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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 6,486 Documents
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Cognitive cyber-physical systems: integrating intelligence, adaptation, and real-time decision-making Tole Sutikno
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2265-2271

Abstract

Cognitive cyber-physical systems (CCPS) represent a critical inflection point in the evolution of intelligent engineering systems, where computation, learning, and physical interaction are no longer separated but fused within a continuous adaptive loop. This editorial positions CCPS as the intermediate layer between foundational computational intelligence and fully autonomous socio-cognitive systems, extending the broader IJECE 2026 intellectual trajectory. In contrast to conventional cyber-physical systems governed by predefined control logic, CCPS embed learning-driven intelligence directly into sensing, communication, and control processes, enabling continuous real-time adaptation in uncertain and dynamically evolving environments. Intelligence in this paradigm is realized as embodied intelligence through continuous interaction across computational and physical layers rather than remaining confined to localized algorithmic modules. Tight coupling among perception, decision-making, and actuation enables continuous refinement of internal representations through environmental feedback. Multi-scale learning mechanisms further support simultaneous fast-response adaptation and long-horizon behavioral optimization. As a result, classical feedback architectures are transformed into predictive cognitive loops capable of anticipatory, context-aware, and autonomous operation under uncertainty. CCPS therefore provide the engineering realization of embodied intelligence, establishing the foundational bridge between computational intelligence and the emergence of future socio-cognitive systems.
Real-time depth measurement and stability control of AUV using regression approximation and filtered pressure data Senanjung Prayoga; Dhaniel Beny Wardhana; Ryan Satria Wijaya
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2417-2430

Abstract

This paper presents the development and experimental validation of a prototype-scale autonomous underwater vehicle (AUV) depth control system using a proportional-integral-derivative (PID) controller with depth feedback from a SEN0257 water-pressure sensor. Raw sensor readings are filtered and calibrated using linear regression, reducing the depth estimation error, as indicated by a decrease in root mean square error (RMSE) from 1.88 to 0.63 cm. The calibrated depth signal is implemented in real time as the feedback source for closed-loop control on the testbed. Controller performance is evaluated by comparing two tuning strategies: Ziegler–Nichols (ZN) closed-loop tuning and manual fine-tuning. Experiments were conducted at depth setpoints of 70 and 100 cm under consistent pool conditions, and additional trials were performed while the AUV executes forward motion to assess robustness under dynamic disturbances. System responses are quantified using rise time, overshoot, settling time, and steady-state error. Results show that calibration significantly improves sensor suitability for feedback, while the fine-tuned PID controller produces a more stable depth response with lower overshoot, smaller steady-state error, and shorter settling time than the ZN controller, despite the faster initial rise achieved by ZN tuning. Overall, combining calibrated pressure-based depth estimation with fine-tuned PID gains enables stable and accurate depth regulation for prototype AUV operation.
A comparative analysis of hybrid FFNN-LSTM and FFNN-RNN architectures for short term electricity load forecasting Temitope Akinyede; Josephine Adenike Akinyede; Paul Kehinde Olulope; Emmanuel Taiwo Fasina; Temitope Adewale Olominu
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2347-2356

Abstract

Short-term accurate forecasting of electricity demand is crucial for power-system operation and energy scheduling and the equilibrium between electricity generation and consumption. The predicting of electric power consumption is however difficult because electricity-demand profiles are non-linear and time varying. In this paper, we consider two hybrid deep learning architectures feedforward neural network long short-term memory (FFNN-LSTM) and feedforward neural network recurrent neural network (FFNN-RNN) for multi-horizon electricity-load forecasting. The architectures we proposed combine the ability of FFNN to represent data non-linearly with the sequential modelling capability of LSTM and RNN. The authors assess the models based on historical electricity-load observations from Ado-Ekiti, at three different forecasting horizons of 24 hours, 72 hours, and 168 hours. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) assessed predictive performance. The findings reveal that FFNN-LSTM achieves a consistently lower RMSE across the evaluated horizons, resulting in greater effectiveness to confine relatively large forecasting errors. On the other hand, the separate LSTM and RNN models achieve lower MAE and MAPE in various scenarios, suggesting stronger short-term reaction to electricity demand. The results, therefore, show a trade-off between forecast stability and sensitivity to rapid load changes. In general, the performance of hybrid architecture is more stable compared to the standalone recurrent models which are more responsive to short-term changes when we look at most of the forecasting horizons. According to the research paper, the forecasting architectures can now be selected for smart grid applications.
Computer vision and artificial intelligence system for real-time detection of the use of personal protective equipment in industrial environments Sebastian Jeremy Alegre Mendoza; Luis Elias Mendoza Hueyta; Felix Rogelio Pucuhuayla-Revatta
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2731-2749

