized AI, Vision-Language Integration, Zero-shot Learning, Artificial Intelligence Applications

Published Papers


  • Open Access

    ARTICLE

    LLM-Powered Multimodal Reasoning for Fake News Detection

    Md. Ahsan Habib, Md. Anwar Hussen Wadud, M. F. Mridha, Md. Jakir Hossen
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070235
    (This article belongs to the Special Issue: Visual and Large Language Models for Generalized Applications)
    Abstract The problem of fake news detection (FND) is becoming increasingly important in the field of natural language processing (NLP) because of the rapid dissemination of misleading information on the web. Large language models (LLMs) such as GPT-4. Zero excels in natural language understanding tasks but can still struggle to distinguish between fact and fiction, particularly when applied in the wild. However, a key challenge of existing FND methods is that they only consider unimodal data (e.g., images), while more detailed multimodal data (e.g., user behaviour, temporal dynamics) is neglected, and the latter is crucial for… More >

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