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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 96 Documents
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Search results for , issue "Vol 11, No 4: August 2021" : 96 Documents clear
Analysis of cyclic prefix length effect on ISI limitation in OFDM system over a Rayleigh-fading multipath Sarah Zanafi; Noura Aknin
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3114-3122

Abstract

In this work, the influence of the cyclic prefix on the performance of the OFDM system is studied. We worked out an OFDM transceiver using a 16 QAM modulation scheme, a comparison of the BER for various lengths of the cyclic prefix has been achieved, and the influence of the noise introduced in the channel has been highlighted, for both a Gaussian and Rayleigh noise. The simulation was carried out on MATLAB where the curves of the BER for various lengths of the cyclic prefix are given and compared. We also adopted as a metric the QAM constellation to show the dispersion of the carriers as a consequence of the transmission channel, the mitigation of this effect by the CP is noticeable.
The future of software engineering: Visions of 2025 and beyond Firoz Khan; R. Lakshmana Kumar; Seifedine Kadry; Yunyoung Nam
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3443-3450

Abstract

In the current technological scenario of the industry and businesses, there has been increasing need of software within systems and also an increasing demand being put onto software-intensive systems. This in effect will lead to a significant evolution of software engineering processes over the next twenty years. This is due to the fact of emerging technological advancements like Industry 4.0 and Internet of Things in the IT field, among other new developments. This paper addresses and tries to analyses the key research challenges being faced by the software engineering field and articulates information that is derived from the key research specializations within software engineering. The paper analyses the past and current trends in software engineering. The future of software engineering is also looked with respect to Industry 4.0 which including emerging technological platforms like Internet of Things. The societal impact aspect of future trends in software engineering is also addressed in this paper.
Advances in lane marking detection algorithms for all-weather conditions Hadhrami Ab Ghani; Rosli Besar; Zamani Md Sani; Mohd Nazeri Kamaruddin; Syabeela Syahali; Atiqullah Mohamed Daud; Aerun Martin
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3365-3373

Abstract

Driving vehicles in all-weather conditions is challenging as the lane markers tend to be unclear to the drivers for detecting the lanes. Moreover, the vehicles will move slower hence increasing the road traffic congestion which causes difficulties in detecting the lane markers especially for advanced driving assistance systems (ADAS). Therefore, this paper conducts a thorough review on vision-based lane marking detection algorithms developed for all-weather conditions. The review methodology consists of two major areas, which are a review on the general system models employed in the lane marking detection algorithms and a review on the types of weather conditions considered for the algorithms. Throughout the review process, it is observed that the lane marking detection algorithms in literature have mostly considered weather conditions such as fog, rain, haze and snow. A new contour-angle method has also been proposed for lane marker detection. Most of the research work focus on lane detection, but the classification of the types of lane markers remains a significant research gap that is worth to be addressed for ADAS and intelligent transport systems.
Tangible user interface design for learners with different multiple intelligence Salintip Sudsanguan; Sakchai Tangwannawit; Thippaya Chintakovid
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3381-3392

Abstract

The creation of learning activities responsive to learners with different basic skills has been limited due to a classroom environment and applied technologies. The goals of this research were to develop Tang-MI, a game with a tangible user interface supporting primary school learners’ analytical skills based on the theory of multiple intelligences (MI), and to present design guidelines for a tangible user interface suitable for learners in different MI groups. In this research, the Tangible user interface for multiple intelligence (Tang-MI) was tested with thirty students initially evaluated for their multiple intelligences. The learners’ usage behavior was observed and recorded while the students performed the assigned tasks. The behavioral data were analyzed and grouped into behaviors occurring before performing the tasks, during the tasks, and after completing the tasks. Based on the learners’ usage behavior, the tangible user interface design guidelines for learners in different MI groups were proposed concerning physical equipment design, question design, interactive program design, audio design, and animated visual feedback design. These guidelines would help educators build learning games that respond to the learners’ intelligence styles and enhance students’ motivation to learn.
Adaptive backstepping control of induction motor powered by photovoltaic generator M. Madark; A. Ba-razzouk; E. Abdelmounim; M. El Malah
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp2842-2855

Abstract

This paper is aimed at addressing the design of an effective adaptive nonlinear control of a photovoltaic (PV) water pumping system powering a submersible induction motor and a centrifugal water pump. Four objectives are achieved using an adaptive Backstepping controller. First, it is applied to ensure maximum power point tracking, and uses the latter as a reference in regulation of the rotor speed to convert the maximum electrical power into maximum mechanical power. Second, the adaptive controller is synthesized to control motor rotor flux and restrict the magnetic circuit to its linear interval. Third, it is used to online estimate the rotor time-constant and the load torque disturbance estimation. Finally, this controller is employed to limit the stator currents to protect induction motor windings. Mathematical modelling of the main elements of the system is presented. A sliding mode rotor flux estimator is employed in the output feedback control of the whole system. DC-AC converter is controlled by pulse width modulation. The feasibility, the robustness and the effectiveness of the proposed adaptive nonlinear controller are evaluated through simulations in MATLAB/Simulink environment.
Comparative analysis of ReliefF-SVM and CFS-SVM for microarray data classification Mochamad Agusta Naofal Hakim; Adiwijaya Adiwijaya; Widi Astuti
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3393-3402

