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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 13, No 5: October 2024" : 75 Documents clear
Gamma and ultraviolet radiation radiation analysis: an internet of things-based dosimetric study Baena-Navarro, Rubén; Alcala-Varilla, Luis; Torres-Hoyos, Francisco; Carriazo-Regino, Yulieth; Parodi-Camaño, Tobías
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7344

Abstract

This study presents the implementation of an internet of things (IoT)-based device for the accurate and continuous measurement of gamma and ultraviolet (UV) radiation in a rural area of Sincelejo, Colombia. The device, calibrated with an error margin below 5%, allowed for the reliable collection of data during the year 2022. An average effective dose rate of gamma radiation of (0.998±0.037) mSv/year was recorded, a value that approaches the recommended limit. Additionally, the inverse square law of radiation was confirmed, observing a decrease in radiation with an increase in altitude. Concurrently, a constant risk of high to extremely high UV radiation exposure was detected throughout the year. These findings emphasize the need for constant monitoring and the implementation of UV protection measures in the region. The integration of IoT in environmental dosimetry has proven to be an invaluable tool for detailed tracking of radiation levels, significantly contributing to the understanding of radiation in rural areas. The exploration of more advanced sensors and data analysis tools in future research is recommended to further improve the accuracy and utility of these devices.
Handwritten Arabic words detection using Faster R-CNN in IFN/ENIT dataset Mowaffaq AL-Taee, May; Ben Hassen Neji, Sonia; Frikha, Mondher
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8189

Abstract

Recognizing Arabic offline handwritten words still faces various challenges because of the diversity of writing styles and the overlap between the words and characters. Therefore, building an effective system to solve these challenges has always been difficult, which has led to a lack of published research in this field. This study introduces two new models to recognize handwritten Arabic words based on the Faster region-convolution neural network (Faster R-CNN). These models employ two pre-trained networks during the feature extraction phase: The visual geometry group-16 (VGG-16) network and the residual network (ResNet50) network. To help with overlapping detections and make localization more accurate, a soft non-maximum suppression (Soft-NMS) strategy is used in post-processing. Models are independently trained and tested on two groups of data from the Institut Für Nachrichtentechnik/Ecole Nationale d’Ingénieurs de Tunis (IFN/ENIT) dataset. The first group includes one word in each image, while the second contains multiple words. Test results showed that the proposed models give excellent results compared to others. The results of VGG16 and ResNet50 with the first dataset reached accuracy rates of 100% and 99.5%, respectively. Meanwhile, the accuracy of the second group reached 91.4% and 100% with VGG16 and ResNet50, respectively.
Performance evaluation of software defined networking into vanets system Taher, Younus Hasan; Alsaadi, Israa; Saad, Mohammed Ayad; Ali, Adnan Hussein; Essa, Mohammed; Rashid, Ahmed Hashim
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.4675

Abstract

Vehicular ad hoc networks (VANETs) is an important topic nowadays. A lot of research deal and attracts consideration owing to potential for increasing traffic and travel efficiency, improving road safety for vehicles, providing convenience and comfort to both drivers and passengers. The need for a packet delivery ratio (PDR) and low delivery delay time in communication are the key elements in modern life especially when traveling in vehicles. To satisfy these demands; researchs in VANET systems aims to develop some new technologies. One of these technologies is using software-defined- network (SDN) to enhance communication between vehicles on the road. Because of this, project evaluates using SDN protocol with two most viable VANET protocols which are ad hoc on demand distance vector (AODV) and optimized link state routing (OLSR) in LTE communication. Two performance metrics are used to evaluate the performances, the PDR and the delivery delay time. The simulation is performed in the varying density network and varying speed vehicles. The simulation results show that SDN displays better performance than AODV and OLSR in both PDR and delivery delay time. SDN uses global views of SDN controller to determine the shortest route with the highest vehicle density. Additionally, it solves the local maximum issue and adds dense connectivity.
Braille letter recognition in deep convolutional neural network with horizontal and vertical projection Rahmat, Romi Fadillah; Purnamawati, Sarah; Mardianto, Willy; Faza, Sharfina; Sulaiman, Riza; Nadi, Farhad; Lubis, Arif Ridho
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7222

