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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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Low-cost integrated circuit packaging defect classification system using edge impulse and ESP32CAM Kamaruddin, Muhammad Adni; Mispan, Mohd Syafiq; Jidin, Aiman Zakwan; Mohd Nasir, Haslinah; Mohd Nor, Nurul Izza
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp156-162

Abstract

Defects in integrated circuit (IC) packaging are inevitable. Several factors can cause defects in IC packaging such as material quality, errors in machine and human handling operations, and non-optimized processes. An automated optical inspection (AOI) is a typical method to find defects in the IC manufacturing field. Nevertheless, AOI requires human assistance in the event of uncertain defect classification. Human inspection often misses very tiny defects and is inconsistent throughout the inspection. Therefore, this study proposed a low-cost IC packaging defect classification system using edge impulse and ESP32-CAM. The method involves training a deep learning model (i.e., convolutional neural network (CNN)) using a dataset of non-defective and defective ICs on Edge Impulse. For defective ICs, the top surface of the ICs is deliberately scratched to imitate the cosmetic defects. ICs with scratch-free on their top surfaces are considered non-defective ICs. A successfully trained model using Edge Impulse is subsequently deployed on ESP32-CAM. The model is optimized to fit the limited resources of the ESP32-CAM. By using the built-in camera in ESP32-CAM, the trained model can perform a real-time image classification of non-defective/defective ICs. The proposed system achieves 86.1% prediction accuracy by using a 1,571 image dataset of defective and non-defective ICs.
Innovative power sharing and secondary controls for meshed microgrids Ben Hassi, Youssef Amine Ait; Hennane, Youssef; Berdai, Abdelmajid
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp99-113

Abstract

In alternating current (AC) microgrids, the prevalent approach for controlling the power distribution between generators and loads is droop control. This decentralized technique ensures accurate power sharing; however, its utility is restricted by significant drawbacks. Notably, in scenarios involving dissimilar power sources, mismatched impedance lines, or meshed microgrids, conventional droop control fails to ensure effective reactive power sharing among inverters, often leading to notable circulating currents. Hence, the primary objective of this paper is twofold: firstly, to examine limitations inherent to conventional droop control; secondly, to introduce a robust power-sharing methodology for AC microgrids. This novel approach is specifically designed to achieve consistent sharing of active and reactive power across meshed topology microgrids. The technique considers the presence of distributed power loads and the dynamic nature of the topology. Despite the attainment of satisfactory active and reactive power sharing, deviations in voltage and frequency occasionally manifest. To address this issue, a supplementary control mechanism is proposed as a third phase. This secondary control method focuses on reinstating the microgrid's voltage and frequency to rated values, all while upholding the precision of power sharing. The efficacy of this multi-stage methodology is rigorously validated through simulations using MATLAB/Simulink and practical experimentations.
Utilization meta-analysis to identify the convenience of eBooks (visual and audio) for learning Mailool, Jefri; Arlinwibowo, Janu; Linguistika, Yulia
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp529-539

Abstract

This research aims to conclude the influence of eBooks in the learning process throughout the world. The meta-analysis design taken was a group contrast between control and experimental groups with a random effect size model. The criteria used are time “data published 2018–2023,” published in English, type of publication is a quantitative research article, the research design is a difference between control and experimental groups, containing complete data “mean, sample size, and standard deviation,” and recorded in the Scopus database. Data collection was guided by the PRISMA method. The results of the analysis showed that the data were heterogeneous and free from publication bias. The results of the analysis showed that there was a large “positive” effect as indicated by a p-value <0.001<5% “95% confidence interval” and a total effect size=0.86 [0.61; 1.11]. It can be concluded based on the latest findings that eBooks have an equally good effect on all conditions which are influenced by the type of competency developed, the eBook information base, the type of eBook, and class size.
Intrusion detection and prevention using Bayesian decision with fuzzy logic system Sekar, Satheeshkumar; Parvathy, Palaniraj Rajidurai; Gupta, Gopal Kumar; Rajagopalan, Thiruvenkadachari; Basavaraddi, Chethan Chandra Subhash Chandra Basappa; Padmanaban, Kuppan; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1200-1208

