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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 9,174 Documents
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News classification using light gradient boosted machine algorithm Muhammad Hatta Rahmatul Kholiq; Wiranto Wiranto; Sari Widya Sihwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp206-213

Abstract

News classification is a complex issue as people are easily convinced of misleading information and lack control over the spread of fake news. However, we ca n break the problem of spreading fake news with artificial intelligence (AI), which has developed rapidly. This study proposes a news classification model using a light gradient boosted machine (LightGBM) algorithm. The model is analyzed using two feature extraction techniques, count vectorizer and Tfidf vectorize r and compared with a deep learning model using long - short term memory (LSTM). The experimental evaluation showed that all LightGBM models outperform LSTM. The best model is the count vectorizer Li ghtGBM, which achieves an accuracy value of 0.9933 and an area under curve (AUC) score of 0.9999.
An efficient authentication and key-distribution protocol for wireless multimedia sensor network Basavaraj Patil; Sangappa Ramachandra Biradar
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp347-354

Abstract

To provide security and privacy for multimedia data transmission, efficient techniques for authorizing and authenticating network users and nodes are required. These challenges have made it a vital and significant area of research in the present decade. Du e to resource constraints, existing systems are unable to provide adequate protection against vulnerable behaviors and security assaults such as black-hole, Sybil, man-in-the-middle, and other similar attacks. In this paper, an effective enhanced engineere d cementitious composites (ECC) and crypto-based authentication with a key exchange mechanism is proposed. The method boosts the effective authentication mechanism and reduces the number of vulnerable activities in the network. The simulation results demon strate that the suggested technique is robust to malicious assaults and performs mutual authentication efficiently. A cost-benefit analysis validates that the processing, communication, and storage requirements are much reduced when compared to existing ap proaches. Furthermore, an informal security analysis demonstrates that the suggested protocol is secure and adaptable to real-time scenarios.
ThreatNet: advanced threat detection, region-based convolutional neural network framework Anurag Singh; Naresh Kumar; Seifedine Kadry
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1007-1015

Abstract

It is critical for many countries to ensure public safety in detecting and identifying threats in a night, commercial places, border areas and public places. Majority of past research in this area has focused on the use of image-level categorization and object-level detection techniques. As an X-ray and thermal security image analysis strategy, object separation can considerably improve automatic threat detection when used in conjunction with other techniques. In order to detect possible threats, the effects of introducing segmentation deep learning models into the threat detection pipeline of a large imbalanced X-ray and thermal dataset were investigated. With the purpose of boosting the number of true positives discovered, a faster regional convolutional neural network (R-CNN) model was trained on a balanced dataset to identify probable hazard zones in X-ray and thermal security pictures. In order to get the final results, we combined the two models i.e faster R-CNN with Mask RCNN into a single detection pipeline using the transfer learning technique, which outperforms baseline and end-to-end instance segmentation methods using less number of the practical dataset, with mAPs ranging from 94.88 percent to 91.40 percent helps in detecting the person with guns, knives, pliers to avoid cross border threats.
Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT Mounir Grari; Idriss Idrissi; Mohammed Boukabous; Omar Moussaoui; Mostafa Azizi; Mimoun Moussaoui
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1062-1073

Abstract

The impact of wildfires, even following the fire's extinguishment, continues to affect harmfully public health and prosperity. Wildfires are becoming increasingly frequent and severe, and make the world's biodiversity in a growing serious danger. The fires are responsible for negative economic consequences for individuals, corporations, and authorities. Researchers are developing new approaches for detecting and monitoring wildfires, that make use of advances in computer vision, machine learning, and remote sensing technologies. IoT sensors help to improve the efficiency of detecting active forest fires. In this paper, we propose a novel approach for predicting wildfires, based on machine learning. It uses a regression model that we train over NASA's fire information for resource management system (FIRMS) dataset to predict fire radiant power in megawatts. The analysis of the obtained simulation results (more than 99% in the R2 metric) shows that the ensemble learning model is an effective method for predicting wildfires using an IoT device equipped with several sensors that could potentially collect the same data as the FIRMS dataset, such as smart cameras or drones.
Digital platform based on geomarketing as an improvement in micro and small enterprises Teófilo Crisóstomo-Berrocal; Fernando Sierra-Liñan; Cabanillas Carbonell-Michael
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp395-403

Abstract

After the situation generated by the pandemic caused by COVID-19, micro and small enterprises (MSEs) faced a complex reality, having to cope with business uncertainty. This research proposes a digital platform based on geomarketing as a growth and support strategy for MSEs, with the objective of improving their labor and capital productivity, through the incorporation of the technological factor, which will have a great impact on them, helping them to continue operating and not having to close their businesses. The platform was developed under the agile Scrum methodology because it is adaptable to the constant changes in the mobile application development process, having as indicators labor productivity and capital productivity. Finally, the results revealed that labor productivity increased by 30.86 percent, meaning that, for every hour worked per person, more sales were made. As for capital productivity, it decreased by 1.47 percent, meaning that investment decreased for each value added of each product sold.
The enhancement of the dual-layer phosphorus configuration in color uniformity and luminous flux of a light emitting diode Phuc Dang Huu; Phung Ton That; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp766-772