Abstract

Cancer is one of the main causes of death in the world where the World Health Organization (WHO) recognized cancer as among the top causes of death in 2018. Thus, detecting cancer symptoms is paramount in order to cure and subsequently reduce the casualties due to cancer disease. Many studies have been developed data mining approaches to detect symptoms of cancer through a classifying human gene data expression. One popular approach is using microarray data based on DNA. However, DNA microarray data has many dimensions that can have a detrimental effect on the accuracy of classification. Therefore, before performing classification, a feature selection technique must be used to eliminate features that do not have important information to support the classification process. The feature selection techniques used were ReliefF and correlation-based feature selection (CFS) and a classification technique used in this study is support vector machine (SVM). Several testing schemes were applied in this analysis to compare the performance of ReliefF and CFS with SVM. It showed that the ReliefF outperformed compared with CFS as microarray data classification approach.
Breast cancer diagnosis system using hybrid support vector machine-artificial neural network Tze Sheng Lim; Kim Gaik Tay; Audrey Huong; Xiang Yang Lim
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3059-3069

Abstract

Breast cancer is the second most common cancer occurring in women. Early detection through mammogram screening can save more women’s lives. However, even senior radiologists may over-diagnose the clinical condition. Machine learning (ML) is the most used technique in the diagnosis of cancer to help reduce human errors. This study is aimed to develop a computer-aided detection (CAD) system using ML for classification purposes. In this work, 80 digital mammograms of normal breasts, 40 of benign and 40 of malignant cases were chosen from the mini MIAS dataset. These images were denoised using median filter after they were segmented to obtain a region of interest (ROI) and enhanced using histogram equalization. This work compared the performance of artificial neural network (ANN), support vector machine (SVM), reduced features of SVM and the hybrid SVM-ANN for classification process using the statistical and gray level co-occurrence matrix (GLCM) features extracted from the enhanced images. It is found that the hybrid SVM-ANN gives the best accuracy of 99.4% and 100% in differentiating normal from abnormal, and benign from malignant cases, respectively. This hybrid SVM-ANN model was deployed in developing the CAD system which showed relatively good accuracy of 98%.
Comparative analysis of short-term demand predicting models using ARIMA and deep learning Halima Bousqaoui; Ilham Slimani; Said Achchab
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3319-3328

Abstract

The forecasting consists of taking historical data as inputs then using them to predict future observations, thus determining future trends. Demand prediction is a crucial component in the supply chain’s process that allows each member to enhance its performance and its profit. Nevertheless, because of demand uncertainty supply chains usually suffer from many problems such as the bullwhip effect. As a solution to those logistics issues, this paper presents a comparative analysis of four time series demand forecasting models; namely, the autoregressive integrated moving Average (ARIMA) a statistical model, the multi-layer perceptron (MLP) a feedforward neural network, the long short-term memory model (LSTM) a recurrent neural network and the convolutional neural network (CNN or ConvNet) a deep learning model. The experimentations are carried out using a real-life dataset provided by a supermarket in Morocco. The results clearly show that the convolutional neural network gives slightly better forecasting results than the Long short-term memory network.
Real-time cloud system for managing blood units and convalescent plasma for COVID-19 patients Dhuha Basheer Abdulla; Mohammed Dherar Younus
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3593-3600

Abstract

In health care systems, blood management services are essential to saving lives. In such systems, when a unit of blood is required, if the system is not able to provide it on time, sometimes this may lead to patient death, especially in critical cases. Unfortunately, even if the required blood unit is available within the system, contradictions may occur and the required blood unit may not be allocated to critical cases on time, due to the allocation of these units to lower priority cases or due to the isolated operate of blood banks within these systems. So, to overcome these obstacles, we proposed a real-time system on a cloud, to managing blood units within the whole health care system. This system will allocate blood units depends on the deadline and the severity of the case that needs blood, in addition to the types, quantities, and position of available blood units. Where, this system eliminated the need for human intervention in managing blood units, in addition to offering the ability to easily develop the system to deal with new urgent requirements, which need new methods of managing blood units; as is happening today with the COVID-19 epidemic. This system increases the performance, transparency, reliability, and accuracy of blood unit management operations while reducing the required cost and effort.
Companies’ perception toward manufacturing execution systems Adil Aramja; Oualid Kamach; Rachid Elmeziane
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3347-3355

Abstract

The use of information systems in manufacturing sector is very crucial to reach a high level of operational excellence and improve companies’ competitiveness. The use of such systems will definitely increase in the upcoming years, considering the digitalization strategies. Manufacturing execution systems gained a lot of attention in recent years due to showcased benefits in production management operations. Companies that adopted such systems witnessed an increase in process efficiency and enhancements with regards to cost savings and products quality. This paper seeks to analyze what makes the usage of manufacturing execution systems successful among manufacturing companies. We analyzed how the integration capabilities of such systems with other business applications and the company profile impact their usage and consequently the perceived benefits. A case study was conducted with 51 manufacturing companies and data were analyzed using partial least square structural equation modeling technique. The results confirmed the positive and significant impact of the company profile and solution integration capabilities on system usage. In addition, a ranking of solution modules importance for companies was also provided.

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