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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 96 Documents
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Search results for , issue "Vol 11, No 4: August 2021" : 96 Documents clear
Text classification model for methamphetamine-related tweets in Southeast Asia using dual data preprocessing techniques Narongsak Chayangkoon; Anongnart Srivihok
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.pp3617-3628

Abstract

Methamphetamine addiction is a prominent problem in Southeast Asia. Drug addicts often discuss illegal activities on popular social networking services. These individuals spread messages on social media as a means of both buying and selling drugs online. This paper proposes a model, the “text classification model of methamphetamine tweets in Southeast Asia” (TMTA), to identify whether a tweet from Southeast Asia is related to methamphetamine abuse. The research addresses the weakness of bag of words (BoW) by introducing BoW and Word2Vec feature selection (BWF) techniques. A domain-based feature selection method was performed using the BoW dataset and Word2Vec. The BWF dataset provided a smaller number of features than the BoW and TF–IDF dataset. We experimented with three candidate classifiers: Support vector machine (SVM), decision tree (J48) and naive bayes (NB). We found that the J48 classifier with the BWF dataset provided the best performance for the TMTA in terms of accuracy (0.815), F-measure (0.818), Kappa (0.528), Matthews correlation coefficient (0.529) and high area under the ROC Curve (0.763). Moreover, TMTA provided the lowest runtime (3.480 seconds) using the J48 with the BWF dataset.
Smart element aware gate controller for intelligent wheeled robot navigation Nadia Adnan Shiltagh Al-Jamali; Mahmood Z. Abdullah
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.pp3022-3031

Abstract

The directing of a wheeled robot in an unknown moving environment with physical barriers is a difficult proposition. In particular, having an optimal or near-optimal path that avoids obstacles is a major challenge. In this paper, a modified neuro-controller mechanism is proposed for controlling the movement of an indoor mobile robot. The proposed mechanism is based on the design of a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. This controller is updated to overcome the rigid and dynamic barriers in the indoor area. The proposed controller is implemented with a mobile robot known as Khepera IV in a practical manner. The practical results demonstrate that the proposed mechanism is very efficient in terms of providing shortest distance to reach the goal with maximum velocity as compared with the MENN. Specifically, the MEEG is better than MENN in minimizing the error rate by 58.33%.
Assessment of voltage stability based on power transfer stability index using computational intelligence models Ahmed Majeed Ghadban; Ghassan Abdullah Salman; Husham Idan Hussein
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.pp2790-2797

Abstract

In this paper, the importance of voltage stability is explained, which is a great problem in the EPS. The estimation of VS is made a priority so as to make the power system stable and prevent it from reaching voltage collapse. The power transfer stability index (PTSI) is used as a predictor utilized in a PSN to detect the instability of voltages on weakened buses. A PSI is used to obtain a voltage assessment of the PSNs. Two hybrid algorithms are developed. The (CA-NN) and the (PSO-NN). After developing algorithms, they are compared with the actual values of PTSI NR method. The algorithms installed on the 24 bus Iraqi PS. The actual values of PTSI are the targets needed. They are obtained from the NR algorithm when the input data is Vi, δi, Pd, Qd for the algorithm. The results indicate that a weak bus that approaches voltage collapse and all results were approximately the same. There is a slight difference with the actual results and demonstrated classical methods are slower and less accurate than the hybrid algorithms. It also demonstrates the validation and effectiveness of algorithms (CA-NN, and PSO-NN) for assessing voltage-prioritizing algorithms (CA-NN). The MATLAB utilized to obtain most of the results.
A four-element UWB MIMO antenna using SRRs for application in satellite communications Chafai Abdelhamid; Hedi Sakli; Nizar Sakli
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.pp3154-3167

Abstract

This paper proposes a method for designing a new ultra wide band (UWB) multiple-input multiple-output (MIMO) antenna with two and four elements. First we presented an ultra-wide band antenna we studied these performances. Then, we studied the application of metamaterials to the design of MIMO antennas for miniaturization and the performance of antennas, in order to guarantee the proper functioning of the MIMO system with a much reduced separation distance between the radiating elements (λ/12), where the coupling can be very weak. The application of these circular double ring SRRs materials on the front plan of the antenna has contributed to the increasing of the antenna performance is studied in terms of S-Parameters, efficiency, diversity gain (DG), radiation properties and envelop correlation coefficient (ECC). It offers advantages such as the reduction of weight and congestion that is beneficial for their integration into satellite communications systems.
An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people Nachirat Rachburee; Wattana Punlumjeak
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.pp3434-3442

Abstract

The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we proposed an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life.
RescueAlert-an accident detection and rescue mechanism Uttkarsh Kumar Singh; Sahil Yadav; Sonali Joshi; Stuti Singh; Kayalivizhi Jayavel
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.pp3356-3364

Abstract

With the increase of vehicles and cars of different kind and the large movement that occurs every day on the roads it was natural to observe an increase in traffic accidents, but the real dilemma lies in how to make the rescue process efficient. The problem that we want to solve is the response of ambulances towards accidents and the lengthy registration process of patients in hospitals. In the above two scenarios, the manual process of calling the ambulance leads to delay in rescue of patients from an accident and the delay in registration of patient leads to delay in medication or treatment of the patient. We want to make the process more efficient by automating accident detection for increasing the efficiency of the ambulance rescue process and by sending the details of the patient before the patient reaches the hospitals for faster treatment of patients. Along with this, alert messages will be sent to the family or friends of the patients to notify them as soon as an accident is detected.
Performance analysis of OFDM-IM scheme under STO and CFO Suyoto Suyoto; Agus Subekti; Arief Suryadi Satyawan; Vita Awalia Mardiana; Nasrullah Armi; Dayat Kurniawan
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.pp3293-3299

