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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 9,138 Documents
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Investigation of linear models for control of water flow and temperature in a water supply system Asset, Askhat; Mansurova, Madina; Zhmud, Vadim; Kopesbaeva, Aksholpan; Dzheksenbaev, Nurbolat
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp113-123

Abstract

In some cases, the object model is a set of parallel models of the same general appearance, but with different parameters. The most common model is a model in the form of a serial connection of a first- or second-order filter and a delay link. An example is the water supply system of a large residential building or a group of houses. From the most general considerations, we can expect that such an object can be approximately described by a simpler model, replacing the sum of identical-looking models with different parameters with a single model of this type with averaged parameters, however, finding many parameters simply in the form of an average is, apparently, an unreasonable approach. It seems more reasonable to find the parameters by the approximating model by numerical optimization, in which the integral from the module or from the square of the deviation of the output signal of such a model from the output signal of the exact model is minimized when the test signal is applied. For linear models, the most reasonable test signal is a single step effect. This article tests this hypothesis and provides the results of this test.
Design of adaptive array using least mean square beamformer Vidya Pramod Kodgirwar; Kalyani R. Joshi; Shankar B. Deosarkar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp932-941

Abstract

This paper introduces an 8-element linear array designed for adaptive array applications, using least mean square (LMS) algorithm to enhance the directivity of the array. Microstrip antenna has been optimized at 2.3 GHz, a pivotal frequency ranges relevant to 4G and 5G applications. This design is thoughtfully extended to encompass 8-elements, achieved through the art of parameterization using computer simulation technology (CST) microwave studio. This geometry of 8-element exhibits considerable promise, significantly elevating the gain from 6.13 dBi for a single element to an impressive 15.5 dBi for all eight-element array. To further empower the array’s adaptability and beam-steering capabilities, the LMS algorithm is simulated. This intelligent algorithm computes complex weights, thoughtfully with various angles, including those for the interested user at 60° and 30°, as well as potential interferers at 10° and 15°, as simulated in MATLAB. These meticulously calculated weights are effectively applied to antenna elements using CST, facilitating beam steering in various directions. During CST simulations, notable peaks in performance emerge at 54° and 28°, strategically aligned with nulls at 10° and 15°. Remarkably, these results exhibit a remarkable degree of concurrence with those obtained through MATLAB simulations, affirming effectiveness of the proposed adaptive array design.
Design and development of frameworks for CPU verification efficiency improvement Sheetal Singrihalli Hemaraj; Shylashree Nagaraja; Sunitha Yariyur Narasimhaiah; Madhu Patil
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1361-1369

Abstract

Bug finding is a critical component of the verification flow and is resource intensive.In a typical week, a debug engineer writes triages, which take up significant amount of time that could be spent debugging another unique issue, and the lack of standardization in scripting causes maintainability issues in functional verification bug triage. A framework that allows customizable triage script generation is developed based on inputs from the engineer deploying YAML isn’t another markup language (YAML) files and practical extraction and report language (PERL) scripting, and this methodology is made automated and is standardized across projects to ensure maximum benefit going forward. The use of auto-triage in the project of functional verification bug triage has contributed to a 18% increase in triaged signatures on average, from 40% before its use to 58% after. A similar earlier project vs. current project comparison shows a 20% uplift. The triaged inputs that are parsed are currently being fed to a machine learning algorithm, which will help further improve the debug efficiency. As part of future work, the information from input YAML files can be used to analyze simulation failure attributes, hence improving the overall efficiency of debugging.
Distributed denial of service attacks classification system using features selection and ensemble techniques Leila Bagdadi; Belhadri Messabih
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1868-1878

Abstract

Distributed denial-of-service (DDoS) attacks are expanding threat to online services and websites. These attacks overwhelm targets with traffic from multiple sources to exhaust resources and make services unavailable. The frequency of DDoS attacks exhibits an ongoing upward trajectory over time. This persistent escalation highlights the need for effective countermeasures. While machine learning approaches have been extensively investigated for binary classification of DDoS attacks, multi-class classification has received comparatively less examination in the literature despite its greater practical utility. In this paper, we propose an intrusion detection system for detecting and classifying DDoS attacks, based on two main axes: feature selection for selecting the best relevant features and ensemble learning technique for improving performance by combining weak learners. The proposed model has been trained and evaluated on the CICDDoS2019 dataset. Experimental evaluation demonstrates improved performance using a subset of 16 relevant features identified, with a test accuracy of 82.35% attained for discriminating between the 12 classes represented in the dataset. By aggregating attacks sharing common characteristics resulting in 7 classes, the approach achieves surpassing 97% accuracy. Additionally, a binary classification delineating benign and DDoS attacks attain 99.90% accuracy.
Machine learning approach for intrusion detection system using dimensionality reduction Deepa Manikandan; Jayaseelan Dhilipan
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp430-440

Abstract

As cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.
Dual tri-isolated DC H-6 inverter with minimal power components design Maheswaran, Anusuya; Ramanujam, Geetha; Rengaraju, Ilango; Arumugam, Iyyappan S.; Ramachandran, Gayathri
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp711-719

