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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 6,301 Documents
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Optimizing hourly air quality index forecasting: a particle swarm optimization-enhanced hybrid approach combining convolutional and recurrent neural networks Khan, Darakhshan; Patankar, Archana B.; Kakar, Jyotika
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp333-341

Abstract

Air pollution is still a serious worldwide issue, and accurate air quality index (AQI) prediction is needed. This paper proposes a hybrid deep learning model integrating 1D convolutional neural networks (Conv1D) and long short-term memory (LSTM) networks, optimized with particle swarm optimization (PSO) to enhance AQI forecasting. The model was evaluated at six urban areas: Bandra, Thane, Mazgaon, Kurla, Nerul, and Malad, and compared with a single LSTM network. PSO adjusted hyperparameters like hidden units, batch size, epochs, and learning rate was used to improve predictive accuracy. The Conv1D+LSTM hybrid model drastically decreased RMSE by 49.19% (Bandra), 33.97% (Thane), 5.24% (Mazgaon), 20.52% (Kurla), 35.85% (Nerul), and 27.54% (Malad), and R² Score improvements up to 751.2%. Training logs indicated smoother convergence with loss decrease at faster rates compared to LSTM, showing better learning efficiency and generalization. By combining spatial and temporal feature extraction with automated hyperparameter tuning, this model captures sophisticated pollution patterns which increases the reliability of AQI prediction. Enhancements in the future can be adding regularization methods and more feature inputs to improve the accuracy.
Facial emotion recognition under face mask occlusion using vision transformers Maghari, Ashraf Yunis; Telbani, Ameer M.
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp395-403

Abstract

Facial emotion recognition (FER) systems face significant challenges when individuals wear face masks, as critical facial regions are occluded. This paper addresses this limitation by employing vision transformers (ViT), which offer a promising alternative with reduced computational complexity compared to traditional deep learning methods. We propose a ViT-based FER framework that fine-tunes a pre-trained ViT architecture to enhance emotion recognition under mask-induced occlusion. The model is fine-tuned and evaluated on the AffectNet dataset, which originally represents eight emotion categories. These categories are restructured into five broader classes to mitigate the impact of occluded features. The model’s performance is assessed using standard metrics, including accuracy, precision, recall, and F1 score. Experimental results demonstrate that the proposed framework achieves an accuracy of 81%, outperforming several state-of-the-art approaches. These findings highlight the potential of vision transformers in recognizing emotions under masked conditions and support the development of more robust FER systems for real-world applications in healthcare, surveillance, and human–computer interaction. This work introduces a scalable and effective approach that integrates self-attention, synthetic mask augmentation, and emotion class restructuring to improve emotion recognition under facial occlusion.
IDPS: A machine learning framework for real-time intrusion detection and protection system for malicious internet activity Fabiha, Raisa; Reberio, Stein Joachim; Farazi, Zubayer; Nur, Fernaz Narin; Sultana, Shaheena; Islam, A. H. M. Saiful
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp437-449

Abstract

With the increasing frequency and complexity of cyber threats, there is a pressing need for effective real-time solutions to detect and prevent malicious activities. This study introduces a novel machine learning-based architecture for real-time cybersecurity to enhance accurate identification and prevention of malicious cyber activities. The proposed framework combines advanced machine learning algorithms with Wireshark network traffic analysis to effectively detect and classify a wide range of cyberattacks, providing timely and actionable insights to cybersecurity professionals. A core component of this system is a prototype blocker, which is seamlessly integrated with Cisco infrastructure, enabling proactive intervention by blocking suspicious IP addresses in real-time. In addition, a user-friendly web application enhances system operability by offering intuitive data visualization and analytical tools, enabling rapid and informed decision-making. This comprehensive approach not only strengthens network security and protects digital assets but also equips defenders with the capability to respond effectively to the dynamic landscape of cyber threats.
Application of the model reference adaptive system method in sensorless control for elevator drive systems using 3-Phase permanent magnet synchronous motors Khoi, Tran Van; Anh, An Thi Hoai Thu; Hieu, Tran Trong
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp149-157

