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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 25, No 2: February 2022" : 64 Documents clear
Predicting students' learning styles using regression techniques Ahmad Mousa Altamimi; Mohammad Azzeh; Mahmoud Albashayreh
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1177-1185

Abstract

Traditional learning systems have responded quickly to the COVID pandemic and moved to online or distance learning. Online learning requires a personalization method because the interaction between learners and instructors is minimal, and learners have a specific learning method that works best for them. One of the personalization methods is detecting the learners' learning style. To detect learning styles, several works have been proposed using classification techniques. However, the current detection models become ineffective when learners have no dominant style or a mix of learning styles. Thus, the objective of this study is twofold. Firstly, constructing a prediction model based on regression analysis provides a probabilistic approach for inferring the preferred learning style. Secondly, comparing regression models and classification models for detecting learning style. To ground our conceptual model, a set of machine learning algorithms have been implemented based on a dataset collected from a sample of 72 students using visual, auditory, reading/writing, and kinesthetic (VARK's) inventory questionnaire. Results show that regression techniques are more accurate and representative for real-world scenarios than classification algorithms, where students might have multiple learning styles but with different probabilities. We believe that this research will help educational institutes to engage learning styles in the teaching process.
Robust least square approach for optimal development of quadratic fuel quantity function for steam power stations Ikenna Onyegbadue; Cosmas Ogbuka; Theophilus Madueme
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp732-740

Abstract

Ordinary Least Square (OLS) and Robust Least Square (RLS) consisting of Least Absolute Residual and Bi-square approaches were deployed to obtain the fuel consumption characteristic curve and the coefficients of the quadratic fuel consumption function for thermal stations in Nigeria. Results were compared based on convergence property, Root Mean Square Error, R-Square value, Adjusted R-Square value, and Width Interval of coefficients. Valve Point loading effects of Egbin and Sapele power stations were used to develop the quadratic fuel consumption characteristic curve and function. The average difference in width interval for the coefficients a, b, c, d, and e of the two stations, after comparing Bi-square and OLS technique, were 0.02084, 8.5055, 1856.565, 520.8855, and 0.0082, respectively. The R-square values obtained from the Bi-Square technique were superior to the OLS technique with arithmetic differences of 0.6196 and 0.5254 for Egbin and Sapele generating stations, respectively. Bi-Square technique also offered better adjusted R-Square value for Egbin and Sapele stations with arithmetic differences of 0.622 and 0.5287, respectively. Bi-Square technique offered smaller Root Mean Square Error than the Ordinary Least Square technique for both stations. The coefficients obtained from Bi-Square technique were used to develop the fuel quantity function for the studied stations.
A review of optimisation and least-square problem methods on field programmable gate array-based orthogonal matching pursuit implementations Muhammad Muzakkir Mohd Nadzri; Afandi Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp920-930

Abstract

Orthogonal matching pursuit (OMP) is the most efficient algorithm used for the reconstruction of compressively sampled data signals in the implementation of compressive sensing. OMP operates in an iteration-based nature, which involves optimisation and least-square problem (LSP) as the main processes. However, optimisation and LSP processes comprise complex mathematical operations that are computationally demanding, and software-based implementations are slow, power-consuming, and unfit for real-time applications. To fill the research gap, we reviewed the optimisation and LSP techniques implemented on the FPGA platform as the hardware accelerator. Aspects that contributed to the performance, algorithm, and methods involved in the implemented works were discussed and compared. The methods were found to be improved when modified or combined. However, the best approach still depends on the requirement of the system to be developed, and this review is significant as a reference.
An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices Idriss Idrissi; Mostafa Azizi; Omar Moussaoui
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1140-1150

Abstract

Machine learning (ML) and deep learning (DL) have achieved amazing progress in diverse disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of algorithms for analyzing and clustering unlabeled data; it allows identifying hidden patterns or performing data clustering over provided data without the need for human involvement. There is no prior knowledge of actual abnormalities when using UL methods in anomaly detection (AD); hence, a DL-intrusion detection system (IDS)- based on AD depends intensely on their assumption about the distribution of anomalies. In this paper, we propose a novel unsupervised AD Host-IDS for internet of things (IoT) based on adversarial training architecture using the generative adversarial network (GAN). Our proposed IDS, called “EdgeIDS”, targets mostly IoT devices because of their limited functionality; IoT devices send and receive only specific data, not like traditional devices, such as servers or computers that exchange a wide range of data. We benchmarked our proposed “EdgeIDS” on the message queuing telemetry transport (MQTTset) dataset with five attack types, and our obtained results are promising, up to 0.99 in the ROC-AUC metric, and to just 0.035 in the ROC-EER metric. Our proposed technique could be a solution for detecting cyber abnormalities in the IoT.
Free space optical communication system for indoor applications based on printed circuit board design Alsharef Mohammad; Mohammed S. Alzaidi; Mahmoud M. A. Eid; Vishal Sorathiya; Sunil Lavadiya; Shobhit K. Patel; Ahmed Nabih Zaki Rashed
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1030-1037

