This study explores the application of deep convolutional neural networks (CNNs) for accurate pill identification, addressing the limitations of traditional human-based methods. Using a dataset of 1,250 images across 10 household remedy drugs, various CNN architectures, including YOLO models, were tested under different conditions. Results showed that natural lighting was optimal for imprinted pills, while a lightbox improved detection for plain pills. The YOLOv5-tiny model demonstrated the best detection accuracy, and efficientNet_b0 achieved the highest classification performance. While the model showed strong results, its generalization is limited by sample size and drug variability. Nonetheless, this approach holds promise for enhancing medication safety and reducing errors in outpatient care.
The increasing complexity of pharmaceutical treatments requires precise pill identification to ensure patient safety. Traditional methods for pill reconciliation rely on human experts, which are time-consuming and prone to errors. Deep Convolutional Neural Networks (CNNs), particularly effective in image processing, offer a promising solution for automating and enhancing these processes.
คณะเทคโนโลยีสารสนเทศ
Facial Expression Recognition (FER) has attracted considerable attention in fields such as healthcare, customer service, and behavior analysis. However, challenges remain in developing a robust system capable of adapting to various environments and dynamic situations. In this study, the researchers introduced an Ensemble Learning approach to merge outputs from multiple models trained in specific conditions, allowing the system to retain old information while efficiently learning new data. This technique is advantageous in terms of training time and resource usage, as it reduces the need to retrain a new model entirely when faced with new conditions. Instead, new specialized models can be added to the Ensemble system with minimal resource requirements. The study explores two main approaches to Ensemble Learning: averaging outputs from dedicated models trained under specific scenarios and using Mixture of Experts (MoE), a technique that combines multiple models each specialized in different situations. Experimental results showed that Mixture of Experts (MoE) performs more effectively than the Averaging Ensemble method for emotion classification in all scenarios. The MoE system achieved an average accuracy of 84.41% on the CK+ dataset, 54.20% on Oulu-CASIA, and 61.66% on RAVDESS, surpassing the 71.64%, 44.99%, and 57.60% achieved by Averaging Ensemble in these datasets, respectively. These results demonstrate MoE’s ability to accurately select the model specialized for each specific scenario, enhancing the system’s capacity to handle more complex environments.
คณะวิทยาศาสตร์
Photocatalytic materials decorated with bi-metallic nanoparticles (Bi-Metallic NPs/ photocatalyst) was synthesized for the degradation of aflatoxin B1. Bi-metallic NPs/ photocatalyst were synthesized by ultrasonic irradiation. The as-synthesized was characterized the chemical characteristics by the transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction analysis (XRD), Fourier transform infrared spectrometer (FT-IR), zeta potential analyzer, and UV-visible spectrophotometer. Bi-metallic NPs/photocatalyst was used to evaluate the degradation efficiency of AFB1 in household wastewater under visible light. The degradation process was analyzed using high-performance liquid chromatography (HPLC) at a wavelength of 365 nm, revealing that AFB1 was completely degraded 100% within 2 minutes. This superior performance is attributed to its highly porous structure, increased specific surface area, and reduced electron-hole recombination rate, which demonstrate that the developed nanomaterial has successfully achieved AFB1 degradation.
วิทยาลัยการจัดการนวัตกรรมและอุตสาหกรรม
The Age-Defying Dates Palm Serum is an innovative skincare product formulated with date palm extract, known for its high antioxidant content that helps reduce wrinkles and retain skin moisture. Combined with hyaluronic acid, the serum enhances hydration and skin elasticity. This research explores factors influencing consumers’ purchasing intentions, revealing a preference for anti-aging properties, product safety, and natural ingredients. This serum aims to provide a novel option in the skincare market by utilizing high-quality natural extracts.