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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 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. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2,901 Documents
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Leveraging pretrained transformers for enhanced air quality index prediction model Velusamy, Santhana Lakshmi; Madhaya Shanmugam, Vijaya
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7968

Abstract

Air pollution mitigation is essential to ensure sustainable development, as it directly affects climate change, economic productivity, and social well-being. Despite the availability of numerous prediction techniques, machine learning (ML) remains the optimal solution for forecasting air pollution. Constructing a prediction model for a region with limited data poses a challenge. This study presents a novel technique that combines temporal fusion transformer (TFT) with transfer learning to create an inventive air quality index (AQI) prediction model, effectively utilizing temporal insights and prior knowledge. The TFT is an advanced deep neural architecture engineered to enhance time series forecasting through the fusion of sequence modelling and global temporal patterns. By fusing TFT with transfer learning, the research pioneers a fresh approach to AQI prediction for region with data scarcity issue, capitalizing on cross-domain knowledge transfer. Utilizing meteorological and pollutant data from the Cochin region, a hybrid AQI prediction model is constructed through TFT and transfer learning. Employing a preexisting TFT model trained on Trivandrum data, transfer learning technique is utilized to adapt the model for predicting AQI in the Cochin region. The study demonstrates that integrating TFT with transfer learning yields superior accuracy compared to an exclusive TFT-based approach.
SeeAround: an offline mobile live support system for the visually impaired Sebban, Othmane; Azough, Ahmed; Lamrini, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7904

Abstract

The inability of blind or partially-sighted people to understand visual content and real-life situations reduces their standard of living, especially in a world mainly tailored for sighted individuals. Despite the progress made by certain devices to assist them in using touch, sound, or other senses, these solutions often fall short of bridging the comprehension gap. Our work proposes an intuitive, user-friendly mobile-based framework named "SeeAround" that is capable of automatically providing real-time audio descriptions of the user's immediate visual surroundings. Our solution addresses this challenge by leveraging key point detection, image captioning, text-to-speech (TTS), optical character recognition (OCR), and translation algorithms to offer comprehensive support for visually impaired individuals. Our system architecture relies on convolutional neural networks (CNNs) such as Inception-V3, Inception-V4, and ResNet152-V2 to extract detailed features from images and employs a multi-gated recurrent unit (GRU) decoder to generate word-by-word natural language descriptions. Our framework was integrated into mobile applications and optimized with TensorFlow lite pre-trained models for easy integration on the Android platform.
Web system to enhance technical supervision of incidents at the hydrocarbons regulatory institution in Lima–2023 Caceres Sanchez, Cynthia Elvia; Diburga Evangelista, Luis Alfredo; Cano Lengua, Miguel Angel; Rosas Culcos, Fredy Robert
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7701

Abstract

In this article, the implementation of a web system was carried out to improve the process of technical supervision of incidents of a hydrocarbon regulatory company because time was lost in carrying out each process; this research was developed using the SCRUM methodology as it is an agile methodology and adapted to our research. Using the process, events and artifact, it was possible to design the prototypes of the system, architecture, and database. Finally, the implementation was carried out among other important points obtained as results; the average level of optimization of the incident assignment process, derived from the observations, is 91.05% efficiency in assigning incidents to specialists. Regarding the 95% confidence interval for this indicator, it is between 88.98% and 93.11% efficiency, representing two standard deviations with respect to the mean. Regarding the average response time to incidents in all states, obtained from observations, it is 15 days. The 95% confidence interval for this indicator ranges between 14 and 18 days, which represents two standard deviations from the mean. The system is intuitive and not complex. With the implementation of the web system, processes are automated and end user satisfaction is obtained.
Human-centered design approach for enhancing supply chain management systems in SMEs: insights from Malaysia Ahmad, Sabrina; Zainudin, Nurhamizah; Ermahani A. Jalil, Intan; Hefri Ariyanto, Hepy; Ahmad, Mazida
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7751

Abstract

A reliable supply chain management (SCM) system is crucial to small and medium enterprises (SMEs) to meet the increasing demands of supply chain operations. However, the misalignment between the functional SCM system and the complex, dynamic, and diverse needs of the supply chain stakeholders is paramount. This paper presents an effort to adopt human-centered design (HCD) in the process of identifying requirements for a SCM system, aimed at providing valuable support to SMEs. The HCD places a strong emphasis on shaping design choices in alignment with users' tasks, needs, and preferences, instead of requiring users to adapt their behaviors to fit the system. The survey method is employed to get the SMEs' perspectives on the potential benefits of incorporating HCD into the requirements of the SCM system. The findings showed that a minimum of 80% of the respondents agreed that HCD brings numerous benefits to the development of SCM systems for SMEs in Malaysia.
Non-centroid-based discrete differential evolution for data clustering Poonthong, Tanapon; Wetweerapong, Jeerayut
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8811

Abstract

Data clustering can find similarities and hidden patterns within data. Given a predefined number of groups, most partitional clustering algorithms use representative centers to determine their corresponding clusters. These algorithms, such as K-means and optimization-based algorithms, create and update centroids to give (hyper) spherical shape clusters. This research proposes a non-centroid-based discrete differential evolution (NCDDE) algorithm to solve clustering problems and provide non-spherical shape clusters. The algorithm directs the population of discrete vectors to search for data group labels. It uses a novel discrete mutation strategy analogous to the continuous mutation in classical differential evolution. It also combines a sorting mutation to enhance convergence speed. The algorithm adaptively selects crossover rates in high and low ranges. We use the UCI datasets to compare the NCDDE with other continuous centroid-based algorithms by intra-cluster distance and clustering accuracy. The results show that NCDDE outperforms the compared algorithms overall by intra-cluster distance and achieves the best accuracy for several datasets.
Real-time IoT security framework for detecting a person with a weapon using Raspberry Pi, Google Vertex AI, and AWS Schutte, Storm; Uddin, Jia
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7627

