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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 3: March 2022" : 64 Documents clear
Cost analysis of on-premise versus cloud-based implementation of moodle in Kufa University during the pandemic Abdulmohson, Abdulhussein; Kadhim, Mohammed Falih; Hussein Anssari, Othman M.; Al-Jobouri, Ahmed A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1787-1794

Abstract

Many organizations often use physical hardware resources, such as storage devices and firewall, to store their essential applications and data. Recently, the Coronavirus pandemic represented a significant challenge for the University of Kufa, which utilize an on-premise data center for e-learning since 2009. Whether the learning management system (LMS) is installed on an on-premise data center or the cloud, it is crucial for any university, to decide which implementation is more suitable because of the differences between the two options, especially in terms of cost. This study uses the total cost of ownership (TCO) model to highlight the cost aspect when using on-premise datacenter versus cloud-based implementation for e-learning and to determine which option is cost effective. The results may help other universities, inside or outside Iraq, deciding which implementation is more suitable (financially) for the organization. The final results show that the cloud-based solution costs approximately 20% less than the currently used on-premise option. Despite all drawbacks of on-premise datacenter such as unstable electricity, bad Internet service, and costing more than cloud hosting, it maybe still more convenient in the case of the University of Kufa due to the sensitive data stored in the data center.
Accurate skin cancer diagnosis based on convolutional neural networks Diab, Amal G.; Fayez, Nehal; El-Seddek, Mervat Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1429-1441

Abstract

Although melanoma is not the most common type of skin cancer, it is supposed to extend to other areas of the body if not early diagnosed. Melanoma is the deadliest form of skin cancer and accounts for about 75% of deaths associated with skin cancer. The present study introduces an automated technique for skin cancer prediction, detection, and diagnosis including trending noninvasive and nonionizing techniques that combines deep learning methods to diagnose melanoma with high accuracy. Computer-aided diagnosis (CAD) using medical images is utilized to distinguish benign and malignant tumors, which can assist physicians in early identification of symptoms, thus lowering the mortality rate. The CAD system consists of four phases; detection of the region of interest (RoI), using data augmentation techniques, processing RoI using convolutional neural network (CNN) to extract the most important features, and finally the extracted CNN features are input to a support vector machine (SVM) classifier to decode the two classes benign (B) and malignant (M). Two datasets, ISIC and CPTAC-CM, were utilized to train the CNNs. GoogleNet, ResNet-50, AlexNet, and VGG19 were investigated and compared. The accuracy of the proposed CAD system has reached 99.8% for ISIC database and 99.9% for CPTAC-CM database.
Development of a new linearizing controller using Lyapunov stability theory and model reference control Mfoumboulou, Yohan Darcy; Mnguni, Mkhululi Elvis Siyanda
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1328-1343

Abstract

One of the most challenging aspects in the nonlinear control of a magnetic levitation (Maglev) system is to find an efficient control algorithm to achieve the stability and accuracy of the closed-loop system. The challenge is then to develop a linearizing control algorithm to maintain a steel ball at a desired position. In this paper, a novel linearizing control algorithm is proposed, which consists of the Lyapunov direct method (LDM) and the model reference control (MRC). The Lyapunov function is developed using the nonlinear equations of the magnetic levitation system, and the reference model is a linear second order system. Two control methods are developed to guarantee system robustness and output stability. Firstly, a new integral linear quadratic regulator (ILQR) is designed for the reference model. Then, an additional innovative proportional gain is combined with the linearizing controller to make the nonlinear control signal stronger. The simulation results indicate that the proposed linearizing controller has excellent set-point tracking, no time delay, fast rising and settling times, and achieves states stability.
Smart agriculture monitoring system for outdoor and hydroponic environments Edwin, Bijolin; Veemaraj, Ebenezer; Parthiban, Pradeepa; Devarajan, Joseph Pushparaj; Mariadhas, Vargheese; Nainar, Ahila Arumuga; Reddy, Maheshwar
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1679-1687

Abstract

Agriculture plays an important role in economic aspects in most countries like India. Numerous problems associated with farming are continuously affecting the actions that are happening in the country. A potential resolution for such issues to be eradicated, one should combine the technological advancements with current ongoing agricultural practices. Good agricultural practice will increase crop productivity and reduce unwanted water usage. Many authors have done research on temperature, nutrition, and pH-controlled systems. But no one concentrated on alert messages sent to the mobile phone. The main objective of the proposed system measures various natural aspects that use a global system for mobile communication (GSM) module that is connected to an Arduino to transfer the data that is obtained by the sensors to an internet of things (IoT) application programming interface (API) which is a kind of cloud computing of obtained data, this data can be analyzed if needed, and an alert short message service (SMS) is sent to the cell phone/mobile phone. The alert message can be done through conversational artificial intelligence (CAI). It is the collection of technologies behind triggering the message that will be sent automatically to the mobile as an SMS if any of the sensor values that are generating are not under already specified threshold values.
Adopting the cybersecurity concepts into curriculum: the potential effects on students’ cybersecurity knowledge Azzeh, Mohammad; Mousa Altamimi, Ahmad; Albashayreh, Mahmood; AL-Oudat, Mohammad A
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1749-1758

