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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Adopting MQTT for a multi protocols IoMT system Bilal Asaad Mubdir; Hassan Mohammed Ali Bayram
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp834-844

Abstract

Coronavirus disease (COVID-19) altered the way of caregiving and the new pandemic forced the health systems to adopt new treatment protocols in which remote follow-up is essential. This paper introduces a proposed system to link a remote healthcare unit as it is inside the hospital. Two different network protocols; a global system for mobile communication (GSM) and Wi-Fi were used to simulate the heath data transfer from the two different geographical locations, using Raspberry Pi development board and Microcontroller units. Message queuing telemetry transport (MQTT) protocol was employed to transfer the measured data from the healthcare unit to the hospital’s Gateway. The gateway is used to route the aggregated health data from healthcare units to the hospital server, doctors’ dashboards, and the further processing. The system was successfully implemented and tested, where the experimental tests show that the remote healthcare units using a GSM network consumed about 900 mWh. A high percentage of success data packets transfer was recorded within the network framework as it reaches 99.89% with an average round trip time (RTT) of 7.5 milliseconds and a data transfer rate up to 12.3 kbps.
Real-time Arabic scene text detection using fully convolutional neural networks Rajae Moumen; Raddouane Chiheb; Rdouan Faizi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1634-1640

Abstract

The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed.
Distributed reflection denial of service attack: A critical review Riyadh Rahef Nuiaa; Selvakumar Manickam; Ali Hakem Alsaeedi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5327-5341

Abstract

As the world becomes increasingly connected and the number of users grows exponentially and “things” go online, the prospect of cyberspace becoming a significant target for cybercriminals is a reality. Any host or device that is exposed on the internet is a prime target for cyberattacks. A denial-of-service (DoS) attack is accountable for the majority of these cyberattacks. Although various solutions have been proposed by researchers to mitigate this issue, cybercriminals always adapt their attack approach to circumvent countermeasures. One of the modified DoS attacks is known as distributed reflection denial-of-service attack (DRDoS). This type of attack is considered to be a more severe variant of the DoS attack and can be conducted in transmission control protocol (TCP) and user datagram protocol (UDP). However, this attack is not effective in the TCP protocol due to the three-way handshake approach that prevents this type of attack from passing through the network layer to the upper layers in the network stack. On the other hand, UDP is a connectionless protocol, so most of these DRDoS attacks pass through UDP. This study aims to examine and identify the differences between TCP-based and UDP-based DRDoS attacks.
Frequency based signal processing technique for pulse modulation ground penetrating radar system Che Ku Nor Azie Hailma Che Ku Melor; Ariffuddin Joret; Maryanti Razali; Asmarashid Ponniran; Muhammad Suhaimi Sulong; Rosli Omar
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4104-4112

Abstract

This paper discusses the method of processing the pulse modulation (PM) ground penetrating radar (GPR) system to detect an embedded object underground. The proposed technique is using frequency domain operation which can be classified based on two parameters which are magnitude and phase. The process of detecting the position and depth of iron objects in dry sandy soil is easier to identify using the techniques and parameters that have been introduced. The selection of the Dipole antenna as a sensor device to detect iron objects has been designed in a frequency range of 70 MHz to 80 MHz. Based on the simulation, the proposed technique seems to be able to detect underground iron objects. By using the magnitude value, the underground iron object that can be detected as displayed in GPR radargram is in the depth range from 0 mm until 1000 mm. Meanwhile, by using the phase value, the embedded underground iron object detected is in the range of depth between 900 mm and 1000 mm. Therefore, based on this promising result, the proposed technique and parameters are considered to be used in
Insights on critical energy efficiency approaches in internet-of-things application Sharath S. M.; Manjunatha P.; Shwetha H. R.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp2925-2933

Abstract

Internet-of-things (IoT) is one of the proliferated technologies that result in a larger scale of connection among different computational devices. However, establishing such a connection requires a fault-tolerant routing scheme. The existing routing scheme results in communication but does not address various problems directly linked with energy consumption. Cross layer-based scheme and optimization schemes are frequently used scheme for improving the energy efficiency performance in IoT. Therefore, this paper investigates the approaches where cross-layer-based schemes are used to retain energy efficiencies among resource-constrained devices. The paper discusses the effectivity of the approaches used to optimize network performance in IoT applications. The study outcome of this paper showcase that there are various open-end issues, which is required to be addressed effectively in order to improve the performance of application associated with the IoT system.
Beam division multiple access for millimeter wave massive MIMO: Hybrid zero-forcing beamforming with user selection Hong Son Vu; Kien Truong; Minh Thuy Le
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp445-452

