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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 9,174 Documents
 585   586   587   588   589 
Half-face based recognition using principal component analysis Ahmed M. Alkababji; Sara Raed Abd
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1404-1410

Abstract

Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.
Eye blink detection using CNN to detect drowsiness level in drivers for road safety Pothuraju Vishesh; Raghavendra S; SantoshKumar Jankatti; Rekha V
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp222-231

Abstract

Blinking is a regular bodily function and it is the semiautomatic fast closing of the eyelid. A specific blink is examined by dynamic folding of the eyelid. It is a vital function of the eye which helps in spread of tears across and eliminates irritants from the shallow of cornea. In this research work we made use of convolution neural network, the deep learning concepts and image processing to detect drowsiness level in drivers. To train the blink detection model the mobilenet V2 is used as base. The loss function used for training was RMSprop and the optimizer is binary cross entropy. The dlib facial landmark was exploited to perceive and pre-process the detected faces. The dataset used for the training model is selected from the “Xiaoyang Tan” of nanjing university of aeronautics and astronautics. Based on the experimental outcome the projected method achieves an accuracy of 97%. The prototype developed serves as a base for further development of this process to achieve better road safety.
A new SDN-based load balancing algorithm for IoT devices Hind Sounni; Najib El kamoun; Fatima Lakrami
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp1209-1217

Abstract

Nowadays, the emergence of the Internet of things devices has wholly revolutionized the customer's communication habits. The information can be collected at any time and anywhere. However, the mobility of communication devices in a dense network results inseveral problems, such as unbalanced network load and increased bandwidth demands, which decrease the network performance. To deal with these issues, this paper proposes a new load balancing algorithm based on the software-defined Network to enhance the performance of mobile devices communication over a Wi-Fi network. The use of the software-defined network automatizes the configuration of the network through a centralized controller; it provides programmability and an overall view of the network, along with optimizing resource allocation based on real-time network information, which facilitates the implementation of our algorithm. The proposed algorithm is implemented and evaluated through simulation using mininet-WiFi. The results indicate that our proposed method provides an efficient network load balancing and improves performancdevices' performance.
Optimized scheduling of scientific workflows based on iterated local search Jihad, Alaa Abdalqahar; Faraj Al-Janabi, Sufyan T.; Yassen, Esam Taha
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.pp1615-1624

Abstract

Recent years have witnessed a great interest in scientific applications with large data and processing-intensive, so cloud computing is used which provides the resources needed to implement and run these applications. One of the challenges in the management of scientific workflow applications is scheduling them to solve many combinatorial optimization problems, including reducing execution time, cost, resource utilization, and energy consumption. Due to the fact that the iterated local search algorithm (ILS) has been successfully applied to solve many combinatorial optimization problems, this paper investigates the performance of ILS in solving the scientific workflow scheduling problem which is a highly constrained problem. The main components that are different from one problem to others are the ILS parameters, local search, and perturbation, which must be carefully designed. The performance of the standard ILS has been examined and compared with the latest technology. The experimental results show that the proposed algorithm (ILS) obtained good results compared to the best-known results in the literature. This is due to the ILS being an adaptable metaheuristic, which can be simply adapted to different search situations and instances.
Single line to ground fault detection and location in medium voltage distribution system network based on neural network Ahmed K. Abbas; Sumaya Hamad; Nuha A. Hamad
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp621-632

Abstract

The aim of this project was to detect and locate the single ground failure lines that occurs in medium voltage (MV) networks on the transmission lines (TL). Compared with anther faults, single line-to-ground (SLG) is the most frequent. The neural network (NN) algorithm was advanced in order to discover and locate SLG faults. The network is simulated through simulated numerous defects at various locations, as well as changing earth resistance from (or 100 Ω) to TL to gather all of the data. In the electromagnetic transients’ program (EMTP) program software, the existing fault have been measured. In addition, the waves were evaluated by utilize MATLAP's fastfourier-transform to calculate the waves of top three of them, On the MV network are fifty hundred faults are simulated all data in the neural network at MATLAB were trained and examined to improve the NN algorithm according to this data. Comparing all the simulated location faults that have been applied with those all locations detected in the NN algorithm, the overall error between them has been found to be very low and not to exceed 0.7. The Simulink circuit was created from this algorithm and checked in order to predict each failure could occur in the future in the MV network.
Smart solution for reducing COVID-19 risk using internet of things Akshay Rajeshkumar; Senthilkumar Mathi
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp474-480

