arXiv subjects

Ngoc Hong Tran

Publications and source records attributed to Ngoc Hong Tran.

4 recordsLinked to original sources

arXiv2026

Evaluating Automated Testing on an Open-Source Web Application Using Cypress

End-to-end automated testing is increasingly used in web software development to ensure system quality and shorten response times during development. However, the true effectiveness of automated testing depends on many factors including execution time, stability of test results, and maintainability of the test suite as the application continues to evolve. In this paper, we evaluate the effectiveness of end-to-end automated testing using the Cypress framework for an open-source web application. We deployed the experiment with 27 test cases. The test's effectiveness is measured by execution speed, reliability, and maintainability. The experimental results show that the Cypress-based end-to-end test suite has short and stable execution times. It suits frequent runs during software development. The majority of test cases achieved consistent results across multiple runs, while flakiness only occurred in a few tests which involve complex interactive functions. Furthermore, the study highlights the impact of element locator strategies and Page Object Model (POM) architecture on test suite maintainability, demonstrating that resilient data-cy attributes significantly reduce maintenance overhead when UI changes occur.

cs.SE↗

arXiv2025

Application of machine learning models to predict the relationship between air pollution, ecosystem degradation, and health disparities and lung cancer in Vietnam

Lung cancer is one of the major causes of death worldwide, and Vietnam is not an exception. This disease is the second most common type of cancer globally and the second most common cause of death in Vietnam, just after liver cancer, with 23,797 fatal cases and 26,262 new cases, or 14.4% of the disease in 2020. Recently, with rising disease rates in Vietnam causing a huge public health burden, lung cancer continues to hold the top position in attention and care. Especially together with climate change, under a variety of types of pollution, deforestation, and modern lifestyles, lung cancer risks are on red alert, particularly in Vietnam. To understand more about the severe disease sources in Vietnam from a diversity of key factors, including environmental features and the current health state, with a particular emphasis on Vietnam's distinct socioeconomic and ecological context, we utilize large datasets such as patient health records and environmental indicators containing necessary information, such as deforestation rate, green cover rate, air pollution, and lung cancer risks, that is collected from well-known governmental sharing websites. Then, we process and connect them and apply analytical methods (heatmap, information gain, p-value, spearman correlation) to determine causal correlations influencing lung cancer risks. Moreover, we deploy machine learning (ML) models (Decision Tree, Random Forest, Support Vector Machine, K-mean clustering) to discover cancer risk patterns. Our experimental results, leveraged by the aforementioned ML models to identify the disease patterns, are promising, particularly, the models as Random Forest, SVM, and PCA are working well on the datasets and give high accuracy (99%), however, the K means clustering has very low accuracy (10%) and does not fit the datasets.

cs.LG↗

arXiv2019

A Security-Aware Access Model for Data-Driven EHR System

Digital healthcare systems are very popular lately, as they provide a variety of helpful means to monitor people's health state as well as to protect people against an unexpected health situation. These systems contain a huge amount of personal information in a form of electronic health records that are not allowed to be disclosed to unauthorized users. Hence, health data and information need to be protected against attacks and thefts. In this paper, we propose a secure distributed architecture for healthcare data storage and analysis. It uses a novel security model to rigorously control permissions of accessing sensitive data in the system, as well as to protect the transmitted data between distributed system servers and nodes. The model also satisfies the NIST security requirements. Thorough experimental results show that the model is very promising.

cs.CR↗

arXiv2018

An Effective Privacy-Preserving Data Coding in Peer-To-Peer Network

Coding Opportunistically (COPE) is a simple but very effective data coding mechanism in the wireless network. However, COPE leaves risks for attackers easily getting the private information saved in the packets, when they move through the network to their destination nodes. Hence in our work, a lightweight cryptographic approach, namely SCOPE, is proposed to consolidate COPE against the honest-but-curious and malicious attacks. Honest-but-curious attack serves adversaries who accurately obey the protocol but try to learn as much private information as possible for their curiosity. Additionally, this kind of attack is not destructive consequently. However, it may leave the backdoor for the more dangerous attacks carrying catastrophes to the system. Malicious attack tries to learn not only the private information but also modifies the packet on harmful purposes. In our work, the SCOPE protocol is defensive to the both attacks. The private information in the COPE packet are encrypted by Elliptic Curve Cryptography (ECC), and an additional information is inserted into SCOPE packets served for the authentication process using the lightweight hash Elliptic Curve Digital Signature Algorithm (ECDSA). We then prove our new protocol is still guaranteed to be a secure method of data coding, and to be light to effectively operate in the peer-to-peer wireless network

cs.CR↗