Abstract

Nowadays, intrusion detection and prevention method has comprehended the notice to decrease the effect of intruders. denial of service (DoS) is an attack that formulates malicious traffic is distributed into an exacting network device. These attackers absorb with a valid network device, the valid device will be compromised to insert malicious traffic. To solve these problems, the Bayesian decision model with a fuzzy logic system based on intrusion detection and prevention (BDFL) is introduced. This mechanism separates the DoS packets based on the type of validation, such as packet and flow validation. The BDFL mechanism uses a fuzzy logic system (FLS) for validating the data packets. Also, the key features of the algorithm are excerpted from data packets and categorized into normal, doubtful, and malicious. Furthermore, the Bayesian decision (BD) decide two queues as malicious and normal. The BDFL mechanism is experimental in a network simulator environment, and the operations are measures regarding DoS attacker detection ratio, delay, traffic load, and throughput.
Influence of metal particles shape on direct current voltage electric properties of nanofluids Fahmi, Daniar; Akbar, Muhammad Fadlan; Yulistya Negara, I Made; Hernanda, I Gusti Ngurah Satriyadi; Asfani, Dimas Anton; Zaidan, Risyad Alauddin; Fadhilah, Arkan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp56-66

Abstract

It is widely recognized that the application of nanoparticles has the potential to improve the dielectric properties of transformer oil. Nevertheless, there is a scarcity of studies that have utilized pure nanofluids, and in practical applications, it is inevitable for transformer oil to become contaminated. Therefore, this study conducted tests to investigate how the shape and size of metal contaminants impact the dielectric performance of Fe3O4 nanofluids. The findings from the levitation voltage test indicate that as the size and diameter of the particle increase, the levitation voltage value measured also increases, and conversely. Moreover, a higher concentration of nanoparticles leads to a higher measured levitation voltage value. On the other hand, the breakdown voltage test results demonstrate that larger and sharper particles result in lower measured breakdown voltage values, and vice versa. The simulation outcomes regarding electric field distribution reveal that larger and sharper particles correspond to higher measured electric field values, while the opposite is true for smaller and less sharp particles.
Review of gait recognition systems: approaches and challenges Mandlik, Sachin B.; Labade, Rekha; Chaudhari, Sachin Vasant; Agarkar, Balasaheb Shrirangao
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp349-355

Abstract

Gait recognition (GR) has emerged as a significant biometric identification technique, leveraging an individual's walking pattern for various applications such as surveillance, forensic analysis, and person identification. Despite its non-intrusive nature, GR systems face challenges due to their sensitivity to pose variations, limiting functionality in real-world scenarios where people exhibit diverse walking styles and body orientations. This review paper aims to comprehensively discuss GR systems, focusing on approaches and challenges in designing accurate and robust systems capable of handling bodily variations. GR's prominence spans across domains including surveillance, security, healthcare, and human-computer interaction, positioning it as a versatile biometric modality complementary to the traditional methods like fingerprint and face recognition. The review offers an in-depth analysis of GR systems, detailing silhouette-based, model-based, and deep-learning approaches. Silhouette-based methods capture gait information by analyzing the outline and locomotion of a person’s silhouette, while model-based approaches utilize skeletal models to describe gait patterns. The paper elucidates the challenges and limitations of GR systems, encompassing factors such as walking conditions, clothing, viewpoint, and environmental influences. Additionally, it explores potential future directions in GR research, highlighting the technology’s ongoing evolution and integration into diverse applications. As a valuable resource, this review serves researchers, practitioners, and policymakers by providing insights into the current state of GR systems and avenues for further research and development. It underscores the importance of addressing challenges to enhance GR’s accuracy and robustness, ensuring its continued relevance in biometric identification across various domains.
Energy analysis of active photovoltaic cooling system using water flow Kristi, Ant. Ardath; Susanto, Erwin; Risdiyanto, Agus; Junaedi, Agus; Darussalam, Rudi; Rachman, Noviadi Arief; Fudholi, Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1-14

