คอลเล็กชัน (Collections)
พบ 6 รายการ
(ค้นหา: "classification")
บทความประชุมวิชาการ
ระดับนานาชาติ
GraphBERT4FakeNews: Graph-aware Bidirectional Transformer Learning for English Fake News Detection
The rapid dissemination of fake news through online news platforms and social media has become a major challenge, significantly influencing public opinion, political decision-making, and social stability. Recent fake news detection methods have achieved promising performance using deep contextual language models such as Bidirectional Encoder Representations from Transformers (BERT). However, most existing approaches process news articles independently and primarily focus on textual semantics while overlooking structural relationships among related news articles. Consequently, valuable graph-based information, including semantic similarity, shared entities, common publishers, and topical relationships, remains underutilized. This paper proposes GraphBERT4FakeNews, a graph transformer framework that jointly learns contextual semantic representations and graph structural information for English fake news detection. First, BERT generates contextualized embeddings from news articles. A news graph is then constructed by connecting semantically related articles according to contextual similarity. Subsequently, a GraphBERT encoder performs graph-aware self-attention to capture both local and long-range structural dependencies without relying on conventional graph convolution operations. Finally, a classification layer predicts whether a news article is genuine or fake. The proposed framework is evaluated on the publicly available FakeNewsNet benchmark, including the GossipCop and PolitiFact datasets. Experimental results demonstrate that GraphBERT4FakeNews effectively integrates semantic understanding with graph structural reasoning, leading to improved fake news detection performance compared with conventional language models and graph neural network approaches.
บทความประชุมวิชาการ
ระดับนานาชาติ
CGRN4FakeNews: Convolutional Gated Recurrent Network for Fake News Detection
This study aims to (1) examine the effectiveness of the Convolutional Gated Recurrent Network for
Fake News Detection (CGRN4FakeNews) model in fake news classification and (2) compare its performance
with baseline methods across different datasets. The model was evaluated using the PolitiFact and
GossipCop subsets of the FakeNewsNet dataset. The research process involved text preprocessing and data
cleaning, including word tokenization, lowercasing, the removal of URLs, HTML tags, numerical values,
punctuation marks, and redundant whitespace, as well as stopword handling. The processed text data were
then transformed into vector representations using a word embedding technique for model training and
evaluation. The model performance was assessed based on Accuracy, Precision, Recall, F1-score, and
Confusion Matrix metrics. The experimental results indicate that the proposed CGRN4FakeNews model
effectively distinguishes between real and fake news articles, achieving classification accuracies of 95.75%
on the PolitiFact dataset and 91.12% on the GossipCop dataset. These findings demonstrate the
effectiveness and suitability of the CGRN4FakeNews model for fake news detection tasks.
บทความประชุมวิชาการ
ระดับนานาชาติ
BERTGRU4Sentiment: Bidirectional Encoder Representations from Transformers with Gated Recurrent Unit
This study proposes BERTGRU4Sentiment, a deep learning model that combines fine-tuned Bidirectional Encoder Representations from Transformers (BERT) with a Gated Recurrent Unit (GRU) for multilingual sentiment analysis. The proposed model is evaluated on four benchmark datasets: Wongnai (Thai), Amazon Reviews (English), Twitter/X (English), and IMDB (English), covering both three-class (negative, neutral, positive) and two-class (negative, positive) classification tasks. To address class imbalance, a mixed over- and under-sampling strategy is applied to all datasets. Results demonstrate that BERTGRU4Sentiment consistently outperforms traditional baseline models including Decision Tree, Random Forest, XGBoost, and LSTM across all datasets, achieving accuracy of 69.49% on Wongnai, 93.67% on Amazon Reviews, 71.73% on Twitter, and 83.97% on IMDB. The findings confirm that BERTGRU4Sentiment enables richer contextual feature extraction, and the proposed architecture is effective for multilingual sentiment classification tasks.
วารสารวิชาการ
ระดับนานาชาติ (SCOPUS) Q2
Digital Workforce Matching: A Machine Learning Approach for Skill-Based Job Classification and Recom
This research presents an integrated machine learning approach for optimizing digital workforce matching in Thailand's evolving digital economy. The study develops a novel job recommendation system combining Natural Language Processing (NLP) with Random Forest classification to analyze job market data from Thailand's leading recruitment platforms. Using FastText for initial job classification and a Random Forest model for skill-based matching, the system achieves 75% accuracy in job recommendations across 20 digital job categories. The methodology incorporates automated skill extraction, cross-validated model comparison, and a user-friendly web interface for practical applications. Our findings reveal distinct skill clusters and job-skill relationships in Thailand's digital sector, with the Random Forest model outperforming traditional Decision Tree approaches by 4% in accuracy metrics. The system demonstrates robust performance in real-world testing, achieving 86.67% accuracy in matching previously unseen job postings. This research contributes to both theoretical understanding of skill-based job matching and practical workforce development, offering insights for curriculum development and career planning for workforce development stakeholders in Thailand's digital sector.
