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All Journal Bulletin of Electrical Engineering and Informatics
Puangnak, Korn
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Electrical & Electronics Engineering

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Comparative analysis of 5G network performance at Thailand's premier shopping centers Daengsi, Therdpong; Srimuk, Pachara; Puangnak, Korn; Phanthuna, Nattapong; Prajong, Amnaj; Pornpongtechavanich, Phisit
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10011

Abstract

This paper evaluates 5G network performance across three well-known shopping malls in Bangkok: Icon Siam, Siam Paragon, and CentralWorld. The study focuses on assessing key quality of service (QoS) metrics, consisting of download (DL) speed, upload (UL) speed, and latency. Measurements were taken in various zones within each mall; including high, ground, and outdoor areas through field tests using two different mobile network operators (MNO-1 and MNO-2). The findings indicate noticeable differences in performance, with Icon Siam recording the highest average DL speed of 273.6 Mbps (MNO-1) and the outdoor zone at Siam Paragon having the lowest at 11.2 Mbps (MNO-2). While MNO-1 provided more stable UL speeds, MNO-2 showed greater variability. Latency results also highlighted MNO-1’s stronger network efficiency, often staying below 20 ms, apart from a slight increase in outdoor areas. Statistical analyses, using ANOVA and t-Test, revealed significant disparities in QoS parameters depending on location and MNO, with outdoor areas often underperforming. These results underline the importance of in-building distributed antenna systems (IB-DAS) and improved infrastructure for boosting 5G performance. Furthermore, this study offers insights that can be useful to improve network quality in high-traffic locations.
Ransomware and artificial intelligence: a comprehensive systematic review of reviews Daengsi, Therdpong; Pornpongtechavanich, Phisit; Boonpoor, Paradorn; Wattanachukul, Kathawut; Puangnak, Korn; Phanrattanachai, Kritphon; Wuttidittachotti, Pongpisit; Horkaew, Paramate
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11107

Abstract

This study provides a comprehensive synthesis of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) in ransomware defense. Using a “review of reviews” methodology based on the PRISMA, this paper gathers insights on how AI is transforming ransomware detection, prevention, and mitigation strategies in the past five years (2020-2024). The findings highlight the effectiveness of hybrid models, which combine multiple analysis techniques such as code inspection (static analysis) and behavior monitoring during execution (dynamic analysis). The study also explores anomaly detection and early warning mechanisms before encryption that tackle ransomware’s growing complexity. It also examines key challenges in ransomware defense, such as techniques designed to deceive AI driven detection, and the lack of strong and diverse datasets. It highlights AI’s role in early detection and real-time response systems, enhancing scalability and resilience. With the systematic review of reviews approach, the contributions of this study are systematically consolidating research insights from multiple review articles, identifying effective AI models, and bridging theory with practice to foster collaboration among academia, industry, and policymakers. Future research directions are anticipated and practical recommendations for cybersecurity practitioners are provided. Finally, it presents a roadmap for advancing AI-driven countermeasures, for the protection of key systems and infrastructures against evolving ransomware threats.
Co-Authors Boonpoor, Paradorn Daengsi, Therdpong Horkaew, Paramate Phanrattanachai, Kritphon Phanthuna, Nattapong Pornpongtechavanich, Phisit Prajong, Amnaj Srimuk, Pachara Wattanachukul, Kathawut Wuttidittachotti, Pongpisit