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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 29, No 1: January 2023" : 64 Documents clear
Proposed security mechanism for preventing fake router advertisement attack in IPv6 link-local network Mahmood A. Al-Shareeda; Selvakumar Manickam; Murtaja Ali Saare; Navaneethan C. Arjuman
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp518-526

Abstract

The design of router discovery (RD) is a trust mechanism to confirm the legitimacy of the host and router. Fake router advertisement (RA) attacks have been made possible by this RD protocol design defect. Studies show that the standard RD protocol is vulnerable to a fake RA attack where the host will be denied a valid gateway. To cope with this problem, several prevention techniques have been proposed in the past to secure the RD process. Nevertheless, these methods have a significant temporal complexity as well as other flaws, including the bootstrapping issue and hash collision attacks. Thus, the SecMac-secure router discovery (SecMac-SRD) technique, which requires reduced processing time and may thwart fake RA assaults, is proposed in this study as an improved secure RD mechanism. SecMac-SRD is built based on a UMAC hashing algorithm with ElGamal public key distribution cryptosystem that hides the RD message exchange in the IPv6 link-local network. Based on the obtained expected results display that the SecMac-SRD mechanism achieved less processing time compared to the existing secure RD mechanism and can resist fake RA attacks. The outcome of the expected results clearly proves that the SecMac-SRD mechanism effectively copes with the fake RA attacks during the RD process.
A new density core graph-cut class decomposition to improve neural network classification performance Eakawat Tantamjarik; Thitipong Tanprasert
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp496-508

Abstract

This research presents a new pre-processed class decomposition technique called density core graph-cut (DCGC). The method uses supervised clustering instead of a traditional unsupervised one to decompose the class. Supervised clustering requires additional label information to function and with that it gains a better understanding of the distribution. DCGC employs nearest neighbors to form a density core graph for each class. Then, the edges of each graph to be removed or cut is identified utilizing class information. Lastly, it yields final clusters by grouping all connected cores and assigning the remaining samples to a cluster where the nearest core belongs. Training neural network classifiers on complex label data will yield a better accuracy with the modified class representation. Intuitively, the decision boundaries separating classes based on the modified labels are less complex, and classifiers’ chance to reach these hyperplanes is higher. The results present that training neural networks using label representations from DCGC significantly helps improve the classification accuracy of neural networks on syntactic datasets as high as 30%. For real-world problems, the experiment presents a mixed result in which some datasets moderately benefit from DCGC.
Earthquake prediction in Iraq using machine learning techniques Nada Badr Jarah; Kadhim Mahdi Hashim; Abbas Hanon Hassin
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp322-329

Abstract

This study deals with addressing the scientific achievements and the history of earthquake prediction in Iraq, in addition to attempting to discuss the possibility of machine learning to predict earthquakes from a theoretical perspective. The idea of predicting earthquakes gives at least a little time to protect people and reduce earthquake damage. In Iraq, we notice an increase in the occurrence of earthquakes, especially in the southern regions, where they form a strange phenomenon because they are plain areas and far from the seismic fault line, due to the errors that accompany excessive oil extraction and in random and unstudied ways, and geological studies raise fears in predicting an increase in earthquakes for the coming years. We have explored the possibility of applying machine learning technology to predict earthquakes in Iraq, and follow-up recording of tremors at different stations in Iraq through three centers of seismic sensor networks. In addition to the earthquake catalog in Iraq (1900-2019). This study may pave the way for more research to develop an integrated and accurate earthquake prediction system using machine-learning technologies.
Hybrid approach for multi-objective optimization path planning with moving target Baraa M. Abed; Wesam M. Jasim
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp348-357

Abstract

Path planning algorithms are the most significant area in the robotics field. Path planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Path planning optimization refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOO present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. Several objectives are considered as part of this study, including path security, length, and smoothness, when planning paths for autonomous mobile robots in a dynamic environment with a moving target. Particle swarm optimization (PSO) algorithms are combined with bat algorithms (BA) to make a balance between exploration and exploitation. PSO algorithms used to optimize two important parameters of the bat algorithm. The proposed solution is tested through several simulations based on varying scenarios. The results demonstrate that mobile robots can travel clearly and safely along short paths and smoothly, proving this method's efficiency.
Decentralized secondary control for frequency regulation based on fuzzy logic control in islanded microgrid Ilyas Bennia; Abdelghani Harrag; Yacine Daili; Allal Bouzid; Josep M. Guerrero
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp85-100

Abstract

This paper presents a fuzzy-based decentralized secondary control for frequency restoration and active power-sharing in an islanded microgrid, this controller uses the local frequency error to generate an extra term for compensating the deviation and maintaining accurate active power-sharing. No communication infrastructures are needed, event detection, time dependent-protocols, and state estimation are not required. Its design and implementation are straightforward, also, it offers quick dynamic frequency recovery with accurate active power-sharing. To verify the validity of the proposed controller, several tests have been carried out: frequency restoration and active power-sharing during load disturbances, synchronization with plug and play (PnP) ability, as well the communication latency impact. Moreover, the effect of data drop-out and interferences were analyzed in real-time simulation using Truetime. The results have shown the high capability and the fast response of frequency restoration while maintaining the active power-sharing, no oscillations and ripples were presented in steady-state response, likewise, a smooth dynamic response during PnP test is observed. The communication latency and interferences have no impact on the proposed controller that showing a significant improvement in settling time 50% without frequency packet losses and 81%, 90% in the presence of 30% and 70% of frequency packet losses respectively.
An intelligent oil accident predicting and classifying system using deep learning techniques Yasmen Samhan Abd ElWahab; Mona Mohamed Nasr; Fahad Kamal Al Sheref
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp460-471