Abstract

Brail is a written mode of communication utilized by individuals with visual impairments to engage in interpersonal exchanges. The braille writing system consists of patterns printed on specialized paper that feature embossed dots. Braille documents enable the visually impaired to acquire knowledge and information exclusively through the application of their sense of contact. Comprehending braille is not a simple undertaking, particularly for the general populace. Because braille is not a required subject in Indonesian education, the majority of the population lacks proficiency in the language. This may therefore result in a minor communication barrier between visually impaired individuals and non-impaired individuals. In order to address this challenge, the present study employs digital image processing via the deep convolutional neural network (DCNN) technique to facilitate comprehension of braille document contents by non-braille speakers. This study employs a deep learning technique that is highly accurate, effective at image processing, and capable of recognizing complex patterns. This study employed the following image processing methods: grayscaling, filtering, contrast enhancement, thresholding, morphological operation, and resizing. Following testing in this investigation, it was determined that the proposed method accurately identifies embossed braille images with a precision of 99.63%.
Bio-engineered strategies for osteochondral defect repair Alnaimat, Feras; Owida, Hamza Abu; Turab, Nidal M.; Al-Nabulsi, Jamal I.
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7316

Abstract

Due to the absence of blood vessels and nerves, the regenerative potential of articular cartilage is significantly constrained. This implies that the impact of a ruptured cartilage extends to the entire joint. Osteoarthritis, a health condition, may arise due to injury and the progressive breakdown of joint tissues. The progression of osteoarthritis can be accelerated by the extensive degradation of articular cartilage, which is ranked as the third most prevalent musculoskeletal disorder necessitating rehabilitation, following low back pain and fractures. The existing therapeutic interventions for cartilage repair exhibit limited efficacy and seldom achieve complete restoration of both functional capacity and tissue homeostasis. Emerging technological advancements in the field of tissue engineering hold significant promise for the development of viable substitutes for cartilage tissue, capable of exhibiting functional properties. The overarching strategy involves ensuring that the cell source is enriched with bioactive molecules that facilitate cellular differentiation and/or maturation. This review provides a comprehensive summary of recent advancements in the field of cartilage tissue engineering. Additionally, it offers an overview of recent clinical trials that have been conducted to examine the latest research developments and clinical applications pertaining to weakened articular cartilage and osteoarthritis.
Enhancing classification in high-dimensional data with robust rMI-SVM feature selection Chin, Fung Yuen; Goh, Yong Kheng
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7938

Abstract

Dealing with high-dimensional datasets presents notable challenges for classification modelling, primarily due to complexity and susceptibility to overfitting. Traditional feature selection methods frequently struggle to guarantee improved classification performance by including more features. Instead, they often rely on utilising the entire feature set. To address these challenges, a robust feature selection algorithm known as ranked mutual information for support vector machines (rMI-SVM) has been introduced. This approach mitigates the risk of overfitting by selecting features that augment the classification model with additional information, thereby ensuring enhanced performance as more features are selected. rMI-SVM can accommodate datasets with missing values regardless of data linearity as it does not require additional parameters or preset the number of features needed. The proposed method offers a solution to the challenges posed by high-dimensional data, and explicitly identifies the optimal number of features required for a classification model, thus circumventing the necessity of using the full feature set. These findings are supported by receiver operating characteristic (ROC) curves, which highlight the effectiveness of rMI-SVM in outperforming existing baselines and delivering a superior classification model performance.
A study on social media addiction analysis on the people of Bangladesh using machine learning algorithms Mim, Minjun Nahar; Firoz, Mehedi; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.5680

