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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 66 Documents
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Search results for , issue "Vol 21, No 3: March 2021" : 66 Documents clear
Enhancement in data security and integrity using minhash technique Sa'ed Abed; Lamis Waleed; Ghadeer Aldamkhi; Khaled Hadi
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.pp1739-1750

Abstract

Data encryption process and key generation techniques protect sensitive data against any various attacks. This paper focuses on generating secured cipher keys to raise the level of security and the speed of the data integrity checking by using the MinHash function. The methodology is based on applying the cryptographic algorithms rivest-shamir-adleman (RSA) and advanced encryption standard (AES) to generate the cipher keys. These keys are used in the encryption/decryption process by utilizing the Pearson Hash and the MinHash techniques. The data is divided into shingles that are used in the Hash function to generate integers and in the MinHash function to generate the public and the private keys. MinHash technique is used to check the data integrity by comparing the sender’s and the receiver’s encrypted digest. The experimental results show that the RSA and AES algorithms based on the MinHash function have less encryption time compared to the normal hash functions by 17.35% and 43.93%, respectively. The data integrity between two large sets is improved by 100% against the original algorithm in terms of completion time, and 77% for small/medium data and 100% for large set data in terms of memory utilization.
Predicting heart failure using a wrapper-based feature selection Minh Tuan Le; Minh Thanh Vo; Nhat Tan Pham; Son V.T Dao
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.pp1530-1539

Abstract

In the current health system, it is very difficult for medical practitioners/physicians to diagnose the effectiveness of heart contraction. In this research, we proposed a machine learning model to predict heart contraction using an artificial neural network (ANN). We also proposed a novel wrapper-based feature selection utilizing a grey wolf optimization (GWO) to reduce the number of required input attributes. In this work, we compared the results achieved using our method and several conventional machine learning algorithms approaches such as support vector machine, decision tree, K-nearest neighbor, naïve bayes, random forest, and logistic regression. Computational results show not only that much fewer features are needed, but also higher prediction accuracy can be achieved around 87%. This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.
An energy efficient optimized cluster establishment methodology for sensor nodes in WSN Shivshanker Biradar; T. S. Vishwanath
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.pp1548-1556

Abstract

The compatibility of WSN is with various applications such as; healthcare and environmental monitoring. Whereas nodes present in that network have limited ‘battery-life’ that cause difficulty to replace and recharge those batteries after deployment. Energy efficiency is a major problem in the present situation. In present, many algorithms based on energy efficiency have been introduced to improvise the conservation of energy in WSN. The LEACH algorithm improvises the network lifetime in comparison to direct transmission and multi-hop, but it has several limitations. The selection of CHs can be randomly done that doesn’t confirm the optimal solution, proper distribution and it lacks during complete network management. The centralized EE optimized cluster establishment approach (OCEA) for sensor nodes is proposed to decrease the average energy dissipation and provide significant improvement. The proposed EE WSN model with the sensor nodes is examined under a real-time scenario and it is compared with state-of-art techniques where it balances the energy consumption of the network and decreasing the cluster head number.
High sensitivity sapphire FBG temperature sensors for the signal processing of data communications technology Mahmoud M. A. Eid; Ashraf S. Seliem; Ahmed Nabih Zaki Rashed; Abd El-Naser A. Mohammed; Mohamed Yassin Ali; Shaimaa S. Abaza
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.pp1567-1574

Abstract

This study has outlined the fiber bragg grating (FBG) temperature sensors signal processing for data communications by using OptiGrating simulation software. The reflectivity of the silica and sapphire fiber grating spectrum is reported against the grating wavelength for internal and external temperature variations. As well as apodized Gaussian reflectivity of the silica and sapphire fiber grating spectrum is simulated and clarified against the grating wavelength for high temperature variations. The temperature sensitivity of sapphire FBG nearly 0.11 pm/0C, where its value is three times higher than silica FBG. It is observed that silica and Sapphire FBG sensors were tested up to 1000 0C by using Gaussian apodization type, side lobes in reflectivity spectrum are totally suppressed.
Noise-robust classification with hypergraph neural network Nguyen Trinh Vu Dang; Loc Tran; Linh Tran
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.pp1465-1473