Abstract

This study discusses the problem of oil and gas faults that lead to spills or explosions that lead to a lot of losses in human life, oil field extraction, and costs. Petrol is an important field in our lives because it controls all aspects of human life and their way of life, so our research focused on petrol and its problems in order to introduce a better way of life. The data used in this research was taken from the 3w database that was prepared by Petrobras, the Brazilian oil holding. The 9 classes classified in that work include the normal state that indicates the factors that will not lead to a problem. Deep learning classification techniques were used in this study. 99% accuracy was obtained in that model, and it refers to a successful prediction and classification of each class. Different results were observed when different hidden layers, optimizers, neurons, epochs, and activation functions were used. 99% was achieved when using Adam's optimizer and Tanh's activation function.
Integrated approach of brain segmentation using neuro fuzzy k-means Jawwad Sami Ur Rahman; Sathish Kumar Selvaperumal
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp270-276

Abstract

A proposed method using neuro-fuzzy k-means for the segmentation process of brain has been developed successfully, simulated and assessed. The proposed method has been assessed by using clinical brain images of magnetic resonance imaging (MRI) technology, to segment the three main tissues of the brain. The proposed system is able to segment the three important regions of the brain, which are white matter, grey matter and cerebrospinal fluid (CSF) more accurately, as compared to the benchmarked algorithms. Furthermore, the developed method’s misclassification rate (MR) has been significantly minimized by 88%, 27%, 88%; 82%, 71%, 84%; and 82%, 29%, 83%, as compared to k-means, fuzzy logic, and radial basis function (RBF) for white matter, grey matter and CSF, respectively. Also, from the visual interpretation, it is observed that the brain’s edges are well preserved and the tissues are clearly segmented. From these measures, the proposed integrated approach is shown to be accurate in segmenting the MRI brain tissue with reduced misclassified pixels.
Various object detection algorithms and their comparison Debani Prasad Mishra; Kshirod Kumar Rout; Sivkumar Mishra; Surender Reddy Salkuti
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp330-338

Abstract

This paper presents a detailed and comparative analysis of various object detection algorithms. The challenge of object detection is taken care of while studying various algorithms. Throughout the year various methods have been discovered in this field, each having its advantages and drawbacks. This paper aims at providing the systematic study of all the popular algorithms including the conventional ones. Although many methods and techniques come up each year and each of them having superiority other the previous models but at the same time even complexity increases. In this paper, some famous and basic methods of object detection and tracking are discussed. Using these developed techniques good results can be obtained and also the comparison of the efficiency of all the models can be done. Real-time applications and the outcomes are also discussed.
Variational selective segmentation model for intensity inhomogeneous image Tammie Christy Saibin; Abdul Kadir Jumaat
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp277-285

Abstract

Variational selective image segmentation models aim to extract a particular object in an image depending on a set of user-defined prior points. The current model suffers from high computational costs due to the traditional total variation function that results in a slow segmenting process. In addition, it is not designed to segment images with intensity inhomogeneities. In this research, we formulate a new variational selective image segmentation model based on the Gaussian function. A Gaussian function is proposed to replace the traditional total variation function to regularize the variational level set function. To segment images with intensity inhomogeneities, the local image fitting idea was in corporate into the formulation. The efficiency of the proposed model was then assessed by recording the computation time while the accuracy was measured using Jaccard and Dice similarity values. Numerical experiments using synthetic, natural, and medical images demonstrate that the proposed model is about 6 times faster than the existing model, while the Jaccard and Dice values are about 11% and 7% higher, respectively, compared to the existing model. In the future, this research can be extended further into a 3-dimensional modeling and vector-valued image framework.
Support system of self-assessment and gap analysis for new normal tourism standards Soawanee Prachayagringkai; Marut Buranarach; Pongpisit Wuttidittachotti
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp384-395

Abstract

Tourism after the outbreak of the emerging epidemic of COVID-19 has drastically changed. Tourist attractions will be certified with Green National Park and New Normal Standards. Starting in the year 2021 onwards, Thailand's national parks are important tourist destinations, of which 155 nationwide will be subject to complying with such standards to ensure safety, hygiene and environmentally friendly service starting in the year 2021 onwards. This research aims to develop a support system for self-assessment and gap analysis based on Smart Self-Assessment for New Normal Tourism Standards to enable the national parks to assess themselves and be prepared for future actual assessments. The system development focuses on user data import design and report output, system performance test, self-assessment score percentage difference tests, and system performance evaluation by the experts. The percentage difference of self-assessment scores is found at 0.0 for all items after adding details in some of the work lists based on the experts’ opinions, whereas, the performance testing indicates that the system developed is applicable and highly efficient (= 4.40, S.D.= 0.54).

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