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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 70 Documents
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Search results for , issue "Vol 15, No 4: August 2025" : 70 Documents clear
To ensure public safety internet of things and convolutional neural network algorithm for a surveillance system enabled with 5G priya, Chandrasekar; Kumuthapriya, Kesavan; Sagayamary, Savarimuthu; Livingston, L. M. Merlin; Venkatesan, Marimuthu
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4268-4278

Abstract

Public safety and security are top priorities in the constantly urbanizing society and research develops and implements a smart surveillance system using fifth generation (5G) of wireless communication technology and internet of things (IoT) technologies to improve public safety. It developed a comprehensive and responsive monitoring solution using machine learning methods, especially convolutional neural networks (CNNs). IoT devices, including high-definition cameras, environmental sensors, and drones, are carefully deployed in urban centers, transit hubs, and essential infrastructure. These devices provide data to a central processing unit through the 5G network and CNNs analyze incoming data in real-time. The CNNs are taught to recognize objects, anomalies, faces, and license plates. These tasks help the system identify risks, odd activities, and intriguing people and warn authorities of real-time irregularities and security issues, simplifying emergency responses. Predictive analytics analyzes previous data to forecast security issues, enabling preventative steps and data are protected by strict privacy protections. According to this analysis, 5G-enabled IoT surveillance systems and machine learning may improve public safety, situational awareness, and emergency response times and approach ensures that security advancements respect privacy and integrity.
Enhancing anomaly detection performance using ResNet50 and BiLSTM networks on benchmark datasets Ramoliya, Dipak; Ganatra, Amit
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3727-3736

Abstract

Detection of abnormal activity from large video sequences is one of the biggest challenges because of ambiguity in different activities. Over the last many years, several cameras have been placed to cover the public and private sectors to monitor abnormal human activity and surveillance. In recent years, deep learning and computer vision have significantly impacted this kind of surveillance. Intelligent systems that can automatically identify unusual events in video streams are currently in high demand. A deep learning-based combinational model has been proposed to detect abnormal activity from input video streams. The proposed study uses a combination of convolution and sequential models. A ResNet50 network with a residual connection was used for initial feature extraction. The proposed bidirectional long short-term memory (BiLSTM) network has improved the extracted ResNet50 features. Simulation of the proposed model was experimented on two benchmark datasets for anomaly detection UCF Crime and ShanghaiTech. Simulation of proposed architecture has achieved 97.55% and 91.94% remarkable accuracy for UCF Crime and ShanghaiTech datasets respectively.
DriveGuard: enhancing vehicle breakdown assistance through mobile geolocation technology Ariff, Mohamed Imran Mohamed; Halim, Abdul Hadi Abdul; Ahmad, Samsiah; Abdullah, Mohammad Nasir; Zulkifli, Zalikha; Salleh, Khairulliza Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3957-3964

Abstract

The DriveGuard mobile application addresses the growing demand for efficient vehicle breakdown assistance by connecting users to nearby workshops using advanced geolocation technologies. With the rise in private vehicle ownership, sudden breakdowns are increasingly common, necessitating quick access to assistance. DriveGuard utilizes GPS, GSM/CDMA Cell IDs, and Wi-Fi positioning for precise location tracking, enabling users to locate assistance rapidly and accurately. Developed through the waterfall model, the application offers a user-friendly interface built with the Flutter framework. Test results indicate high functionality and user satisfaction, achieving usability ratings between 88% and 90%. DriveGuard’s design improves road safety by reducing waiting times for emergency services, alleviating the stress often associated with breakdown situations. Future work will focus on expanding service options, enhancing security, and refining user interactions to provide a more comprehensive roadside assistance tool. DriveGuard demonstrates the potential of mobile technology in promoting safe and efficient transportation.
A hybrid model to mitigate data gaps and fluctuations in tax revenue forecasting Taufik, Rahman; Aristoteles, Aristoteles; Ilman, Igit Sabda
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4099-4108

