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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.
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Articles 75 Documents
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Search results for , issue "Vol 14, No 1: February 2025" : 75 Documents clear
A common vocabulary for semantic interoperability of Moroccan e-government services Laaz, Naziha; Benaddi, Hanane
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.v14i1.7450

Abstract

Interoperability is a critical factor for the success of e-government services, as it enables different public information systems to communicate in a consistent and accurate manner. Governments are making significant efforts to improve their public e- services interactions and promote e-government interoperability. Morocco has developed an e-government interoperability framework that lists compliance rules and references for the development of public information systems. Unfortunately, Moroccan public administrations still work independently and operate as siloed organizations. To deal with this problem, it is essential to implement a common vocabulary (CV) for public services that public administrations can share to formalize public data, enhance exchange between information systems, and ensure data interoperability. In this light, this work presents a CV to standardize public services data, define concepts and relationships. The standardized vocabulary is defined using RDF/XML serialization format and incorporates fundamental declarations to ensure digital communication in Moroccan public services. The approach is illustrated through a case study of e-health service. The study shows the potential added value of creating a national vocabulary. It helps public administrations to structure data, interoperate more effectively and accelerate digital transformation.
Fuzzy logic method for making push notifications on monitoring system of IoT-based electric truck charging Al Madani Kurniawan, Aqsha; Khaula Amifia, Lora; Iskandar Riansyah, Moch.; Furizal, Furizal; Suwarno, Iswanto; Ma’arif, Alfian; Maghfiroh, Hari
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.v14i1.7412

Abstract

To minimize the negligence when charging electric vehicles, it is deemed important to have an internet of things (IoT) based monitoring system using a notification feature. The monitoring system of electric vehicle battery charging used a voltage divider and temperature sensor (DS18B20) installed on the Arduino Mega 2560 microcontroller with the addition of an ESP8266 Wi-Fi module for sending microcontroller data into the Blynk platform. A notification feature was added as the reminder that the battery has been overcharging or overheating. This study applied the Mamdani fuzzy logic method to determine the conditions when notifications must appear. The results of the application of the Mamdani fuzzy logic method were able to determine the conditions for push notifications to appear using the parameters as desired; by so doing, it is possible to create a battery monitoring system with accurate push notification feature to prevent the battery from being overcharged and overheated.
Exploring Bengali speech for gender classification: machine learning and deep learning approaches Dewan Arpita, Habiba; Al Ryan, Abdullah; Fahad Hossain, Md.; Sadekur Rahman, Md.; Sajjad, Md; Noor Islam Prova, Nuzhat
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.v14i1.8146

Abstract

Speech enables clear and powerful idea transmission. The human voice, rich in tone and emotion, holds unique beauty and significance in daily life. Vocal pitches vary by gender and are influenced by emotions and languages. While people naturally perceive these nuances, machines often struggle to capture these subtle distinctions. Machines may struggle to detect these nuances, but people effortlessly perceive them. This project aims to use various machine learning (ML) and deep learning (DL) techniques to reliably determine an individual’s gender from a corpus of Bengali conversations. Our dataset comprises 3185 Bengali speeches, with 1100 delivered by males, 1035 by women, and 1050 by those who identify as third gender. We employed six distinct feature extraction techniques to examine the audio data: roll-off, spectral centroid, chroma-stft, spectral bandwidth, zero crossing rate, and Mel-frequency cepstral coefficients (MFCC). Extreme gradient boosting (XGBoost), support vector machines (SVM), K-nearest neighbors (KNN), decision trees classifier (DTC), and random forest (RF) were employed as the five ML algorithms to comprehensively analyze the dataset. For a full study, we also included 1D convolutional neural networks (CNN) from the DL area. The 1D CNN performed extraordinarily well, exceeding the accuracy of all other algorithms with a stunning 99.37%.
Facial micro-expression classification through an optimized convolutional neural network using genetic algorithm Santosh Naidana, Krishna; Yarra, Yaswanth; Prasanna Divvela, Lakshmi
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.v14i1.8048

