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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal 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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Stock market prediction of Bangladesh using multivariate long short-term memory with sentiment identification Md. Ashraful Islam; Md. Rana Sikder; Sayed Mohammed Ishtiaq; Abdus Sattar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5696-5706

Abstract

The prediction of stock market trends is a challenging task due to its dynamic and volatile nature. Research has shown that predicting the stock market, especially in developing nations like Bangladesh, is challenging due to the presence of multiple external factors in addition to technical ones. To address this, this study proposed a novel dataset that includes not only technical stock market data from 2014 to 2021, but also external factors such as news sentiment and other economic indicators like inflation, gross domestic product (GDP), exchange rate, interest rate, and current balance. The goal is to provide a comprehensive view of the Dhaka Stock Exchange (DSE), the largest stock market in Bangladesh. The main objective of this study is to predict the trend of DSE by taking into account both technical stock market data and relevant external factors, and to compare the predictions made with and without using external factors. The study utilized a multivariate long short-term memory (LSTM) neural network for the stock market trend prediction. The experimental results showed that the use of external factors improved the accuracy of the LSTM-based stock market trend predictions by approximately 24%.
Wind power prediction using a nonlinear autoregressive exogenous model network: the case of Santa Marta, Colombia Jordan Guillot; Diego Restrepo Leal; Carlos Robles-Algarín; Ingrid Oliveros; Paola Andrea Niño-Suárez
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4856-4867

Abstract

The monitoring of wind installations is key for predicting their future behavior, due to the strong dependence on weather conditions and the stochastic nature of the wind. However, in some places, in situ measurements are not always available. In this paper, active power predictions for the city of Santa Marta-Colombia using a nonlinear autoregressive exogenous model (NARX) network were performed. The network was trained with a reliable dataset from a wind farm located in Turkey, because the meteorological data from the city of Santa Marta are unavailable or unreliable on certain dates. Three training and testing cases were designed, with different input variables and varying the network target between active power and wind speed. The dataset was obtained from the Kaggle platform, and is made up of five variables: date, active power, wind speed, theoretical power, and wind direction; each with 50,530 samples, which were preprocessed and, in some cases, normalized, to facilitate the neural network learning. For the training, testing and validation processes, a correlation coefficient of 0.9589 was obtained for the best scenario with the data from Turkey, while the best correlation coefficient for the data from Santa Marta was 0.8537.
Ranking load in microgrid based on fuzzy analytic hierarchy process and technique for order of preference by similarity to ideal solution algorithm for load shedding problem Tung Giang Tran; Thai An Nguyen; Trong Nghia Le; Ngoc Au Nguyen; Thi Ngoc Thuong Huynh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4919-4930

Abstract

This paper proposes a method to rank the loads in the microgrid by means of a weight that combines the criteria together in terms of both technical and economic aspects. The fuzzy analytic hierarchy process technique for order of preference by similarity to ideal solution (fuzzy AHP TOPSIS) algorithm is used to calculate this combined weight. The criteria to be considered are load importance factor (LIF), voltage electrical distance (VED) and voltage sensitivity index (VSI). The fuzzy algorithm helps to fuzzy the judgment matrix of the analytic hierarchy process (AHP) method, making it easier to compare objects with each other and remove the uncertainty of the AHP method. The technique for order of preference by similarity to ideal solution (TOPSIS) algorithm is used to normalize the decision matrix, determine the positive and negative ideal solutions to calculate the index of proximity to the ideal solution, and finally rank all the alternatives. The combination of fuzzy AHP and TOPSIS algorithms is the optimal combination for decision making and ranking problems in a multi-criteria environment. The 19-bus microgrid system is applied to calculate and demonstrate the effectiveness of the proposed method.
Bio-inspired algorithm for decisioning wireless access point installation Aphirak Thitinaruemit; Suchada Sitjongsataporn; Sethakarn Prongnuch
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4996-5005

