Dòng Nội dung
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Processing and classifying IP packet data on the Internet based on machine learning / Vuong Xuan Chi, Nguyen Kim Quoc // . - . - . - ISSN: 2615-9015
 // Tạp chí Khoa học và Công nghệ - Đại học Nguyễn Tất Thành . - . - tr. 01-11. - ISSN:



Ký hiệu phân loại (DDC): 004.6
Nowadays, the continuous development of information technology, communication over the Internet is increasing rapidly, and network congestion has become an alarming issue. To develop communication network infrastructure in a large city, a country, or globally, streamlining and controlling network data flow to optimize communication processes and minimize network congestion is crucial and necessary. In this study, the authors analyze and process data according to the delay of Internet Protocol (IP) packets, using machine learning models with the Random Forest (RF) and the Support Vector Machines (SVM) method to classify IP packets. The primary goal of classifying packets by delay is to optimize network performance by prioritizing processing of low-delay packets, ensuring stable and uninterrupted online services such as video streaming and voice calls. Furthermore, it is easy to manage and control packet traffic, hence minimizing network congestion at the router.
Số bản sách: (0) Tài liệu số: (1)
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UGGNet : Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis / Tran Cao Minh, Nguyen Kim Quoc, Phan Cong Vinh, Dang Nhu Phu, [...] // EAI Endorsed Transactions on Contex-aware Systems and Applications. - . - . - ISSN: 2409-0026

DOAJ, 2024
8 tr. : picture, tables ; 24 cm.
Ký hiệu phân loại (DDC): 616
In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for the early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2% on the "Breast Ultrasound Images Dataset."
 Số bản sách: (0) Tài liệu số: (1)