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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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Data augmentation and enhancement for multimodal speech emotion recognition Jonathan Christian Setyono; Amalia Zahra
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5031

Abstract

Humans’ fundamental need is interaction with each other such as using conversation or speech. Therefore, it is crucial to analyze speech using computer technology to determine emotions. The speech emotion recognition (SER) method detects emotions in speech by examining various aspects. SER is a supervised method to decide the emotion class in speech. This research proposed a multimodal SER model using one of the deep learning based enhancement techniques, which is the attention mechanism. Additionally, this research addresses the imbalanced dataset problem in the SER field using generative adversarial networks (GAN) as a data augmentation technique. The proposed model achieved an excellent evaluation performance of 0.96 or 96% for the proposed GAN configuration. This work showed that the GAN method in the multimodal SER model could enhance performance and create a balanced dataset.
Design and performance analysis of Cobb angle measurement from X-ray images Adibatti, Spurthi; Sudhindra, K. R.; Manisha Shivaram, Joshi
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5335

Abstract

Scoliosis seems to be the most frequently used type of spinal abnormality. Cobb angle measurement essentially relates to the quantification of the spinal stenosis in degrees, also it is a globally standardized approach for assessing scoliosis. Based on a recent survey, there really is no accurate and comprehensive technique for calculating the Cobb angle programmatically. This problem is crucial in the medical field pertaining to Cobb angle measurement to identify the exact position of Cobb in X-ray images. However, multiple investigations have demonstrated that there is inter and intra-observer variance when assessing the Cobb angle physically. The goal is to create a computer-assisted solution to reduce user-based Cobb angle measuring errors. The preprocessing filters and semi-automatic methods determine the overall architectural curved spine. Using the Cobb technique, results are inaccuracies and improper in the estimation of a scoliotic curvature's peak or bottom vertebra. The curve-fitting approach was used in this investigation to reduce uncertainty. Every patient had a digitally recorded poster anterior radiography image. The polynomial estimation is fitted using user-defined curvature midpoints that correspond to vertebral intersection points. The Cobb angle is computed by taking the first characteristic of the fitted polynomial function and dividing it by the number of vertebrae.
A fine tune robust transfer learning based approach for brain tumor detection using VGG-16 Islam, Rakibul; Akhi, Amatul Bushra; Akter, Farzana
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5646

Abstract

Brain tumor recognition by magnetic resonance imaging (MRI) is crucial because it improves survival rates and allows them to plan treatments accordingly. An accumulation of abnormal cells known as a brain tumor can spread to nearby tissues and endanger the patient. Magnetic resonance imagery is the primary imaging technique which determines the extent of brain tumors. Deep learning techniques rapidly grew in computer vision due to ample data for model training and improved designs on applications. MRI has shown promising results when using deep learning approaches to identify and classify brain tumors. This study uses MRI data and a convolutional neural network (CNN) to create a reliable transfer learning model that classifies tumors under four classes. Brain tumors' unwanted parts are excised, the quality is improved, and the cancer is coloured. By eliminating artefacts, decreasing noise, and boosting the image. The number of MRI images has increased using two augmentation techniques. A number of CNN architectures, including VGG19, VGG16, MobileNet, InceptionV3, and MobileNetV2 analyzed the augmented dataset. Where VGG-16 provides the accuracy of highest level. The best model underwent a hyperparameter ablation investigation, which led to the suggested hyper-tuned VGG16 obtaining 99.21% test and validation accuracy and 99.01% test accuracy.
The use of CaO:Eu3+ and Zn2SiO4:Mn2+ phosphors to increase the color quality and illumination intensity of WLEDs Thanh Tung, Ha; An Nguyen Thi, Dieu; Xuan Le, Phan
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4754

Abstract

The lumen efficacy in remote phosphor structure displayed remarkable enhancements, which is notable for the development of white light-emitting diodes (WLEDs). Nevertheless, its quality of colour is deemed not as good as that of the conformal or in-cup phosphor structure. Therefore, the goal of this research is to achieve a better quality of colour and significant luminous flux value for remote phosphor configuration by using extra phosphor layers. In particular, the two-layer and three-layer structures with the implementation of green and red phosphors are proposed. Comparing these two structures can help pinpoint the best suited for the WLED production. The assessment of each structure’s effect on the WLEDs’ optical parameters was determined under various correlated temperatures of colour (5,600-8,500 K). The outcomes indicated that the three-layer structure enhanced the quality of colour with greater efficiency compared to the two-layer structure due to the increased color rendering index (CRI), color quality scale (CQS), and photoluminescence (PL), and reduced colour deviation. The scattering improvement of the three-layer structure is a key factor of these accomplishments, proven by the scattering theory of Mie. Therefore, the three-layer structure is potential for developing WLED production.
Solutions of economic load dispatch problems for hybrid power plants using Dandelion optimizer Hung Duc Nguyen; Ly Huu Pham
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5245

