Lung cancer is a leading cause of mortality worldwide, and early detection is crucial in improving treatment outcomes and reducing death rates. However, diagnosing medical images, such as Computed Tomography scans (CT scans), is complex and requires a high level of expertise. This study focuses on developing and evaluating the performance of Convolutional Neural Network (CNN) models, specifically the Visual Geometry Group 16 (VGG16) architecture, to classify lung cancer CT scan images into three categories: Normal, Benign, and Malignant. The dataset used consists of 1097 CT images from 110 patients, categorized according to these severity levels. The research methodology began with data collection and preparation, followed by training and testing the VGG16 model and comparing its performance with other CNN architectures, including Residual Network with 50 layers (ResNet50), Inception Version 3 (InceptionV3), and Mobile Neural Network Version 2 (MobileNetV2). The experimental results indicate that VGG16 achieved the highest classification performance, with a Test Accuracy of 98.18%, surpassing the other models. This accuracy highlights VGG16’s strong potential as a supportive diagnostic tool in medical imaging. However, a limitation of this study is the dataset size, which may reduce model accuracy when applied to new data. Future studies should consider increasing the dataset size, using Data Augmentation techniques, fine-tuning model parameters, and employing advanced models such as 3D CNN or Vision Transformers. Additionally, incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) to interpret model decisions would enhance transparency and reliability. This study confirms the potential of CNNs, particularly VGG16, for classifying lung cancer CT images and provides a foundation for further development in medical applications.

This research evaluates the performance of various sentiment analysis models, including traditional machine learning approaches (Naive Bayes, KNN, CART), a deep learning model (LSTM), and the transformer-based model RoBERTa using an Amazon book reviews dataset. ROBERTa outperformed all other models, achieving an accuracy of 96.30% and an F1-score of 98.11%, underscoring its superior ability to process complex and semantically diverse textual data. Traditional models, while computationally efficient, demonstrated limitations in capturing nuanced textual relationships, and the LSTM model, although competitive, faced scalability challenges and overfitting issues. These results demonstrate how transformer-based architectures such as RoBERTa offer advantages in real-world applications, particularly in e-commerce and social media sentiment analysis. This study underscores the superior capabilities of RoBERTa for sentiment analysis, particularly in processing semantically diverse and context-rich textual data that traditional models struggle to capture. Future work will explore optimizing RoBERTa's computational efficiency and expanding its applications to multilingual and cross-domain sentiment analysis tasks.

Introduction: Museums face challenges in managing and retrieving timely news content due to fragmented information systems. This study investigates how semantic web technologies can enhance contextual accuracy and accessibility in museum information retrieval.Methods: We created a domain-specific ontology integrated with relational databases via Ontology-Based Data Access (OBDA). A semantic search system accepting natural language queries was implemented and evaluated by experts using standard information retrieval metrics.Results: The system achieved strong performance with precision of 0,85, recall of 0,96, and F1-score of 0,88, demonstrating effective semantic retrieval of museum news.Conclusions: The findings demonstrate that semantic web technologies improve the accessibility and contextual relevance of museum news, contributing to digital heritage information management.

Purpose/Background: Small and medium-sized enterprises (SMEs) that deploy Enterprise Resource Planning (ERP) systems face a persistent paradox: although ERP centralises organisational data, frontline users frequently lack the technical expertise to navigate complex menu structures, preventing efficient information retrieval for decision-making. This study presents a preliminary Research and Development (R&D) effort to design, build, and qualitatively evaluate a prototype intelligent question-answering system that connects to Odoo ERP through application programming interfaces (APIs) via Retrieval-Augmented Generation (RAG), enabling natural-language access to real SME business data. Methods: A single-organisation R&D case study was conducted at an SME in Khon Kaen Province, Thailand. The development cycle comprised problem analysis and requirement specification, system architecture design, prototype construction, integration and deployment, iterative testing and refinement, and multidimensional evaluation. The prototype was implemented with Chainlit (conversational interface), FastAPI (orchestration and tool-calling layer), and Odoo XML-RPC/JSON-RPC APIs (structured data retrieval). A fixed set of 20 test questions spanning four complexity levels (easy, moderate, complex, out-of-scope) was evaluated by three automated tools (OpenAI Evals, DeepEval, Ragas), by real-task verification against live ERP data, by five domain experts using 5-point Likert-scale questionnaires, and by three end users from the case-study organisation, who additionally completed the System Usability Scale (SUS). Given the very small expert and user samples, all human evaluation results are reported descriptively as preliminary, exploratory indicators rather than as statistically generalisable measures. Results: Automated evaluation achieved indicative pass rates of 95.00% (OpenAI Evals, 19/20), 90.00% (Ragas, 18/20), and 85.00% (DeepEval, 17/20). Descriptive expert feedback (n = 5) yielded an overall mean of 3.82 (high level), and descriptive end-user feedback (n = 3) yielded an overall satisfaction mean of 4.33 (highest level). The SUS score was 66.67/100, sitting at the boundary between ‘OK’ and ‘Good’ and revealing a divergence between high stated satisfaction and lower confidence in independent system use (item 9, raw mean = 2.33) and a stronger perceived need for expert assistance (item 4, raw mean = 2.67). These results are interpreted as preliminary diagnostic signals for further development rather than as confirmatory evidence. Conclusions: This preliminary R&D study suggests that a RAG-based ERP chatbot can meaningfully simplify ERP data access for SME users, while exposing persistent gaps in multi-step reasoning, user confidence, and data privacy boundaries that must be addressed in subsequent development cycles. The SUS pattern, in particular, suggests a ‘novelty effect’ in which users are enthusiastic about the natural-language interface yet remain anxious about correctness and stability during real tasks. Originality/Value: This work contributes a transparent, replicable preliminary R&D blueprint that combines (i) live API-mediated RAG over structured ERP data, (ii) a complementary multi-tool automated evaluation set (OpenAI Evals + DeepEval + Ragas), (iii) descriptive expert and end-user feedback, and (iv) SUS-based usability assessment, all documented for a single Thai SME using Odoo 18. The study explicitly positions itself as an early step toward larger, multi-site, and on-premise deployments of trustworthy ERP-integrated conversational agents.