Qianqian Hu, Chuhan Li, Mohan Zhang, Fang Liu^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-17, 2026, DOI:10.32604/cmc.2025.068225 - 10 November 2025

Abstract Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform, the demands of visual communication keep increasing for promoting traditional cultural artifacts online. As an effective medium, posters serve to attract public attention and facilitate broader engagement with cultural artifacts. However, existing poster generation methods mainly rely on fixed templates and manual design, which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts. Therefore, we propose CAPGen, an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language More >

Haider Ali, Rashadul Islam Sumon, Abdul Rehman Khalid, Kounen Fathima, Hee Cheol Kim^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5445-5462, 2025, DOI:10.32604/cmc.2025.065992 - 30 July 2025

Abstract Artificial intelligence is reshaping radiology by enabling automated report generation, yet evaluating the clinical accuracy and relevance of these reports is a challenging task, as traditional natural language generation metrics like BLEU and ROUGE prioritize lexical overlap over clinical relevance. To address this gap, we propose a novel semantic assessment framework for evaluating the accuracy of artificial intelligence-generated radiology reports against ground truth references. We trained 5229 image–report pairs from the Indiana University chest X-ray dataset on the R2GenRL model and generated a benchmark dataset on test data from the Indiana University chest X-ray and… More >

Smita Mahajan¹, Shilpa Gite^1,2, Biswajeet Pradhan^3,*, Abdullah Alamri⁴, Shaunak Inamdar⁵, Deva Shriyansh⁵, Akshat Ashish Shah⁵, Shruti Agarwal⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2001-2026, 2025, DOI:10.32604/cmes.2025.058456 - 30 May 2025

Abstract The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs. However, humans naturally use speech for visualization prompts. Therefore, this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks (GANs) model, leveraging Speech-to-Text translation along with the CLIP + VQGAN model. The proposed method involves translating speech prompts into text, which is then used by the Contrastive Language-Image Pretraining (CLIP) + Vector Quantized Generative Adversarial Network (VQGAN) model to generate images. This paper outlines the steps required to implement such a… More >

Yue Liu¹, Qinglang Guo², Chunyao Yang¹, Yong Liao^1,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2555-2572, 2025, DOI:10.32604/cmc.2025.060318 - 16 April 2025

Abstract Processing police incident data in public security involves complex natural language processing (NLP) tasks, including information extraction. This data contains extensive entity information—such as people, locations, and events—while also involving reasoning tasks like personnel classification, relationship judgment, and implicit inference. Moreover, utilizing models for extracting information from police incident data poses a significant challenge—data scarcity, which limits the effectiveness of traditional rule-based and machine-learning methods. To address these, we propose TIPS. In collaboration with public security experts, we used de-identified police incident data to create templates that enable large language models (LLMs) to populate data More >

Liqing Wang^*, Yiheng Xiao

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 619-631, 2024, DOI:10.32604/iasc.2024.052971 - 06 September 2024

Abstract Neural Machine Translation is one of the key research directions in Natural Language Processing. However, limited by the scale and quality of parallel corpus, the translation quality of low-resource Neural Machine Translation has always been unsatisfactory. When Reinforcement Learning from Human Feedback (RLHF) is applied to low-resource machine translation, commonly encountered issues of substandard preference data quality and the higher cost associated with manual feedback data. Therefore, a more cost-effective method for obtaining feedback data is proposed. At first, optimizing the quality of preference data through the prompt engineering of the Large Language Model (LLM), More >