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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Paramate Horkaew
Suranaree University of Technology
Computer Science & IT Electrical & Electronics Engineering

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Structural adaptive anisotropic recursive filter for blind medical image deconvolution Tanawat Kwanpak; Paramate Horkaew
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 2: June 2019
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (846.872 KB) | DOI: 10.52549/ijeei.v7i2.869

Abstract

Performance of radiographic diagnosis and therapeutic intervention heavily depends on the quality of acquired images. Over decades, a range of pre-processing for image enhancement has been explored. Among the most recent proposals is iterative blinded image deconvolution, which aims to identify the inheritant point spread function, degrading images during acquisition. Thus far, the technique has been known for its poor convergence and stability and was recently superseded by non-negativity and support constraints recursive image filtering. However, the latter requires a priori on intrinsic properties of imaging sensor, e.g., distribution, noise floor and field of view. Most importantly, since homogeneity assumption was implied by deconvolution, recovered degrading function was global, disregarding fidelity of underlying objects. This paper proposes a modified recursive filtering with similar non-negativity constraints, but also taking into account local anisotropic structure of content. The experiment reported herein demonstrates its superior convergence property, while also preserving crucial image feature.
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 Kathawut Wattanachukul Korn Puangnak Kritphon Phanrattanachai Paradorn Boonpoor Phisit Pornpongtechavanich Pongpisit Wuttidittachotti Tanawat Kwanpak Therdpong Daengsi