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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 75 Documents
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Search results for , issue "Vol 13, No 6: December 2024" : 75 Documents clear
Business intelligence for decision-making in the collection area of a municipality Daza, Alfredo; Salazar Casas, Daniela Belen; Alarcón Cajas, Yohan Roy
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.5704

Abstract

The large volume of data in systems in the collection area leads to the lack of adequate management of information, as well as dissatisfaction on the part of the user. The purpose of the study is to implement business intelligence (BI) technology to improve the effectiveness of the information and the satisfaction of the attention of the users of a municipality of Lima in the area of collection; therefore, the phases of the Ralph Kimball method with the following phases: project planning; definition of requirements; design of technological architecture; in the dimensional modeling a snowflake scheme was made with 9 dimensions and 1 table made, in the physical design it was implemented in the MySQL management system and in the extract, transform, and load (ETL) development the migration, transformation and cleaning of the data from the online transaction processing (OLTP) database to online analytical processing (OLAP) was executed; obtaining as results that BI managed to increase the level of information efficiency by 53.32%, as well as the level of user satisfaction (LUS) by 1.90%, concluding that BI allows to meet the needs of the user since it maintains a clean, secure and reliable data structure.
Improving Arabic handwritten text recognition through transfer learning with convolutional neural network-based models Lamtougui, Hicham; El Moubtahij, Hicham; Fouadi, Hassan; Satori, Khalid
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8178

Abstract

Arabic handwritten text recognition is a complex and challenging research domain. This study proposes an offline Arabic handwritten word recognition system based on transfer learning. The system exploits four pre-trained convolutional neural network (CNN) architectures, namely VGG16, ResNet50, AlexNet, and InceptionV3. In addition, a specialized image recognition model derived from the ImageNet dataset is incorporated. A combination strategy is designed to combine transfer learning with specific fine-tuning techniques, aiming to improve recognition accuracy. The study is conducted on the IFN/ENIT dataset, which includes images of Tunisian City and village names. The results show that the proposed system achieves a recognition accuracy of 94.73%, which is significantly higher than the accuracy rates achieved by previous approaches. These results suggest that the proposed system is a promising approach for Arabic handwritten text recognition.
Exploring the synergy: AI and ML in very large scale integration design and manufacturing Gonsai, Sima K.; Sheth, Kinjal Ravi; Patel, Dhavalkumar N.; Tank, Hardik B.; Desai, Hitesh L.; Rana, Shilpa K.; Bharvad, Suresh Laxmanbhai
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8594

Abstract

With the rapid advancements in very large scale integration (VLSI) and integrated circuit (IC) technology, the complexity of devices has escalated significantly. Designing a VLSI chip is essential for scaling up the capabilities of chips to meet the growing demands of modern applications, like artificial intelligence (AI), IoT, and high-performance computing. Chip testing and verification also emerges as crucial tasks to ensure optimal device functionality. Testing verifies the integrity of a circuit’s gates and connections, ensuring accurate operation. Throughout the chip’s design and development life cycle, design, testing and verification composes a substantial portion of the effort. AI and machine learning (ML) are used in many different research domains to improve predicted accuracy, automate difficult jobs, provide data-driven insights, and optimise workflows. This study aims to showcase the vital role of AI/ML in reducing complexity in VLSI chip design life cycle by automating test pattern generation and fault detection, enhancing efficiency and accuracy, and significantly reducing the time and resources needed for design verification and optimization.
A study on the impact of layout change to knowledge distilled indoor positioning systems Mazlan, Aqilah; Ng, Yin Hoe; Tan, Chee Keong
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7157