Abstract

Workplace safety in high-risk industrial sectors, such as heavy machinery maintenance, remains a critical challenge due to the improper use or omission of personal protective equipment (PPE). This study proposes an intelligent system based on computer vision and artificial intelligence, complemented by electronic devices for real-time detection and monitoring of helmet, vest, glove, and boot use. The research follows a quantitative approach with a quasi-experimental design, validated through controlled tests using a mannequin equipped with protective elements to simulate real working conditions. The system uses a camera connected to a Raspberry Pi running the YOLOv8 detection model, achieving 96.5% accuracy and 91.8% recall on a test set of 300 images. The model was trained on 2,250 images and validated with an additional 300. Detection results are transmitted to an ESP32 microcontroller, which controls local alerts through LEDs, an LCD screen, and a buzzer, as well as automatic notifications via Telegram over Wi-Fi. A sliding window filtering technique is applied to reduce false positives, improving monitoring reliability and reinforcing a preventative safety culture. It is concluded that integrating artificial intelligence into automated PPE detection offers an efficient, practical, and replicable solution for improving workplace safety in industrial environments.
Design science research in developing a religious chatbot based on Bulugh al-Maram Aris Tjahyanto; Irmasari Hafidz; Faizal Johan Atletiko
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2782-2794

Abstract

Chatbots have recently gained significant popularity. For instance, ChatGPT has become a preferred tool for many individuals seeking instant answers without relying on human responses. This immediacy sets chatbots apart from books, which require users to search for information manually. This time-consuming process does not align with millennials' preference for convenience and efficiency. Studying hadith independently using the Bulugh al-Maram book demands considerable time and effort. The limited use of natural language processing technologies in religious chatbots restricts their ability to handle complex inquiries effectively. A chatbot capable of answering hadith-related questions could greatly assist the public in studying hadith texts by providing direct responses without extensive searching. This chatbot was designed for web browsers, utilizing deep learning as its core technology. This research led to the development of a chatbot prototype for learning hadith from Bulugh al-Maram. Built using the design science research (DSR) methodology, the prototype achieves an intent recognition rate (IRR) of 86.82%. However, its capabilities are below the BERT model, demonstrating a strong ability to accurately interpret user questions and statements.
Performance and quality analysis of brain MRI image transmission over free-space optical communication systems under severe atmospheric conditions Entidhar Mhawes Zghair; Seham Hashem; Ali Hammadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2526-2536

Abstract

Reliable transfer of brain magnetic resonance imaging (MRI) data over atmospheric free-space optical (FSO) links is a key enabler of telemedicine, yet conventional FSO studies judge link quality by communication metrics such as the bit error rate (BER) alone, which cannot guarantee the structural and contrast fidelity that diagnosis demands. This study proposes a quality-aware FSO transmission framework for brain MRI in which link performance is assessed jointly through BER, peak signal-to-noise ratio (PSNR), and the structural similarity index (SSIM). A physical-layer FSO channel is modelled in OptiSystem 20 and co-simulated with MATLAB R2023b, which performs image serialization, reconstruction, and quality analysis. Thirty axial T2-weighted slices (256×256, 8-bit) from the public IXI dataset are transmitted at 1550 nm over clear-air, rain, and fog channels at 500, 1000, and 2000 m. Adopting conservative diagnostic thresholds of PSNR ≥ 30 dB and SSIM ≥ 0.85, the link is diagnostically usable in clear air at all tested distances (PSNR = 42.1 dB, SSIM = 0.98 at 500 m) and in rain up to 2000 m (PSNR ≥ 31.2 dB), whereas fog degrades quality below the thresholds at every distance, reaching PSNR = 22.7 dB and SSIM = 0.68 at 2000 m. A concatenated forward-error-correction (FEC) scheme is then shown to restore diagnostic quality under fog up to 1000 m, extending the usable fog range, while 2000 m remains infeasible and motivates hybrid FSO/RF operation. The framework provides quantitative deployment limits for FSO-based medical image transport.
CLARA: a unified hybrid ai-human receptionist framework using WebRTC and Monorepo architecture Dhanush Sridhar Babu; Nagashree Nagesh; Amble Nagendra Aashuthosh; Adithya Nimmala Chandra; Muthusamy Naveen Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2769-2781

Abstract

The rapid digitalization of enterprise infrastructure has caused a major change in institutional communication. This change requires the traditional front desk to transform into a high-tech center for security and logistics. However, current automated solutions often create what is known as the “automation paradox.” In this situation, removing human oversight increases the importance and complexity of the remaining interactions. This work outlines the design, implementation, and evaluation of conversational low-latency AI receptionist agent (CLARA). CLARA is a new hybrid system which solves the problems of unattended kiosks by combining a conversational AI, which uses the Google Gemini API, with a low-latency, real-time WebRTC video calling system. The design features a strong 3-Tier Monorepo built with React, TypeScript, Express.js, Socket.IO, and PostgreSQL. This setup ensures type safety and consistency in the code across the entire stack. Key contributions of this project include a custom Socket.IO signaling server for secure peer-to-peer connections, a “Human-in-the-Loop” workflow that reduces handoff issues, and an “Offline-First” strategy ensuring the system keeps working during network disruptions. Performance tests show that CLARA achieves less than 200 ms glass-to-glass latency and handles high traffic well, proving it is an effective and reliable solution for modern visitor management.
AI-driven PUE optimization in hyperscale data centers using real-time telemetry Hussein Yassin Al-Adawi
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2688-2697

