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All Journal International Journal of Advances in Applied Sciences
Wong See Wan
Vise Trading
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Program defect prediction model based on topology aware node evaluation pool graph topology model Dan Li; Poh Soon JosephNg; Peng Yin Choo; Koo Yuen Phan; Wong See Wan
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp583-593

Abstract

Traditional fuzzing struggles with efficiency, as maximizing code coverage does not guarantee the discovery of additional vulnerabilities. To solve this, the study introduces topology-aware node evaluation (TANE-Pool), a deep learning model that proactively predicts defects to guide the fuzzing process. The model analyzes the structural properties of a program’s attributed control flow graph (ACFG) via a diffusion attention mechanism. This process identifies fragile code regions and generates a static vulnerability score (SVS) for each basic block. The fuzzer then uses this score to prioritize test cases, concentrating its efforts on the areas most likely to contain flaws. Evaluated on the Juliet test suite and a set of real-world programs, TANE Pool demonstrates superior prediction accuracy. Its integration into a fuzzer significantly enhances the rate of vulnerability discovery, proving that a defect-prediction-guided approach is a more efficient and effective strategy for software security testing.
Artificial intelligence-powered image recognition retail checkout systems Malyssa Alias; Dhaifina Saidi; Lim Jia Huey; Lee Qing Fang; Durghaashini S. Ragunathan; JosephNg Poh Soon; Phan Koo Yuen; Lim Jit Theam; Wong See Wan
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp187-196

Abstract

The integration of artificial intelligence (AI) with big data analytics leads to substantial transformations in the retail sector. This research explores the impact of AI-powered image recognition checkout systems on the retail industry, focusing on operational efficiency, customer experience, and resource waste. Employing a mixed-methods approach, this study combines usability testing and data analytics to assess the viability of this technology in attaining automation and accuracy in retail operations. The study focuses on the creation of robust, resource-efficient systems that foster long-term industrial growth. The findings show that AI-powered solutions not only speed the checkout process but also contribute to sustainable infrastructure by reducing resource consumption and increasing energy efficiency. This report offers significant information, like the impact of AI-powered image recognition checkout systems on operational efficiency, customer experience, and the role of AI in promoting sustainable infrastructure for retailers and governments looking to advance the digitalization of the retail industry.
Co-Authors Dan Li Dhaifina Saidi Durghaashini S. Ragunathan JosephNg Poh Soon Koo Yuen Phan Lee Qing Fang Lim Jia Huey Lim Jit Theam Malyssa Alias Peng Yin Choo Phan Koo Yuen Poh Soon JosephNg