Abstract

Convolutional neural networks (CNN)-based indoor positioning systems (IPS) have gained significant attention over the past decade due to their ability to provide precise localization accuracy. However, the use of CNNs in these systems comes with a higher computational cost. To tackle this issue, recent studies have introduced knowledge distilled positioning schemes to mitigate the computational burden. Despite the clear possibility of performance degradation due to signal fluctuations, there remains a lack of investigation into the performance of knowledge distilled and CNN based indoor positioning schemes in dynamic indoor environment. To fill this research gap, this paper investigates the practicality of implementing knowledge distilled-based indoor positioning schemes in real-world by analyzing the impact of indoor layout change on these schemes. Results demonstrate that in the case of layout change, the knowledge distilled-based indoor positioning schemes without teaching assistant can still achieve good performance, with an improvement of 11.56% in average positioning error compared to simple CNN model, while taking only 49.05% of the complex CNN model’s execution time. However, the knowledge distilled-based indoor positioning scheme with teaching assistant fails under the same condition as the inclusion of teacher assistant leads to increased error in modeling the received signal strengths (RSS) and locations relationship.
Representative power distribution network: a review of available models Masdzarif, Nur Diana Izzani; Ibrahim, Khairul Anwar; Gan, Chin Kim; Au, Mau Teng
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7787

Abstract

In recent decades, there has been an increasing penetration of new smart grids (SG) and distributed generations (DG) that are connected to the distribution network (DN). Thus, it is critical for utilities to analyze and assess their impact on the power system networks, which often necessitates major decisions about network operation and planning. Consequently, researchers are constantly developing new and improved methods of advanced control and operation to address these challenges. Unfortunately, there are a limited number of realistic DN models that are made publicly available by the utilities for the development, testing, and evaluation of such new methods. This is mainly caused by the utilities' concerns and reluctance to reveal the public's real and “sensitive” network information. Although international standard test systems such as IEEE and CIGRE are publicly available, these test network models are customized based on the US DN and are not representative of the other networks that operate under different network settings. This paper presents a brief literature survey of existing and prominent representative DNs with a special emphasis on identifying the general description, and application, as a comparison for future development of test network in Malaysia.
Advanced spatial adaptive channel estimation for efficient mmWave communication Vadgave, Rajkumar M.; S., Manjula
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8304

Abstract

This study explores the intricacies posed by the unique features of 5G/6G wireless sensor networks (WSNs) to guarantee dependable and long-lasting connectivity. The increasing energy consumption in 5G/6G networks due to higher data rates and more complex architectures emphasizes the necessity for energy-efficient techniques. The WSN resources are limited, specially designed resource allocation and management techniques are essential. In this paper, a unique analogue combining design called advanced spatial adaptive channel estimation (ASACE) and an optimization model for channel state information (CSI) estimation that takes use of the low-rank characteristics of channel matrix sparsity are presented. Gradient descent (GD) optimization is incorporated to improve the suggested approach, demonstrating improvements in residual errors and computing complexity. The optimization problem aims to find the gains and orientations of wideband channel paths. Moreover, a comparative analysis is conducted between the suggested model and many cutting-edge methods, emphasizing error minimization. This thorough analysis offers a nuanced viewpoint on the effectiveness and efficiency of the suggested ASACE approach in the context of wideband cross-entropy (CE) and optimization, which makes a significant contribution to the area.
Machine learning-based detection of fake news in Afan Oromo language Salau, Ayodeji Olalekan; Arega, Kedir Lemma; Tin, Ting Tin; Quansah, Andrew; Sefa-Boateng, Kwame; Chowdhury, Ismatul Jannat; Braide, Sepiribo Lucky
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8016

Abstract

This paper presents a machine learning-based (ML) approach for identifying fake news on web-based social media networks. Data was acquired from Facebook to develop the model which was used to identify Afan Oromo's false news. The system architecture uses algorithms, such as support vector machines (SVM), k-nearest neighbor (KNN), and convolutional neural networks (CNNs) to detect and classify fake news. Existing models have limitations in understanding reported news accuracy compared with verified news. This study successfully resolved the challenges in the detection of social media fake news detection for the Afan Oromo language with the use of ML models and natural language processing (NLP) techniques. The results show that the SVM approach achieved a precision, recall, and F1-score, of 0.92, 0.92, and 0.90.
A stacked ensemble approach to identify internet of things network attacks through traffic analysis Rawashdeh, Adnan; Alkasassbeh, Mouhammd; Alauthman, Mohammad; Almseidin, Mohammad
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7811