Abstract

Social media has become a fundamental element of contemporary life, providing countless benefits but also posing substantial concerns. While technology improves connectedness and information exchange, excessive use raises issues about social and personal well-being. The emergence of social media addiction emphasizes its influence on everyday routines and mental health, with many people favoring online activities above vital tasks, resulting in real repercussions. Twitter, Facebook, and Snapchat have a significant impact on emotional well-being, adding to global rates of despair and anxiety. To measure the frequency of social media reliance, we studied data from 1,417 individuals using machine learning methods such as decision tree (DT) classifier, random forest (RF) classifier, support vector classifier (SVC), k-nearest neighbors (K-NN), and multinomial naive Bayes (NB). Understanding the behavioral patterns that drive addiction allows us to create tailored therapies to encourage healthy digital behaviors. This study highlights the critical necessity to address social media addiction as a complicated societal issue. Our major goal is to determine the amount of people who are addicted to social media.
CMOS low noise amplifier technologies: trends for enhancing satellite receivers and mobile communications Singh, Rashmi; Mehra, Rjaesh
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8002

Abstract

The emerging demand for high-data-rate wireless communication systems and high-resolution radars, particularly in the millimeter-wave (mm-wave) spectrum, has captured significant attention within both the industrial and academic landscape. Recognized as the fundamental building block for satellite receivers, the low noise amplifier (LNA) plays a pivotal role in meeting these growing requirements. In Today's world a continuously increasing number of connected devices and resource-intensive digital content load to an incessant data being generated, transformed, and facilitated. In this paper, authors summarize the different technologies and techniques employed to design various LNAs with enhanced bandwidth, higher gain, low noise figure (NF), minimal power consumption, and less chip area.
Deep learning based photovoltaic generation on time series load forecasting Loganathan, Umasankar; Nagarajan, Geetha; Gopinath, Srimathy; Chandrasekar, Vignesh
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7836

Abstract

In recent years, solar irradiance forecasting has become essential to managing, developing, and effectively integrating photovoltaic (PV) systems properly into the smart grid. The foundation of a conventional variational autoencoder (VAE) is an entirely coupled layer that includes both decoder and encoder components. In this study, a novel deep attention-driven model for forecasting named bidirectional long short-term memory (BiLSTM) which is combined with the VAE model is introduced as an enhanced version of the VAE. BiLSTM is integrated at the encoder side of VAE to effectively extract and learn temporal dependencies that are embedded in the panel irradiance data. Additionally, a self-attention mechanism (SAM) is added to bilateral variational autoencoder (BiVAE) which is known as BiVAE-SAM that highlights the important characteristics. The proposed BiVAE-SAM permits the VAE’s capacity to design the temporal dependency. The examined models are assessed using sun irradiance measurements from New York City, Turkey, Canopy, Los Angeles, California, and Florida. The outcomes exhibit that the proposed BiVAE-SAM model performs better mean absolute percentage error (MAPE) with values of 1.7935, 0.7828, 1.3491 and 2.8346 respectively for California, Los Angeles, New York City, and Florida, over existing stacked denoising auto-encoders (SDA) model at projecting solar irradiance.
Transfer learning for improved electrocardiogram diagnosis of cardiac disease: exploring the potential of pre-trained models Sayed Ismail, Sharifah Noor Masidayu; Abdul Razak, Siti Fatimah; Ab. Aziz, Nor Azlina
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7053

Abstract

Predicting the onset of cardiovascular disease (CVD) has been a hot topic for researchers for years, and recently, the concept of transfer learning has been gaining traction in this field. Transfer learning (TL) is a process that involves transferring information gained from one task or domain to another related task or domain. This paper comprehensively reviews recent advancements in pre-trained TL models for CVD, focusing on electrocardiogram (ECG) signals. Forty-three articles were chosen from Scopus and Google Scholar sources and reviewed, focusing on the type of CVD detected, the database used, the ECG input format, and the pre-training model used for transfer learning. The results show that more than 80% of the studies utilize 2-dimensional (2D) ECG input from the two most utilized available ECG datasets: MIT-BIH arrhythmia (ARR) and MIT-BIH normal sinus rhythm. alexnet, visual geometry group (VGG), and residual network (ResNet) are among the pre-trained TL models with the highest number used among reviewed articles. Additionally, the development of pre-trained TL models over time has made it possible to detect CVD with ECG signals. It can also address limited data problems, promote the development of more dependable and resilient detection systems, and aid medical professionals in diagnosing CVD and other diseases.

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