Abstract

Realtime crime scene detection is a vital issue for ensuring security in various environments. Building on recent advancements in machine learning algorithms, this paper presents an IoT framework for real-time weapon and face detection. By deploying a convolutional neural network (CNN) architecture in Vertex AI and utilizing the portable camera module of a Raspberry Pi, to detect whether a person is carrying a weapon. This is achieved by pre-processing, which we resize and annotate the images. Then, train and validate the CNN model with the annotated label dataset. The trained model is saved in Google Cloud’s Vertex AI portal. Then we tested the model by uploading live images from a camera as well as a few video clips, to a Django application in amazon web hosting services (AWS) to Vertex AI. The model exhibited an accuracy of 97.2% along with a F1 score of 0.97. In addition, the model outperforms the other state-of-the-art models by less trainable parameters and higher accuracy.
Deep hybrid neural network for automatic classification of heart arrhythmias using 12-lead electrocardiograms Sultan, Daniyar; Baikuvekov, Meirzhan; Omarov, Batyrkhan; Kassenkhan, Aray; Nuralykyzy, Saltanat; Zhekambayeva, Maigul
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8285

Abstract

This research introduces a novel convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) hybrid network for the automatic classification of heart arrhythmias using 12-lead electrocardiograms (ECGs). By merging the spatial feature extraction capabilities of CNNs with the temporal precision of BiLSTM networks, our approach sets a new standard in cardiac diagnostics. The proposed model was tested against the comprehensive CPSC2018 dataset, demonstrating superior performance with an accuracy of 90.67%, precision of 93.27%, recall of 96.35%, and an F-score of 94.78%, surpassing existing state-of-the-art methods. These results underscore the effectiveness of integrating spatial and temporal data analysis, offering a robust and reliable tool for medical practitioners. This study represents a significant advancement in automated ECG analysis, paving the way for improved diagnosis and treatment of heart diseases, and contributing to enhanced patient outcomes in cardiac care.
Optimization of perovskite solar cell with MoS2-based HTM layer using hybrid L27 Taguchi-GRA based genetic algorithm Ezwan Kaharudin, Khairil; Salehuddin, Fauziyah; Ahmad Jalaludin, Nabilah; Suhaila Mohd Zain, Anis; Arith, Faiz; Aisah Mat Junos, Siti; Ahmad, Ibrahim
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8455

Abstract

This article proposes an optimization method to predictively model the perovskite solar cell with molybdenum disulfide (MoS2) based inorganic hole transport material (HTM) for improved fill factor (FF) and power conversion efficiency (PCE) by finding the most optimum thickness and donor/acceptor concentration for each layer via a hybrid L27 Taguchi grey relational analysis (GRA) based genetic algorithm (GA). Numerical simulation of the device is carried out by employing one-dimensional solar cell capacitance simulator (SCAPS-1D) while the optimization procedures are developed based on combination of multiple methods; L27 Taguchi orthogonal array, GRA, multiple linear regression (MLR), and GA. The results of post-optimization reveal that the most optimum layer parameters for improved FF and PCE are predicted as follows; SnO2F thickness (0.855 μm), SnO2F donor concentration (9.206×1018 cm-3), TiO2 thickness (0.011 μm), TiO2 donor concentration (9.306×1016 cm-3), CH3NH3PbI3 thickness (0.897 μm), CH3NH3PbI3 donor concentration (0.906×1013 cm-3), MoS2 thickness (0.154 μm), and MoS2 acceptor concentration (9.373×1017 cm-3). Both FF and PCE of the device are improved by ~1.1% and ~12.6% compared to the pre-optimization.
Cascaded H-bridge 7-level inverter application for air exhaust fan drive control of Thu Thiem road tunnel Thu Anh, An Thi Hoai; Cuong, Tran Hung; Khoi, Tran Van
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8490

Abstract

The Thu Thiem road tunnel in Vietnam is crucial in reducing traffic congestion and ensuring the safety of those passing through, thanks to its ventilation system that generates clean air. This fresh air production is primarily supported by two exhaust fans at both ends of the tunnel in the eastern and western towers. However, the fans have a power capacity of several hundred kW and operate at kilovolt-level voltage, which is unsuitable for conventional inverters. Therefore, this paper proposes a 7-level inverter to feed the exhaust fan drive motor. The 7-level inverter improves the output voltage quality, and the output current and voltage have reduced the harmonic distortion significantly. The outstanding advantages of this inverter are verified through MATLAB/Simulink simulation software compared to a 3-level inverter.
A simplified approach to establishing the impact of software source code changes on requirements specifications Mugambi Muthengi, Fredrick; Muchangi Mugo, David; Makau Mutua, Stephen; Mueni Musyoka, Faith
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8736

Abstract

This paper proposes an improved approach to establishing the impact of source code changes in software features. An association of the methods affected by the changes made and functional requirements of the software reveals the likely impact of the changes made in the software. Changes in source code are exhibited in the operational behaviour of the software. Functional requirements and source code artefacts play a key role in assessing the impact of the changes made. In this study, we investigated the possibility of establishing the association between the changed methods and the functional requirements. The study found out that changes made in the methods can be mapped to the functional requirements that the methods are implementing. The motivation in this endeavour was to assess the impacted software requirements which translate to the likely software features affected by the changes made in the software. With the intent of the software users being seen in the requirements statements and the method naming by the developers, a mapping of the two software artefacts would help developers find out the impacted software features when assessing the overall effect of the committed changes.

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