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K-link Application

K-link Application

Abstract

A platform that aims to connect students from all faculties and departments to promote joint activities and develop effective social and collaborative skills, focusing on: Promoting learning and self-development through reviewing lessons and collaborative learning that are relevant to all faculties and departments in the university, creating a space for negotiation and exchange of knowledge, and supporting joint activities to build relationships and cooperation among students.

Objective

จากสถิติโดยเฉลี่ยแล้วในแต่ละวันคนเราจะพบเจอคนแปลกหน้าประมาณ 47 คน นั้นเท่ากับว่าเรา จะพบเจอคนหน้าตาใหม่ๆมากถึง 17,155 คน ต่อปี ซึ่งในการพบเจอกันของผู้คนโดยปกติแล้วเป็นเรื่องที่ เป็นไปไม่ได้เลยที่จะเจอคนที่ชอบหรือมีความต้องการอะไรคล้ายๆกัน เราจึงเกิดแนวคิดที่จะนำพาคนเหล่านั้นให้มาเจอกันได้ง่ายขึ้นจากการสร้างแอปพลิเคชัน ที่จะ ส่งเสริมการทำกิจกรรมร่วมกันของนักศึกษาทั้งจาก ต่างคณะและภายในคณะ เดียวกัน และยังส่งเสริมการ ทบทวนบทเรียน ทำแบบฝึกหัด เพื่อพัฒนาความรู้ในบทเรียนร่วมกัน ที่จะมีรายวิชาจากทุกคณะ และทุก สาขาภายใต้คอนเซ็ปต์ที่ว่า KMITL GO and grow up together

Other Innovations

French Parisian Bathroom Model

คณะสถาปัตยกรรม ศิลปะและการออกแบบ

French Parisian Bathroom Model

The design and construction of a detailed bathroom model with structural components aim to provide a comprehensive understanding of plumbing and electrical systems in bathrooms. This project enables learners to study the intricacies of bathroom infrastructure through a highly detailed model.

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KinderForest : Puzzle Building Game with VR Technology

คณะเทคโนโลยีสารสนเทศ

KinderForest : Puzzle Building Game with VR Technology

KinderForest : Puzzle Building Game with VR Technology is designed to utilize Virtual Reality (VR) technology with the primary aim of promoting creative problem-solving skills and basic practical application abilities among players. This project presents the game in an Augmented Virtual Reality (AR VR) format, emphasizing physical engagement of players during gameplay while fostering creativity and fundamental application skills. The project team has chosen to utilize Unreal Engine 5.1 and Oculus Quest 2 virtual reality glasses to develop the game in the form of augmented virtual reality technology. Within the game, there will be various levels that require creative thinking and different approaches to pass. Time constraints will be a crucial element in completing missions and progressing through these levels. Players will physically move their bodies in response to in-game movements. Each level will present unique challenges that will necessitate both physical movement and problem-solving skills. The game will provide different rewards based on the outcomes of mission completion, and players will be informed of their results once they have successfully passed a level.

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Detection of Durian Leaf Diseases Using Image Analysis and Artificial Intelligence

คณะเทคโนโลยีการเกษตร

Detection of Durian Leaf Diseases Using Image Analysis and Artificial Intelligence

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.

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