Meile Li¹, Yinuo Jiang^2,*

Journal on Artificial Intelligence, Vol.7, pp. 509-516, 2025, DOI:10.32604/jai.2025.073792 - 28 November 2025

Abstract This paper conducts topic mining and analysis of research literature in the domestic smart library field based on the BERTopic model, aiming to reveal its topic development context and evolution trends. Journal literature in the smart library field collected by CNKI (China National Knowledge Infrastructure) from 2015 to 2024 was analyzed using the BERTopic model and dynamic topic modeling for topic mining and evolution trend analysis. The study found that the domestic smart library field involves multiple core topics, identifying a diversified topic structure centered around “data”, “user”, “5g”, etc. The research results provide data More >

Lina Corgiolu, Giuseppe Giannaccare^*, Alberto Cuccu

Oncology Research, Vol.33, No.10, pp. 2725-2740, 2025, DOI:10.32604/or.2025.067221 - 26 September 2025

Abstract Topical chemotherapy is increasingly used to treat ocular surface tumors as a primary therapy and an adjuvant treatment after surgical excision. The most employed topical agents include mitomycin C (MMC), 5-fluorouracil (5-FU), and interferon alpha-2b (IFNα2b), each with distinct mechanisms of action, efficacy profiles, and toxicity risks. Although these agents offer effective tumor control and allow for a non-invasive approach in many cases, ocular surface complications requiring medical or surgical management can occur. This summarizes the adverse effect and outilines practical strategies for their prevention and treatment. MMC is the most potent agent but also… More >

Weisi Chen^1,*, Walayat Hussain^2,*, Junjie Chen¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1559-1583, 2025, DOI:10.32604/cmc.2025.065916 - 29 August 2025

Abstract Topic modeling is a fundamental technique of content analysis in natural language processing, widely applied in domains such as social sciences and finance. In the era of digital communication, social scientists increasingly rely on large-scale social media data to explore public discourse, collective behavior, and emerging social concerns. However, traditional models like Latent Dirichlet Allocation (LDA) and neural topic models like BERTopic struggle to capture deep semantic structures in short-text datasets, especially in complex non-English languages like Chinese. This paper presents Generative Language Model Topic (GLMTopic) a novel hybrid topic modeling framework leveraging the capabilities… More >

Etan Eigner^1,*, Yacov Reisman², Nicola Fazza¹, Ameer Nsair¹, Valentin Shabataev¹, Ariel Zisman¹

Canadian Journal of Urology, Vol.32, No.3, pp. 229-235, 2025, DOI:10.32604/cju.2025.063332 - 27 June 2025

Abstract Inflatable penile prosthesis (IPP) implantation is the gold standard treatment for patients with erectile dysfunction who are refractory to medical therapy. The standard placement of the reservoir in the space of Retzius (SOR) may be contraindicated in patients with prior pelvic or abdominal surgery due to altered anatomy and increased risk of complications. This has led to the development of alternative ectopic reservoir placement techniques. In this narrative review, we summarize the literature on various ectopic reservoir approaches, including low and high submuscular placements, submuscular techniques with counter incisions or transfascial fixation, midline submuscular placement, 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 >

Rudraneel Dutta^*, Shreya Mohanty

Journal on Artificial Intelligence, Vol.6, pp. 177-192, 2024, DOI:10.32604/jai.2024.050706 - 19 July 2024

Abstract Traditionally, exam preparation involves manually analyzing past question papers to identify and prioritize key topics. This research proposes a data-driven solution to automate this process using techniques like Document Layout Segmentation, Optical Character Recognition (OCR), and Latent Dirichlet Allocation (LDA) for topic modelling. This study aims to develop a system that utilizes machine learning and topic modelling to identify and rank key topics from historical exam papers, aiding students in efficient exam preparation. The research addresses the difficulty in exam preparation due to the manual and labour-intensive process of analyzing past exam papers to identify… More >

Pakorn Santakij¹, Samai Srisuay^2,*, Pongporn Punpeng¹

Computer Systems Science and Engineering, Vol.48, No.3, pp. 665-689, 2024, DOI:10.32604/csse.2024.045066 - 20 May 2024

Abstract Social media has revolutionized the dissemination of real-life information, serving as a robust platform for sharing life events. Twitter, characterized by its brevity and continuous flow of posts, has emerged as a crucial source for public health surveillance, offering valuable insights into public reactions during the COVID-19 pandemic. This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets. Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.… More >

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 - 30 January 2024

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… More >

Elias Hossain¹, Abdullah Alshahrani², Wahidur Rahman^3,*

Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 109-123, 2023, DOI:10.32604/iasc.2022.029511 - 05 February 2024

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)… More >

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 - 27 December 2023

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: More >