Abstract

Massive multiple-input multiple-output (MIMO) systems are considered a promising solution to minimize multiuser interference (MUI) based on simple precoding techniques with a massive antenna array at a base station (BS). This paper presents a novel approach of beam division multiple access (BDMA) which BS transmit signals to multiusers at the same time via different beams based on hybrid beamforming and user-beam schedule. With the selection of users whose steering vectors are orthogonal to each other, interference between users is significantly improved. While, the efficiency spectrum of proposed scheme reaches to the performance of fully digital solutions, the multiuser interference is considerably reduced.
Elevator controller based on implementing a random access memory in FPGA Azzad Bader Saeed
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1053-1062

Abstract

Previous techniques of elevator controllers suffer from two main challenges: processing time, and software size. In this work these challenges have been overcame by implementing a controller random access memory (RAM) in a fast FPGA for a proto-type of two-floors elevator, as known the RAM and FPGA are fast devices. A look-up-table LUT (which is fast technique) has been proposed for this work, this LUT has represented a proposed relation between 10 and 7 lines, the states of the sensors and switches have been represented by the 10 input lines, and the commands for the motors of slide door and traction machine have been represented by the 7 output lines. The proposed LUT has been schematically realize by a (10×7) bits RAM which has been implemented in field programmable gate arrays (FPGA). The proposed system has been performed using 'ISE Design Suit' software package and FPGA Spartan6 SP-605 evaluation kit, the clock frequency of this FPGA is 200 MHz which is respectively high. The processing time and software size of the proposed controller had reached to 20ns and 3.75 MB, which they are less than that obtained from the results of previous techniques.
A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest X-ray images Ihssan S. Masad; Amin Alqudah; Ali Mohammad Alqudah; Sami Almashaqbeh
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5530-5540

Abstract

Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time.
Improving radiologists’ and orthopedists’ QoE in diagnosing lumbar disk herniation using 3D modeling Sanaa Abu Alasal; Mohammad Alsmirat; Asma’a Al-Mnayyis; Qanita Bani baker; Mahmoud Al-Ayyoub
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4336-4344

Abstract

This article studies and analyzes the use of 3D models, built from magnetic resonance imaging (MRI) axial scans of the lumbar intervertebral disk, that are needed for the diagnosis of disk herniation. We study the possibility of assisting radiologists and orthopedists and increasing their quality of experience (QoE) during the diagnosis process. The main aim is to build a 3D model for the desired area of interest and ask the specialists to consider the 3D models in the diagnosis process instead of considering multiple axial MRI scans. We further propose an automated framework to diagnose the lumber disk herniation using the constructed 3D models. We evaluate the effectiveness of increasing the specialists QoE by conducting a questionnaire on 14 specialists with different experiences ranging from residents to consultants. We then evaluate the effectiveness of the automated diagnosis framework by training it with a set of 83 cases and then testing it on an unseen test set. The results show that the the use of 3D models increases doctors QoE and the automated framework gets 90% of diagnosis accuracy.
An algorithm for characterizing skin moles using image processing and machine learning Zaid Sanchez; Alicia Alva; Mirko Zimic; Christian del Carpio
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3539-3550

Abstract

Melanoma, the most serious type of skin cancer, forms in cells (melanocytes) that produce melanin, the pigment that gives color to the skin. There are low-income regions that lack specialized dermatologists, causing skin cancer to be diagnosed in advanced stages. In Peru, in high Andean communities with low resources, the problem is aggravated by the high incidence of ultraviolet radiation and lack of medical resources to make the diagnosis. Normally, mole images are obtained from dermatoscopes. The present work seeks to use mole images obtained from smartphones to make the classification of them as suspected or not suspected of being melanoma, by means of a feature extraction algorithm. The first step is to make color and lighting corrections. After this, the image is segmented using the K-Means algorithm, and we obtain the areas of the mole and skin. With the segmented mole we proceed to extract the main visual characteristics and then use classification algorithms such as support vector machine (SVM), random forest and naïve bayes, which obtained an accuracy of 0.9473, 0.7368 and 0.6842, respectively. These results show that it is possible to use images obtained from smartphones to develop a classification algorithm with 94.73% accuracy to detect melanoma in skin moles.

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