Abstract

The rapid growth of hyperscale data centers has significantly increased energy consumption, making power usage effectiveness (PUE) one of the most critical metrics for evaluating operational efficiency and sustainability. This study presents a real-world case study of a hyperscale data center environment and investigates the potential of artificial intelligence (AI)-driven optimization using real-time telemetry data. Operational reports, rack-level power measurements, and facility energy consumption records were analyzed to evaluate current performance and identify opportunities for efficiency improvement. Historical operational analysis revealed a substantial reduction in PUE from approximately 2.70 under legacy operating conditions to 2.01 following infrastructure modernization and operational optimization initiatives. To support future efficiency improvements, a telemetry-driven AI optimization framework was developed as a decision-support architecture for adaptive cooling and energy management. The framework integrates real-time operational telemetry, including IT load, facility power consumption, environmental conditions, cooling demand, and historical PUE behavior, to support intelligent operational decision-making. A synthetic six-month time-series dataset with 5-minute intervals was generated based on observed operational patterns to enable long-term analytical evaluation. The primary contribution of this research is the development of a telemetry-driven AI optimization framework that combines operational data analysis, sustainability-oriented decision support, and adaptive cooling optimization within a hyperscale data center environment. Analytical evaluation and benchmark comparison indicate that AI-assisted optimization could potentially reduce PUE to a projected range between 1.4 and 1.6. These results represent optimization scenarios rather than experimentally validated production outcomes. The findings highlight the potential of AI-enabled infrastructure management to improve operational efficiency, sustainability performance, and strategic decision-making within hyperscale data center environments.
A compact tri-band THz patch antenna using slot-loaded radiator and defected ground structure Tran-Thi Bich Ngoc; Truong Thi Phuong Nhi
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2549-2558

Abstract

Terahertz (THz)- based antennas are essential for future 6G sensing and short range communications, especially when multi-band flexibility and compact di mensions are required. This paper presents a compact microstrip patch an tenna designed for THz applications with tri-band operation. The antenna is formed out of a rectangular patch filled with slots, a defective ground structure (DGS), and a microstrip feedline. Slot loading and the DGS generate various current paths, exciting several resonant modes and enhancing impedance match ing across the resonant frequency range. The proposed design is implemented on a 30 µm thick RO3003 substrate with a relative permittivity of 3 and loss tan gent of 0.001. The antenna has physical dimensions of 0.27×0.28×0.03 mm3. Simulation results show the antenna operates at three frequencies with a remark ably low return loss of up to-61.35 dB over the range of 1.59-3.2 THz. It offers tri-band gains of 6.01 dBi, 7.30 dBi, and 7.31 dBi, with a voltage standing wave ratio (VSWR) close to the ideal value of 1 for most bands. Furthermore, field distributions and power breakdown calculations are performed to better under stand the radiation mechanisms and efficiency characteristics at THz frequen cies. The antenna radiated power across the band, achieving up to 86.5% of the power accepted. These results indicate that the suggested design provides tri-band capabilities while being a simple, compact and efficient candidate for the demand for improved antennas in next-generation wireless communication systems.
Incremental pattern mining with novelty-based pruning for dynamic analysis and adaptive e-learning recommendations Emad Mahmoud Alazazi; Ahmed Sultan Alhegami
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2607-2621

Abstract

Traditional pattern mining algorithms become increasingly inefficient in dynamic e learning environments, where learner interactions accumulate continuously. When new data arrive, these algorithms typically discard previously discovered patterns and recompute everything from scratch—a costly process that this work seeks to avoid. The proposed incremental intelligent maximal frequent itemsets (IIMFIs) framework integrates three complementary mechanisms: incremental maintenance, maximal pattern extraction, and novelty based pruning. Rather than restarting computation with each new batch, IIMFIs stores and selectively updates previously discovered patterns. The novelty threshold \theta controls pruning of highly similar candidates, further reducing the search space. Experiments on the OULAD dataset split chronologically into four batches—demonstrate several advantages. First, IIMFIs compresses the pattern set considerably: at the lowest support threshold, it reduces 2,597 patterns generated by classical algorithms to only 342, achieving a compression factor of 7.6×. Second, incremental updates achieve a 4.2× speedup over full restart and run 28% faster than restarting FP Growth. Third, activating novelty pruning (θ=0.7) improves recommendation precision by 15.5% while maintaining diversity above 0.95. Notably, F1@10 scores remain identical to those obtained from full recomputation, confirming that compactness does not compromise quality. These results demonstrate that IIMFIs provides an efficient and scalable solution for adaptive e-learning environments.

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