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• Solution for Taking Attendance of Students using Smartphone Devices

  Tran Trung Hieu, Pham Quang Dung, Hoang Thi Ha, Le Thi Minh Thuy, Phan Trong Tien

  Date Received: 11-11-2024 / Date Accepted: 31-07-2025 / Date Published: 31-07-2025

   Abstract PDF (VIETNAMESE)

  In this article, we analyze the attendance activities, server resources, smartphones, and internet usage of lecturers and students at the Vietnam National University of Agriculture (VNUA), with the aim of proposing an information technology solution that leverages available resources, optimizes costs, and provides a convenient solution to meet the attendance needs of both students and lecturers of the VNUA. By method of investigation and collection of data, method of analysis and synthesis of theory related to technologies, experimental research, professional methods, we have successfully built a Website using Spring Boot Restful Web Services, ReactJS, SignaturePad technology, MTCNN (Multi-Task Cascaded Convolutional Neural Networks) face detection algorithms, FaceNet feature extraction, SVC (Support Vector Classification) recognition model training support VNUA lecturers and students to easily take attendance using other optional methods ranging from simple to complex. The website is currently being tested to apply to attendance activities in classes of the VNUA's Faculty of Information Technology.

  DOI: https://doi.org/10.31817/tckhnnvn.2025.23.7.08

• Content-based Image Retrieval with Convolutional Neural Networks and Binary Hashing Method

  Nguyen Thi Huyen, Tran Thi Thu Huyen, Vu Thi Luu

  Date Received: 20-07-2020 / Date Accepted: 02-09-2020

   Abstract PDF (VIETNAMESE)

  Content-based image retrieval has received great attention in recent years because this method overcomes the disadvantages of the text-based image retrieval that is not affected by the lack of or wrong of the text attached to the image. In addition, deep learning methods such as convolutional neural networks have demonstrated their ability to process large-sized data, especially computer vision and image processing. The aims of this study was develop a content-based image retrieval program and method to reduce image query time using the convolutional neural network (CNN). Also, we combined CNN with a binary hashing method to improve image retrieval time. The experimental results on CIFAR-10 and MNIST data sets showed that combining CNN with the binary hashing method for content-based image retrieval achieved an accuracy of approximately 89% on CIFAR-10,98% on MNIST and significantly improved retrieval time.