วารสารวิชาการ
ระดับชาติ (TCI) TCI กลุ่ม 2
Efficiency Comparison of Classification Models for Pali and Sanskrit in Thai Languages Using Machine
งานวิจัยนี้มีวัตถุประสงค์เพื่อทดสอบและเปรียบเทียบประสิทธิภาพของแบบจำลองการจำแนกคำบาลีและสันสกฤตในภาษาไทยโดยใช้เทคนิคการเรียนรู้ของเครื่อง เน้นการพัฒนาความแม่นยำในการแยกแยะคำศัพท์ที่มาจากสองภาษานี้ ซึ่งมีความใกล้เคียงกันในด้านการออกเสียงและการเขียน ในการวิจัยใช้แบบจำลอง 5 ชนิด ได้แก่ แบบจำลองป่าไม้สุ่ม แบบจำลองต้นไม้ตัดสินใจ แบบจำลองการคำนวณเพื่อนบ้านใกล้สุด แบบจำลองนาอีฟเบย์ และแบบจำลองซัพพอร์ตเวกเตอร์แมชชีน ผ่านกระบวนการตรวจสอบ 10-fold cross-validation เพื่อวัดประสิทธิภาพของแต่ละแบบจำลอง ผลการวิจัยพบว่า แบบจำลองซัพพอร์ตเวกเตอร์แมชชีน มีความสามารถสูงสุดในการจำแนกคำบาลีและสันสกฤตในภาษาไทย โดยมีค่าความแม่นยำสูงถึงร้อยละ 95.75 และค่าความถูกต้องร้อยละ 90.90 รองลงมา คือ แบบจำลองการคำนวณเพื่อนบ้านใกล้สุดมีค่าความแม่นยำร้อยละ 92.86 และแบบจำลองนาอีฟเบย์ที่มีค่าความแม่นยำร้อยละ 81.29 ขณะที่แบบจำลองแบบจำลองป่าไม้สุ่มมีประสิทธิภาพต่ำที่สุด โดยมีค่าความแม่นยำเพียงร้อยละ 55.27 ข้อค้นพบเหล่านี้แสดงให้เห็นถึงความเหมาะสมของการใช้แบบจำลองซัพพอร์ตเวกเตอร์แมชชีน ในการแยกแยะภาษาบาลีและสันสกฤตได้อย่างมีประสิทธิภาพ และสามารถนำผลลัพธ์ไปประยุกต์ใช้ในงานด้านการจำแนกภาษา การแปลภาษา รวมถึงการพัฒนาเครื่องมือด้านการเรียนการสอนภาษาในอนาคต
วารสารวิชาการ
ระดับนานาชาติ (SCOPUS) Q3
Comparison of capability of data classification models to predict consistent results for depression
This research compares the capability of data classification models to predict consistent results for a subject’s depression potentiality, track the subject behaviour and recognise facial expressions during PHQ-9 assessments. This research is motivated by the necessity for depression screening and diagnosis, which traditionally relies on observations by experienced physicians or clinical psychologists of symptoms in conjunction with data from questionnaires. However, the field still requires a suitable technological approach that gives more accurate and consistent results. All data used in the present research were collected by combining technologies and compared by using classification models, the goal being to find the machine-learning model that most accurately predicts consistent results for the subjects’ PHQ-9 assessment, behaviours and emotions. The subjects were screened by clinical psychologists and divided into three groups: (i) subjects suffering from depression but not receiving treatment (undertreated subjects), (ii) subjects undergoing depression treatment (subjects undergoing treatment) and (iii) subjects without depression disorder (normal subjects). Related studies have compared the accuracy of classification models to one another. The four most frequently applied classification models in depression-related studies are (i) decision tree (ii) support vector machine, (iii) naïve Bayes and (iv) neural network. All models were analysed, designed and developed before being tested experimentally. The accuracy of the experimental results was tested by using the data analysis tool RapidMiner Studio. The results show that the decision tree model is not only the most accurate for predicting depression potentiality, tracking behaviour and recognising facial expressions during PHQ-9 assessments but also the most suitable.