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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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Radial basis network estimator of oxygen content in the flue gas of debutanizer reboiler Shafanda Nabil Sembodo; Nazrul Effendy; Kenny Dwiantoro; Nidlom Muddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3044-3050

Abstract

The energy efficiency in the debutanizer reboiler combustion can be monitored from the oxygen content of the flue gas of the reboiler. The measurement of the oxygen content can be conducted in situ using an oxygen sensor. However, soot that may appear around the sensor due to the combustion process in the debutanizer reboiler can obstruct the sensor’s function. In-situ redundancy sensors’ unavailability is a significant problem when the sensor is damaged, so measures must be made directly by workers using portable devices. On the other hand, worker safety is a primary concern when working in high-risk work areas. In this paper, we propose a software-based measurement or soft sensor to overcome the problems. The radial basis function network model makes soft sensors adapt to data updates because of their advantage as a universal approximator. The estimation of oxygen content with a soft sensor has been successfully carried out. The soft sensor generates an estimated mean square error of 0.216% with a standard deviation of 0.0242%. Stochastics gradient descent algorithm with momentum acceleration and dimension reduction using principal component analysis successfully improves the soft sensors’ performance.
An evaluation of machine learning algorithms coupled to an electronic olfactory system: a study of the mint case Amkor, Ali; Maaider, Kamal; El Barbri, Noureddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4335-4344

Abstract

The aim of this investigatation is to compare the utility of machine learning algorithms in distinguishing between untreated and processed mint beside in predicting the spray day of the insecticide. Within seven days, mint treated samples with the malathion insecticide are collected, and their aromas are Studied using a laboratory-manufactured sensor array system based on commercial metallic semiconductor (MOS) gas sensors. To distinguish the mint type, some results of machine learning algorithms were compared to know the decision trees (DT), Naive Bayes, support vector machines (SVM), and ensemble classifier. Furthermore, to predict the treatment day support vector machines regression (SVMR) and partial least squares regression (PLSR) were compared. Regarding the best results, in the discrimination case, a success rate of 92.9% was achieved by the ensemble classifier while in the prediction case, a correlation coefficient of R=0.82 was reached by the SVMR. Good results are achieved if the right gas sensor array system is designed and realized coupled with a good choice of the appropriate machine learning algorithms.
Economic dispatch by optimization techniques Ali Abttan, Rana; Hasan Tawafan, Adnan; Jaafar Ismael, Samar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2228-2241

Abstract

The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.
Mass estimation of citrus limetta using distance based hand crafted features and regression analysis Shobha Rani Narayana Murthy; Arun Sri Krishna; Vishwa Prasad Laxmisagara; Pavan H. Srinath; Vinay Madhusudan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3700-3708

Abstract

Sorting and grading are qualitative operational tasks performed in food processing industries. Realization of higher accuracy in mass estimation is the key inclination of this work. In this work, an automated technique for mass estimation of citrus limetta is devised based on the geometrical features derived from pre-processed images. Dataset includes 250 data samples of citrus limetta, whose images are acquired in different orientations. Two novel handcrafted distance-based geometrical features along with four conventional geometrical features were employed for regression analysis. Predictive modeling is conducted with configuration of 150 training and 100 testing data samples and subject to regression analysis for mass estimation. Multiple linear and support vector regression models with linear, polynomial and radial basis function (RBF) kernels were employed for mass estimation with two different model configurations, conventional and conventional with handcrafted features, for which an R2 score of 0.9815, root mean squared error (RMSE) of 10.94 grams, relative averages of accuracy and error of 96.61% and 3.39% respectively is achieved for the proposed model and configuration which was validated using k-fold cross-validation. Through comparison with performance of model with conventional and conventional with handcrafted features configurations, it was established that inclusion of handcrafted features was able to increase the performance of the models.
Design of Savonius model wind turbine for power catchment Liew Hui Fang; Rosemizi bin Abd Rahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2285-2299

Abstract

In this study, the fossil fuel usage by-product is carbon dioxide, which is known as the primary cause in global warming. Alternatively, wind energy is a clean alternative energy source compared the fuel consumption can cause smoke pollution. The goal of the work is to develop a pollution controller device model Savonius wind turbine to represent the characterized actual speed wind turbine concepts into convert kinetic energy into electric energy from campus and monitoring all output data display on the cloud. The wind speed operation is enabled through the use of ESP8266 as internet of things (IoT) platform and the alternating current (AC) direct current (DC) harvesting circuit into improve stability of the wind energy performance. Secondly, a magnet coil synchronous generator is used, which is a grid coupled through a diode rectifier and voltage source converter. The parameters that have been measured using wireless fidelity (Wi-Fi) module ESP8266 are considering wind speed, current, voltage and power. The wind speed with 7.8 MPH can produce a maximum output voltage and output current of 1.104 V and 4.321 μA, respectively. Blynk applications functional as role present performance monitoring kit wind turbine analysis with more precise and efficient in anywhere and anytime.
Privacy protection domain-user integra tag deduplication in cloud data server Mohanaprakash Thottipalayam Andavan; Nirmalrani Vairaperumal
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4155-4163

