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.
คณะวิศวกรรมศาสตร์
Currently, lithium batteries are widely used in electronic devices and electric vehicles, making the estimation of their State of Health (SOH) crucial. Accurate SOH estimation helps extend battery lifespan, reduce maintenance costs, and prevent safety issues such as overheating or explosions. This project aims to study and analyze mathematical models of batteries and develop SOH estimation techniques using Neural Networks to enhance accuracy and evaluation speed. The experiment involved collecting charge and discharge data from three lithium battery cells under controlled temperature conditions while maintaining a constant current. The current, voltage, and time data were recorded and analyzed to determine the battery capacity for each cycle. These data were then used to train a Neural Network model. The results demonstrated an effective method for predicting battery health status. The outcomes of this project can contribute to the development of a Battery Management System (BMS) that improves battery efficiency and longevity. Additionally, it provides a foundation for applying artificial intelligence techniques in the energy sector effectively.
คณะอุตสาหกรรมอาหาร
Bio-calcium powders were extracted from Asian sea bass bone by heat-treated alkaline with fat removal and bleaching supplementary method. Cereal bars (CBs) were fortified with produced bio-calcium at 3 levels: (1) increased calcium (IS-Ca; calcium ≥10% Thai RDI), (2) good source of calcium (GS-Ca; calcium ≥15% Thai RDI), and (3) high calcium (H-Ca; calcium ≥30% Thai RDI) which were consistent with the notification of the Ministry of Public Health, Thailand: No. 445; Nutrition claim issued in B.E. 2023. Moisture content, water activity, color, calcium content and FTIR analysis of bio-calcium powders were measured. Dimension, color, water activity, pH and texture of fortified CBs were determined. Produced bio-calcium could be classified as a dried food with light yellow-white color. Calcium contents in bio-calcium powder was 23.4% (w/w). Dimension, weight and color except b* and ΔE* values of fortified CBs were not different (P > 0.05) from those of the control. Fortifying of bio-calcium resulted in harder texture CBs. An increase of fortified bio-calcium amounts decreased carbohydrate and fat but increased of protein, ash and calcium in the fortified CBs. Shelf life of CBs was to be shorten by fortification of bio-calcium powder because of the increment of moisture, water activity and pH. Yield of bio-calcium production was 40.30%. Production cost of bio-calcium was approximately 7,416 Bth/kg while cost of fortified CBs increased almost 2-3 times compared to the control. Calcium contents in IS-Ca (921.12 mg/100g), GS-Ca (1,287.10 mg/100g) and H-Ca (2,639.70 mg/100g) cereal bars could be claimed as increased calcium, good source of calcium and high calcium, respectively. In conclusion, production of cereal bar fortified with Asian sea bass bone bio-calcium powder as a fortified food was possible. However, checking the remained hazardous reagents in bio-calcium powder must be carried out before using in food products and analysis of calcium bioavailability, sensory acceptance and shelf life of the developed products should be determined in further studies.
วิทยาลัยการจัดการนวัตกรรมและอุตสาหกรรม
Diabetes is a significant global health issue, particularly due to complications related to diabetic wounds. Studies indicate that approximately 15-25% of diabetic patients develop foot ulcers, with more than 50% of severe cases leading to amputation. This results in a substantial decline in the quality of life for patients. Current treatments for diabetic wounds face challenges such as antibiotic-resistant bacterial infections and delayed wound healing, highlighting the need for innovative solutions to accelerate the healing process and reduce the risk of limb loss. Cotylelobium lanceolatum Craib, a medicinal plant long utilized in traditional Thai medicine, is known for its anti-inflammatory and wound-healing properties. This study focuses on developing an extract from Cotylelobium lanceolatum Craib in the form of nano silver (Nano Silver) to enhance the effectiveness of diabetic wound treatment. Nano silver technology enables deeper penetration into the skin, provides potent antibacterial activity, and promotes wound healing by reducing inflammation and stimulating tissue regeneration. The development of nano silver derived from Cotylelobium lanceolatum Craib extract is expected to help reduce chronic wounds in diabetic patients, lower the risk of infection, and decrease the incidence of limb amputation and mortality associated with diabetic wound complications. This research represents a significant step toward creating a safer and more effective treatment alternative for diabetic wound care.