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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 64 Documents
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Search results for , issue "Vol 25, No 2: February 2022" : 64 Documents clear
Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers Mohammed Boukabous; Mostafa Azizi
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1131-1139

Abstract

Sentiment analysis (SA) is widely used today in many areas such as crime detection (security intelligence) to detect potential security threats in realtime using social media platforms such as Twitter. The most promising techniques in sentiment analysis are those of deep learning (DL), particularly bidirectional encoder representations from transformers (BERT) in the field of natural language processing (NLP). However, employing the BERT algorithm to detect crimes requires a crime dataset labeled by the lexiconbased approach. In this paper, we used a hybrid approach that combines both lexicon-based and deep learning, with BERT as the DL model. We employed the lexicon-based approach to label our Twitter dataset with a set of normal and crime-related lexicons; then, we used the obtained labeled dataset to train our BERT model. The experimental results show that our hybrid technique outperforms existing approaches in several metrics, with 94.91% and 94.92% in accuracy and F1-score respectively.
A predictive maintenance system for wireless sensor networks: a machine learning approach Mohammed Almazaideh; Janos Levendovszky
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1047-1058

Abstract

Predictive maintenance system (PdM) is a new concept that helps system operators evaluate the current status of their systems, and it also assists in predicting the future quality of these systems and scheduling maintenance action. This paper proposes a PdM model that utilizes machine learning to predict the system’s operational status after M active steps based on L previous observations implemented by a feedforward neural network (FFNN). We use quantization and encoding schemes to reduce the complexity of the system. We apply the proposed model to build a PdM system for wireless sensors networks (WSNs), where our concern is to predict the state of the system as far as the quality of data transfer is concerned. The FFNN provides a forward prediction of the operational status of the network after M consecutive time steps in the future, based on the previous L readings of quality of service (QoS) requirements of WSN. We also demonstrate the relation between complexity and accuracy. We found that larger M leads to higher complexity and larger prediction error, where larger L entails higher complexity and smaller prediction error. We also investigate how quantization and encoding can reduce complexity to implement a real-time PdM system.
First order surface grating fiber coupler under the period chirp and apodization functions variations effects Alsharef Mohammad; Mohammed S. Alzaidi; Mahmoud M. A. Eid; Vishal Sorathiya; Sunil Lavadiya; Shobhit K. Patel; Ahmed Nabih Zaki Rashed
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1020-1029

Abstract

The paper has demonstrated the first order surface grating fiber coupler under the period chirp and apodization functions variations effects. The Fiber coupler transmittivity/reflectivity, the fiber coupler grating index change and the fiber coupler mesh transmission cross-section are clarified against the grating length with the quadratic/cubic root period chirp and Gaussian/uniform apodization functions. The fiber coupler delay and dispersion are simulated and demonstrated with grating wavelength with quadratic/cubic root period chirp and Gaussian/uniform apodization function. As well as the fiber coupler output pulse intensity is simulated against the time period with the quadratic/cubic root period chirp and Gaussian/uniform apodization function. The fiber coupler peak intensity variations against the transmission range variations is also demonstrated by OptiGrating simulation software.
Classification of chest X-ray images using a hybrid deep learning method Panida Songram; Phatthanaphong Chomphuwiset; Khanabhorn Kawattikul; Chatklaw Jareanpon
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp867-874

Abstract

This work presents a technique for classifying X-ray images of the chest (CXR) by applying deep learning-based techniques. The CXR will be classified into three different types, i.e. (i) normal, (ii) COVID-19, and (iii) pneumonia. The classification challenge is raised when the X-ray images of COVID-19 and pneumonia are subtle. The CXR images of the chest are first proceeded to be standardized and to improve the visual contrast of the images. Then, the classification is performed by applying a deep learningbased technique that binds two deep learning network architectures, i.e., convolution neural network (CNN) and long short-term memory (LSTM), to generate a hybrid model for the classification problem. The deep features of the images are extracted by CNN before the final classification is performed using LSTM. In addition to the hybrid models, this work explores the validity of image pre-processing methods that improve the quality of the images before the classification is performed. The experiments were conducted on a public image dataset. The experimental results demonstrate that the proposed technique provides promising results and is superior to the baseline techniques.
Power quality improvement of distribution systems asymmetry caused by power disturbances based on particle swarm optimization-artificial neural network Ismael Kareem Saeed; Kamal Sheikhyounis
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp666-679

Abstract

With an increase of non-linear load in today’s electrical power systems, the rate of power quality drops and the voltage source and frequency deteriorate if not properly compensated with an appropriate device. Filters are most common techniques that employed to overcome this problem and improving power quality. In this paper an improved optimization technique of filter applies to the power system is based on a particle swarm optimization with using artificial neural network technique applied to the unified power flow quality conditioner (PSO-ANN UPQC). Design particle swarm optimization and artificial neural network together result in a very high performance of flexible AC transmission lines (FACTs) controller and it implements to the system to compensate all types of power quality disturbances. This technique is very powerful for minimization of total harmonic distortion of source voltages and currents as a limit permitted by IEEE-519. The work creates a power system model in MATLAB/Simulink program to investigate our proposed optimization technique for improving control circuit of filters. The work also has measured all power quality disturbances of the electrical arc furnace of steel factory and suggests this technique of filter to improve the power quality.
Music genres classification by deep learning Yifeng Hu; Gabriela Mogos
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1186-1198

