Jieren Cheng^1,2, Xiaolong Chen^1,*, Wenghang Xu³, Shuai Hua³, Zhu Tang¹, Victor S. Sheng⁴

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1779-1793, 2023, DOI:10.32604/cmc.2023.042980

Abstract In the realm of Multi-Label Text Classification (MLTC), the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches. Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content, often overlooking intrinsic textual cues such as label statistical features. In contrast, these endogenous insights naturally align with the classification task. In our paper, to complement this focus on intrinsic knowledge, we introduce a novel Gate-Attention mechanism. This mechanism adeptly integrates statistical features from the text itself into the semantic fabric, enhancing the model’s capacity… More >

Jebran Khan¹, Kashif Ahmad², Kyung-Ah Sohn^1,3,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2869-2894, 2023, DOI:10.32604/csse.2023.040159

Abstract In recent years, the growing popularity of social media platforms has led to several interesting natural language processing (NLP) applications. However, these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning (ML) and NLP techniques. This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication. These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form. The intuition of the proposed scheme is to generate adversarial examples… More >

Semi Park, Kyungho Lee^*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 813-826, 2023, DOI:10.32604/cmc.2023.035313

Abstract According to BBC News, online hate speech increased by 20% during the COVID-19 pandemic. Hate speech from anonymous users can result in psychological harm, including depression and trauma, and can even lead to suicide. Malicious online comments are increasingly becoming a social and cultural problem. It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level. To achieve a healthy and safe Internet environment, studies should focus on institutional and technical topics. The detection of toxic comments can create a safe online environment. In this study, to detect malicious comments, we… More >

Shabir Hussain¹, Muhammad Ayoub², Yang Yu¹, Junaid Abdul Wahid¹, Akmal Khan³, Dietmar P. F. Moller⁴, Hou Weiyan^1,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5355-5377, 2023, DOI:10.32604/cmc.2023.036779

Abstract As the COVID-19 pandemic swept the globe, social media platforms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the proposed aspect2class annotation algorithm, we… More >

Fethi Fkih^1,2,*, Mohammed Alsuhaibani¹, Delel Rhouma^1,2, Ali Mustafa Qamar¹

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5871-5886, 2023, DOI:10.32604/cmc.2023.035910

Abstract Text classification is an essential task for many applications related to the Natural Language Processing domain. It can be applied in many fields, such as Information Retrieval, Knowledge Extraction, and Knowledge modeling. Even though the importance of this task, Arabic Text Classification tools still suffer from many problems and remain incapable of responding to the increasing volume of Arabic content that circulates on the web or resides in large databases. This paper introduces a novel machine learning-based approach that exclusively uses hybrid (stylistic and semantic) features. First, we clean the Arabic documents and translate them to English using translation tools.… More >

Jae-Seok Jang¹, Hyuk-Yoon Kwon^2,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3471-3489, 2023, DOI:10.32604/csse.2023.035570

Abstract Question-answering (QA) models find answers to a given question. The necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data sets. In this paper, we deal with the QA pair matching approach in QA models, which finds the most relevant question and its recommended answer for a given question. Existing studies for the approach performed on the entire dataset or datasets within a category that the question writer manually specifies. In contrast, we aim to automatically find the category to which the question belongs by employing the text classification model and… More >

Mesfer Al Duhayyim^1,*, Badriyya B. Al-onazi², Mohamed K. Nour³, Ayman Yafoz⁴, Amal S. Mehanna⁵, Ishfaq Yaseen⁶, Amgad Atta Abdelmageed⁶, Gouse Pasha Mohammed⁶

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2755-2772, 2023, DOI:10.32604/csse.2023.034609

Abstract Natural Language Processing (NLP) for the Arabic language has gained much significance in recent years. The most commonly-utilized NLP task is the ‘Text Classification’ process. Its main intention is to apply the Machine Learning (ML) approaches for automatically classifying the textual files into one or more pre-defined categories. In ML approaches, the first and foremost crucial step is identifying an appropriate large dataset to test and train the method. One of the trending ML techniques, i.e., Deep Learning (DL) technique needs huge volumes of different types of datasets for training to yield the best outcomes. The current study designs a… More >

Fahriye Gemci^1,*, Turgay Ibrikci², Ulus Cevik³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3703-3713, 2023, DOI:10.32604/csse.2023.032706

Abstract The study aims to find a successful solution by using computer algorithms to detect remote homologous proteins, which is a significant problem in the bioinformatics field. In this experimental study, structural classification of proteins (SCOP) 1.53, SCOP benchmark, and the newly created SCOP protein database from the structural classification of proteins—extended (SCOPe) 2.07 were used to detect remote homolog proteins. N-gram method and then Term Frequency-Inverse Document Frequency (TF-IDF) weighting were performed to extract features of the protein sequences taken from these databases. Next, a smoothing process on the obtained features was performed to avoid misclassification. Finally, the proteins with… More >

Zhiping Liang^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4519-4534, 2023, DOI:10.32604/cmc.2023.037035

Abstract In view of the various adverse effects, fake news detection has become an extremely important task. So far, many detection methods have been proposed, but these methods still have some limitations. For example, only two independently encoded unimodal information are concatenated together, but not integrated with multimodal information to complete the complementary information, and to obtain the correlated information in the news content. This simple fusion approach may lead to the omission of some information and bring some interference to the model. To solve the above problems, this paper proposes the Fake News Detection model based on BLIP (FNDB). First,… More >

Monirah Al-Ajlan^*, Mourad Ykhlef

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 19-34, 2023, DOI:10.32604/cmc.2023.033753

Abstract There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms. Among them is cyberbullying, which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively. An alarmingly high percentage of people, especially teenagers, have reported being cyberbullied in recent years. A variety of approaches have been developed to detect cyberbullying, but they require time-consuming feature extraction and selection processes. Moreover, no approach to date has examined… More >