Abstract

Computer vision facilitates machines to interpret the visual world using various computer aided detection (CAD)-based techniques. It plays a crucial role in micro-expression auto classification. A micro-expression is a brief facial movement which reveals a genuine emotion that a person tries to conceal, it usually lasts for a short duration and is imperceptible with normal vision. To reveal people’s genuine emotions, an automatic micro-expression screening using convolutional neural network (CNN) is in great need. Traditional methods for micro-expression recognition (MER) suffer from low classification accuracy due to inadequate CNN hyperparameters selection. The proposed approach addresses these challenges by using an optimized CNN with adequate learning rate, batch size, epochs, and dropout rate. Real-coded genetic algorithm (RCGA) has been employed for the hyperparameter optimization. In this experimentation, features are extracted from the onset and apex frames of microexpression video clips of CASME II dataset. The proposed model's performance is measured using various metrics, including accuracy, precision, and recall. The proposed approach’s performance is then compared with an optimized CNN using random search algorithm. The empirical investigation of existing CNN-based methods has proven efficacy of our proposed model.
Integration of genetic algorithm and mesoscopic modeling for the optimization of membrane separation processes Umarova, Zhanat; Makhanova, Zlikha; Zhumatayev, Nurlybek; Kopzhassarova, Asylzat; Suieuova, Nabat; Imanbayeva, Aigul; Yegenova, Aliya
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.v14i1.8268

Abstract

This article is dedicated to the development of an innovative approach to optimizing membrane separation processes. The paper introduces the integration of a genetic algorithm (GA) and mesoscopic modeling to enhance the efficiency and accuracy of process parameter optimization. The GA is employed for evolutionary search of optimal parameters, such as pressure, temperature, and membrane material characteristics. The use of evolutionary principles allows for efficient exploration of parameter space, identifying optimal solutions. Mesoscopic modeling serves as a tool for detailed analysis and visualization of membrane separation processes. It involves modeling the interaction of molecules with the membrane surface, enabling a more accurate consideration of the physicochemical aspects of the process. The integration of the GA and mesoscopic modeling creates a unique tool for membrane separation process optimization. The developed approach contributes not only to improving component separation efficiency but also to minimizing energy consumption. The method presented in the article has been successfully tested on model membrane process systems and demonstrated significant improvements compared to traditional optimization methods. The research results confirm the potential of the proposed approach for application in membrane technology industries, opening new perspectives in the field of separation process optimization.
Optimizing neural radiance field: a comprehensive review of the impact of different optimizers on neural radiance fields Pinjarkar, Latika; Nittala, Aditya; P. Mattada, Mahantesh; Pinjarkar, Vedant; Neole, Bhumika; Kogundi Math, Manisha
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.v14i1.8315

Abstract

Neural radiance field (NeRF) is a form of deep learning model that may be used to depict 3D scenes from a collection of photos. It has been demonstrated that NeRF can produce photorealistic photographs of fresh perspectives on a scene even from a small number of input images. However, the optimizer that is employed can have a significant impact on the quality of the final reconstruction. Finding an effective optimizer is one of the biggest challenges while learning NeRF models. The optimizer is responsible for making changes to the model's parameters to minimize the discrepancy between the model's predictions and the actual data. We cover the many optimizers that have been used to train NeRF models in this study. We present research results contrasting the effectiveness of multiple optimizers and examine the benefits and drawbacks of each optimizer. For training NeRF models, four different optimizers viz. Adaptive moment estimation (Adam), AdamW, root mean square propagation (RMSProp), and adaptive gradient (Adagrad) are trained. The most effective optimizer for a given assignment will vary depending on a variety of elements, including the size of the dataset, the complexity of the scene, and the level of accuracy that is required.
X-band and Ku-band PIN diode loaded reflectarray unit cells with adaptive frequency switching Inam Abbasi, Muhamamad; Dali Khan, Sher; Hashim Dahri, Muhammad; Mohd Ibrahim, Imran; Hafizah Sulaiman, Noor
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.v14i1.8798