Abstract

This study examines the effect of adopting cybersecurity concepts on the information and technology (IT) curriculum and determines the potential effect on students' knowledge of cybersecurity practices and level of awareness. To this end, a pilot study was first conducted to measure the current level of cybersecurity awareness. The results revealed that students do not have much knowledge of cybersecurity. Thus, a four-step approach was proposed to infuse the relevant cybersecurity topics in five matched courses based on the latest cybersecurity curricular guidelines (CSEC2017). A sample of 42 students was selected purposively without prior knowledge of cybersecurity and divided identically into experimental and control groups. Students in the experimental group were asked to take five consecutive courses over five semesters. In each course, groups went through a pre-test for the infused topics. Then, the experimental group taught the corresponding infused topics. A post-test was administered to both groups at the end of each course, and the t-test was conducted. The results found significant differences between marks of prior and post-tests for 11 out of 14 infused topics. These satisfactory results would encourage universities to infuse cybersecurity concepts into their curriculum.
A machine learning for environmental noise classification in smart cities Ali, Yaseen Hadi; Rashid, Rozeha A.; Abdul Hamid, Siti Zaleha
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1777-1786

Abstract

The sound at the same decibel (dB) level may be perceived either as annoying noise or as pleasant music. Therefore, it is necessary to go beyond the state-of-the-art approaches that measure only the dB level and also identify the type of the sound especially when the sound is recorded using a microphone. This paper presented a case study that considers the ability of machine learning models to identify sources of environmental noise in urban areas and compares the sound levels with the recommended levels by the World Health Organization (WHO). The approach was evaluated with a dataset of 44 sound samples grouped in four sound classes that are highway, railway, lawnmowers, and birds. We used mel-frequency cepstral coefficients for feature extraction and supervised algorithms that are Support vector machine (SVM), k-nearest neighbors (KNN), bootstrap aggregation (Bagging), and random forest (RF) for noise classification. We evaluated performance of the four algorithms to determine the best one for the classification of sound samples in the data set under consideration. The findings showed that the noise classification accuracy is in the range of 95%-100%. Furthermore, all the captured data exceeded the recommended levels by WHO which can cause adverse health effects.
A human vision based system for biometric images recognition Boukhari, Wassila; Benyettou, Mohamed; Abderrahim, Belmadani
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1508-1517

Abstract

In this paper, a universal biometric system based on human vision is proposed. From recent biological and physiological results, A human identification system that approximates the natural vision and recognition of individuals is conceived. Liquid state machine (LSM), as a recurrent spiking neural network, is highly inspired by the brain neural architecture with low training cost. However, input dimension of large scale images requires efficient processing at the cost of performance or resource overhead. This paper propose a new neural input coding for images based on frequency signals rather than pixels. Each image is filtered and fragmented then the LSM liquid (or reservoir) will receive, first, high frequency signals, then low frequency signals from each fragment. The two sets of output neurons states corresponding to each type of filter will be matched to the entire enrollment database. A weighted sum rule between the matching results will determine the right class of a biometric image. The system was tested on three different biometric datasets: face, palmprint and off-line signature, results show the reliability of the proposed approach.
Notice of Retraction A parallel time series algorithm for searching similar sub-sequences Saeed, Firas Mahmood; Ali, Salwa M.; Al-Neama, Mohammed W.
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1652-1661

Abstract

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ijeecs.iaes@gmail.com, ijeecs@iaesjournal.com.-----------------------------------------------------------------------Dynamic time warping (DTW) is an important metric for measuring similarity for most time series applications. The computations of DTW cost too much especially with the gigantic of sequence databases and lead to an urgent need for accelerating these computations. However, the multi-core cluster systems, which are available now, with their scalability and performance/cost ratio, meet the need for more powerful and efficient performance. This paper proposes a highly efficient parallel vectorized algorithm with high performance for computing DTW, addressed to multi-core clusters using the Intel quad-core Xeon co-processors. It deduces an efficient architecture. Implementations employ the potential of both message passing interface (MPI) and OpenMP libraries. The implementation is based on the OpenMP parallel programming technology and offloads execution mode, where part of the code sub-sequences on the processor side, which are uploaded to the co-processor for the DTW computations. The results of experiments confirm the effectiveness of the algorithm.
Detecting Arabic textual threats in social media using artificial intelligence: An overview Elzayady, Hossam; S. Mohamed, Mohamed; M. Badran, Khaled; I. Salama, Gouda
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1712-1722

Abstract

Recent studies show that social media has become an integral part of everyone's daily routine. People often use it to convey their ideas, opinions, and critiques. Consequently, the increasing use of social media has motivated malicious users to misuse online social media anonymity. Thus, these users can exploit this advantage and engage in socially unacceptable behavior. The use of inappropriate language on social media is one of the greatest societal dangers that exist today. Therefore, there is a need to monitor and evaluate social media postings using automated methods and techniques. The majority of studies that deal with offensive language classification in texts have used English datasets. However, the enhancement of offensive language detection in Arabic has gotten less consideration. The Arabic language has different rules and structures. This article provides a thorough review of research studies that have made use of artificial intelligence (AI) for the identification of Arabic offensive language in various contexts.
Towards developing impairments arabic speech dataset using deep learning Shareef, Sura Ramzi; Al-Irhayim, Yusra Faisal
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1400-1405

Abstract

The effective and efficient recognition of speech sounds errors for impaired children is important if a defective phonological process is early detected and corrected. This study deals with the topic of classification of speech sound errors in Arabic impairments children when Arabic letters and numbers are incorrectly pronounced. For 18 standard Arabic isolated numerals and characters, we created an impaired children speech recognition system. We utilized the Mel frequency cepstral coefficients throughout the feature extraction step. then deep long short-term memory network recognition phase. We used the developed model with the developed dataset and the classification accuracy was 97.99% and lose 0.18%, additionally, the results have been compared and yielded extremely intriguing results with previously existing recognition rates models.

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