Abstract

This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph neural network are employed to solve the noisy label learning problem. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases. Moreover, the hypergraph neural network methods are at least as good as the graph neural network.
A low-cost IoT-based auscultation training device Heri Andrianto Andrianto; Daniel Perdana Sutanto; Yunus Adhy Prasetyo
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.pp1356-1363

Abstract

Auscultation training devices are needed by teachers and students in health schools to practice auscultation techniques. In this paper, a low-cost IoT-based auscultation training device has been developed using NodeMCU, four proximity sensors, metal as a stethoscope, a switch, an android smartphone, an earphone, and a phantom doll. The message queuing telemetry transport (MQTT) protocol has been used for data communication between NodeMCU and smartphones, therefore an auscultation training hardware can be used by many students who have auscultation training application on their smartphones that subscribe to topics. The results showed that an auscultation training device was able to detect a stethoscope. Auscultation training application on a smartphone successfully plays normal and abnormal breathing sounds based on subscribed topics. With a production cost of less than 15 USD, we offer an inexpensive IoT-based auscultation training device. 
A new class of self-scaling for quasi-newton method based on the quadratic model Basim A. Hassan; Ranen M. Sulaiman
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.pp1830-1836

Abstract

Quasi-Newton method is an efficient method for solving unconstrained optimization problems. Self-scaling is one of the common approaches in the modification of the quasi-Newton method. A large variety of self-scaling of quasi-Newton methods is very well known. In this paper, based on quadratic function we derive the new self-scaling of quasi-Newton method and study the convergence property. Numerical results on the collection of problems showed the self-scaling of quasi-Newton methods which improves overall numerical performance for BFGS method.
Sectoral dual-polarized MIMO antenna for 5G-NR band N77 base station Muhsin Muhsin; Afina Lina Nurlaili; Aulia Saharani; Indah Rahmawti Utami
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.pp1611-1621

Abstract

Massive internet of things (IoT) in 5G has many advantages as a future technology. It brings some challenges such as a lot of devices need massive connection. In this case, multiple-input multiple-output (MIMO) systems offer high performance and capacity of communications. There is a challenge of correlation between antennas in MIMO. This paper proposes three-sectors MIMO base station antenna for 5G-New Radio (5G-NR) band N77 with dual polarized configuration to reduce the correlation. The proposed antenna has a maximum coupling of -16.90 dB and correlation below 0.01. The obtained bit error rate (BER) performance is very close to non-correlated antennas with bandwidth of 1.87 GHz. It means that the proposed antenna has been well designed.
Optimized and efficient deblurring through constraint conditional modelling Ravikumar H C; P Karthik
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.pp1503-1512

Abstract

Image deburring technique refers to restoring an image from the degraded version named blurred. Blurring can be caused due to various phenomena such as optical system, motion blur and other phenomena. Moreover, to deblur the image it is essential to know the blurring process characteristics and it is one of the difficult task. In past several deblurring algorithm have been proposed to approximate the kernel blur, however they lack the efficiency and expensive to be applied for the real world scenario. In this paper, we have proposed a CCM (constraint conditional model) to deblur the image; it learns the direct mapping from the degraded to the absolute clean image. Moreover, the main aim of CCM is to restore the image in its original form, the best advantage of CCM is that it provides handsome tradeoff between the image quality and efficiency. Moreover CCM is evaluated on the three different standard datasets by considering the different performance metrics and through the comparison analysis observation has made that CCM approach outperforms the other techniques.
A hybrid de-noising method for mammogram images Rashid Mehmood Gondal; Saima Anwar Lashari; Murtaja Ali Saare; Sari Ali Sari
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.pp1435-1443

Abstract

In general, mammogram images contaminated with noise which directly affect images quality. Several methods have been proposed to de-noise these images, however, there is always a risk of losing valuable information. In order to overcome the loss of information, the present study proposed a hybrid denoising method for mammogram images. The proposed hybrid method works in two steps: Firstly, preprocessing with mathematical morphology was applied for image enhancement. Secondly, global unsymmetrical trimmed median filter (GUTM) is applied to de-noise image. Experimental results prove that proposed method work well for mammogram images. Hence, the study provided an alternative method for denoising mammogram images.

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