Abstract

The internet of things (IoT) has increased exponentially in connected devices worldwide in recent years. However, this rapid growth also introduces significant security challenges since many IoT devices have vulnerabilities that can be exploited for cyber-attacks. Anomaly detection using machine learning algorithms shows promise for identifying abnormal network traffic indicative of IoT attacks. This paper proposes an ensemble learning framework for anomaly detection in IoT networks. A systematic literature review analyzes recent research applying machine learning for IoT security. Subsequently, a novel stacked ensemble model is presented, combining multiple base classifiers (random forest, neural network, support vector machine (SVM)) and meta-classifiers (gradient boosting) for improved performance. The model is evaluated on the IoTID20 dataset, using network traffic features to detect anomalies across binary, multi-class, and multi-label classifications. Experimental results demonstrate that the ensemble model achieved 99.7% accuracy and F1 score for binary classification, 99.5% accuracy for multi-class, and 91.2% accuracy for multi-label classification, outperforming previous methods. The model provides an effective anomaly detection approach to identify malicious activities and mitigate IoT security threats.
Exploring COVID-19 vaccine sentiment: a Twitter-based analysis of text processing and machine learning approaches Khalaf, Ban Safir; Hamdan, Hazlina; Manshor, Noridayu
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7855

Abstract

In the wake of the 2020 coronavirus disease (COVID-19) pandemic, the swift development and deployment of vaccines marked a critical juncture, necessitating an understanding of public sentiments for effective health communication and policymaking. Social media platforms, especially Twitter, have emerged as rich sources for gauging public opinion. This study harnesses the power of natural language processing (NLP) and machine learning (ML) to delve into the sentiments and trends surrounding COVID-19 vaccination, utilizing a comprehensive Twitter dataset. Traditional research primarily focuses on ML algorithms, but this study brings to the forefront the underutilized potential of NLP in data preprocessing. By employing text frequency-inverse document frequency (TF-IDF) for text processing and long short-term memory (LSTM) for classification, the research evaluates six ML techniques K-nearest neighbors (KNN), decision trees (DT), random forest (RF), artificial neural networks (ANN), support vector machines (SVM), and LSTM. Our findings reveal that LSTM, particularly when combined with tweet text tokenization, stands out as the most effective approach. Furthermore, the study highlights the pivotal role of feature selection, showcasing how TF-IDF features significantly bolster the performance of SVM and LSTM, achieving an impressive accuracy exceeding 98%. These results underscore the potential of advanced NLP applications in real-world settings, paving the way for nuanced and effective analysis of public health discourse on social media.
Modified multicarrier sinusoidal pulse-width modulation for three-phase open-load five-level inverter Suroso, Suroso; Prasetijo, Hari; Susilawati, Hesti; Murdyantoro Am, Eko; Mubyarto, Agung
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7899

Abstract

Five-level voltage source inverter (VSI) is a power inverter topology generating a five-level output voltage waveform. This inverter topology can reduce harmonics distortion to be lower compared to a conventional two-level inverter. In practical, delay of gating signals is unavoidable during switching operation of power semiconductor switches. Adding dead time in the gating signals of VSI’s power switches is mandatory to avoid short circuit during switching operation. However, the dead time of the inverter’s switching signals causes low frequency harmonics and distortion of inverter’s output waveforms. In this paper, a different multicarrier sinusoidal pulse-width modulation (SPWM) method with harmonics suppression capability was proposed and applied in the three-phase open-connection load five-level inverter. The proposed modified SPWM was tested using computer simulation of Powersim (PSIM) software. The measured output waveforms of the five-level VSI at different power factor conditions are presented and analyzed. The total harmonics distortion (THD) values of inverter’s output current were suppressed using the proposed SPWM method to be less than 1%. The test results showed that the proposed modified SPWM method was able to reduce the distortion (THD) of alternating current (AC) waveform, and increase the quality of the inverter’s output power.

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