Abstract

The article exposes a smart device designed for mitigating the coronavirus disease (COVID-19) risk using the internet of things. A portable smart alerting device is designed for ensuring safety in public places which can alert people when the guidelines given by the government were not followed and alert health authorities when any abnormalities found. By doing so, the spread of this fatal disease can be stopped. The modules of the proposed system include the face mask detection module, social distance alerting module, crowd detection and analysis module, health screening module and health assessment module. The proposed system can be placed in any public entrances to monitor people without human intervention. Firstly, the human face images are captured for face mask check, then the crowd analysis of the particular entrance where the person is entering is performed, thereafter health screening of the person is done and the values were imported to the health assessment module to check for any abnormalities. Finally, after all the conditions were met the door is opened automatically. The smart device can be installed and effectively used in many scenarios such as malls, stores, crowded places and campuses to avoid the risk of spread of the coronavirus.
Mobile communication (2G, 3G & 4G) and future interest of 5G in Pakistan: a review Muhammad Saqib Iqbal; Zulhasni Abdul Rahim; Syed Aamer Hussain; Norulhusna Ahmad; Hazilah Mad Kaidi; Robiah Ahmad; Rudzidatul Akmam Dziyauddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1061-1068

Abstract

The use of mobile communication is growing radically with every passing year. The new reality is the fifth generation (5G) of mobile communication technology. 5G requires expensive infrastructural adjustment and upgradation. Currently, Pakistan has one of the most significant numbers of biometrically verified mobile users. However, at the same time, the country lags incredibly in the field of mobile internet adoption, with just half of the mobile device owners avail broadband subscription. It is a viable market with a large segment yet to be tapped. With the advancing progression in Pakistan towards the internet of things (IoT) connectivity, i.e., solar-powered home solutions, smart city projects, and on-board diagnostics (OBD), the urgency for speed, bandwidth and reliability are on the rise. In this paper, Pakistan's prevalent mobile communication networks, i.e., second, third and fourth generation (2G, 3G and 4G), were analyzed and examined in light of the country's demographics and challenges. The future of 5G in Pakistan was also discussed. The study revealed that non-infrastructural barriers influence the low adoption rate, which is the main reason behind the spectrum utilization gap, i.e., the use of 3G, and the 4G spectrum is minimal.
Classifying a type of brain disorder in children: an effective fMRI based deep attempt Abeer M Mahmoud; Hanen Karamti
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp260-269

Abstract

Recent advanced intelligent learning approaches that are based on using neural networks in medical diagnosing increased researcher expectations. In fact, the literature proved a straight-line relation of the exact needs and the achieved results. Accordingly, it encouraged promising directions of applying these approaches toward saving time and efforts. This paper proposes a novel hybrid deep learning framework that is based on the restricted boltzmann machines (RBM) and the contractive autoencoder (CA) to classify the brain disorder and healthy control cases in children less than 12 years. The RBM focuses on obtaining the discriminative set of high guided features in the classification process, while the CA provides the regularization and the robustness of features for optimal objectives. The proposed framework diagnosed children with autism recording accuracy of 91, 14% and proved enhancement compared to literature.
Fast learning neural network based on texture for Arabic calligraphy identification Ahmed Kawther Hussein
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1794-1799

Abstract

Arabic calligraphy is considered a sort of Arabic writing art where letters in Arabic can be written in various curvy or segments styles. The efforts of automating the identification of Arabic calligraphy by using artificial intelligence were less comparing with other languages. Hence, this article proposes using four types of features and a single hidden layer neural network for training on Arabic calligraphy and predicting the type of calligraphy that is used. For neural networks, we compared the case of non-connected input and output layers in extreme learning machine ELM and the case of connected input-output layers in FLN. The prediction accuracy of fast learning machine FLN was superior comparing ELM that showed a variation in the obtained accuracy. 
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.

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