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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 67 Documents
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Search results for , issue "Vol 22, No 3: June 2021" : 67 Documents clear
New ideas and framework for combating COVID-19 pandemic using IoT technologies Hussein M. Haglan; Akeel Sh. Mahmoud; Mustafa Hamid Al-Jumaili; Ahmed J. Aljaaf
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.pp1565-1572

Abstract

The internet of things (IoT) is one of the most advanced technologies that have emerged in the last decade. In recent years, IoT has been used in many medical fields. With the emergence of the Coronavirus pandemic, some IoT technologies were employed to serve the health sector to make quick and accurate decisions to save people's lives. However, there are still many ideas and works not yet implemented that could be applied in many aspects to preserve people's lives. Therefore, it is necessary to collect works and ideas that depend on IoT to produce modern systems quickly to serve the health sector. In this paper, a review of the most recent technologies of IoT against Coronavirus disease (COVID-19) has been done. A comparative and analysis among the previous works have been done to reach the most efficient depending on comparing the services that each work has provided. Besides that, suggest several new ideas that can be adopted as systems use IoT technologies and the expected advantages that can be gain from applying these ideas. A framework for a proposed idea to build a comprehensive monitoring system based on IoT technologies on the patient and hospital sides and expected advantages of implementing the system has been done.
Classification of ECG signals for detection of arrhythmia and congestive heart failure based on continuous wavelet transform and deep neural networks Rashidah Funke Olanrewaju; S. Noorjannah Ibrahim; Ani Liza Asnawi; Hunain Altaf
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.pp1520-1528

Abstract

According to World Health Organization (WHO) report an estimated 17.9 million lives are being lost each year due to cardiovascular diseases (CVDs) and is the top contributor to the death causes. 80% of the cardiovascular cases include heart attacks and strokes. This work is an effort to accurately predict the common heart diseases such as arrhythmia (ARR) and congestive heart failure (CHF) along with the normal sinus rhythm (NSR) based on the integrated model developed using continuous wavelet transform (CWT) and deep neural networks. The proposed method used in this research analyses the time-frequency features of an electrocardiogram (ECG) signal by first converting the 1D ECG signals to the 2D Scalogram images and subsequently the 2D images are being used as an input to the 2D deep neural network model-AlexNet. The reason behind converting the ECG signals to 2D images is that it is easier to extract deep features from images rather than from the raw data for training purposes in AlexNet. The dataset used for this research was obtained from Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH normal sinus rhythm database and Beth Israel Deaconess Medical Center (BIDMC) congestive heart failure database. In this work, we have identified the best fit parameters for the AlexNet model that could successfully predict the common heart diseases with an accuracy of 98.7%. This work is also being compared with the recent research done in the field of ECG Classification for detection of heart conditions and proves to be an effective technique for the classification.
Fire-fighting UAV with shooting mechanism of fire extinguishing ball for smart city Nastaran Reza Nazar Zadeh; Ameralden H. Abdulwakil; Mike Joshua R. Amar; Bernadette Durante; Christian Vincent Nico Reblando Santos
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.pp1320-1326

Abstract

With the growth of technology and massive city development, firefighting services have become more challenging to cope with a smart-city concept. One of the challenges that firefighters are facing is reaching the top floors of high-raised buildings. Firefighters need heavy and oversized pieces of equipment to reach top floors, which they sometimes fail to deliver on time due to big cities' traffic. The proposed solution to this global problem is using firefighting unmanned aerial vehicle (UAV) to reach the top floors fast and efficiently; It can also provide a better vision for the firefighting team and slow down the spread of fire using fire extinguishing ball. In this paper, a noble design for a Firefighting UAV with shooting and dropping mechanism of fire extinguishing ball has been developed and successfully tested. A Camera with night vision has been integrated into the UAV to provide a helpful aid for firefighters. The UAV has a controller with a 2.4 GHz radio frequency (RF) signal and video surveillance to regulate the UAV's movement. The controller is also for activating the shooting and dropping mechanism. The researchers examined the behavior of the drone in terms of its stability and functionality.
A hybrid deep learning model for air quality time series prediction Samit Bhanja; Abhisek Das
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.pp1611-1618

Abstract

Air quality (mainly PM2.5) forecasting plays an important role in the early detection and control of air pollution. In recent times, numerous deep learning-based models have been proposed to forecast air quality more accurately. The success of these deep learning models heavily depends on the two key factors viz. proper representation of the input data and preservation of temporal order of the input data during the feature’s extraction phase. Here we propose a hybrid deep neural network (HDNN) framework to forecast the PM2.5 by integrating two popular deep learning architectures, viz. Convolutional neural network (CNN) and bidirectional long short-term memory (BDLSTM) network. Here we build a 3D input tensor so that CNN can extract the trends and spatial features more accurately within the input window. Here we also introduce a linking layer between CNN and BDLSTM to maintain the temporal ordering of feature vectors. In the end, our proposed HDNN framework is compared with the state-of-the-art models, and we show that HDNN outruns other models in terms of prediction accuracy.
LTE network performance evaluation based on effects of various parameters on the cell range and MAPL Asaad. S. Daghal; Haider Mohammed Turki Al-Hilfi
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.pp1770-1776

