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  • Open Access

    ARTICLE

    News Modeling and Retrieving Information: Data-Driven Approach

    Elias Hossain1, Abdullah Alshahrani2, Wahidur Rahman3,*

    Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 109-123, 2023, DOI:10.32604/iasc.2022.029511

    Abstract This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling. The Methodology of this study is categorized into three phases: the Text Classification Approach (TCA), the Proposed Algorithms Interpretation (PAI), and finally, Information Retrieval Approach (IRA). The TCA reflects the text preprocessing pipeline called a clean corpus. The Global Vectors for Word Representation (Glove) pre-trained model, FastText, Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BOW) for extracting the features have been interpreted in this research. The PAI manifests the Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network… More >

  • Open Access

    ARTICLE

    A Video Captioning Method by Semantic Topic-Guided Generation

    Ou Ye, Xinli Wei, Zhenhua Yu*, Yan Fu, Ying Yang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1071-1093, 2024, DOI:10.32604/cmc.2023.046418

    Abstract In the video captioning methods based on an encoder-decoder, limited visual features are extracted by an encoder, and a natural sentence of the video content is generated using a decoder. However, this kind of method is dependent on a single video input source and few visual labels, and there is a problem with semantic alignment between video contents and generated natural sentences, which are not suitable for accurately comprehending and describing the video contents. To address this issue, this paper proposes a video captioning method by semantic topic-guided generation. First, a 3D convolutional neural network is utilized to extract the… More >

  • Open Access

    ARTICLE

    Topic Modelling and Sentiment Analysis on YouTube Sustainable Fashion Comments

    Hsu-Hua Lee, Minh T. N. Nguyen*

    Journal of New Media, Vol.5, No.1, pp. 65-80, 2023, DOI:10.32604/jnm.2023.045792

    Abstract YouTube videos on sustainable fashion enable the public to gain basic knowledge about this concept. In this paper, we analyse user comments on YouTube videos that contain sustainable fashion content. The paper’s main objective is to help content creators and business managers effectively understand the perspectives of viewers, thus improving video quality and developing business. We analysed a dataset of 17,357 comments collected from 15 sustainable fashion YouTube videos. First, we use Latent Dirichlet Allocation (LDA), a topic modelling technique, to discover the abstract topics. In addition, we use two approaches to rank these topics: ranking based on proportion and… More >

  • Open Access

    ARTICLE

    A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

    Bishrul Haq1, Sher Muhammad Daudpota1, Ali Shariq Imran2, Zenun Kastrati3,*, Waheed Noor4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 115-137, 2023, DOI:10.32604/cmc.2023.040638

    Abstract The Internet has become one of the significant sources for sharing information and expressing users’ opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a semi-supervised approach with minimal human… More >

  • Open Access

    ARTICLE

    Ectopic Overexpression of EuCHIT30.7 Improves Nicotiana tabacum Resistance to Powdery Mildew

    Lanjun Li1, Degang Zhao1,2,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.11, pp. 3043-3061, 2023, DOI:10.32604/phyton.2023.031175

    Abstract Various strains of powdery mildew (PM), a notorious plant fungal disease, are prevalent and pose a significant threat to plant health. To control PM, transgenic technology can be used to cultivate more resistant plant varieties. In the present study, we utilized the rapid amplification of cDNA ends (RACE) technique to clone the full-length cDNA sequence of the EuCHIT30.7 gene to explore plant genes with disease resistance functions. Bioinformatics analysis revealed that this gene belongs to the GH18 family and is classified as a class III chitinase. The EuCHIT30.7 gene is expressed throughout the Eucommia ulmoides plant, with the most abundant… More >

  • Open Access

    ARTICLE

    Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports

    Yan Li1, Xiaoguang Zhang1,*, Tianyu Gong1, Qi Dong1, Hailong Zhu1, Tianqiang Zhang1, Yanji Jiang2,3

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3691-3705, 2023, DOI:10.32604/cmc.2023.040492

    Abstract Automatic text summarization (ATS) plays a significant role in Natural Language Processing (NLP). Abstractive summarization produces summaries by identifying and compressing the most important information in a document. However, there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics. In particular, Chinese complaint reports, generated by urban complainers and collected by government employees, describe existing resident problems in daily life. Meanwhile, the reflected problems are required to respond speedily. Therefore, automatic summarization tasks for these reports have been developed. However, similar to traditional… More >

  • Open Access

    EDITORIAL

    Hot Topics of Molecular and Cellular Biomechanics in 2022

    Guixue Wang1,2,*

    Molecular & Cellular Biomechanics, Vol.20, No.2, pp. 63-66, 2023, DOI:10.32604/mcb.2023.044564

    Abstract The analysis of biomechanical characteristics plays an important role in mastering the technical characteristics of athletes, providing guidance for the formulation and prevention of sports injury training plans and providing theoretical support for research on injury prevention and stability control in the sports field. With the importance of data analysis, the application scope of artificial intelligence methods is more extensive. For example, intelligent training systems can be used for athletes’ personalized and professional training, real-time monitoring and feedback of training data, and further reduce the risk of sports injury. However, deep learning methods process a large number of medical images… More >

  • Open Access

    ARTICLE

    ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

    Haein Lee1, Seon Hong Lee1, Kyeo Re Lee2, Jang Hyun Kim3,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6023-6037, 2023, DOI:10.32604/cmc.2023.039104

    Abstract Environmental, social, and governance (ESG) factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate value. Recently, non-financial indicators have been considered as important for the actual valuation of corporations, thus analyzing natural language data related to ESG is essential. Several previous studies limited their focus to specific countries or have not used big data. Past methodologies are insufficient for obtaining potential insights into the best practices to leverage ESG. To address this problem, in this study, the authors used data from two platforms: LexisNexis, a platform that provides media monitoring, and… 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 >

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