Abstract

The fast advancement of intelligent new applications has led to the creation of high-performance antennas. Reflectarrays (RAs), also known as planar reflectors, are seen as promising antennas for several such modern-day applications. This work presents a comprehensive investigation of frequency switchable RA antennas operating in the X-band and Ku-band frequency ranges. Various strategic configurations of combined slots have been suggested, using integrated P-layer, I-layer, and N-layer (PIN) diodes, with the purpose of creating unit cells in RAs that may switch frequencies and exhibit a gradual change in phase distribution. The frequency variation achieved in X-band for the ON state of PIN diodes is from 8.13 GHz to 11.69 GHz, whereas for the OFF state it is from 8.13 GHz to 11.68 GHz. Similarly, for Ku-band ON and OFF states of PIN diodes provided frequency variations of 13.6 GHz to 17.1 Ghz and 12.8 Ghz to 16.6 GHz respectively. Frequency tunability of 0.85 GHz and 0.72 GHz has been successfully achieved in X-band and Ku-band.
Distributed denial-of-service attack detection short review: issues, challenges, and recommendations Ahasan Habib, AKM; Imtiaz, Ahmed; Tripura, Dhonita; Omar Faruk, Md.; Anwar Hossain, Md.; Ara, Iffat; Sarker, Sohag; Zainul Abadin, A F M
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.v14i1.8377

Abstract

An attacker can attack a network in several methods when there are a lot of device connections. Distributed denial-of-service (DDoS) attacks could result from this circumstance, which could damage resources and corrupt data. Therefore, irregularity in traffic data must be detected to identify malicious behavior in a network, which is critical for maintaining the integrity of current cyber-physical systems (CPS) as well as network security. This article attempts to study and compare various approaches to detecting DDoS attacks and expresses data paths for packet filtering for high-speed networks (HSN) performance, using machine or deep learning techniques used in intrusion detection systems (IDSs) and flow-based IDSs. The study presents a comprehensive DDoS attack taxonomy, categorizes detection strategies, and highlights the HSN accuracy assessment features. By exposing the problems and difficulties associated with DDoS attacks on HSN, several investigation paths are proposed to assist researchers in determining and developing the best solution.
Intrusion detection system in lightweight devices: issues and challenges Musa Shanono, Nuruddeen; Muslim, Zulkiflee; Azman Abu, Nur; Rahayu Selamat, Siti; Nahar, Haniza
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.v14i1.8038

Abstract

Intrusion detection system (IDS) is a crucial component in ensuring the security of computer networks. It helps in identifying and responding to unauthorized access attempts or malicious activities within a network. The focus of this systematic review is on IDS specifically designed for lightweight devices. This systematic review aims to provide an abstract understanding of the current state of IDSs for lightweight devices. It involves a comprehensive analysis of existing research papers, evaluating the methodologies, techniques, and performance metrics used in these IDS solutions. The goal of the systematic review is to provide a critical assessment and analysis of the literature on IDS in lightweight devices, closing the research gap in this field. The review analyzed and evaluated 55 studies out of 678 initially identified. The findings of the study are presented in the paper, which includes insights into the state-of-the-art proposals in the field, challenges and limitations of existing solutions, and recommendations for future research directions. The outcome of this paper can help the advancement of IDS for lightweight devices.
Analyzing 5G performance: investigating altitude-induced variations Daengsi, Therdpong; Sriamorntrakul, Pakkasit; Chatchalermpun, Surachai; Phanrattanachai, Kritphon
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.v14i1.8425

Abstract

Since the launch of fifth generation (5G) services in Thailand in 2020, there have been continuous improvements in 5G coverage. Currently, 5G coverage extends to most areas throughout the country. However, coverage issues persist not only in rural areas but also in high-rise buildings in urban areas. Consequently, a study was conducted within such buildings. This paper assesses the performance of 5G at different altitude test points. The chosen location for the field tests was a high-rise building within a crowded public hospital, which receives numerous patients every weekday, in the major urban area of Bangkok. Two smartphones from the same manufacturer, both supporting 5G technology and equipped with the Speedtest application, were employed as tools for this study. Tests were carried out on the third and twenty-fourth floors of the high-rise building for data collection. The primary finding of this study reveals that download speeds exhibited a significant decrease with increasing altitude of the test points, as evidenced by statistical analysis (p-values0.001). This implies an issue with altitude-induced variations, indicating a need for the improvement of indoor 5G coverage in high-rise buildings.

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