Abstract

In this letter, performance analysis of orthogonal frequency division multiplexing with index modulation (OFDM-IM) is presented in term of bit error rate (BERs). The analysis considers its performance under two impairments, symbol time offset (STO) and carrier frequency offset (CFO) in frequency-selective fading channel. As orthogonal multicarrier system, OFDM-IM is subject to both inter-symbol interference (ISI) and inter-carrier interference (ICI) in a frequency-selective fading channel. OFDM-IM is a new multicarrier communication system, where the active subcarriers indices are used to carry additional bits of information. In general, in the previous existing works, OFDM-IM are evaluated only for near-ideal communication scenarios by only incorporating the CFO factor. In this work, the OFDM-IM performance is investigated and compared with conventional OFDM in the presence of two impairments, STO and CFO. Simulation results show that OFDM-IM outperforms the conventional OFDM with the presence of STO and CFO, especially at high SNR areas.
Modified digital space vector pulse width modulation realization on low-cost FPGA platform with optimization for 3-phase voltage source inverter Shalini Vashishtha; Rekha K. 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.pp3629-3638

Abstract

The realization of power electronic applications on hardware is a challenging task. The digital control circuit strategies are used to overcome the analog control strategies by providing great flexibility with simple equipment and higher switching frequencies. In this manuscript, an area optimized, modified digital space vector (DSV) pulse width modulation is designed and realized on low-cost FPGA. The modified digital space vector pulse width modulation (DSVPWM) uses a phase-locked loop (PLL) to generate clocks using the digital clock manager (DCM). These DCM clocks are used in the DSVPWM module to synchronize the other sub-modules. The voltage generation unit generates the three-phase (3-Ф) voltages and is used in the alpha-beta generation and sector determination unit. The reference active vectors are made by the reference generation unit and used in switching time calculation. The PWM pulses are generated using switching time generation, and lastly, the dead time occurrence unit generates the final SVPWM gate pulses. The modified DSVPWM is synthesized and implemented on Spartan-3E FPGA. The modified DSVPWM utilizes 17% slices, works at 102.45 MHz, and consumes 0.070 W total power. The simulation results and the resource utilization of modified DSVPWM are represented in detail. The modified DSVPWM is compared with existing PWM approaches on different Spartan-series FPGAs with better chip area improvement
Small intestine bleeding detection using color threshold and morphological operation in WCE images A. Al Mamun; M. S. Hossain; P. P. Em; A. Tahabilder; R. Sultana; M. A. Islam
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.pp3040-3048

Abstract

Wireless capsule endoscopy (WCE) is a significant modern technique for observing the whole gastroenterological tract to diagnose various diseases like bleeding, ulcer, tumor, Crohn's disease, polyps etc in a non-invasive manner. However, it will make a substantial onus for physicians like human oversight errors with time consumption for manual checking of a vast amount of image frames. These problems motivate the researchers to employ a computer-aided system to classify the particular information from the image frames. Therefore, a computer-aided system based on the color threshold and morphological operation has been proposed in this research to recognize specified bleeding images from the WCE. Besides, A unique classifier, quadratic support vector machine (QSVM) has been employed for classifying the bleeding and non-bleeding images with the statistical feature vector in HSV color space. After extensive experiments on clinical data, 95.8% accuracy, 95% sensitivity, 97% specificity, 80% precision, 99% negative predicted value and 85% F1 score has been achieved, which outperforms some of the existing methods in this regard. It is expected that this methodology would bring a significant contribution to the WCE technology. 
AlertNet: Deep convolutional-recurrent neural network model for driving alertness detection P. C. Nissimagoudar; A. V. Nandi; Aakanksha Patil; Gireesha H. M.
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.pp3529-3538

Abstract

Drowsy driving is one of the major problems which has led to many road accidents. Electroencephalography (EEG) is one of the most reliable sources to detect sleep on-set while driving as there is the direct involvement of biological signals. The present work focuses on detecting driver’s alertness using the deep neural network architecture, which is built using ResNets and encoder-decoder based sequence to sequence models with attention decoder. The ResNets with the skip connections allow training the network deeper with a reduced loss function and training error. The model is built to reduce the complex computations required for feature extraction. The ResNets also help in retaining the features from the previous layer and do not require different filters for frequency and time-invariant features. The output of ResNets, the features are input to encoder-decoder based sequence to sequence models, built using Bi-directional long-short memories. Sequence to Sequence model learns the complex features of the signal and analyze the output of past and future states simultaneously for classification of drowsy/sleepstage-1 and alert stages. Also, to overcome the unequal distribution (class-imbalance) data problem present in the datasets, the proposed loss functions help in achieving the identical error for both majority and minority classes during the raining of the network for each sleep stage. The model provides an overall-accuracy of 87.92% and 87.05%, a macro-F1-core of 78.06%, and 79.66% and Cohen's-kappa score of 0.78 and 0.79 for the Sleep-EDF 2013 and 2018 data sets respectively.

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