Abstract

This paper presents the bio-inspired algorithms for decisioning wireless access point (AP) installation. In order to achieve the desired coverage capability of APs, the bio-inspired algorithms are applied for robust competition and optimization. The main objective is to determine the optimal number of APs with the high coverage capability in the concerning area using the genetic and ant colony optimization algorithms. Received signal strength indicator (RSSI) and line-of-sight (LoS) gradient approach are the most important parameters for AP installation depending on the AP signal strength. Practical experiments are tested on the embedded system using Xilinx Kria KR260 and Raspberry Pi Zero 2W boards at the tested room size about 16 m wide and 40 m long inside the building. Xilinx Kria KR260 board is used to calculate the number of AP installation and localization compared to Xcode. Then, Raspberry Pi Zero 2W board is the representation of wireless AP for measuring the signal in the testing area. Experiment results show that maximum received signals strength is equal to -35 dBm at 6 m and there are six APs installation with high coverage area and maximum received signal strength at the area of 16×40 m2.
Improved spectral mismatch and performance of a phosphor-converted light-emitting diode solar simulator Napat Watjanatepin; Khanittha Wannakam; Paiboon Kiatsookkanatorn; Chaiyant Boonmee; Patcharanan Sritanauthaikorn
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4931-4941

Abstract

A phosphor-converted light-emitting diode (LED) solar simulator is an illuminance device that produced irradiance intensity and spectral close to the sunlight. It is determined as spectral mismatch, non-uniformity of irradiance, and temporal instability. This paper has improved the LED solar simulator (LSS) system to have a spectral distribution consistent with the AM1.5G spectrum at 100%. It was developed as a new prototype to have the AAA class spectral characteristics, time instability, and inconsistency according to IEC 60904-9. The results showed that an optimal approach was to use phosphor-converted natural white LED (pc-nWLED), combining a monochromatic near-infrared (NIR) (730, 800, 850, 940, and 1,000 nm) as well as the proposed LSS system capable of generating 1,000 W/m2 irradiation over the test plane of 125×125 mm and operated continuously in a constant temperature LED state for at least 2 hours, therefore suitable for demonstration of solar cell features under standard test condition (STC) in the laboratory.
Runner profile optimisation of gravitational vortex water turbine Ridwan Arief Subekti; Sastra Kusuma Wijaya; Arief Sudarmaji; Tinton Dwi Atmaja; Budi Prawara; Anjar Susatyo; Ahmad Fudholi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4777-4788

Abstract

This study discusses the numerical optimisation and performance testing of the turbine runner profile for the designed gravitational water vortex turbine. The initial design of the turbine runner is optimised using a surface vorticity algorithm coded in MATLAB to obtain the optimal stagger angle. Design validation is carried out using computational fluid dynamics (CFD) Ansys CFX to determine the performance of the turbine runner with the turbulent shear stress transport model. The CFD analysis shows that by optimising the design, the water turbine efficiency increases by about 2.6%. The prototype of the vortex turbine runner is made using a 3D printing machine with resin material. It is later tested in a laboratory-scale experiment that measures the shaft power, shaft torque and turbine efficiency in correspondence with rotational speeds varying from 150 to 650 rpm. Experiment results validate that the optimised runner has an efficiency of 45.3% or about 14% greater than the initial design runner, which has an efficiency of 39.7%.
Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review Rahmat Budiarsa; Retantyo Wardoyo; Aina Musdholifah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5662-5673

Abstract

Face recognition technology has been used in many ways, such as in the authentication and identification process. The object raised is a piece of face image that does not have complete facial information (occluded face), it can be due to acquisition from a different point of view or shooting a face from a different angle. This object was raised because the object can affect the detection and identification performance of the face image as a whole. Deep leaning method can be used to solve face recognition problems. In previous research, more focused on face detection and recognition based on resolution, and detection of face. Mask region convolutional neural network (mask R-CNN) method still has deficiency in the segmentation section which results in a decrease in the accuracy of face identification with incomplete face information objects. The segmentation used in mask R-CNN is fully convolutional network (FCN). In this research, exploration and modification of many FCN parameters will be carried out using the CNN backbone pooling layer, and modification of mask R-CNN for face identification, besides that, modifications will be made to the bounding box regressor. it is expected that the modification results can provide the best recommendations based on accuracy.
Reliable and efficient webserver management for task scheduling in edge-cloud platform Sangeeta Sangani; Rudragoud Patil
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5922-5931