Abstract

An active water-cooling system is one of several technologies that has been proven to be able to reduce heat losses and increase electrical energy in photovoltaic (PV) module. This research discusses a comparative experimental study of three pump activation controls in cooling of PV module with the aim of evaluating specifically the PV output power, net energy gain, water flow rate, and module temperature reduction. The three pump activation controls being compared are continuously active during the test, active based on setpoint temperature, and active by controlling the pump voltage using pulse width modulation (PWM) control in adjusting water flow rate smoothly. The results show that controlling the pump voltage using PWM in the PV cooling process produces energy of 437.95 Wh, slightly lower than the others and the average module cooling temperature is 35.24 °C, higher of 1-3 °C than the others. Nevertheless, PWM control of cooling pump has resulted the percentage of net energy gain of 9.94%, greater than other controls, and with an average flow rate of 2.17 L/min, more efficient than the others. Thus, this control is quite effective as it can produce higher net PV energy yield and lower water consumption.
Sailfish-cat algorithm-enhanced generative adversarial network for attack detection in internet of things-Fog network authentication Niranjan, Pallavi Kanthamangala; Venkatesh, Ravikumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1109-1122

Abstract

The internet of things (IoT) has emerged as a prominent and influential concept within the realm of computing. Various attack detection methods are devised for detecting attacks in IoT-Fog environment. Despite all these efforts, attack detection still remained as a challenging task due to factors such as low latency, resource constraints of IoT devices, scalability issues, and distribution complexities. All these challenges are addressed in this paper by designing an efficient attack detection technique named as sailfish- cat optimization-based generative adversarial network (SaCO-based GAN) tailored for the IoT-Fog framework. This proposed approach introduces the SaCO-based GAN for IoT-Fog attack detection utilizing deep learning and feature-based classification, validated through experiments showing superior performance metrics. Notably, the SaCO optimization technique is utilized to train the GAN. Experimental results demonstrate the efficacy of the SaCO-based GAN with a maximum recall of 92.15%, a maximum precision of 91.21%, and a maximum F-Measure of 92.16%, outperforming existing techniques in IoT-Fog attack detection. The paper recommends enhancing scalability, implementing real-time detection strategies, rigorously testing robustness against diverse attack scenarios, and integrating with existing IoT security frameworks for practical deployment.
Development of an internet of things based smart cold storage with inventory monitoring system Angappan, Suganya; Nataraj, Aarthi; Krishnan, Loganathan Navaneetha; Palanisamy, Anbarasu
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp89-98

Abstract

Consuming fresh drupes, vegetables can help lower a chance of developing several chronic diseases. Unfortunately, the post-harvest life cycle's storage stage is where fruits and vegetables (FVs) lose the most of all the food that is produced annually. A failure to recognize important ambient environmental conditions when using cold storage seems to be the main causes of this elevated loss rate. The current monitoring systems for cold storage are only able to measure warmth and moistness and ignoring further crucial acceptable surrounding factors of radiance and gas quantity. Serious matter gets handled in order to lower the system’s harm degree. The real time intelligent monitoring and notification system (RT-IMNS) for icy container is described briefly in the paper. It employs a device management platform (IoT)-enabled technique to continuously monitor hotness, comparative moisture, brightness, fume quantity and alert staff when dangerous thresholds are reached.
Seasonal auto-regressive integrated moving average with bidirectional long short-term memory for coconut yield prediction Jayanna, Niranjan Shadaksharappa; Lingaraju, Raviprakash Madenur
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp783-791

Abstract

Crop yield prediction helps farmers make informed decisions regarding the optimal timing for crop cultivation, taking into account environmental factors to enhance predictive accuracy and maximize yields. The existing methods require a massive amount of data, which is complex to acquire. To overcome this issue, this paper proposed a seasonal auto-regressive integrated moving average-bidirectional long short-term memory (SARIMA-BiLSTM) for coconut yield prediction. The collected dataset is preprocessed through a label encoder and min-max normalization is employed to change non-numeric features into numerical features and enhance model performance. The preprocessed features are selected through an adaptive strategy-based whale optimization algorithm (AS-WOA) to avoid local optima issues. Then, the selected features are given to the SARIMA-BiLSTM to predict the coconut yields. The proposed SARIMA-BiLSTM is adaptable to handling a widespread of various seasonal patterns and captures spatial features. The SARIMA-BiLSTM performance is estimated through the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE). SARIMA-BiLSTM attains 0.84 of R2, 0.056 of MAE, 0.081 of MSE, and 0.907 of RMSE which is better when compared to existing techniques like multilayer stacked ensemble, convolutional neural network and deep neural network (CNN-DNN) and autoregressive moving average (ARIMA).

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