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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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Utilization of idle time slot in spectrum sensing under noise uncertainty Iyad Khalil Tumar; Adnan Mohammad Arar; Ayman Abd El Saleh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp431-444

Abstract

Spectrum sensing in cognitive radio (CR) is a critical process as it directly influences the accuracy of detection. Noise uncertainty affects the reliability of detecting vacant holes in the spectrum, thus limiting the access of that spectrum by secondary users (SUs). In such uncertain environment; SUs sense the received power of a primary user (PU) independently with different measures of signal-to-noise ratio (SNR). Long sensing time serves in mitigating the effect of noise uncertainty, but on the cost of throughput performance of CR system. In this paper, the scheme of an asynchronous and crossed sensing-reporting is presented. The scheme reduces energy consumption during sensing process without affecting the detection accuracy. Exploiting the included idle time (????????) in sensing time slot; each SU collects power samples with higher SNR directly performs the reporting process to a fusion center (FC) consecutively. The FC terminates the sensing and reporting processes at a specific sensing time that corresponds to the lowest SNR (????????????????????????????). Furthermore, this integrated scheme aims at optimizing the total frame duration (????????). Mathematical expressions of the scheme are obtained. Analytical results show the efficiency of the scheme in terms of energy saving and throughput increment under noise uncerainty.
Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks Abdelladim Hadioui; Yassine Benjelloun Touimi; Nour-eddine El Faddouli; Samir Bennani
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1675-1688

Abstract

Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example.
A novel method for determining fixed running time in operating electric train tracking optimal speed profile An Thi Hoai Thu Anh; Nguyen Van Quyen
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp4881-4890

Abstract

Tracking the optimal speed profile in electric train operation has been proposed as a potential solution for reducing energy consumption in electric train operation, at no cost to improve infrastructure of existing Metro lines as well. However, the optimal speed profile needs to meet fixed running time. Therefore, this paper focuses on a new method for determining the fixed running time complied with the scheduled timetable when trains track the optimal speed profile. The novel method to ensure the fixed running time is the numerical-analytical one. Calculating accelerating time ta, coasting time tc, braking time tb via values of holding speed vh, braking speed vb of optimal speed profile with the constraint condition: the running time equal to the demand time. The other hands, vh and vb are determined by solving nonlinear equations with constraint conditions. Additionally, changing running time suit for each operation stage of metro lines or lines starting to conduct schedules by the numerical-analytical method is quite easy. Simulation results obtained for two scenarios with data collected from electrified trains of Cat Linh-Ha Dong metro line, Vietnam show that running time complied with scheduled timetables, energy saving by tracking optimal speed profile for the entire route is up to 8.7%, if the running time is one second longer than original time, energy saving is about 11.96%.
Offline signature recognition system using oriented FAST and rotated BRIEF Abdel-Karim Al-Tamimi; Asseel Qasaimeh; Kefaya Qaddoum
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4095-4103

Abstract

Despite recent developments in offline signature recognition systems, there is however limited focus on the recognition problem facet of using an inadequate sample size for training that could deliver reliable and easy to use authentication systems. Signature recognition systems are one of the most popular biometric authentication systems. They are regarded as non-invasive, socially accepted, and adequately precise. Research on offline signature recognition systems still has not shown competent results when a limited number of signatures are used. This paper describes our proposed practical offline signature recognition system using the oriented FAST and rotated BRIEF (ORB) feature extraction algorithm. We focus on the practicality of the proposed system, which requires only the minimum number of signatures per user to achieve a high level of fidelity. We manifest the practicality of our approach with a signature database of 300 signatures from 100 different individuals, implying that only two signatures are needed per person to train the proposed system. Our proposed solution achieves a 91% recognition rate with a median matching time of only 7 ms.
Solar/wind pumping system with forecasting in Sharjah, United Arab Emirates Waleed Obaid; Abdul-Kadir Hamid; Chaouki Ghenai
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.pp2752-2759

Abstract

This paper demonstrates a water pumping hybrid power system design. The proposed system was designed for water related applications in Sharjah (Latitude 25.29 °N and Longitude 55 °E), United Arab Emirates. The proposed water hybrid system has two primary renewable power systems: solar PV panels and wind turbines. The proposed hybrid system considers the changes in weather conditions (humidity, wind speed, and temperature) since wind speed affects the performance of the wind turbines and solar panels are affected by solar irradiance. The following components are involved in the proposed design: battery (to store the power from solar panels), voltage regulator circuit (for getting stable DC voltage), three-phase rectifier (to convert the reduced AC voltage to DC), three-phase transformer (for reducing the obtained AC voltage), and DC electric motor (the main output of the proposed water pumping system). The proposed water pumping system relies on neural network blocks to achieve weather forecasting by obtaining solar irradiance values from the input temperature, wind speed, and humidity in a span of five years. Both MATLAB and Simulink are used simulate the performance of the proposed system under different weather conditions by changing the values according to the measured weather conditions values over five years.
MRI image segmentation using machine learning networks and level set approaches Layth Kamil Adday Almajmaie; Ahmed Raad Raheem; Wisam Ali Mahmood; Saad Albawi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp793-801

Abstract

The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones.
myBas driving cycle for Kuala Terengganu city J. S. Norbakyah; M. I. Nordiyana; I. N. Anida; A. F. Ayob; A. R. Salisa
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2054-2061

Abstract

Driving cycles are series of data points that represent vehicle speed versus time sequenced profile developed for specific road, route, city or certain location. It is widely utilized in the application of vehicle manufacturers, environmentalists and traffic engineers. Since the vehicles are one of the higher air pollution sources, driving cycle is needed to evaluate the fuel consumption and exhaust emissions. The main objectives in this study are to develop and characterize the driving cycle for myBAS in Kuala Terengganu city using established k-means clustering method and to analyse the fuel consumption and emissions using advanced vehicle simulator (ADVISOR). Operation of myBAS offers 7 trunk routes and one feeder route. The research covered on two operation routes of myBAS which is Kuala Terengganu city-feeder and from Kuala Terengganu to Jeti Merang where the speed-time data is collected using on-board measurement method. In general, driving cycle is made up of a few micro-trips, defined as the trip made between two idling periods. These micro-trips cluster by using the k-means clustering method and matrix laboratory software (MATLAB) is used in developing myBAS driving cycle. Typically, developing the driving cycle based on the real-world in resulting improved the fuel economy and emissions of myBAS.
Laboratory investigation of the impact of air pollution on partial discharge inception voltage of insulators in a specific region Y. Shafiei; F. Faghihi; H. Heydari; A. H. Salemi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp4634-4640

Abstract

Studying the discharge characteristics of transmission line insulators in the presence of pollution, particularly when the contaminated layer is wet by rain, fog or condensation, is necessary for selecting the proper insulation. Therefore, identifying the major air pollutants as well as the most effective ones on the discharge performance of outdoor insulators is mandatory. A systematic approach has been proposed to evaluate the impact of dominant air pollutants of an area on partial discharge (P.D) inception voltage of specimen insulators. Based on the suggested method, determining the pollution constituents, defining the dominant pollutant of the area, finding the most commonly used insulators for medium and above distribution voltages within the geographical boundaries of the Central Province of Iran, as well as examining the effect of dominant air pollutant of the region on partial discharge inception voltage of designated insulators by laboratory measurements, are the necessary steps toward a comprehensive study of the subject.
Association rule hiding using integer linear programming Suma B.; Shobha G.
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.pp3451-3458

Abstract

Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.
Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor Al Khowarizmi; Rahmad Syah; Mahyuddin K. M. Nasution; Marischa Elveny
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2696-2703

Abstract

Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.

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