Journal of Computer Science Research

Innovating Artificial Intelligence for Workforce Preparation and Knowledge Development

Shuo Xu — Wed, 19 Jun 2024 00:00:00 +0800

Artificial intelligence (AI) transforms workplaces by streamlining operations, automating tasks, and enhancing decision-making. To bridge the knowledge gap in AI best practices, a workshop was created for executives, integrating change management principles. The workshop aimed to help participants understand AI's role, use AI tools for predictive analytics, and develop strategies for leveraging AI in change initiatives. This paper outlines the workshop's impact on building confidence, knowledge, and positive attitudes towards AI in the workplace.

Research on the Spatiotemporal Distribution Relationship between Regional Rainfall and Taxi Supply in Singapore

Yuqi Wang — Mon, 01 Jul 2024 00:00:00 +0800

This quantitative correlational study intends to investigate the spatiotemporal relationship between rainfall and taxi supply in Singapore. Over the period of 4 months, coordinates of all available taxis in Singapore, as well as rain value data from 66 weather stations located around the island, were collected every minute from public Application Programming Interfaces (API). Singapore was divided into a grid of 3km by 2km rectangles, with each region minutely assigned a taxi supply count and a rain value weighted based on distance to the weather station. To obtain groups where taxis behaved similarly, the data on weekends and weekdays were separated, then divided spatially and temporally. A non-linear correlation coefficient was calculated for each category. It was hypothesized that rainfall notably reduces taxi supply in most regions, an effect most pronounced in the evening rush hours (18:00 – 21:00) on all days of the week. The results do not fully validate this hypothesis, displaying that though taxi supply levels were generally decreased in situations with rainfall, they could likewise reach low levels in scenarios without.

A Multi-model Fusion Strategy for Android Malware Detection Based on Machine Learning Algorithms

Shuguang Xiong, Huitao Zhang — Wed, 05 Jun 2024 00:00:00 +0800

In the digital age, the widespread use of Android devices has led to a surge in security threats, especially malware. Android, as the most popular mobile operating system, is a primary target for malicious actors. Conventional antivirus solutions often fall short in identifying new, modified, or zero-day attacks. To address this, researchers have explored various approaches for Android malware detection, including static and dynamic analysis, as well as machine learning (ML) techniques. However, traditional single-model ML approaches have limitations in generalizing across diverse malware behaviors. To overcome this, a multi-model fusion approach is proposed in this paper. The approach integrates multiple machine learning models, including logistic regression, decision tree, and K-nearest neighbors, to improve detection accuracy. Experimental results demonstrate that the fusion method outperforms individual models, offering a more balanced and robust approach to Android malware detection. This methodology showcases the potential of ensemble techniques in enhancing prediction accuracy, providing valuable insights for future research in cybersecurity.