Abstract

This study clarified an overview of wired and wireless optical communication system block diagram with practical applications. Freespace optical (FSO) communication is a trending field that is rising so fast to replace electromagnetic waves in a communication, so we have presented a theoretical circuit as an example and modified it to fit and work in communication purposes, simulation is used and then practical work is done and printed circuit board (PCB) is designed. Light emitting diode (LED) have been used as transmitter and Photo Transistor as a receiver and variable resistance to change voltage sent to the LED that indicates the change in the transmitted signal.
Evaluation quality of service for internet of things based on fuzzy logic: a smart home case study Lairedj Aboubaker Saddik; Ben Ahmed Khalifa; Bounaama Fateh
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp825-839

Abstract

The development of the internet of thing (IoT) technology has become a major concern in sustainability of quality of service (SQoS) in terms of efficiency, measurement, and evaluation of services, such as our smart home case study. Based on several ambiguous linguistic and standard criteria, this article deals with quality of service (QoS). We used fuzzy logic to select the most appropriate and efficient services. For this reason, we have introduced a new paradigmatic approach to assess QoS. In this regard, to measure SQoS, linguistic terms were collected for identification of ambiguous criteria. This paper collects the results of other work to compare the traditional assessment methods and techniques in IoT. It has been proven that the comparison that traditional valuation methods and techniques could not effectively deal with these metrics. Therefore, fuzzy logic is a worthy method to provide a good measure of QoS with ambiguous linguistic and criteria. The proposed model addresses with constantly being improved, all the main axes of the QoS for a smart home. The results obtained also indicate that the model with its fuzzy performance importance index (FPII) has efficiently evaluate the multiple services of SQoS.
Third harmonic current minimization using third harmonic blocking transformer Vishnuprasada Vittal Bhat; Pinto Pius
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp697-709

Abstract

Zero sequence blocking transformers (ZSBTs) are used to suppress third harmonic currents in 3-phase systems. Three-phase systems where single-phase loading is present, there is every chance that the load is not balanced. If there is zero-sequence current due to unequal load current, then the ZSBT will impose high impedance and the supply voltage at the load end will be varied which is not desired. This paper presents Third harmonic blocking transformer (THBT) which suppresses only higher harmonic zero sequences. The constructional features using all windings in single-core and construction using three single-phase transformers explained. The paper discusses the constructional features, full details of circuit usage, design considerations, and simulation results for different supply and load conditions. A comparison of THBT with ZSBT is made with simulation results by considering four different cases.
Network intrusion detection system: machine learning approach Ameera S. Jaradat; Malek M. Barhoush; Rawan S. Bani Easa
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1151-1158

Abstract

The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. Recently, intrusion detection systems employ machine learning techniques, due to the fact that machine learning techniques proved to have the ability of learning and adapting in addition to allowing a prompt response. This work proposes a model for intrusion detection and classification using machine learning techniques. The model first acquires the data set and transforms it in the proper format, then performs feature selection to pick out a subset of attributes that worth being considered. After that, the refined data set was processed by the Konstanz information miner (KNIME). To gain better performance and a decent comparative analysis, three different classifiers were applied. The anticipated classifiers have been executed and assessed utilizing the KNIME analytics platform using (CICIDS2017) datasets. The experimental results showed an accuracy rate ranging between (98.6) as the highest obtained while the average was (90.59%), which was satisfying compared to other approaches. The gained statistics of this research inspires the researchers of this field to use machine learning in cyber security and data analysis and build intrusion detection systems with higher accuracy.
Analyzing and detecting hemorrhagic and ischemic strokebased on bit plane slicing and edge detection algorithms Warqaa Shaher Alazawee; Zobeda Hatif Naji; Weaam Talaat Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1003-1010

Abstract

Nowadays, in the medical world, analyzing and diagnosing acute brain stroke and its location is a difficult process. In many hospitals, however, striking symptoms with the use of computed tomography (CT) imaging for patients is an important step in screening and diagnosis. Furthermore, computer-assisted accurate detection of diseased brain regions Because of the several sorts of strokes, their uneven form, and their great intensity and size, aided design is extremely challenging. Using the bit plan slice technique and the canny detector, we created and suggested a novel approach. Our algorithm produces excellent outcomes. The results demonstrate that our proposed algorithm is an accurate and reliable technique. This study also indicates that this system can detect two different types of strokes: hemorrhagic and ischemic strokes. The results of a comparison study of our suggested technique and other methods such as negative and logarithmic transformation methods are also included in this article.
Internet of things and multi-class deep feature-fusion based classification of tomato leaf disease Rina mahakud; Binod Kumar Pattanayak; Bibudhendu Pati
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp995-1002

Abstract

A deep transfer learning (deep-TL) classification model has been proposed to diagnose tomato leaf disease. The main challenge of inaccurate classification of a convolution neural network (CNN) model was the availability of the small-sized dataset. This model deals with the challenges like availability of small-sized and imbalanced datasets. The proposed Alex support vector machine (SVM) fused hybrid classification (ASFHC) model is based on fully fusion technology that avoids overfitting to classify the type of disease in tomato leaves. The proposed model achieves the best performance in terms of accuracy by data augmentation of the training data. It uses a pre-trained network for feature extraction with the modification of architecture by concatenating two layers FC6 and FC7 (fully connected layer), plus a linear SVM classifier for classification of the disease. The uniqueness of the research is although the dataset is not balanced, the performance of the model has achieved the maximum. Compared with VGG 16 and VGG 19, the proposed model (ASFHC) has been evaluated using different measuring parameters, indicating remarkable computation time for implementation in the internet of things (IoT) domain. The overall accuracy attained by the model is 99.62%.

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