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  • Open Access

    ARTICLE

    Tackling Faceless Killers: Toxic Comment Detection to Maintain a Healthy Internet Environment

    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 >

  • Open Access

    ARTICLE

    Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student’s Tweets during COVID-19

    Shabir Hussain1, Muhammad Ayoub2, Yang Yu1, Junaid Abdul Wahid1, Akmal Khan3, Dietmar P. F. Moller4, Hou Weiyan1,*

    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 >

  • Open Access

    ARTICLE

    Novel Machine Learning–Based Approach for Arabic Text Classification Using Stylistic and Semantic Features

    Fethi Fkih1,2,*, Mohammed Alsuhaibani1, Delel Rhouma1,2, 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 >

  • Open Access

    ARTICLE

    Question-Answering Pair Matching Based on Question Classification and Ensemble Sentence Embedding

    Jae-Seok Jang1, Hyuk-Yoon Kwon2,*

    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 >

  • Open Access

    ARTICLE

    Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

    Mesfer Al Duhayyim1,*, 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 >

  • Open Access

    ARTICLE

    Deep Learning Algorithm for Detection of Protein Remote Homology

    Fahriye Gemci1,*, 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 >

  • Open Access

    ARTICLE

    Fake News Detection Based on Multimodal Inputs

    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 >

  • Open Access

    ARTICLE

    Firefly-CDDL: A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

    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 >

  • Open Access

    ARTICLE

    An Intelligent Deep Neural Sentiment Classification Network

    Umamaheswari Ramalingam1,*, Senthil Kumar Murugesan2, Karthikeyan Lakshmanan2, Chidhambararajan Balasubramaniyan3

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1733-1744, 2023, DOI:10.32604/iasc.2023.032108

    Abstract A Deep Neural Sentiment Classification Network (DNSCN) is developed in this work to classify the Twitter data unambiguously. It attempts to extract the negative and positive sentiments in the Twitter database. The main goal of the system is to find the sentiment behavior of tweets with minimum ambiguity. A well-defined neural network extracts deep features from the tweets automatically. Before extracting features deeper and deeper, the text in each tweet is represented by Bag-of-Words (BoW) and Word Embeddings (WE) models. The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2 (TSA2), Twitter-Sanders-Apple3 (TSA3), and Twitter-DataSet (TDS). TSA2 and TDS consist of… More >

  • Open Access

    ARTICLE

    Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

    P. Ramya1,*, B. Karthik2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2379-2391, 2023, DOI:10.32604/iasc.2023.026291

    Abstract Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning. Mining core features and performing the text classification still exist as a challenging task. Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach. This paper presented the text document classification that has wide applications in information retrieval, which uses movie review datasets. Here the document indexing based on controlled vocabulary, adjective, word sense disambiguation, generating hierarchical categorization of web pages, spam detection, topic labeling, web search, document summarization, etc. Here the… More >

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