Syed Atir Raza^1,2, Muhammad Shoaib Farooq¹, Uzma Farooq³, Hanen Karamti ⁴, Tahir Khurshaid^5,*, Imran Ashraf^6,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3149-3173, 2025, DOI:10.32604/cmc.2025.063255 - 03 July 2025

Abstract Urdu, a prominent subcontinental language, serves as a versatile means of communication. However, its handwritten expressions present challenges for optical character recognition (OCR). While various OCR techniques have been proposed, most of them focus on recognizing printed Urdu characters and digits. To the best of our knowledge, very little research has focused solely on Urdu pure handwriting recognition, and the results of such proposed methods are often inadequate. In this study, we introduce a novel approach to recognizing Urdu pure handwritten digits and characters using Convolutional Neural Networks (CNN). Our proposed method utilizes convolutional layers… More >

Noor Ayesha¹, Muhammad Mujahid², Abeer Rashad Mirdad², Faten S. Alamri^3,*, Amjad R. Khan²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1883-1899, 2025, DOI:10.32604/cmc.2025.063560 - 09 June 2025

Abstract Large language models (LLMs) and natural language processing (NLP) have significant promise to improve efficiency and refine healthcare decision-making and clinical results. Numerous domains, including healthcare, are rapidly adopting LLMs for the classification of biomedical textual data in medical research. The LLM can derive insights from intricate, extensive, unstructured training data. Variants need to be accurately identified and classified to advance genetic research, provide individualized treatment, and assist physicians in making better choices. However, the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize. Such an… More >

Sangmin Kim¹, Byeongcheon Lee¹, Muazzam Maqsood², Jihoon Moon^3,*, Seungmin Rho^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2079-2108, 2025, DOI:10.32604/cmes.2025.061653 - 30 May 2025

Abstract The increased accessibility of social networking services (SNSs) has facilitated communication and information sharing among users. However, it has also heightened concerns about digital safety, particularly for children and adolescents who are increasingly exposed to online grooming crimes. Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims. However, research on grooming detection in South Korea remains limited, as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations, leading to inaccurate classifications. To address these issues, this study proposes a novel… 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 >

Huu-Tuong Ho¹, Thi-Thuy-Hoai Nguyen², Duong Nguyen Minh Huy³, Luong Vuong Nguyen^1,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3945-3973, 2025, DOI:10.32604/cmc.2025.063099 - 19 May 2025

Abstract Natural Language Processing (NLP) has become essential in text classification, sentiment analysis, machine translation, and speech recognition applications. As these tasks become complex, traditional machine learning and deep learning models encounter challenges with optimization, parameter tuning, and handling large-scale, high-dimensional data. Bio-inspired algorithms, which mimic natural processes, offer robust optimization capabilities that can enhance NLP performance by improving feature selection, optimizing model parameters, and integrating adaptive learning mechanisms. This review explores the state-of-the-art applications of bio-inspired algorithms—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—across core NLP tasks. We analyze More >

Faheem Shaukat¹, Naveed Ejaz^1,2, Rashid Kamal^3,4, Tamim Alkhalifah^5,*, Sheraz Aslam^6,7,*, Mu Mu⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5595-5622, 2025, DOI:10.32604/cmc.2025.061702 - 19 May 2025

Abstract Automated and accurate movie genre classification is crucial for content organization, recommendation systems, and audience targeting in the film industry. Although most existing approaches focus on audiovisual features such as trailers and posters, the text-based classification remains underexplored despite its accessibility and semantic richness. This paper introduces the Genre Attention Model (GAM), a deep learning architecture that integrates transformer models with a hierarchical attention mechanism to extract and leverage contextual information from movie plots for multi-label genre classification. In order to assess its effectiveness, we assess multiple transformer-based models, including Bidirectional Encoder Representations from Transformers… More >

Mohammed Abdelmajeed^1,*, Jiangbin Zheng¹, Ahmed Murtadha¹, Youcef Nafa¹, Mohammed Abaker², Muhammad Pervez Akhter³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3471-3491, 2025, DOI:10.32604/cmc.2025.059870 - 16 April 2025

Abstract Arabic Dialect Identification (DID) is a task in Natural Language Processing (NLP) that involves determining the dialect of a given piece of text in Arabic. The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect. Despite the effectiveness of these solutions, the performance heavily relies on the amount of labeled examples, which is labor-intensive to attain and may not be readily available in real-world scenarios. To alleviate the burden of labeling data, this paper introduces a novel solution that leverages… More >

Lei Zhang^1,2,*, Hangyuan Deng^1,2

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1-11, 2025, DOI:10.32604/cmc.2025.062666 - 26 March 2025

Abstract NJmat is a user-friendly, data-driven machine learning interface designed for materials design and analysis. The platform integrates advanced computational techniques, including natural language processing (NLP), large language models (LLM), machine learning potentials (MLP), and graph neural networks (GNN), to facilitate materials discovery. The platform has been applied in diverse materials research areas, including perovskite surface design, catalyst discovery, battery materials screening, structural alloy design, and molecular informatics. By automating feature selection, predictive modeling, and result interpretation, NJmat accelerates the development of high-performance materials across energy storage, conversion, and structural applications. Additionally, NJmat serves as an… More >

Josua Käser¹, Thomas Nagy¹, Patrick Stirnemann¹, Thomas Hanne^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 201-217, 2025, DOI:10.32604/cmc.2025.061527 - 26 March 2025

Abstract We analyze the suitability of existing pre-trained transformer-based language models (PLMs) for abstractive text summarization on German technical healthcare texts. The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field. The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts, even if the model is not specifically trained in that language. Through experiments, the research questions explore the performance of transformer language models in dealing with complex syntax constructs, the difference… More >

Alaa Thobhani^1,*, Beiji Zou¹, Xiaoyan Kui^1,*, Amr Abdussalam², Muhammad Asim³, Sajid Shah³, Mohammed ELAffendi³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2247-2280, 2025, DOI:10.32604/cmes.2025.059192 - 03 March 2025

Abstract Image captioning has seen significant research efforts over the last decade. The goal is to generate meaningful semantic sentences that describe visual content depicted in photographs and are syntactically accurate. Many real-world applications rely on image captioning, such as helping people with visual impairments to see their surroundings. To formulate a coherent and relevant textual description, computer vision techniques are utilized to comprehend the visual content within an image, followed by natural language processing methods. Numerous approaches and models have been developed to deal with this multifaceted problem. Several models prove to be state-of-the-art solutions… More >