Abstract

In this paper, an economic load dispatch problem (ELD) is solved for reaching optimal power output of hybrid systems in addition to cost minimization. The systems consider the forbidden working zones (FWZs), dynamic load demand, wind farms, and solar photovoltaic fields (SPs). The cost minimization solutions for the ELD problem are found by applying the Dandelion optimizer (DO), the salp swarm algorithm (SSA), and the particle swarm optimization (PSO). In the study case, the power system consists of six thermal power plants (TPs), two wind farms, and two SPs. In addition, the variation of load demand over 24 hours of one day is applied. DO and SSA can achieve the best cost of $15443.0753 for the first system, but PSO cannot. However, DO is the most stable method reaching the standard deviation of 0.0184 for fifty runs but that of SSA and PSO is about 1.0439 and 8.9664. For the second system, DO can reach smaller cost than PSO by $11.17, $137.74 and $323.09 for the best, mean and worst solutions among fifty found solutions. As a result, DO is strongly recommended for solving the ELD problem.
A comparative study of Gaussian mixture algorithm and K-means algorithm for efficient energy clustering in MWSN Ahmad, Iman Ameer; Jawad Al-Nayar, Muna Mohammed; Mahmood, Ali M.
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5707

Abstract

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving energy of up to 92% at 4,500 rounds.
A novel automated feature selection based approach to recognize cauliflower disease Shakil, Rashiduzzaman; Akter, Bonna; Javed Mehedi Shamrat, F M; Haider Noori, Sheak Rashed
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5359

Abstract

Cauliflower disease is a primary cause of reduced cauliflower yield. Preventing cauliflower disease requires early diagnosis. In the scope of this study, we suggested an agro-medical expert system that would make it easier to diagnose cauliflower disease. In this method, a digital image must be taken off the phone or handled device to diagnose cauliflower sickness. A data augmentation technique was initially used to construct a vast data set. The disease-affected parts of the cauliflower were then segmented using k-means clustering. Following that, ten statistical and gray-level co-occurrence matrix (GLCM) features were retrieved from the segmented pictures. After choosing the top n features (N ranged from 5 to 10), the synthetic minority oversampling technique (SMOTE) approach was used to handle training datasets with different amounts of each feature. After that, we utilized five machine learning (ML) algorithms and evaluated their performance using seven performance evaluation matrices for both augmented and non-augmented datasets. The same procedure was performed on both datasets. Then, we use both datasets to test how well the classifier works. Logistic regression (LR) is the most accurate method for the top nine features in the augmented dataset (90.77%).
Improving the color deviation of white light emitting diode by employing Sr3Ga2Ge4O14:Cr3+ phosphor Ha Thanh Tung; Huu Phuc Dang; Dieu An Nguyen Thi
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5163

Abstract

The pomising uses of near infrared (NIR) phosphor converted light emitting diodes (pc-LEDs), including non-destructive testing and biological implementations, are endless. It is still difficult to create wideband and NIR phosphors with sufficient heating steadiness for a variety of uses. The study herein introduces the phosphor Sr3Ga2Ge4O14:Cr3+, its creation procedure through expreiments as well as its influneces on LED devices. The Sr3Ga2Ge4O14:Cr3+ (SGGO:Cr3+) super-wideband NIR phosphor was effectively synthesized in this study with 431 nm stimulation, and a spectrum adjustment 750 nm-900 nm was made in a two-step superwideband radiation having its full width under half maximum (FWHM) changing among 257 and 336 nm. The inner quantum performance (IQP) for SGGO:0.15Cr3+ is 36.67%, and it has an FWHM of 257 nm. At 423 K, the emitting strength was still 76% of ambient temperature. The potential of SGGO:Cr3+ for various uses was eventually demonstrated by the employment of SGGO:0.03Cr3+ as well as SGGO:0.15Cr3+ samples accompanied by blue illumination chips under 430 nm for the task of creating NIR pc-LED gadgets then putting in nighttime sight, human palm puncture, flora lighting. It was shown that SGGO:Cr3+ has a wide range of use possibilities.
A novel method for estimating propagation pathloss in millimeter-wave communication systems Thanh Quang, Vu; Huu Duc, Do; Van Yem, Vu; Phuong Thao, Hoang Thi
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.6614

Abstract

In millimeter wave communication systems, the construction of a path loss model that is close to empirical one plays an important role in planning coverage, system capacity, and link budgets. In this paper, we propose a novel approach applied unsupervised machine learning for propagation pathloss estimation in millimeter wave communication systems based on the iterative procedure of cooperative and iterative evaluation exchange (Co-IEE) algorithm. The propagation path loss models of the time city–an urban area in the center of Hanoi, Vietnam in both line-of-sight (LOS) and non line-of-sight (NLOS) are estimated and calculated using the proposed approach and then they are compared to the minimum mean square error (MMSE). The estimated results of the proposed model show the approximation with the optimum one returned by MMSE method. Moreover, the proposed model of estimating path loss can solve the problem of sensitivity with outliers existing in MMSE and give more choices for path loss exponents.
Texture features analysis technique to detect mass lesion in digitized mammogram images Ayman A. AbuBaker; Yazeed Yasin Ghadi
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4701

Abstract

Mass lesions are one of the breast cancer tumors. Mammogram images are the first screening tool to detect tumors in the women breast, but due to radiologist fatigue, number of false positive (FP) and false negative (FN) rates are increased. The main objective of this paper is to develop an intelligent computer aided diagnosis (CAD) system that can accurately detect mass lesions in digitized mammogram images. The proposed method has three stages. The first stage is a preprocessing stage, where the mass lesion is enhanced using a customized Laplacian filter. Then, multi-statistical filters are implemented to detect a potential mass lesion in the mammogram images. In the final stage, the number detected FP regions are reduced using five texture features. The proposed algorithm is evaluated using 45 mammogram images and the algorithm achieved an accuracy rate of 97% in detecting mass lesion with 83% sensitivity rate and 98% specificity rate.

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