Abstract

This study addresses the critical challenge of advancing tax revenue forecasting models to effectively handle distinctive data gaps and inherent fluctuations in tax revenue data. These challenges are evident in Lampung Province, Indonesia, where limited temporal granularity and non-linear variability hinder accurate fiscal planning. Despite advancements in statistical, machine learning, and hybrid approaches, existing models often fall short in simultaneously managing these challenges. A hybrid model integrating random forest regressors for data interpolation and Long Short-Term Memory for capturing complex temporal patterns was proposed. The model was evaluated, achieving an R² of 0.86, root mean squared error (RMSE) of 9.65 billion, and mean absolute percentage error (MAPE) of 3.49%. Although the model has limitations in generalizing to unseen data, the results demonstrate that it outperforms existing forecasting models regarding accuracy and reliability. Integrating random forest regressors and long short-term memory delivers a tailored solution to the complexities of tax revenue forecasting, contributing to fiscal forecasting and setting a foundation for further exploration into hybrid approaches.
Integrating time-frequency features with deep learning for lung sound classification Chang, Su Yuan; Markom, Marni Azira; Choong, Zhi Sheng; Markom, Arni Munira; Kamaruddin, Latifah Munirah; Tan, Erdy Sulino Mohd Muslim
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3737-3747

Abstract

Deep learning has transformed medical diagnostics, especially in analyzing lung sounds to assess respiratory conditions. Traditional methods like CT scans and X-rays are impractical in resource-limited settings due to radiation exposure and time consumption, while conventional stethoscopes often lead to misdiagnosis due to subjective interpretation and environmental noise. This study evaluates deep learning models for lung sound classification using the International Conference on Biomedical Health Informatics 2017 dataset, comprising 920 annotated samples from 126 subjects. Pre-processing includes down sampling, segmentation, normalization, and audio clipping, with feature extraction techniques like spectrogram and Mel-frequency cepstral coefficients (MFCC). The adopted automatic lung sound diagnosis network (ASLD-Net) model with triple feature input (time domain, spectrogram, and MFCC) achieved the highest accuracy at 97.25%, followed by the dual feature model (spectrogram and MFCC) at 95.65%. Single-input models with spectrogram and MFCC performed well, while the time domain input alone had the lowest accuracy.
Thematic review of light detection and ranging and photogrammetric technologies in unmanned aerial vehicles: comparison, advantages, and disadvantages Gómez-Moya, Diego Alexander; Garcés-Gómez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3748-3758

Abstract

The development of unmanned aerial vehicles (UAVs) has positively influenced various remote sensing techniques, making them more accessible to different types of users. Among these, photogrammetry and light detection and ranging (LiDAR) stand out for their versatility and possibilities in terrain modeling. This study evaluates the advantages of each one in various fields of knowledge and industry, comparing their possibilities in terms of positional accuracy, completeness, and efficiency in terrain modeling. It is evident that the use of these techniques in different areas generates an opportunity to implement algorithms or processes in mapping and cartography. Regarding their use, the advantage of the LiDAR sensor is identified in inhospitable and inaccessible areas covered by vegetation and with problems in the geodetic network. On the other hand, the versatility of photogrammetry is shown in small areas with exposed soil. The advantage of point cloud fusion or the combination of techniques in the construction industry and in archaeological and architectural surveys is also noted. Finally, emphasis is placed on variables to consider, such as georeferencing techniques, the ground control point (GCP) network, algorithms and software, and flight plan reviews, in order to improve their accuracy.
Gene set imputation method-based rule for recovering missing data using deep learning approach Al-Rahayfeh, Amer; Atiewi, Saleh; Almiani, Muder; Mughaid, Ala; Razaque, Abdul; Abu-Salih, Bilal; Alweshah, Mohammed; Alrawajfeh, Alaa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4296-4317

Abstract

Data imputation enhances dataset completeness, enabling accurate analysis and informed decision-making across various domains. In this research, we propose a novel imputation method, a spectral clustering based on a gene set using adaptive weighted k-nearest neighbor (AWKNN), and an imputation of missing data using a convolutional neural network algorithm for accurate imputed data. In this research, we have considered the Kaggle water quality dataset for the imputation of missing values in water quality monitoring. Data cleaning detects inaccurate data from the dataset by using the median modified Weiner filter (MMWFILT). The normalization technique is based on the Z-score normalization (Z-SN) approach, which improves data organization and management for accurate imputation. Data reduction minimizes unwanted data and the amount of capacity required to store data using an improved kernel correlation filter (IKCF). The characteristics and patterns of data with specific columns are analyzed using enhanced principal component analysis (EPCA) to reduce overfitting. The dataset is classified into complete data and missing data using the light- DenseNet (LIGHT DN) approach. Results show the proposed outperforms traditional techniques in recovering missing data while preserving data distribution. Evaluation based on pH concentration, chloramine concentration, sulfate concentration, water level, and accuracy.
Blockchain as a digital governance tool: A systematic review Patricio-Peralta, Cesar; Villacorta, Jimmy Ramirez; Sánchez, Milton Amache; Meneses, Jacker Paredes; Mondragon, Jesús Zamora; Terrones, Luis Segura; Santos, Paul Torres; Manrique, César Veliz; Peralta, Walter Patricio
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3986-3995