Abstract

Improving sensorless control performance in elevator drive systems using three-phase permanent magnet synchronous motors (PMSM) has become increasingly popular to reduce costs and enhance system stability. The primary operation of the elevator involves motor mode when the cabin moves upward and shifts to generator mode or braking mode under the influence of gravity when moving downward. This presents significant challenges for sensorless control. To address these issues, the model reference adaptive system (MRAS) based on the mathematical d-q axis model of the PMSM is proposed to estimate rotor speed and position. Combined with field-oriented control (FOC), this method optimizes performance and precisely controls motor torque without requiring physical sensors. Additionally, a low-pass filter is employed to process input signals, such as voltage and current, to improve estimation accuracy and optimize speed response. Simulation results from MATLAB/Simulink demonstrate highly accurate speed responses, particularly under continuous load variations.
Autonomous mobile robot implementation for final assembly material delivery system Firdaus, Ahmad Riyad; Sholihuddin, Imam; Hutasoit, Fania Putri; Naba, Agus; Suciningtyas, Ika Karlina Laila Nur
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp158-173

Abstract

This study presents the development and implementation of an autonomous mobile robot (AMR) system for material delivery in a final assembly environment. The AMR replaces conventional transport methods by autonomously moving trolleys between the warehouse, production stations, and recycling areas, thereby reducing human intervention in repetitive logistics tasks. The proposed system integrates a laser-SLAM navigation approach, customized trolley design, RoboShop programming, and robot dispatch system coordination, enabling real-time route planning, obstacle detection, and material scheduling. Experimental validation demonstrated high accuracy in path following, with root mean square error values ranging between 0.001 to 0.020 meters. The AMR achieved an average travel distance of 118.81 meters and a cycle time of 566.90 seconds across three final assembly stations. The overall efficiency reached 57%, primarily due to reduced idle time and optimized material replenishment. These results confirm the feasibility of AMR deployment as a scalable and flexible intralogistics solution, supporting the transition toward Industry 4.0 smart manufacturing systems.
Deep learning architecture for detection of fetal heart anomalies Ansari, Nusrat Jawed Iqbal; Edinburgh, Maniroja M.; Nikita, Nikita
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp414-422

Abstract

Research has demonstrated that artificial intelligence (AI) techniques have shown tremendous potential over the past decade for analyzing and detecting anomalies in the fetal heart during ultrasound tests. Despite their potential, the adoption of these algorithms remains limited due to concerns over patient privacy, the scarcity of large well-annotated datasets and challenges in achieving high accuracy. This research aims to overcome these limitations by proposing an optimal solution. Two methods such as deterministic image augmentation techniques and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) showcase the framework's capacity to seamlessly and effectively expand original datasets to 14 times and 17 times respectively, thereby effectively tackling the problem of data scarcity. It uses an annotation tool to precisely categorize anomalies identified in the echocardiogram dataset. Segmentation of the annotated data is done to highlight region of interest. Nine distinct fetal heart anomalies are identified with respect to the fewer covered in existing research. This study also investigates the state-of-the-art architectures and optimization techniques used in deep learning models. The results clearly indicate that the ResNet-101 model demonstrated superior precision accuracy of 99.15%. To ensure the reliability of the proposed model, its performance underwent thorough evaluation and validation by certified gynecologists and fetal medicine specialists.
Efficiency enhancement of off-grid solar system Kumar, Satish; Ansari, Asif Jamil; Singh, Anil Kumar; Gangwar, Deepak
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp111-120

Abstract

This paper presents the design and implementation of a sensor-enabled off-grid solar charge controller aimed at maximizing the utilization of renewable energy. The proposed system integrates solar and load power sensors to minimize solar energy wastage. A microcontroller is employed to efficiently monitor and regulate battery voltage, solar power generation, and load demand. This system is designed to optimize solar energy usage, reduce dependency on the electrical grid, and lower electricity bills. Additionally, a main supply controller board with a display is introduced, along with a smart scheduler for appliance management. Prior to deployment, total solar power wastage was recorded at 93.1 watts per day. After implementing the proposed solution, wastage was reduced to 13.1 watts per day—reflecting an 85.92% reduction. These results confirm the system’s effectiveness in reducing energy loss, increasing self-consumption, and promoting energy sustainability in off-grid environments. It is important to note that this value may vary based on factors such as temperature, cloud cover, fog, and irradiation levels.
Artificial intelligence of things solution for Spirulina cultivation control Elbaati, Abdelkarim; Kobbi, Mariem; Afli, Jihene; Chiha, Abdelrahim; Amor, Riadh Haj; Neji, Bilel; Beyrouthy, Taha; Krichen, Youssef; Alimi, Adel M.
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp488-504