Abstract

The cloud with strong storage management has recently developed in the big data world which can confirm the data integrity and keep just a single data duplicate. Many cloud auditing storage techniques have been developed to overcome the data deduplication (DD) problem, but they are vulnerable and can't resist brute force attacks (BFA). There is some privacy leakage problem that occurred in the present method. In this article, an original strategy called domain-user integra tag (DUIT) has been presented which comprises inter and intra deduplication with file tag and symmetric encryption key. The DUIT has two phases, the first one is random tag generation for Intra deduplication and the other is random ciphertext (CT) generation for encryption. The benefit of the DUIT is the security of individual user’s files would not reveal to people in general, hence we proved that the DUIT is protected from the BFA. Finally, an experiment has conducted in Linux processor and C program software. The outcome of DUIT demonstrates that our method has reduced the computation cost (CC) by 27% and 35% and searching complexity (SC) by 10% and 26% related with the previous methods. It is decided that the DUIT achieves the low CC and SC.
Real-time face detection in digital video-based on Viola-Jones supported by convolutional neural networks Tameem Hameed Obaida; Abeer Salim Jamil; Nidaa Flaih Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3083-3091

Abstract

Face detection is a critical function of security (secure witness face in the video) who appear in a scene and are frequently captured by the camera. Recognition of people from their faces in images has recently piqued the scientific community, partly due to application concerns, but also for the difficulty this characterizes for the algorithms of artificial vision. The idea for this research stems from a broad interest in courtroom witness face detection. The goal of this work is to detect and track the face of a witness in court. In this work, a Viola-Jones method is used to extract human faces and then a particular transformation is applied to crop the image. Witness and non-witness images are classified using convolutional neural networks (CNN). The Kanade-Lucas-Tomasi (KLT) algorithm was utilized to track the witness face using trained features. In this model, the two methods were combined in one model to take the advantage of each method in terms of speed and reduce the amount of space required to implement CNN and detection accuracy. After the test, the results of the proposed model showed that it was 99.5% percent accurate when executed in real-time and with adequate lighting.
Fake accounts detection on social media using stack ensemble system Amna Kadhim Ali; Abdulhussein Mohsin Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3013-3022

Abstract

In today’s world, social media has spread widely, and the social life of people have become deeply associated with social media use. They use it to communicate with each other, share events and news, and even run businesses. The huge growth in social media and the massive number of users has lured attackers to distribute harmful content through fake accounts, leading to a large number of people falling victim to those accounts. In this work, we propose a mechanism for identifying fake accounts on the social media site Twitter by using two methods to preprocess data and extract the most effective features, they are the spearman correlation coefficient and the chi-square test. For classification, we used supervised machine learning algorithms based on the ensemble system (stack method) by using random forest, support vector machine, and Naive Bayes algorithms in the first level of the stack, and the logistic regression algorithm as a meta classifier. The stack ensemble system was shown to be effective in achieving the best results when compared to the algorithms used with it, with data accuracy reaching 99%.
Distributed rule execution mechanism in smart home system Agung Setia Budi; Hurriyatul Fitriyah; Eko Setiawan; Rakhmadhany Primananda; Rizal Maulana
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4439-4448

Abstract

Smart home systems become an interesting topics in the last few years. Many researchers have been studied some features. Most of smart home system use a centralized architecture know as centralized smart home system (CSHS). The centralizedmechanism is easy to manage and to configure. However, in fault-tolerant systemparadigm it produces a problem. The entire system will fail, if the master station fails.Another problem of CSHS is centralized mechanism gives more data-flow. This condition makes the system has a greater delay time. To solve the problem, we proposea distributed rule execution mechanism (DREM). Compared to the centralized mechanism, the DREM allows a device to provide its service without any commands fromthe master station. In this mechanism, since the information does not need to go tothe master station, the data-flow and the delay-time can be decreased. The experimentresults show that the DREM is able to mask the failure in the master station by directlytransmit the data from trigger device to service device. This mechanism makes the services provision without master station possible. The mathematical analysis also shows that the delay time of the service provision of the DREM is less than the delay time ofCSHS.
Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram Raheem Ogla; Ali Adel Saeid; Shaimaa H. Shaker
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2571-2581

Abstract

The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.

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