Digital misinformation presents a dual challenge: achieving high detection accuracy while ensuring interpretability. This paper introduces X-FRAME (Explainable FRAMing Engine), a hybrid framework that combines semantic representations from XLM-RoBERTa with theory-informed features related to psycholinguistic framing, source credibility, and social context. Unlike fact-checking systems that verify claims directly, X-FRAME detects linguistic, contextual, and stylistic indicators statistically associated with misinformation. Evaluated across eight publicly available datasets totaling $\mathrm{286,260}$ samples, X-FRAME achieves $86\%$ accuracy and $81\%$ recall on the minority Fake class, significantly outperforming text-only and features-only baselines. The model demonstrates cross-domain adaptability potential, attaining $97\%$ accuracy on formal news articles and $72\%$ on social media content. Importantly, X-FRAME provides transparent, human-understandable rationales via Local Interpretable Model-agnostic Explanations (LIME) and Permutation Importance, anchoring predictions in interpretable features such as sensationalism and speaker credibility. This work advances misinformation detection by unifying high performance with explainability and cross-domain adaptability.

Detection and classification have an essential role in the world of e-commerce applications. The recommendation method that is commonly used is based on information text attached to a product. This results in several recommendation errors caused by invalid text information. In this study, we propose the development of a fashion category (FC-YOLOv4) model in providing category recommendations to sellers based on fashion accessory images. The resulting model was then compared to YOLOv3 and YOLOv4 on mobile devices. The dataset we use is a collection of 13,689, which consists of five fashion categories and five accessories' categories. Accuracy and speed analysis were performed by looking at mean average precision (mAP) values, intersection over union (IoU), model size, loading time, average RAM usage, and maximum RAM usage. From the experimental results, an increase in mAP was obtained by 99.84% and an IoU of 88.49 when compared to YOLOv3 and YOLOv4. Based on these results, it can be seen that the models we propose can accurately identify fashion and accessories categories. The main advantage of this paper lies in i) providing a model with a high level of accuracy and ii) the experimental results presented on a smartphone.

Over the past few decades, research on object detection has developed rapidly, one of which can be seen in the fashion industry. Fast and accurate detection of an E-commerce fashion product is crucial to choosing the appropriate category. Nowadays, both new and second-hand clothing is provided by E-commerce sites for purchase. Therefore, when categorizing fashion clothing, it is essential to categorize it precisely, regardless of the cluttered background. We present recently acquired tiny product images with various resolutions, sizes, and positions datasets from the Shopee E-commerce (Thailand) website. This paper also proposes the Fashion Category—You Only Look Once version 4 model called FC-YOLOv4 for detecting multiclass fashion products. We used the semi-supervised learning approach to reduce image labeling time, and the number of resulting images is then increased through image augmentation. This approach results in reasonable Average Precision (AP), Mean Average Precision (mAP), True or False Positive (TP/FP), Recall, Intersection over Union (IoU), and reliable object detection. According to experimental findings, our model increases the mAP by 0.07 percent and 40.2 percent increment compared to the original YOLOv4 and YOLOv3. Experimental findings from our FC-YOLOv4 model demonstrate that it can effectively provide accurate fashion category detection for properly captured and clutter images compared to the YOLOv4 and YOLOv3 models.

This study aims to propose a new approach to semantic segmentation of sidewalk images in rice fields using the YOLOv8 algorithm, with the objective of enhancing agricultural monitoring and analysis. The experimental process involved preparing the development environment, extracting data from JSON, and training the model using YOLOv8. Evaluation reveals consistent and accurate sidewalk detection with a confidence score of 0.9-1.0 across various environmental conditions. Confusion matrix and precision-recall analysis confirmed the robustness and accuracy of the model. These findings validate the effectiveness of the approach in detecting and measuring sidewalks with high precision, potentially improving agricultural monitoring. The novelty of this study lies in the utilization of the RIFIS-D algorithm as an integral part of a hybrid approach with YOLOv8. This hybridization enriches the model with additional capability to detect the distance between the sidewalk and the tractor, addressing specific needs in agricultural applications. This contribution is significant in the advancement of automatic navigation and monitoring technology in agriculture, enabling the implementation of more sophisticated and efficient systems in field operations.

The use of Unmanned Aircraft has grown significantly in Thailand and worldwide, particularly for operations below 450 feet. However, unlike manned aviation, there remains a lack of integrated digital platforms to manage flight plans that align with regulatory and operational requirements specific to low altitude activity. This study employed both secondary research and expert interviews to gather technical and regulatory user requirements. The data were analyzed and validated using Structural Equation Modeling to identify key variables influencing safety operations. Based on these findings, a standardized low altitude flight plan format was developed and converted into a prototype web platform called GoFly. The system enables operators to register aircraft and pilot credentials and to submit flight plans digitally. This platform addresses the current fragmentation in Thailand’s flight planning process by centralizing operations and enhancing regulatory compliance. The study contributes to the foundational development of a digital Unmanned Aircraft Traffic Management system tailored for emerging airspace users in Thailand and demonstrates potential scalability to other international regulatory contexts.