Abstract

Since musical genre is one of the most common ways used by people for managing digital music databases, music-genre-classification is a crucial task. There are many scenarios for its use, and the main one explored here is eventually being placed on Spotify, or Netease music, as an external component to recommend songs to users. This paper provides various deep neural networks developed based on python, together with the effect of these models on music genres classification. In addition, the paper illustrates the technologies for audio feature extraction in industrial environment by mel frequency cepstral coefficients (MFCC), audio data augmentation in
A review on power quality issues in electric vehicle interfaced distribution system and mitigation techniques Basaralu Nagasiddalingaiah Harish; Usha Surendra
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp656-665

Abstract

Electric vehicles (EV) penetration in the distribution systems is evident and intended to grow day by day. Power quality issues pop up in the distribution system with an increase in EV penetration. Distribution networks need to consider the power quality issues developed due to the penetration of EVs for planning and designing the system. The power quality issues, including voltage imbalance, total harmonic distortion, distribution transformer failure, and related issues, are anticipated due to EV penetration in distribution systems. Detailed review of power quality issues and mitigation techniques are detailed in this paper. Discussion on the effect of these power quality issues on the distribution systems and corresponding mitigation measures are detailed. Power quality impact mitigation techniques have been discussed recently, which exploits the bidirectional power flow of vehicle to grid vehicle to grid (V2G) and grid to vehicle grid-to-vehicle (G2V). Methods and methodologies that mitigate power quality problems in the EV penetrated distribution system is discussed. Bidirectional power flow during EV charging and discharging and power quality issues in this topology is detailed in this review paper. A discussion on future trends and different possible future research paradigms is discussed as the review's conclusion.
Measurement of an electroretinogram signal and display waves on graphical user interface by laboratory virtual instrument engineering workbench Mustafa F. Mahmood; Huda Farooq Jameel; Mayss Alreem Nizar Hammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp980-988

Abstract

The electroretinogram (ERG) is an electrophysiological recording method that measures the retinal electrical potential. The electrical reaction is quantified by electrical interaction of the indicator electrode with the cornea or at various levels inside the retina. However, such ERG systems suffer from certain limitations and challenges, such as high cost, low a/b-wave amplitude, and the outcomes do not provide any information about patients. In this work, we designed and implemented a real-time prototype for an ERG system for measuring eye waves via diode-transistor logic (DTL)- electrode and AD624AD-model. In addition, a graphical user interface (GUI) via virtual instrument engineering workbench (LabVIEW) was used. The developed system achieved high amplitude for ERG a/b-waves of about 100 and 700 mV. In terms of a/b-waves in the system, the findings show that this study has good results for optimizing the measurement of ERG signals. The method showed satisfactory accuracy of about 92.5% for 10 participants aged 20-60 years and comprising both genders
Hybrid dynamic chunk ensemble model for multi-class data streams Varsha Sachin Khandekar; Pravin Shrinath
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1115-1122

Abstract

In the analysis more specifically in the classification of continuous data stream using machine learning algorithms joint occurrence of concept drift and imbalanced issue becomes more provocative. Also, imbalance issue is again more challenging when the data stream is multi-class with minority class and that is too with data-difficulty factors. Incremental learning with ensemble models found more promising in handling theses issues. But most of the approaches are for two-class data streams which can’t be utilized for multiclass data streams. In this paper we have designed hybrid dynamic chunk ensemble model (HDCEM) for the classification of multi-class insect-data stream for handling imbalance and concept drift issue. To deal with imbalance issue we have proposed effective split bagging algorithm which has achieved better performance on minority class recall and F-measure on arriving dynamic chunks of data from multi-class data stream. HDCEM model can adapt to abrupt and gradual drift because it has combined features of both online and chunk-based learning together. It has achieved average 78% minority class recall in abrupt insect data stream and 71% in gradual drift insect stream.
Optical fiber sensors: review of technology and applications Mahmoud M. A. Eid
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp1038-1046

Abstract

There is a huge increase in the usage of optical fiber sensors in various fields, especially the field of communications, as these sensors have been employed in a promising industry, namely, the internet of things. This industry is witnessing a growing demand for more sensors as well as employing them in new applications, and there is an urgent need to invent new sensors to meet our requirements in providing more time, luxury, and effort with the highest quality and the best possible performance. But there is now a lot of information about optical sensors as well as many classifications and applications. There are also some developments in a scientific research yard. The main objective of this paper is to introduce short, effective, and concentrated points in optical fiber sensors such as a brief historical background, their structure, and their different operation principles, different classifications for these sensors according to different categories, and finally advantages of fiber optical sensors compared to traditional electronic sensors. I hope this content will be very useful to anyone interested in these types of sensors. This review is done with particular assurance on the recently published information.

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