Abstract

This systematic review explores the implementation of blockchain technology as a digital governance tool, focusing specifically on the Peruvian context. In the digital transformation era, blockchain has established itself as an innovative solution to manage and authenticate information. This research focuses on optimizing administrative and governmental processes in Peru, a country where document verification is crucial in legal, financial, educational, and medical procedures. The methodology used follows the problem/population, intervention, comparison, outcome, context (PICOC) model. 56 high-impact articles were selected in Scopus, prioritizing those in the areas of engineering, computer science, and business, and published between 2022 and 2025. The objective was to define the scope and structure of the research questions. These questions address the implementation of blockchain and its applications in digital governance to ensure security and reliability in administrative procedures. Through a comprehensive literature review, we seek to provide a comprehensive view of how blockchain could transform the interaction between citizens and the Peruvian government by automating document verification. In addition, successful cases from other countries and similar sectors will be analyzed, evaluating their feasibility and applicability in the Peruvian context. This approach will allow us to identify both the potential benefits and the challenges and implications associated with the integration of blockchain into government processes in Perú.
Exploring the recurrent and sequential security patch data using deep learning approaches Alam, Falah Muhammad; Fitrianah, Devi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4160-4171

Abstract

The ever-changing nature of vulnerabilities and the intricacy of temporal connections make the classification of security patch data, both sequential and recurrent, a formidable challenge in cybersecurity. The goal of this research is to improve the efficacy and precision of security patch management by optimizing deep learning models to deal with these issues. In order to assess their performance on the PatchDB dataset, four models were used: recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM). Metrics like F1-score, area under the receiver operating characteristic curve (AUC-ROC), recall, accuracy, and precision were used to evaluate performance. When it came to processing sequential data, the GRU model was the most efficient, with the best accuracy (77.39%), recall (65.63%), and AUC-ROC score (0.8127). With a 75.17% accuracy rate and an AUC-ROC score of 0.7752, the RNN model successfully reduced false negatives. With AUC-ROC scores of 0.7792 and 0.8055, respectively, LSTM and Bi-LSTM had better specificity but more false negatives. To improve cybersecurity operations, decrease mitigation time, and automate the classification of security updates, this study presents a methodology. To improve the models' practicality, future efforts will center on increasing datasets and testing them in real-world settings.
Low-cost portable potentiostat for real-time insulin concentration estimation based on electrochemical sensors Dewi, Fitria Yunita; Aliwarga, Harry Kusuma; Handoko, Djati
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3683-3695

Abstract

Administering incorrect insulin dosages to diabetic patients can be fatal, leading to severe health consequences. Insulin detection, in conjunction with blood glucose monitoring, can significantly enhance diagnostic accuracy. Electrochemical methods for insulin detection offer a low-cost and portable solution. This study presents an insulin concentration estimation system using a customized electrochemical potentiostat operating in real-time via Bluetooth low energy (BLE). Conventional electrochemical sensing, which relies on calibration curves to determine concentration, poses accuracy limitations in portable devices. To address this, we implement a multiple- predictor approach that incorporates peak currents from multiple cycles of cyclic voltammetry responses and the electroactive surface area of a multi- walled carbon nanotube (MWCNT-COOH) modified screen-printed sensor. This modified sensor enhances sensitivity compared to bare screen-printed carbon sensors, making it suitable for low-volume and portable applications. Through cross-validation, our method demonstrated strong performance, achieving a determination coefficient (R²) greater than 0.90 for all training dataset combinations and greater than 0.85 for all testing dataset combinations. Hypothesis testing further confirmed the statistical significance of the electroactive surface area (p=0.006) as predictor, indicating its meaningful contribution to concentration estimation. This approach improves portable detection performance, supporting the development of affordable and reliable personal insulin monitoring systems.

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