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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 65 Documents
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Search results for , issue "Vol 34, No 3: June 2024" : 65 Documents clear
Stacking classifier method for prediction of human body performance Noer Rachmat Octavianto; Antoni Wibowo
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.pp1832-1839

Abstract

A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification based on gradient boosting decision tree (GBDT). By using gradient descent to minimize the error when creating a new model, the algorithm is called gradient boosting. In determining a classification starting from determining the model to the results, usually only using one algorithm method, and combining other methods together with the method is an algorithm called random forest classifier. Among these merging methods are, stacking classifier, voting classifier, and bagging classifier. The conclusion obtained from the results of this research is that the test results show that the stacking classifier achieves the highest accuracy of 76.07%, making it the best method in this research. And the stacking classifier has a precision of 76.96%, recall of 75.83%, and F1-score of 75.81%. This shows that the model has a good balance between the ability to provide true positive results and the ability to recover positive data.
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.
Encrypted image processing using compression and reversible data hiding Yasmina Zine; Meriem Boumehed; Naima Hadj Said
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.pp1593-1602

Abstract

Reversible data hiding within encrypted images reversible data hiding in encrypted images (RDH-EI) is a highly effective technique for image processing in the field of encryption. This paper, propose a RDH-EI technique, which utilizes bit-plane compression and various image scanning directions to generate vacant space for data embedding, referred to as vacating room. Initially, the prediction error of the pre-processed image is computed. Subsequently, each bit-plane image is converted into a bit-stream by following the pixel scan order employed before compression. The compressed image is then encrypted employing a stream cipher. Through the process of substitution, the secret data and additional information are incorporated into the acquired image without any knowledge of the original content or the encrypted key. Finally, the generated image is transmitted or archived. The experiments provide evidence that the proposed method surpasses the most advanced methods currently available.
Optical sensor to improve the accuracy of non-invasive blood sugar monitoring Aliya Zilgarayeva; Nurzhigit Smailov; Sergii Pavlov; Sharafat Mirzakulova; Madina Alimova; Bakhytzhan Kulambayev; Dinara Nurpeissova
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.pp1489-1498

Abstract

Optical sensors offer a painless method of monitoring blood glucose levels using various light technologies to analyze blood characteristics without penetrating the skin. The literature review part reflects the progress in optical sensor technology evaluates its potential in blood glucose monitoring by overcoming the limitations of conventional methods and recognizes the challenges and future prospects in this rapidly developing area of research. The results of empirical studies are then presented. The methodology is presented as a non-invasive method of blood glucose monitoring based on near-infrared spectroscopy. To precisely evaluate blood glucose concentrations, spectroscopy techniques involving absorption and reflection are employed at wavelengths 450, 900, 1350, and 1800 nm. After absorption and reflection of glucose molecules, light is generated. An experimental study of different samples revealed a linear relationship between the final output voltage and sugar concentration. The results demonstrate a correlation between blood glucose level and signal intensity after transmission.
Pole placement tuning of proportional integral derivative feedback controller for knee extension model Saharul Arof; Emilia Noorsal; Saiful Zaimy Yahaya; Zakaria Hussain; Rosfariza Radzali; Faridah Abdul Razak; Harith Firdaus Mustapha
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.pp1566-1581

Abstract

Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle non-linear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization.
Research and implementation of the medical text analysis algorithm for predicting mortality Zhenisgul Rakhmetullina; Saule Belginova; Alibekkyzy Karlygash; Aigerim Ismukhamedova; Shynar Tezekpaeva
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.pp1965-1977

Abstract

Mortality prediction has a role to play in the development of a descriptive measure of the quality of care that provides a fair and equitable means of comparing and evaluating hospitals. This article describes a study of a medical text analysis algorithm for mortality prediction that used big data in the form of unstructured medical notes. The article describes the concept of using text mining technology for medical systems, a method for preprocessing medical data to predict patient mortality, an algorithm for predicting patient deaths based on the logistic regression classifier and presents a software module for implementing the proposed algorithm.
Privacy-preserving authentication approach for vehicular networks Chindika Mulambia; Sudeep Varshney; Amrit Suman
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.pp1674-1681

