Amine Gonca Toprak^1,*, Aytuğ Onan²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085268 - 13 August 2026

Abstract Biomedical texts present significant challenges for natural language processing (NLP) due to their complex terminology, intricate contextual dependencies, and highly domain-specific semantics. This study investigates the effectiveness of knowledge distillation (KD) for biomedical text classification, aiming to develop lightweight, resource-efficient models that remain competitive with larger architectures. A balanced dataset of 25,000 PubMed records was constructed, equally distributed across five biomedical domains. Two teacher models (BERT and PubMedBERT) and five student models (DistilBERT, BioClinicalBERT, BioBERT, DistilBioBERT, and DistilRoBERTa) were evaluated across ten distinct KD configurations. Each student model was also directly fine-tuned to serve as… More >

Shunran Duan, Meijuan Yin^*, Xiangyang Luo, Lunchong Cui, Chenyu Wang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084290 - 13 August 2026

Abstract Few-shot relation classification aims to identify semantic relations between entity pairs under limited annotated data. Although recent prompt learning-based methods have achieved promising performance, they often rely on manually crafted, domain-specific prompt templates, which restrict their transferability across domains. In this paper, building upon the multi-task prompt transfer paradigm of MPT, we propose a Confidence-guided Orthogonality-constraint Prompt Adaptation framework for few-shot relation classification, named COPA. The proposed framework learns a shared domain-invariant prompt matrix together with domain-specific low-rank prompt matrices via multi-domain soft prompt tuning, enabling the transfer of domain-invariant relational knowledge across domains. Unlike… More >

Laura Baitenova¹, Gulnar Mukhamejanova², Gauhar Munaitbas^3,*, Saken Mambetov¹, Zhanna Mukanova¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081004 - 23 July 2026

Abstract Morphological parsing is a fundamental task in natural language processing, particularly for morphologically rich languages where words encode complex grammatical and semantic information. This paper proposes a multi-branch Transformer-enhanced neural framework for joint morphological representation learning, designed to improve segmentation and classification accuracy by integrating complementary feature extraction mechanisms. The proposed architecture combines convolutional layers for capturing local morphological patterns, recurrent layers for modeling sequential dependencies, and Transformer-based self-attention for learning global contextual relationships. This hybrid design enables the model to generate robust and context-aware representations that enhance morphological understanding. The framework is trained using… More >

Naimul Hasan Shadesh^*, Zannatul Ferdous, Bipasha Iasmin

Journal on Artificial Intelligence, Vol.8, pp. 335-357, 2026, DOI:10.32604/jai.2026.078589 - 22 July 2026

Abstract Sentiment analysis is a core task in Natural Language Processing (NLP) that aims to identify opinions and sentiment polarity expressed in textual data. This study presents a systematic empirical evaluation of classical machine learning–based sentiment analysis methods using a unified experimental framework. Several supervised classifiers, including Decision Trees, Logistic Regression, Support Vector Machines (SVM), Random Forests, Naïve Bayes, and K-Nearest Neighbors (KNN), are evaluated on labeled text datasets collected from multiple domains such as product reviews, customer feedback, hotel reviews, and social media content. The experimental pipeline includes standard NLP preprocessing steps—text normalization, tokenization, stopword More >

Ghadi Almaktoom, Suliman Aladhadh, Salim El Khediri^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078774 - 27 May 2026

Abstract The rapid growth of phishing attempts in the enterprise could potentially lead to bankruptcy. The primary focus of the research is on detecting phishing attacks, with no interest in how the data is processed. Attackers use fraudulent methods to obtain valuable, confidential information, resulting in billions of dollars in financial losses for enterprises. In our review, we examined the methods used in phishing-detection studies. We concluded that the two main sections, centralized and decentralized methods, were the centralized ones, which aggregate data in a central server and thus violate data protection regulations, such as GDPR.… More >

Choongwon Kang^1,2, Haein Lee^3,4, Jang Hyun Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078997 - 08 May 2026

Abstract With the rapid growth of social media and frequent anonymous interactions, hate speech has become widespread. As users express diverse opinions in digital spaces, the need for effective detection remains crucial. To address this, we propose a framework applicable to diverse hate speech types, combining sentence-level semantic representation vectors from the pre-trained Bidirectional Encoder Representations from Transformers (BERT) with sentiment score vectors from the Linguistic Inquiry and Word Count (LIWC) dictionary and the Valence Aware Dictionary for sEntiment Reasoning (VADER). This semantic-sentiment fusion integrates three deep learning models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), More >

Ronghao Pan¹, Tomás Bernal-Beltrán¹, Alejandro Rodríguez-González^2,3, Ernestina Menasalvas-Ruíz^2,3, Rafael Valencia-García^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077501 - 09 April 2026

Abstract The digitization of healthcare has resulted in the production of large amounts of structured and unstructured clinical data, creating the need for accurate and efficient named entity recognition (NER) to support medical procedures. This study evaluates and compares three approaches to NER in the medical domain in Spanish: using Large Language Models (LLMs) with In-Context Learning techniques (Zero-Shot, Few-Shot, and Chain-of-Thought); fine-tuning of LLMs; and fine-tuning of encoder-only models. Experiments were conducted on the Meddocan, Meddoprof, Meddoplace and Symptemist benchmark datasets. Fine-tuned encoder-only models achieve the best performance across all datasets, reaching macro-F1 scores of More >

Sara Tehsin^1,*, Inzamam Mashood Nasir¹, Wiem Abdelbaki², Fadwa Alrowais³, Reham Abualhamayel⁴, Abdulsamad Ebrahim Yahya⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076514 - 12 March 2026

Abstract The rapid proliferation of multimodal misinformation on social media demands detection frameworks that are not only accurate but also robust to noise, adversarial manipulation, and semantic inconsistency between modalities. Existing multimodal fake news detection approaches often rely on deterministic fusion strategies, which limits their ability to model uncertainty and complex cross-modal dependencies. To address these challenges, we propose Q-ALIGNer, a quantum-inspired multimodal framework that integrates classical feature extraction with quantum state encoding, learnable cross-modal entanglement, and robustness-aware training objectives. The proposed framework adopts quantum formalism as a representational abstraction, enabling probabilistic modeling of multimodal alignment… More >

Talha Farooq Khan¹, Fahad Ali², Majid Hussain¹, Lal Khan^3,*, Hsien-Tsung Chang^4,5,6,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074557 - 12 March 2026

Abstract The outstanding growth in the applications of large language models (LLMs) demonstrates the significance of adaptive and efficient prompt engineering tactics. The existing methods may not be variable, vigorous and streamlined in different domains. The offered study introduces an immediate optimization outline, named PROMPTx-PE, that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM. The proposed system features a timely selection scheme which is informed by reinforcement learning, a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability More >

Basudev Nath¹, Deepak Sahoo¹, Sudhansu Shekhar Patra², Hassan Alkhiri³, Subrata Chowdhury⁴, Sheraz Aslam^5,6,*, Kainat Mustafa⁷

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.073486 - 12 March 2026

Abstract Accurate, up to date, and quick information related to any disaster supports disaster management team/authorities to perform quick, easy, and cost-effective response to enhance rescue operations to alleviate the possible loss of lives, financial risks, and properties. Due to damaged infrastructure in disaster-affected areas, social media is the only way to share/ exchange real time information. Therefore, ‘X’ (formerly Twitter) has become a major platform for disseminating real-time information during disaster events or emergencies, i.e., floods and earthquake. Rapid identification of actionable content is critical for effective humanitarian response; however, the brief and noisy nature… More >