Abstract

The development in the field of cloud webserver management for the execution of the workflow and meeting the quality-of-service (QoS) prerequisites in a distributed cloud environment has been a challenging task. Though, internet of things (IoT) of work presented for the scheduling of the workflow in a heterogeneous cloud environment. Moreover, the rapid development in the field of cloud computing like edge-cloud computing creates new methods to schedule the workflow in a heterogenous cloud environment to process different tasks like IoT, event-driven applications, and different network applications. The current methods used for workflow scheduling have failed to provide better trade-offs to meet reliable performance with minimal delay. In this paper, a novel web server resource management framework is presented namely the reliable and efficient webserver management (REWM) framework for the edge-cloud environment. The experiment is conducted on complex bioinformatic workflows; the result shows the significant reduction of cost and energy by the proposed REWM in comparison with standard webserver management methodology.
Direct torque control of electric vehicle drives using hybrid techniques Hallikeri Basappa Marulasiddappa; Viswanathan Pushparajesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5026-5034

Abstract

Permanent magnet synchronous motors (PMSM) have the capability of delivering a high torque-to-current ratio, better efficiency and low noise. Because of the above-mentioned factors, PMSMs are commonly employed in variable speed drives, especially in electric vehicle (EV) applications. Without the usage of electromechanical devices, the conventional direct torque control (DTC) can control the speed and torque of PMSM. DTC is highly efficient, fast-tracking and provides smooth torque while limiting its ripple during transient periods. There are many benefits to using a DTC-controlled PMSM drive, including quick and reliable torque reaction, high-performance control speed, and enhanced performance. This research examines the use of the DTC approach to enhance the speed and torque behavior of PMSM. The jellyfish search optimizer (JSO) is used to adjust the DTC's responsiveness and tailor the controller's best gains. In order to train the adaptive neuro-fuzzy inference system (ANFIS) controller, JSO data are utilized. The simulation outcomes demonstrate that the proposed JSO-ANFIS controller achieves a minimal torque ripple of 0.26 Nm and preserves the speed with a harmonic error of 1.21% while contrasted to existing methods.
U-Net transfer learning backbones for lesions segmentation in breast ultrasound images Mohamed Bal-Ghaoui; My Hachem El Yousfi Alaoui; Abdelilah Jilbab; Abdennacer Bourouhou
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5747-5754

Abstract

Breast ultrasound images are highly valuable for the early detection of breast cancer. However, the drawback of these images is low-quality resolution and the presence of speckle noise, which affects their interpretability and makes them radiologists’ expertise-dependent. As medical images, breast ultrasound datasets are scarce and imbalanced, and annotating them is tedious and time-consuming. Transfer learning, as a deep learning technique, can be used to overcome the dataset deficiency in available images. This paper presents the implementation of transfer learning U-Net backbones for the automatic segmentation of breast ultrasound lesions and implements a threshold selection mechanism to deliver optimal generalized segmentation results of breast tumors. The work uses the public breast ultrasound images (BUSI) dataset and implements ten state-of-theart candidate models as U-Net backbones. We have trained these models with a five-fold cross-validation technique on 630 images with benign and malignant cases. Five out of ten models showed good results, and the best U-Net backbone was found to be DenseNet121. It achieved an average Dice coefficient of 0.7370 and a sensitivity of 0.7255. The model’s robustness was also evaluated against normal cases, and the model accurately detected 72 out of 113 images, which is higher than the four best models.

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