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All Journal Journal of ICT Research and Applications Journal of Applied Data Sciences
Natsheh, Essam
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Enhancing Field-Controlled DC Motors with Artificial Intelligence-Infused Fuzzy Logic Controller Natsheh, Essam
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.508

Abstract

Servomotors play a pivotal role in a wide array of everyday and industrial applications. Field-controlled DC motors particularly stand out for positioning tasks owing to their advantageous speed-torque characteristics. An optical encoder, integrated with the rotor, provides feedback to a PID controller, which in turn generates corrective signals for precise motor positioning. To enhance response speed and minimize hunting, the PID controller incorporates fuzzy logic programming. This paper introduces a novel optimization design approach utilizing a Performance-Oriented Rule-Based Controller (PDFCS) in conjunction with various PID fuzzy controller design methods to attain specific performance goals. Given the criticality of constructing membership functions in fuzzy controllers, a self-optimized membership functions algorithm is proposed. Accuracy analysis demonstrates that the proposed design method achieves a 2.9-second reduction in rise time, a 2.0-second decrease in settling time, and a 1.9% reduction in overshoot compared to conventional design methods. Furthermore, robustness analysis reveals a 4.0-second improvement in rise time, a 1.7-second enhancement in settling time, and a 0.79% decrease in overshoot. These findings underscore the superior accuracy and robustness of employing the proposed performance model alongside various PID fuzzy controller design methods, compared to relying solely on conventional design approaches.
AI-enhanced Cybersecurity Risk Assessment with Multi-Fuzzy Inference Natsheh, Essam; Tabook, Fatima Bakhit
Journal of ICT Research and Applications Vol. 19 No. 1 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.1.1

Abstract

The pace and complexity of modern cyber-attacks expose the limits of traditional ‘impact × likelihood’ risk matrices, which compress uncertainty into coarse categories and miss inter-dependent threat dynamics. We propose a three-layer multi-fuzzy inference system (MFIS) that models general infrastructure vulnerabilities and access-control weaknesses separately, then fuses them into a single, continuous 0-25 risk score. The framework was validated on three representative scenarios—catastrophic/continuous, serious/frequent, and minor/few attacks—encompassing sixteen threat criteria. Compared with a crisp 5 × 5 matrix, MFIS cut mean-absolute error and root-mean-square error by 90 to 99% and reproduced expert-panel judgments to within 0.55 points across all scenarios. Nine independent practitioners rated the prototype highly on usability (100% agreement), credibility (100%) and actionability (100%), with 78% willing to recommend adoption. These results demonstrate that MFIS delivers fine-grained, expert-aligned assessments without adding operational complexity, making it a viable drop-in replacement for time- or resource-constrained organizations. By capturing partial memberships and cross-domain interactions, MFIS offers a more faithful, adaptive and explainable basis for prioritizing cyber-defense investments and can be extended to emerging threat domains with modest rule-base updates.
Co-Authors Tabook, Fatima Bakhit