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 >

Di Wu^*, Liming Feng, Xiaoyu Wang

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 977-999, 2025, DOI:10.32604/cmc.2025.060319 - 26 March 2025

Abstract Semi-supervised new intent discovery is a significant research focus in natural language understanding. To address the limitations of current semi-supervised training data and the underutilization of implicit information, a Semi-supervised New Intent Discovery for Elastic Neighborhood Syntactic Elimination and Fusion model (SNID-ENSEF) is proposed. Syntactic elimination contrast learning leverages verb-dominant syntactic features, systematically replacing specific words to enhance data diversity. The radius of the positive sample neighborhood is elastically adjusted to eliminate invalid samples and improve training efficiency. A neighborhood sample fusion strategy, based on sample distribution patterns, dynamically adjusts neighborhood size and fuses sample More >

Ayesha Khaliq¹, Salman Afsar Awan¹, Fahad Ahmad^2,*, Muhammad Azam Zia¹, Muhammad Zafar Iqbal³

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3221-3242, 2024, DOI:10.32604/cmc.2024.053488 - 15 August 2024

Abstract The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity. Current approaches in Extractive Text Summarization (ETS) leverage the modeling of inter-sentence relationships, a task of paramount importance in producing coherent summaries. This study introduces an innovative model that integrates Graph Attention Networks (GATs) with Transformer-based Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA), further enhanced by Term Frequency-Inverse Document Frequency (TF-IDF) values, to improve sentence selection by capturing comprehensive topical information. Our… More >

Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918 - 26 March 2024

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT)… More >

Sarab AlMuhaideb^*, Yasmeen AlNegheimish, Taif AlOmar, Reem AlSabti, Maha AlKathery, Ghala AlOlyyan

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 195-220, 2023, DOI:10.32604/cmc.2023.038368 - 08 June 2023

Abstract Healthcare organizations rely on patients’ feedback and experiences to evaluate their performance and services, thereby allowing such organizations to improve inadequate services and address any shortcomings. According to the literature, social networks and particularly Twitter are effective platforms for gathering public opinions. Moreover, recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors, including healthcare. The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus. The authors collected 12,400 tweets from Arabic patients… More >

Md. Anwar Hussen Wadud¹, M. F. Mridha¹, Jungpil Shin^2,*, Kamruddin Nur³, Aloke Kumar Saha⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.027841 - 15 June 2022

Abstract Offensive messages on social media, have recently been frequently used to harass and criticize people. In recent studies, many promising algorithms have been developed to identify offensive texts. Most algorithms analyze text in a unidirectional manner, where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences. In addition, there are many separate models for identifying offensive texts based on monolingual and multilingual, but there are a few models that can detect both monolingual and multilingual-based offensive texts. In this study, a detection system has been developed for both monolingual… More >

Maojian Chen^1,2,3, Xiong Luo^1,2,3,*, Hailun Shen⁴, Ziyang Huang⁴, Qiaojuan Peng^1,2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 47-63, 2021, DOI:10.32604/cmes.2021.017491 - 24 August 2021

Abstract In the era of big data, E-commerce plays an increasingly important role, and steel E-commerce certainly occupies a positive position. However, it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of staffs. In order to improve the efficiency of purchasers searching for commodities on the steel E-commerce platforms, we propose a novel deep learning-based loss function for named entity recognition (NER). Considering the impacts of small sample and imbalanced data, in our NER scheme, the focal loss, the label smoothing, and the… More >