Himadri Nath Saha¹, Dipanwita Chakraborty Bhattacharya^2,*, Sancharita Dutta³, Arnab Bera³, Srutorshi Basuray⁴, Satyasaran Changdar⁵, Saptarshi Banerjee⁶, Jon Turdiev⁷

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-56, 2026, DOI:10.32604/cmc.2025.070507 - 09 December 2025

Abstract The emergence of Medical Large Language Models has significantly transformed healthcare. Medical Large Language Models (Med-LLMs) serve as transformative tools that enhance clinical practice through applications in decision support, documentation, and diagnostics. This evaluation examines the performance of leading Med-LLMs, including GPT-4Med, Med-PaLM, MEDITRON, PubMedGPT, and MedAlpaca, across diverse medical datasets. It provides graphical comparisons of their effectiveness in distinct healthcare domains. The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making, documentation, drug discovery, research, patient interaction, and public health. The paper addresses deployment challenges of Medical-LLMs, More >

Dawa Chyophel Lepcha^1,*, Bhawna Goyal^2,3, Ayush Dogra⁴, Ahmed Alkhayyat⁵, Prabhat Kumar Sahu⁶, Aaliya Ali⁷, Vinay Kukreja⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1487-1573, 2025, DOI:10.32604/cmes.2025.070964 - 26 November 2025

Abstract Medical image analysis has become a cornerstone of modern healthcare, driven by the exponential growth of data from imaging modalities such as MRI, CT, PET, ultrasound, and X-ray. Traditional machine learning methods have made early contributions; however, recent advancements in deep learning (DL) have revolutionized the field, offering state-of-the-art performance in image classification, segmentation, detection, fusion, registration, and enhancement. This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks, highlighting both foundational models and recent innovations. The article begins by introducing conventional techniques and their limitations, setting the… More >

Hanyong Lee^#, Ye-Chan Park^#, Jaesung Lee^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4977-4993, 2025, DOI:10.32604/cmc.2025.068156 - 23 October 2025

Abstract Restoring texts corrupted by visually perturbed homoglyph characters presents significant challenges to conventional Natural Language Processing (NLP) systems, primarily due to ambiguities arising from characters that appear visually similar yet differ semantically. Traditional text restoration methods struggle with these homoglyph perturbations due to limitations such as a lack of contextual understanding and difficulty in handling cases where one character maps to multiple candidates. To address these issues, we propose an Optical Character Recognition (OCR)-assisted masked Bidirectional Encoder Representations from Transformers (BERT) model specifically designed for homoglyph-perturbed text restoration. Our method integrates OCR preprocessing with a… More >

Luyu Ma^1,*, Xiu Cheng^1,*, Zongyan Xing¹, Yue Wu¹, Weiwei Jiang²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3921-3943, 2025, DOI:10.32604/cmc.2025.067786 - 23 September 2025

Abstract Green consumption (GC) are crucial for achieving the Sustainable Development Goals (SDGs). However, few studies have explored public attitudes toward GC using social media data, missing potential public concerns captured through big data. To address this gap, this study collects and analyzes public attention toward GC using web crawler technology. Based on the data from Sina Weibo, we applied RoBERTa, an advanced NLP model based on transformer architecture, to conduct fine-grained sentiment analysis of the public’s attention, attitudes and hot topics on GC, demonstrating the potential of deep learning methods in capturing dynamic and contextual… More >

Kexuan Niu, Xiameng Si^*, Xiaojie Qi, Haiyan Kang

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2051-2070, 2025, DOI:10.32604/cmc.2025.065377 - 29 August 2025

Abstract Sarcasm detection is a complex and challenging task, particularly in the context of Chinese social media, where it exhibits strong contextual dependencies and cultural specificity. To address the limitations of existing methods in capturing the implicit semantics and contextual associations in sarcastic expressions, this paper proposes an event-aware model for Chinese sarcasm detection, leveraging a multi-head attention (MHA) mechanism and contrastive learning (CL) strategies. The proposed model employs a dual-path Bidirectional Encoder Representations from Transformers (BERT) encoder to process comment text and event context separately and integrates an MHA mechanism to facilitate deep interactions between More >

Jungpil Shin^1,*, Wahidur Rahman², Tanvir Ahmed², Bakhtiar Mazrur², Md. Mohsin Mia², Romana Idress Ekfa², Md. Sajib Rana², Pankoo Kim^3,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4105-4153, 2025, DOI:10.32604/cmc.2025.066910 - 30 July 2025

Abstract Sentiment Analysis, a significant domain within Natural Language Processing (NLP), focuses on extracting and interpreting subjective information—such as emotions, opinions, and attitudes—from textual data. With the increasing volume of user-generated content on social media and digital platforms, sentiment analysis has become essential for deriving actionable insights across various sectors. This study presents a systematic literature review of sentiment analysis methodologies, encompassing traditional machine learning algorithms, lexicon-based approaches, and recent advancements in deep learning techniques. The review follows a structured protocol comprising three phases: planning, execution, and analysis/reporting. During the execution phase, 67 peer-reviewed articles were More >

Ahmet Emre Ergün, Aytuğ Onan^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4855-4877, 2025, DOI:10.32604/cmc.2025.063826 - 19 May 2025

Abstract Large Language Models (LLMs) have significantly advanced human-computer interaction by improving natural language understanding and generation. However, their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges. This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features. Eleven machine learning models were evaluated based on key metrics such as accuracy, precision, recall, and F1-score. The results show that the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) cascade model delivers the best performance, especially when using prompt features, achieving an accuracy of over 97% in… 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 >

Ke Chen^1,2, Cheng Peng^1,2, Xinyang He^1,2, Jiakang Sun^1,2, Xu Liu^1,2, Xiaolin Qin^1,2,*, Yong Zhong^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4003-4019, 2025, DOI:10.32604/cmc.2025.057443 - 06 March 2025

Abstract In natural language processing (NLP), managing multiple downstream tasks through fine-tuning pre-trained models often requires maintaining separate task-specific models, leading to practical inefficiencies. To address this challenge, we introduce AdaptForever, a novel approach that enables continuous mastery of NLP tasks through the integration of elastic and mutual learning strategies with a stochastic expert mechanism. Our method freezes the pre-trained model weights while incorporating adapters enhanced with mutual learning capabilities, facilitating effective knowledge transfer from previous tasks to new ones. By combining Elastic Weight Consolidation (EWC) for knowledge preservation with specialized regularization terms, AdaptForever successfully maintains More >

Md Hasibur Rahman, Mohammed Arif Uddin, Zinnat Fowzia Ria, Rashedur M. Rahman^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1637-1666, 2025, DOI:10.32604/cmes.2024.058329 - 27 January 2025

Abstract The rapid growth of digital data necessitates advanced natural language processing (NLP) models like BERT (Bidirectional Encoder Representations from Transformers), known for its superior performance in text classification. However, BERT’s size and computational demands limit its practicality, especially in resource-constrained settings. This research compresses the BERT base model for Bengali emotion classification through knowledge distillation (KD), pruning, and quantization techniques. Despite Bengali being the sixth most spoken language globally, NLP research in this area is limited. Our approach addresses this gap by creating an efficient BERT-based model for Bengali text. We have explored 20 combinations… More > Graphic Abstract