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  • Open Access


    RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

    Farah Mohammad1,*, Saad Al-Ahmadi2, Jalal Al-Muhtadi1,2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3175-3192, 2024, DOI:10.32604/cmc.2023.042873

    Abstract Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users. It hinders the economic growth of utility companies, poses electrical risks, and impacts the high energy costs borne by consumers. The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data, including information on client consumption, which may be used to identify electricity theft using machine learning and deep learning techniques. Moreover, there also exist different solutions such as hardware-based solutions to detect electricity theft that… More >

  • Open Access


    RoBGP: A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

    Xiaohui Cui1,2,#, Chao Song1,2,#, Dongmei Li1,2,*, Xiaolong Qu1,2, Jiao Long1,2, Yu Yang1,2, Hanchao Zhang3

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3603-3618, 2024, DOI:10.32604/cmc.2024.047321

    Abstract Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer… More >

  • Open Access


    The Entity Relationship Extraction Method Using Improved RoBERTa and Multi-Task Learning

    Chaoyu Fan*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1719-1738, 2023, DOI:10.32604/cmc.2023.041395

    Abstract There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them. However, the volume of internet data is beyond the processing capabilities of the current internet infrastructure. Therefore, engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia. The goal of this paper is to explore the entity relationship based on deep learning, introduce semantic knowledge by using the prepared language model, develop an More >

  • Open Access


    Chinese Cyber Threat Intelligence Named Entity Recognition via RoBERTa-wwm-RDCNN-CRF

    Zhen Zhen1, Jian Gao1,2,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 299-323, 2023, DOI:10.32604/cmc.2023.042090

    Abstract In recent years, cyber attacks have been intensifying and causing great harm to individuals, companies, and countries. The mining of cyber threat intelligence (CTI) can facilitate intelligence integration and serve well in combating cyber attacks. Named Entity Recognition (NER), as a crucial component of text mining, can structure complex CTI text and aid cybersecurity professionals in effectively countering threats. However, current CTI NER research has mainly focused on studying English CTI. In the limited studies conducted on Chinese text, existing models have shown poor performance. To fully utilize the power of Chinese pre-trained language models… More >

  • Open Access


    Leveraging Readability and Sentiment in Spam Review Filtering Using Transformer Models

    Sujithra Kanmani*, Surendiran Balasubramanian

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1439-1454, 2023, DOI:10.32604/csse.2023.029953

    Abstract Online reviews significantly influence decision-making in many aspects of society. The integrity of internet evaluations is crucial for both consumers and vendors. This concern necessitates the development of effective fake review detection techniques. The goal of this study is to identify fraudulent text reviews. A comparison is made on shill reviews vs. genuine reviews over sentiment and readability features using semi-supervised language processing methods with a labeled and balanced Deceptive Opinion dataset. We analyze textual features accessible in internet reviews by merging sentiment mining approaches with readability. Overall, the research improves fake review screening by using More >

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