Abstract

A solid-state process was used to generate the green phosphor Ca3Si2O4N2:Eu2+. The luminescence characteristics, dispersed reflection spectra, and heat quenching were investigated initially, followed by the white light emitting diodes (wLED’s) manufacture by the Eu2+ stimulated Ca3Si2O4N2 phosphor. Based on the concentration of ion Eu2+, a wide green emission range localized between 510 and 550 nm was seen in Eu2+ -doped Ca3Si2O4N2. In Ca3Si2O4N2, the best doping concentration of Eu2+ was 1 mol%. An electric multipolar interaction process conveys energy among Eu2+ ions, with a necessary conversion distance of around 30.08 Å. Blending a near-ultraviolet (n-UV) light emitting diodes (LED) which has a GaN basis (380 nm) with the blue BaMgAl10O17:Eu2+, the green  Ca3Si2O4N2:Eu2+, and the red Ca3Si2O4N2:Eu2+ phosphors yielded a wLED with a 88.25 color-rendering indice Ra at 6029 K correlating color temperature.  Ca3Si2O4N2:Eu2+ appears to be a promising option to apply as a converting phosphor in wLED applications.
Comparison of electric motors used in electric vehicle propulsion system Khalid S. Mohammad; Aqeel S. Jaber
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp11-19

Abstract

Now days, it is vital to use electric vehicles (EVs) instead of traditional cars with internal combustion engines (ICEs) in order to reduce the high level of pollution in the environment, and many researchers are investigating the possible improvements on these vehicles. The main component of EVs is the electric motor and the selection of a motor with high efficiency, excellent dynamic response and high starting torque has a strong effect on the performance of EVs. In addition to that a reasonable price for the electric motor is required. This work focuses on the selection of the most suitable electric motor for EVs. Therefore a detailed study to compare between the performance of the major types of electric motors that are used in EVs is addressed in this paper. The results of this comparative study is tabulated and by careful consideration for all these results, the appropriate electric motor for EVs has been chosen. From the other hand, the artificial intelligent (AI) techniques play a crucial role in the EVs technologies, and several kinds of AI techniques used in EVs applications are overviewed in this work.
Improving SpellChecking: an effective Ad-Hoc probabilistic lexical measure for general typos Hicham Gueddah; Mohamed Nejja; Said Iazzi; Abdellah Yousfi; Si Lhoussain Aouragh
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp521-527

Abstract

Since the era of learning to write by human beings,  mistakes made in typing words have occupied a privileged place in linguistic studies, integrating new disciplines into school curricula such as spelling and dictation. According to exhaustive studies that we have done in the field of spellchecking errors made in typing Arabic texts, very few research works that deal with typographical errors specifically caused by the insertion or missing of the blank-space in words. On the other hand, spelling correction software remains ineffective for handling this type of errors. Failure to process errors due to the insertion/missing of blankspace between and in words leads and brings us back to situations of ambiguity and incomprehension of the meaning of the typed text. To remedy this limitation of correction, we propose in this article an ad-hoc probabilistic method which is based jointly on two approaches. The first approach treats the errors due to deletion or missing of blank-space between or inside words, while the second puts emphasis in correcting space insertion errors in a word of course in addition to other kinds of elementary editing errors (addition, deletion, permutation of characters). Our new approach combines edit distance with n-gram language models to correct the errors already mentioned. Our new approach gave an accuracy rate that reaches 98,14% for missing blank-space errors (noted MBSE) and 89,5% for insertion blank-dpace errors (noted IBSE), which gives an average correction rate of around 95,26%. These results are very encouraging and show the interest and the importance of our approach.
Two cross coupled and Madgwicks filter for estimation of multi-channel dividing systems Nader Abdullah Kadhim; Ali jawad Alrubaie; Ameer Al-khaykan
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp262-270

Abstract

The estimates of Rayleigh fading channels are rapidly changing in multi carrier direct sequence code division multiple access (MC-DS-CDMA) multiplexing systems. The most widely accepted answer to this issue is the conventional solution least square (LS) or mean square error estimator (MMSE) using the recursive least squares algorithm (RLS) or the least mean squares (LMS) algorithm. In much of the previous work, only one Kalman filter was used for estimation. In this paper, a Kalman filter is used with a Madgwicks filter together to satisfy the fading problems. However, this requires a priori evaluation of auto regressive (AR) parameters. A standard solution involves the first matching of the auto-completion function of the applying the AR method to Jakes' problem and then tackling it (YWE). Even more the results procedure is limited to crowd constraints and is related to an AR+ process of noise, an approximation considered. In fact, depending on simulation findings, high-AR models outperform conventional models on the basis of spectral estimate and bit error margins (BER). Nevertheless, in order to save costs of computing, the 5-D model of AR is a possibility. The proposed process outperforms edge of art competitors in terms of bit error rate as demonstrated by results.
Classification of focal liver disease in egyptian patients using ultrasound images and convolutional neural networks Rania Mohamed Abd-ElGhaffar; Mahmoud El-Zalabany; Hossam El-Din Moustafa; Mervat El-Seddek
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp793-802

Abstract

Recently, computer-aided diagnostic systems for various diseases have received great attention. One of the latest technologies used is deep learning architectures for analyzing and classifying medical images. In this paper, a new system that uses deep learning to classify three focal diseases in the liver besides the normal liver is proposed. A pre-trained convolutional neural network is utilized. Two types of networks are used, ResNet50 and AlexNet with fully connected networks (FCNs). After extracting the deep features using deep learning, FCNs can input images in different states of the disease, such as Normal, Hem, HCC, and Cyst. Dataset is obtained from the Egyptian Liver Research Institute. Two classifiers are utilized, the first includes two classes (Normal/Cyst, Normal/Hem, Normal/HCC, HCC/Cyst, HCC/Hem, Cyst/Hem) and the second contains four classes (Normal/Cyst/ HCC/Hem) to distinguish liver images. Using performance criteria, it has been shown that the two-category classifiers have given better results than the four-class classifier, and accordingly a hybrid classifier was suggested to merge the weighted probabilities of the classes obtained by each singular classifier. Experimental results have achieved an accuracy of 96.1% using ResNet50 which means that it can be used as an assistive diagnostic method for classification of focal liver disease.

Page 676 of 918 | Total Record : 9174

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