Special Issues
Table of Content

Emerging Artificial Intelligence Technologies and Applications-II

Submission Deadline: 15 March 2026 View: 560 Submit to Special Issue

Guest Editors

Prof. Dr. Wenfeng Zheng

Email: winfirms@uestc.edu.cn

Affiliation: Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11574, Saudi Arabia; School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China

Homepage:

Research Interests: AI/ML, pattern recognition, intelligent systems, visual reasoning, visual question answering, NLP, surgical robot, geospatial AI, complex dynamics, image fusion, surgical vision, Artificial Neural Network, Computer Graphics, image processing, machine vision, 3D reconstruction, perception and cognition

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Dr. Chao Liu

Email: liu@lirmm.fr

Affiliation: French National Center for Scientific Research (CNRS), LIRMM, 34095 Montpellier, France

Homepage:

Research Interests: visual augmentation and reconstruction, 3D reconstruction of deformable surface, haptics in human-machine interaction, multimodal sensor-based analysis of manipulation skills, surgical robot, medical image processing

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Dr. Lirong Yin

Email: yin.lyra@gmail.com

Affiliation: Department of Hydrology and Atmospheric Sciences, University of Arizona, 85721, Tucson, USA

Homepage:

Research Interests: AI/ML, pattern recognition, visual reasoning, visual question answering, GeoMatics, GeoAI, complex dynamics, Artificial Neural Network

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Summary

Artificial Intelligence (AI) has rapidly advanced in recent years, leading to the emergence of groundbreaking technologies and applications with far-reaching implications. AI has the potential to revolutionize industries, enhance human capabilities, and address complex challenges in innovative ways.


From using convolutional neural networks to extract image features, to advanced natural language processing achieved by Transformer models, and further to employing graph neural networks to analyze complex topological structures, artificial intelligence is driving unprecedented progress. In medical field, AI is facilitating early disease detection and personalized treatment plans, while in finance, it's optimizing risk management and fraud detection. Additionally, autonomous vehicles and smart manufacturing systems are benefiting from AI's ability to process vast amounts of data in real time, enhancing safety and efficiency. With the continued evolution of AI technologies, the potential for their application across diverse domains is immense, promising to reshape the way we live, work, and interact with the world around us.


As AI continues to advance, its impact will be profound, offering transformative solutions to complex problems and paving the way for a more sustainable and inclusive future. This special issue is open to fresh research contributions that introduce novel theories, innovative methodologies, unique application strategies, and investigations of AI across diverse fields. The potential topics encompassed  may include, but are not limited to, the following topics:
· Artificial intelligence
· Multimodal artificial intelligence
· Potential problems, challenges, and applications of large models
· Visual question and answer (VQA), visual reasoning
· Semantic reasoning, semantic representation, knowledge base
· Characterization inference, natural language reasoning
· Machine translation, text sentiment analysis, text classification
· Meta-learning, transfer learning, few-shot learning.
· Contrastive learning, representation learning, reinforcement learning
· Geospatial artificial intelligence, geospatial AI (GeoAI)
· AI in geostatistics, remote sensing, spatio-temporal simulation
· AI for geospatial data acquisition, analysis, planning, and prediction
· Visual augmentation and reconstruction, 3D reconstruction of deformable surfaces
· Visual- and spatial-based perception enhancement and reasoning



Published Papers


  • Open Access

    ARTICLE

    A Keyword-Guided Training Approach to Large Language Models for Judicial Document Generation

    Yi-Ting Peng, Chin-Laung Lei
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073258
    (This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
    Abstract The rapid advancement of Large Language Models (LLMs) has enabled their application in diverse professional domains, including law. However, research on automatic judicial document generation remains limited, particularly for Taiwanese courts. This study proposes a keyword-guided training framework that enhances LLMs’ ability to generate structured and semantically coherent judicial decisions in Chinese. The proposed method first employs LLMs to extract representative legal keywords from absolute court judgments. Then it integrates these keywords into Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback using Proximal Policy Optimization (RLHF-PPO). Experimental evaluations using models such as Chinese Alpaca More >

  • Open Access

    ARTICLE

    HTM: A Hybrid Triangular Modeling Framework for Soft Tissue Feature Tracking

    Lijuan Zhang, Yu Zhou, Jiawei Tian, Fupei Guo, Xiang Zhang, Bo Yang
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.071869
    (This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
    Abstract In endoscopic surgery, the limited field of view and the nonlinear deformation of organs caused by patient movement and respiration significantly complicate the modeling and accurate tracking of soft tissue surfaces from endoscopic image sequences. To address these challenges, we propose a novel Hybrid Triangular Matching (HTM) modeling framework for soft tissue feature tracking. Specifically, HTM constructs a geometric model of the detected blobs on the soft tissue surface by applying the Watershed algorithm for blob detection and integrating the Delaunay triangulation with a newly designed triangle search segmentation algorithm. By leveraging barycentric coordinate theory, More >

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