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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,393 Documents
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Hybrid systems modelling and control using multiple mixed logical dynamical predictive model control: Application to a three-tank spherical system Benaissa, Tahar; Belazreg, Mohamed Fouzi; Halbaoui, Khaled; Djaroum, Belaid; Boukhetala, Djamel
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1148-1158

Abstract

This study employs the mixed logical dynamical (MLD) framework for modelling, simulating, and controlling hybrid dynamical systems. Hybrid systems, which combine continuous-time dynamics and discrete logical events, pose significant challenges for conventional control strategies, such as proportional-integral-derivative (PID) controllers, particularly under complex operational constraints. To address these challenges, the MLD formalism provides a unified representation that integrates differential equations, logical rules, and inequality constraints. Based on the MLD model, a multivariable hybrid model predictive control (HMPC) approach is designed to optimize control system performance and operational efficiency over a prediction time horizon. At each sampling time step, a mixed quadratic programming (MIQP) optimization problem is solved online to determine the control law. The proposed control approach is applied to a three-spherical tank system, where simulation and experimental results demonstrate its effectiveness in ensuring stability, minimizing tracking errors, and satisfying physical constraints. These results underscore the relevance of MLD-based predictive control approaches for the optimization and advanced control of complex multivariable hybrid dynamical systems in industrial fields.
Designing self-healing database fabrics for real-time payment rails Gollapudi, Raghu
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1360-1368

Abstract

Real-time payment platforms operating at scale face an unforgiving operational reality: even brief outages translate directly into failed transactions, regulatory exposure, and eroded customer trust. Database replication and failover automation have matured considerably over the past two decades, yet a troubling blind spot remains. Recovery frameworks built for general-purpose distributed systems were never designed with settlement finality in mind, and that design omission leaves payment operators exposed to split-brain scenarios that generic high-availability tooling cannot reliably prevent. This paper addresses that omission head-on through a self-healing database fabric purpose-built for payment rail environments. The proposed autonomous resilience fabric architecture (ARFA) operates across three coordinated layers: a continuous monitoring layer that harvests telemetry from compute, storage, and network subsystems; a decision layer that fuses rule-based heuristics with an ensemble of isolation forests, recurrent neural networks, and gradient boosting classifiers to separate genuine fault conditions from transient noise; and a deterministic action layer that executes recovery procedures anchored to explicit settlement finality constraints. In fault injection trials covering node crashes, network partitions, replication lag, and performance degradation, the architecture cut average recovery times by 88% against manual baselines, restoring service in roughly 8 seconds rather than the 180 seconds that human-driven remediation typically requires. False positive rates held below 2% across all failure categories, and the system achieved a 98% recovery success rate. Taken together, these results make a practical case that autonomous resilience and regulatory compliance reinforce rather than conflict with each other when the regulatory constraints are designed in from the start.
AI-driven log reduction and storage optimization for security operations Chalaemwongwan, Nutthakorn
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1417-1424

Abstract

In this study, we present an AI-driven framework that integrates semantic log reduction with compliance-aware storage optimization, specifically designed for security operations center (SOC) and managed security service provider (MSSP) environments. Traditional approaches such as uniform compression, keyword filtering, and static tiering often either miss critical anomalies or preserve redundant noise, leading to excessive storage use, slower search performance, and analyst fatigue. The proposed framework addresses these challenges by combining three components: semantic reduction of repetitive entries, anomaly-focused retention supported by self-supervised models, and adaptive tiering aligned with regulatory requirements. Evaluations on HDFS, BGL, CICIDS2017, and Suricata datasets achieved 70%–80% log reduction, 55%–65% storage savings, recall rates above 95%, and a one-third reduction in query latency. These results demonstrate that pre-index reduction, together with anomaly- and compliance-aware retention, offers a scalable and regulator-ready solution for operational security environments.

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