Abstract

In the evolving field of Spirulina cultivation, the integration of the internet of things (IoT) has facilitated the optimization of spirulina growth and significantly enhanced biomass yield in the culture medium. This study outlines a control open-pond system for Spirulina cultivation that employs generative artificial intelligence (AI) and edge computing within an IoT framework. This transformative approach maintains optimal conditions and automates tasks traditionally managed through labor-intensive manual processes. The system is designed to detect, acquire, and monitor basin data via electronic devices, which is then analyzed by a large language model (LLM) to generate precise, context-aware recommendations based on domain-specific knowledge. The final output comprises SMS notifications sent to the farm manager, containing the generated recommendations, which keep them informed and enable timely intervention when necessary. To ensure continued autonomous operation in case of connectivity loss, pre-trained TinyML models were integrated into the Raspberry Pi. These models display alarm signals to alert the farm owner to any irregularities, thereby maintaining system stability and performance. This system has substantially improved the growth rate, biomass yield, and nutrient content of Spirulina. The results highlight the potential of this system to transform Spirulina cultivation by offering an adaptable, autonomous solution.
Evaluating plant growth performance in a greenhouse hydroponic salad system using the internet of things Rattanachu, Chonthisa; Phetjirachotkul, Wiyuda; Chaopisit, Isara; Rothjanawan, Kronsirinut
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp505-517

Abstract

Hydroponic salad cultivation is becoming increasingly popular. However, a common challenge is the lack of time to maintain hydroponic vegetables due to other responsibilities. This study presents a hydroponic system based on the internet of things (IoT) technique, designed to save time by enabling remote control through a mobile application connected to a NodeMCU microcontroller. Various sensors are integrated with the NodeMCU for real-time monitoring and automation. The study also explores the use of RGB LEDs, which significantly accelerated plant growth and reduced cultivation time. A comparative experimental design was employed to evaluate the growth rate of green oak salad vegetables under two different greenhouse systems. The primary factor compared was the greenhouse system type, with plant growth rate as the outcome variable. Each treatment was replicated 10 times. F-tests were used to statistically determine significant differences in growth rates between the two systems across measured intervals. Results showed that the automated greenhouse system produced the highest leaf width and plant weight values. The use of RGB LEDs reduced the cultivation period from 45 days to 30 days, enabling more planting cycles and ultimately increasing overall yield.
An enhanced improved adaptive backstepping–second-order sliding mode hybrid control strategy for high-performance electric vehicle drives Tran, Huu Dat; Pham, Ngoc Thuy
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp121-134

Abstract

This paper proposes an enhanced hybrid speed control strategy, termed improved adaptive backstepping–second-order sliding mode (IABSSOSM), for six-phase induction motor (SPIM) drives in electric vehicle (EV) propulsion systems. The proposed method combines the systematic design framework of Backstepping in the outer speed and flux loops with a second-order sliding mode controller in the inner current loop. An innovation of the approach is the integration of a variable-gain super-twisting algorithm (VGSTA), which dynamically adjusts the control effort based on disturbance levels, thereby minimizing chattering and enhancing robustness against system uncertainties. To further improve disturbance rejection, a predictive torque estimator is incorporated using a forward Euler discretization, enabling accurate torque prediction and proactive compensation. This hybrid structure significantly improves convergence speed, enhances reference speed tracking accuracy, and ensures fast and precise torque response, and its strong resilience to external load disturbances, system parameter variations enable stable and reliable operation under challenging conditions. The effectiveness of the proposed approach is validated through comprehensive simulations using the MATLAB/Simulink.

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