Abstract

Vehicle AdHoc networks have an important role in intelligent transport systems that enhance safety in road usage by transmitting real traffic updates in terms of congestion and road accidents. The dynamic nature of the vehicular AdHoc networks make them susceptible to attacks because once malicious users gain access to the network they can transform traffic data. It is essential to protect the vehicular ad hoc network because any attack can cause unwanted harm, to solve this it is important to have an approach that detects malicious vehicles and not give them access to the network. The proposed approach is a privacy preserving authentication approach that authenticates vehicles before they have access to the vehicular network thereby identifying malicious vehicles. The model was executed in docker container that simulates the network in a Linux environment running Ubuntu 20.04. The model enhances privacy by assigning Pseudo IDs to authenticated vehicles and the results demonstrate effectiveness of the solution in that unlike other models it boasts faster authentication and lower computational overhead which is necessary in a vehicular network scenario.
Intelligent-of-things multiagent system for smart home energy monitoring Ratna kumari Vemuri; Chinni Bala Vijaya Durga; Syed Abuthahir Syed Ibrahim; Nagaraju Arumalla; Senthilvadivu Subramanian; Lakshmi Bhukya
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.pp1858-1867

Abstract

The proliferation of IoT devices has ushered in a new era of smart homes, where efficient energy management is a paramount concern. Multiagent artificial intelligence-of-things (MAIoT) has emerged as a promising approach to address the complex challenges of smart home energy management. This research study examines MAIoT's components, functioning, benefits, and drawbacks. MAIoT systems improve energy efficiency and user comfort by combining multiagent systems and IoT devices. However, privacy, security, interoperability, scalability, and user acceptability must be addressed. As technology advances, MAIoT in smart home energy management will offer more sophisticated and adaptable solutions to cut energy consumption and promote sustainability. This article describes how energy status and internal pricing signals affect group intelligent decision making and the interaction dynamics between consumers or decision makers. In a multiagent configuration based on the new concept of artificial intelligence-of-things, this intelligent home energy management challenge is simulated and illustrated using software and hardware. Based on sufficient experimental simulations, this paper suggested that residential clients can significantly improve their economic benefit and decision-making efficiency.
Enhancing reconnaissance security: a 2-tier deception-driven model approach (2TDDSM) Anazel P. Gamilla; Thelma D. Palaoag; Marlon A. Naagas
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.pp1999-2006

Abstract

The emergence of network security has revolutionized the way educational institutions operate, providing advanced connectivity, enhanced communication, and efficient management of resources. However, with the increasing dependence on interconnected systems, institutions and organizations became vulnerable targets for cyber threats. To address these security challenges, a two-tier deception-driven model specifically designed to for the initial phase of attacks in reconnaissance period where the adversaries is to gather information of the targets. Defending threats in this phase can provide active and proactive defense allowing the administrator to identify potential attackers and understanding their methods, motivation and potential target assets. The model's layered approach creates a resilient defense mechanism that aligns with the advanced deception techniques which aims to misguide potential threats attempting to gather intelligence within the network.
An efficient healthcare system by cloud computing and clustering-based hybrid machine learning algorithm Palayanoor Seethapathy Ramapraba; Moorthy Radhika; Sokkanarayanan Sumathi; Jayavarapu Karthik; Nachiappan Senthamilarasi
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.pp1698-1707

Abstract

Cloud computing, deep learning, clustering, genetic, and ensemble algorithms in healthcare are gaining popularity. This research highlights the relevance and complex repercussions of this integration. Cloud computing is transforming healthcare by providing scalable data storage and application access. It streamlines data exchange between hospitals, researchers, and institutions. Deep learning allows healthcare systems to use artificial intelligence for diagnostics, predictive analytics, and customized medication. Clustering algorithms segment patients, improving therapy and intervention customization. Genetic algorithms can optimize healthcare processes like treatment planning and resource allocation. Ensemble algorithms combine multiple models to improve predicted accuracy, enabling strong healthcare decision-making. This connection has several benefits. Healthcare systems become more efficient and scalable, resulting in cost-effective resource allocation. Access to patient data and apps promotes collaborative research and real-time healthcare. Deep learning algorithms can recognize complex medical data patterns, improving illness diagnosis and treatment results. Clustering algorithms streamline customized healthcare by stratifying individuals by clinical variables. Genetic algorithms optimize resource allocation, assuring healthcare resource efficiency. Ensemble algorithms improve predicted accuracy and clinical decision support system dependability. Its efficiency, accessibility, and prediction accuracy are positives, but security, resource constraints, interpretability, and ethical issues are obstacles.

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