Sajid Ali¹, Qazi Mazhar Ul Haq^1,2,*, Ala Saleh Alluhaidan^3,*, Muhammad Shahid Anwar⁴, Sadique Ahmad⁵, Leila Jamel³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.074395 - 29 January 2026

Abstract Spam emails remain one of the most persistent threats to digital communication, necessitating effective detection solutions that safeguard both individuals and organisations. We propose a spam email classification framework that uses Bidirectional Encoder Representations from Transformers (BERT) for contextual feature extraction and a multiple-window Convolutional Neural Network (CNN) for classification. To identify semantic nuances in email content, BERT embeddings are used, and CNN filters extract discriminative n-gram patterns at various levels of detail, enabling accurate spam identification. The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails, achieving an accuracy of 98.69%, More >

Lingyun Xiang^1,*, Nian Li², Yuling Liu³, Jiayong Hu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073347 - 12 January 2026

Abstract The rapid advancement of large language models (LLMs) has driven the pervasive adoption of AI-generated content (AIGC), while also raising concerns about misinformation, academic misconduct, biased or harmful content, and other risks. Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information. This survey reviews recent progress in detection methods, categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals. Passive detection is further divided into surface linguistic feature-based and language model-based methods, whereas active detection encompasses watermarking-based and semantic retrieval-based More >

Zixuan Guo¹, Houbin Wang², Sameer Kumar^1,*, Yuanfang Chen³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072064 - 12 January 2026

Abstract With the rapid development of digital culture, a large number of cultural texts are presented in the form of digital and network. These texts have significant characteristics such as sparsity, real-time and non-standard expression, which bring serious challenges to traditional classification methods. In order to cope with the above problems, this paper proposes a new ASSC (ALBERT, SVD, Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model. Based on the framework of TextRCNN, the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding. Combined with the dual attention mechanism, the… More >

Woo Hyun Park^*, Dong Ryeol Shin

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069949 - 12 January 2026

Abstract With the recent increase in data volume and diversity, traditional text representation techniques are struggling to capture context, particularly in environments with sparse data. To address these challenges, this study proposes a new model, the Masked Joint Representation Model (MJRM). MJRM approximates the original hypothesis by leveraging multiple elements in a limited context. It dynamically adapts to changes in characteristics based on data distribution through three main components. First, masking-based representation learning, termed selective dynamic masking, integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets, whose predictions are… 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 >

Yahya Tashtoush^1,*, Areen Banysalim¹, Majdi Maabreh², Shorouq Al-Eidi³, Ola Karajeh⁴, Plamen Zahariev⁵

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3113-3134, 2025, DOI:10.32604/cmc.2025.062724 - 16 April 2025

Abstract Social media has emerged as one of the most transformative developments on the internet, revolutionizing the way people communicate and interact. However, alongside its benefits, social media has also given rise to significant challenges, one of the most pressing being cyberbullying. This issue has become a major concern in modern society, particularly due to its profound negative impacts on the mental health and well-being of its victims. In the Arab world, where social media usage is exceptionally high, cyberbullying has become increasingly prevalent, necessitating urgent attention. Early detection of harmful online behavior is critical to… More >

Oluwaseun Peter Ige^1,2, Keng Hoon Gan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1847-1865, 2024, DOI:10.32604/cmes.2024.053373 - 27 September 2024

Abstract Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality. This involves eliminating irrelevant, redundant, and noisy features to streamline the classification process. Various methods, from single feature selection techniques to ensemble filter-wrapper methods, have been used in the literature. Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents. Feature selection is inherently multi-objective, balancing the enhancement of feature relevance, accuracy, and the reduction of redundant features. This… More >

Zixuan Wu¹, Ye Wang^1,*, Lifeng Shen², Feng Hu¹, Hong Yu^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4111-4127, 2024, DOI:10.32604/cmc.2024.054581 - 12 September 2024

Abstract Hierarchical Text Classification (HTC) aims to match text to hierarchical labels. Existing methods overlook two critical issues: first, some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target. Second, error propagation occurs when a misclassification at a parent node propagates down the hierarchy, ultimately leading to inaccurate predictions at the leaf nodes. To address these limitations, we propose an uncertainty-guided HTC depth-aware model called DepthMatch. Specifically, we design an early stopping strategy with uncertainty to More >

Hussein Alaa Al-Kabbi^1,2, Mohammad-Reza Feizi-Derakhshi^1,*, Saeed Pashazadeh³

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 665-682, 2024, DOI:10.32604/iasc.2024.050452 - 06 September 2024

Abstract SMS spam poses a significant challenge to maintaining user privacy and security. Recently, spammers have employed fraudulent writing styles to bypass spam detection systems. This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam. The system comprises five steps, beginning with the preprocessing of SMS data. RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis. Feature extraction is performed using a Convolutional Neural Network (CNN) for word-level analysis and a Bidirectional Long… More >

Ebtesam Ahmad Alomari^*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 43-85, 2024, DOI:10.32604/cmes.2024.052256 - 20 August 2024

Abstract As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its… More >