Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Advances in Applied Sciences International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics
Therdpong Daengsi
Rajamangala University of Technology Phra Nakhon
Earth & Planetary Sciences Education Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Critical success factors for smart-professional disruptor in university Phisit Pornpongtechavanich; Kawitsara Eumbunnapong; Therdpong Daengsi; Prachyanun Nilsook
International Journal of Evaluation and Research in Education (IJERE) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v11i4.22197

Abstract

Current and emerging technologies have changed a lot. Consequently, every year, Gartner Technology has made many new changes in accordance with global developments. For example, in terms of artificial intelligence (AI), mixed reality (MR), extended reality (XR), collaboration platforms, online learning, distributed cloud, internet of behaviors (IoB), and cybersecurity. Due to changes in technology, disruptors have to constantly learn new technology in order to be up to date in the transfer of knowledge to learners. Therefore, in this research, critical success factors (CSFs) have been studied, which help them become highly skilled professionals by developing their own skills with technology to be a successful disruptor at university. The study found the CSFs, which were derived from the synthesis of international research papers. Disruptors' success consists of 12 internal and 10 external success factors. Smart-professional disruptors in universities were assessed using a focus group method with eight experts. Focus group results found that there were seven important internal factors for smart-professional disruptors in universities and seven minor internal factors. Including all internal factors, smart-professional disruptors have 14 factors; external factors are the most important ones for smart-professional disruptors in universities. In total, smart-professional disruptors have a total of 11 external factors.
An integrated EHS-based risk assessment framework for hazardous substance storage: regulatory gap analysis in Thailand Hengheng Jarunongkran; Therdpong Daengsi
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.pp555-572

Abstract

This study examines hazardous substance storage risk assessment within the environmental, health, and safety (EHS) framework, focusing on Thailand’s regulatory context. Using a systematic review and comparative regulatory analysis, the research evaluates existing risk assessment methodologies and identifies structural and implementation gaps in current practices. The findings reveal deficiencies in the integration of toxicological data with legal requirements, fragmented regulatory enforcement, limited standardization, and insufficient practical tools for workplace application. A comparative assessment of international frameworks, including European Union Registration, Evaluation, Authorization, and Restriction of Chemicals (EU REACH), United States Occupational Safety and Health Administration (US OSHA), and Japan’s Chemical Substances Control Law (CSCL), highlights best practices in proactive risk management and transparency. Based on these insights, the study proposes an integrated EHS-based risk assessment framework that aligns Department of Industrial Works (DIW) regulations with international standards such as ISO 14001, ISO 45001, and globally harmonized system (GHS). The proposed model supports proactive risk identification, regulatory coherence, and improved stakeholder awareness, contributing to enhanced chemical safety governance and ASEAN harmonization.
Ransomware and artificial intelligence: a comprehensive systematic review of reviews Therdpong Daengsi; Phisit Pornpongtechavanich; Paradorn Boonpoor; Kathawut Wattanachukul; Korn Puangnak; Kritphon Phanrattanachai; Pongpisit Wuttidittachotti; Paramate Horkaew
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 Hengheng Jarunongkran Kathawut Wattanachukul Kawitsara Eumbunnapong Korn Puangnak Kritphon Phanrattanachai Paradorn Boonpoor Paramate Horkaew Phisit Pornpongtechavanich Phisit Pornpongtechavanich Pongpisit Wuttidittachotti Prachyanun Nilsook