Abstract

Multilevel inverters have been forecasted in recent years for industrial and renewable energy applications due to its inherent characteristics of shaping the output voltage nearer to sinusoidal shape through concatenating several two/three level inverters using isolated DC sources or DC-link capacitors. However, the classical topologies used for synthesizing stepped voltage have several outwards like more number of DC sources or DC-link capacitors and switching devices used. In this paper, an effort has been sighted to bring a new topology for generating stepped voltage to overcome the above mentioned demerits. In addition to this, a new digital pulse width modulation (PWM) strategy is developed in-line with a new topology to eliminate the use of carrier and reference signals. The performance of the proposed topology and developed control strategy are evaluated in MATLAB/ Simulink platform and an laboratory prototype is constructed for experimental investigations to accord the simulation results.
Stress and anxiety detection: deep learning and higher order statistic approach Vaishali M. Joshi; Deepthi D. Kulkarni; Nilesh J. Uke
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1567-1575

Abstract

Today's teenagers are dealing with anxiety and stress. Anxiety, depression, and suicide rates have increased in recent years because of increased social rivalry. The research is focused on detecting anxiety in students due to exam pressure to reduce the potential harm to a person's wellness. Research is performed on databases for anxious states based on psychological stimulation (DASPS) and our own database. The measured signal is divided into sub bands that correspond to the electroencephalogram (EEG) rhythms using the Butterworth sixth-order order filter. In higher dimensional space, the nonlinearities of each sub-band signal are analyzed using higher order statistics third-order cumulants (TOC). We have classified stress and anxiety using the support vector machine (SVM), K-nearest neighbor (K-NN), and deep learning bidirectional long short-term memory (BiLSTM) network. In comparison to previous techniques, the proposed system's performance using BiLSTM is quite good. The best accuracy in this analysis was 87% on the DASPS database and 98% on the own database. Finally, subjects with high stress levels had more gamma activity than subjects with little stress. This could be an important attribute in the classification of stress.
Modification of SHA-512 using Bcrypt and salt for secure email hashing Sean Eljim S. Castelo; Ruben Jolo L. Apostol IV; Dan Michael A. Cortez; Raymund M. Dioses; Mark Christopher R. Blanco; Vivien A. Agustin
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp398-404

Abstract

Email security, particularly against phishing, spoofing, and distributed denial-of-service (DoS) attacks, is a pressing concern given the essential role email plays in accessing various online accounts. The study introduced a modified SHA-512 algorithm, implementing additional security layers including randomly generated salt and the Bcrypt algorithm. The modified SHA-512 was comprehensively evaluated on parameters like hash construction, computational efficiency, data integrity, collision resistance, and attack resistance. The results showed its avalanche percentage exceeded the 50% target, reaching 50.08%. Experimental hash-cracking failed to decode the hashes created by the modified algorithm, verifying its protective efficiency. The algorithm also successfully demonstrated data integrity and collision resistance. This indicates that the enhanced SHA-512 algorithm is an effective, more secure hashing method, particularly applicable to email addresses.
Forecasting livestock feed sales using machine learning techniques: an analysis of the Moroccan market Nebri, Mohamed Amine; Moussaid, Abdellatif; Bouikhalene, Belaid
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1139-1150

Abstract

Agriculture, especially cereals, is important in sustaining economies and food security globally. This study delves into the Moroccan agricultural landscape, specifically focusing on predicting livestock feed sales to assist cereal company producers in optimizing production, streamlining supply chain operations, and enhancing customer satisfaction. Data collected from various markets across Morocco, including sales dates and locations, was combined with climate data and analyzed using advanced machine learning techniques, particularly the gradient boosting regression (GBR) algorithm, which achieved high accuracy with a mean absolute error (MAE) of 0.0203 and a root mean square error (RMSE) of 0.0281. The evaluation of multiple regression models revealed promising results, demonstrating the effectiveness of predictive models in accurately forecasting sales. These findings contribute valuable insights to sales forecasting in the cereal industry by considering weather conditions, production methods, and livestock-related variables, highlighting the importance of leveraging advanced machine learning techniques for optimizing production processes and meeting market demands efficiently in the agribusiness sector.
Hybrid model for brain tumor detection using convolution neural networks Bhagyalaxmi Kuntiyellannagari; Bhoopalan Dwarakanath; Panuganti VijayaPal Reddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1775-1781

Abstract

The development of abnormal cells in the brain, some of which may turn out to be cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) is the most common technique for detecting brain tumors. Information about the abnormal tissue growth in the brain is visible from the MRI scans. In most research papers, machine learning (ML) and deep learning (DL) algorithms are applied to detect brain tumors. The radiologist can make speedy decisions because of this prediction. The proposed work creates a hybrid convolution neural networks (CNN) model and logistic regression (LR). The visual geometry group16 (VGG16) which was pre-trained model is used for the extraction of features. To reduce the complexity, we eliminated the last eight layers of VGG16. From this transformed model, the features are extracted in the form of a vector array. These features fed into different ML classifiers like support vector machine (SVM), and Naïve Bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as Recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.

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