Chuang-Chieh Lin¹, Yung-Shen Huang², Shih-Yeh Chen^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.072890 - 09 December 2025

Abstract With the rapid development of generative artificial intelligence (GenAI), the task of story visualization, which transforms natural language narratives into coherent and consistent image sequences, has attracted growing research attention. However, existing methods still face limitations in balancing multi-frame character consistency and generation efficiency, which restricts their feasibility for large-scale practical applications. To address this issue, this study proposes a modular cloud-based distributed system built on Stable Diffusion. By separating the character generation and story generation processes, and integrating multi-feature control techniques, a caching mechanism, and an asynchronous task queue architecture, the system enhances generation… More >

Yi-Ting Peng^1,*, Chin-Laung Lei²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3969-3992, 2025, DOI:10.32604/cmes.2025.073258 - 23 December 2025

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 >

Jeongsu Park¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3913-3948, 2025, DOI:10.32604/cmes.2025.072261 - 23 December 2025

Abstract Time series anomaly detection is critical in domains such as manufacturing, finance, and cybersecurity. Recent generative AI models, particularly Transformer- and Autoencoder-based architectures, show strong accuracy but their robustness under noisy conditions is less understood. This study evaluates three representative models—AnomalyTransformer, TranAD, and USAD—on the Server Machine Dataset (SMD) and cross-domain benchmarks including the Soil Moisture Active Passive (SMAP) dataset, the Mars Science Laboratory (MSL) dataset, and the Secure Water Treatment (SWaT) testbed. Seven noise settings (five canonical, two mixed) at multiple intensities are tested under fixed clean-data training, with variations in window, stride, and More > Graphic Abstract

Sewwandi Tennakoon¹, Eric Danso¹, Zhenjie Zhao^2,*

Journal on Artificial Intelligence, Vol.7, pp. 517-547, 2025, DOI:10.32604/jai.2025.072628 - 01 December 2025

Abstract Generative Artificial Intelligence (GenAI) systems have achieved remarkable capabilities across text, code, and image generation; however, their outputs remain prone to errors, hallucinations, and biases. Users often overtrust these outputs due to limited transparency, which can lead to misuse and decision errors. This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability. We introduce a methodology that adjusts explanations dynamically according to user expertise, model output confidence, and contextual risk factors, providing guidance that is informative but not overwhelming. The framework was evaluated using outputs… More >

Sanaa Lakrouni^*, Slimane Bah, Marouane Sebgui

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5555-5569, 2025, DOI:10.32604/cmc.2025.067654 - 23 October 2025

Abstract Federated Learning (FL) has recently emerged as a promising paradigm that enables medical institutions to collaboratively train robust models without centralizing sensitive patient information. Data collected from different institutions represent distinct source domains. Consequently, discrepancies in feature distributions can significantly hinder a model’s generalization to unseen domains. While domain generalization (DG) methods have been proposed to address this challenge, many may compromise data privacy in FL by requiring clients to transmit their local feature representations to the server. Furthermore, existing adversarial training methods, commonly used to align marginal feature distributions, fail to ensure the consistency… More >

Hoon Ko¹, Marek R. Ogiela², Libor Mesicek³, Sangheon Kim^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2985-2997, 2025, DOI:10.32604/cmc.2025.065885 - 23 September 2025

Abstract The rapid evolution of AI-driven cybersecurity solutions has led to increasingly complex network infrastructures, which in turn increases their exposure to sophisticated threats. This study proposes a Graph Neural Network (GNN)-based feature selection strategy specifically tailored for Network Intrusion Detection Systems (NIDS). By modeling feature correlations and leveraging their topological relationships, this method addresses challenges such as feature redundancy and class imbalance. Experimental analysis using the KDDTest+ dataset demonstrates that the proposed model achieves 98.5% detection accuracy, showing notable gains in both computational efficiency and minority class detection. Compared to conventional machine learning methods, the More >

Kyle Stein^1,*, Andrew A. Mahyari^1,2, Guillermo Francia III³, Eman El-Sheikh³

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1001-1018, 2025, DOI:10.32604/cmc.2025.068201 - 29 August 2025

Abstract Deep neural networks (DNNs) and generative AI (GenAI) are increasingly vulnerable to backdoor attacks, where adversaries embed triggers into inputs to cause models to misclassify or misinterpret target labels. Beyond traditional single-trigger scenarios, attackers may inject multiple triggers across various object classes, forming unseen backdoor-object configurations that evade standard detection pipelines. In this paper, we introduce DBOM (Disentangled Backdoor-Object Modeling), a proactive framework that leverages structured disentanglement to identify and neutralize both seen and unseen backdoor threats at the dataset level. Specifically, DBOM factorizes input image representations by modeling triggers and objects as independent primitives in the… More >

Xiao Jian Tan^1,2,3,*, Wai Loon Cheor², Ee Meng Cheng^4,5, Chee Chin Lim^3,4, Khairul Shakir Ab Rahman⁶

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2015-2060, 2025, DOI:10.32604/cmc.2025.063407 - 03 July 2025

Abstract This study systematically reviews the applications of generative artificial intelligence (GAI) in breast cancer research, focusing on its role in diagnosis and therapeutic development. While GAI has gained significant attention across various domains, its utility in breast cancer research has yet to be comprehensively reviewed. This study aims to fill that gap by synthesizing existing research into a unified document. A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in the retrieval of 3827 articles, of which 31 were deemed eligible for analysis. The included studies were… More >

Ilyas Haouam^*

Journal on Artificial Intelligence, Vol.7, pp. 85-114, 2025, DOI:10.32604/jai.2025.064145 - 27 May 2025

Abstract This study investigates the integration of Artificial Intelligence (AI) technologies—particularly natural language processing and machine learning—into qualitative research (QR) workflows. Our research demonstrates that AI can streamline data collection, coding, theme identification, and visualization, significantly improving both speed and accuracy compared to traditional manual methods. Notably, our experimental and numerical results provide a comprehensive analysis of AI’s effect on efficiency, accuracy, and usability across various QR tasks. By presenting and discussing studies on some AI & generative AI models, we contribute to the ongoing scholarly discussion on the role of AI in QR exploring its… More >

Hoon Ko¹, Marek R. Ogiela^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 259-278, 2025, DOI:10.32604/cmc.2024.057257 - 03 January 2025

Abstract This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology. By combining the strengths of blockchain and generative AI, the research team aimed to address the timely challenge of safeguarding visual medical content. The participating researchers conducted a comprehensive analysis, examining the vulnerabilities of medical AI services, personal information protection issues, and overall security weaknesses. This multifaceted exploration led to an in-depth evaluation of the model’s performance and security. Notably, the correlation between accuracy, detection rate, and error rate was… More >