Abstract

These days long term evaluation (LTE) is considered the common mobile technology around the world and there is a need to maximize the network performance to satisfy the increased demand in terms of the cell capacity and coverage. These are many parameters in the network configuration and in the surrounded environment, which have great effects on the network performance. Examples of parameters are the system overhead rations, the required capacity of the network, neighbor cell load, and link budget parameters. The determination of the optimum configuration parameters, which achieve the best network performance, is a main step in the planning process in addition to it is continuous step in network optimization phase. In this study, the effects of some parameters will be investigating to get the best parameters that achieve the best network performance in terms of capacity of the cells and coverage area. The study will start by discussing introduction about LTE network components and protocols, and then the main parameters of the protocols will be revising. The study will display the results of changing many parameters related to LTE protocols and surrounding environment parameters on the LTE network performance.
Public key cryptosystem based on multiple chaotic maps for image encryption Yousif S. Najaf; Maher K. Mahmood Al-Azawi
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.pp1457-1466

Abstract

Image is one of the most important forms of information. In this paper, two public key encryption systems are proposed to protect images from various attacks. Both systems depend on generating a chaotic matrix (I) using multiple chaotic maps. The parameters for these maps are taken from the shared secret keys generated from Chebyshev map using public keys for Alice and secret key for Bob or vice versa. The second system has the feature of deceiving the third party for searching for fake keys. Analysis and tests showed that the two proposed systems resist various attacks and have very large key space. The results are compared with other chaos based systems to show the superiority of these two proposed systems.
Implementation multiple linear regresion in neural network predict gold price Musli Yanto; Sigit Sanjaya; Yulasmi Yulasmi; Dodi Guswandi; Syafri Arlis
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.pp1635-1642

Abstract

The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.
Stability and performance evaluation of the speed control of DC motor using state-feedback controller Saad A. Salman; Zeyad Assi Obaid; Haider Salim Hameed
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.pp1372-1378

Abstract

Direct current (DC) motor are widely used in many applications due to its accurate control of speed and position. However, a proper control and operation is still required and might be a challenge for control designers. This paper presents the design of a state-feedback control to evaluate the performance of the speed control of DC motor for different applications. The simulation results were carried out with and without disturbance applied to the system. The proposed control method showed a stable system response with both cases of disturbances. Therefore, it can be used to solidate the control of DC motor in the real application.
Pedestrian age estimation based on deep learning Nawal Younis Abdullah; Mohammed Talal Ghazal; Najwan Waisi
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.pp1548-1555

Abstract

The large-scale distribution of camera networks in the traffic area resulted in the increasing popularity of video surveillance systems. As pedestrian detection and tracking are the critical monitoring targets in traffic surveillance, many studies focus on pedestrian detection algorithms across cameras. This paper addressed the effect of using the age estimation based on deep convolution neural network (CNN) as a convenience for pedestrian monitoring who is crossing at intersections. Two popular deep convolutional neural networks (DCNNs) pre-trained models have been used in this work, which have recently achieved the best performance in facial features extraction tasks: VGG-Face and ResNet-50. We combined these two models to increase the efficiency of the proposed system. We ran our experiments to evaluate the system based on the VGGFace2 dataset consisting of 3.31 million face images. From the experimental results, we observed a gap in the detection performances between those age groups: children from (00-10) years and elderly with 55 years and more. Moreover, it noted that the proposed pedestrian age estimation model performance is high, also a good result can be obtained by using the model for new purpose.
An energy consumption minimization approach in wireless sensor networks Muhsin J. Al-Amery; Mohammed H. Ghadban
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.pp1485-1494

Abstract

There is no doubt that the most challenging aspect in the wireless sensor networks (WSN) is the lifetime, due to limitations in their energy. WSN depends on a specific group of sensor nodes to gather the data from other nodes and forward it to the base station (BS). These nodes are called cluster heads. Having reliable cluster head’s (CH) means longer life to the network. In this paper, a versatile calculation has been acquainted and analyzed for selecting the CH that maintains the least vitality utilization in the network with appropriate life time during every correspondence round. The altered methodology depends on the improved calendar of the time division multiple access (TDMA) plans. This methodology is created to decide the next CH based on lifetime, expended vitality, number of CH’s, and the frequent contact to the BS. A comparative analysis is introduced, the proposed algorithm assistant cluster heads (ACHS) shows energizing outcomes in vitality utilization in WSNs just as expanding the general system dependability with reasonable viability and productivity in terms of lifetime. The ACHS strategy shows a decrease in the WSN vitality utilization up to about 25% and shows an expansion in the system life time by 30% than the upgraded timetable of time TDMA plan approach.

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