Discover the ultimate innovations of the future developed by Thai researchers! Meet the latest technology from KMITL that will transform our way of life and industry.
คณะแพทยศาสตร์
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.
คณะวิศวกรรมศาสตร์
This project aims to develop an AI-powered system for detecting and classifying wall cracks using image processing. It identifies different crack types, assesses severity, and ensures accuracy across various image conditions. The goal is to support preventive maintenance by enabling early detection of structural issues, reducing repair costs, and improving safety.
คณะแพทยศาสตร์
Background: The RGL3 gene plays a role in key signal transduction pathways and has been implicated in hypertension risk through the identification of a copy number variant deletion in exon 6. Genome-wide association studies have highlighted RGL3 as associated with hypertension, providing insights into the genetic underpinnings of the condition and its protective effects on cardiovascular health. Despite these findings, there is a lack of data that confirms the precise role of RGL3 in hypertension. Additionally, the functional impact of certain variants, particularly those classified as variants of uncertain significance, remains poorly understood. Objectives: This study aims to analyze alterations in the RGL3 protein structure caused by mutations and validate the location of the ligand binding sites. Methods: Clinical variants of the RGL3 gene were obtained from NCBI ClinVar. Variants of uncertain significance and likely benign were analyzed. Multiple sequence alignment was conducted using BioEdit v7.7.1. AlphaFold 2 predicted the wild-type and mutant 3D structures, followed by quality assessment via PROCHECK. Functional domain analysis of RasGEF, RASGEF_NTER, and RA domains was performed, and BIOVIA Discovery Studio Visualizer 2024 was used to evaluate structural and physicochemical changes. Results: The analysis of 81 RGL3 variants identified 5 likely benign and 76 variants of uncertain significance (VUS), all of which were missense mutations. Structural modeling using AlphaFold 2 revealed three key domains: RasGEF_NTER, RasGEF, and RA, where mutations induced conformational changes. Ramachandran plot validation confirmed 79.7% of residues in favored regions, indicating an overall reliable structure. Moreover, mutations within RasGEF and RA domains altered polarity, charge, and stability, suggesting potential functional disruptions. These findings provide insight into the structural consequences of RGL3 mutations, contributing to further functional assessments. Discussion & Conclusion: The identified RGL3 mutations induced physicochemical alterations in key domains, affecting charge, polarity, hydrophobicity, and flexibility. These changes likely disrupt interactions with Ras-like GTPases, impairing GDP-GTP exchange and cellular signaling. Structural analysis highlighted mutations in RasGEF and RA domains that may interfere with activation states, potentially affecting protein function and stability. These findings suggest that mutations in RGL3 could have functional consequences, emphasizing the need for further molecular and functional studies to explore their pathogenic potential.
คณะแพทยศาสตร์
Migraine, a prevalent neurological disorder, is the third most common disease globally, causing significant health and financial burdens. It has four stages: prodrome, aura, headache, and postdrome. The prodrome (also known as premonitory) stage is crucial as it precedes the headache by up to 72 hours. Taking medication during the premonitory peroid has shown to prevent the headache phase . However, the symptoms of premonitory period lack specificity, making it difficult for patients to know if they’re experiencing premonitory symptoms. Calcitonin-gene related peptide (cGRP),is a protein that plays a key role in migraine pathogenesis and studies found that salivary cGRP levels increase during the premonitory stage. This study aims to develop and evaluate a lateral flow immunoassay kit for detecting salivary cGRP levels in migraine patients during the prodrome stage. It can serve as a confirmation tool for premonitory symptoms.
คณะวิศวกรรมศาสตร์
The Diabetes Meal Management Application is a digital health tool designed to empower Type 2 diabetic patients in managing their diet and blood sugar levels more effectively. With features like personalized meal recommendations, nutrient tracking, and seamless integration with wearable blood glucose monitors via Blood sugar measuring device (CGM), the application enables users to monitor glucose fluctuations in real time and adjust dietary choices accordingly. Built with the Flutter framework and supported by a backend of Express.js and MongoDB, the application prioritizes a user-friendly interface, ensuring easy navigation and encouraging consistent engagement with meal planning and health tracking. Preliminary user trials show that the application contributes to more stable blood sugar levels and improved adherence to dietary recommendations, helping users reduce health risks associated with diabetes complications. By offering a proactive approach to diabetes management, the application reduces the need for frequent clinical interventions, thus potentially lowering medical costs over time. This project highlights the promising role of digital health solutions in supporting personalized diabetes care, emphasizing the potential for scalable, user-centered interventions that foster long-term health improvements for diabetic patients.
คณะวิทยาศาสตร์
Developing a Smart Farming Simulation Utilizing LoRa Communication and Presenting Knowledge on LoRa Communication System Components
คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.
คณะวิศวกรรมศาสตร์
This project presents an interactive kiosk system designed to facilitate students, staff, and visitors within the university campus. The kiosk provides real-time event updates, news, and university document access via QR codes or email. It integrates a 3D map of the engineering department with navigation assistance, allowing users to locate offices and other facilities efficiently. Additionally, it features a room booking system, enabling users to reserve spaces through an online platform and check in via QR code scanning at the kiosk. By integrating digital technology and smart urban solutions, this system enhances accessibility, campus management, and visitor experience.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
This project aims to study the load transfer in timber building structures by analyzing weight distribution across key structural components such as beams, columns, and floors, as well as the load-bearing behavior of wood under different conditions. The research incorporates structural calculations and modeling to examine load transfer patterns. Additionally, it enhances skills in design, analysis, and teamwork, providing practical knowledge applicable to real-world construction projects.