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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 58 Documents
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Search results for , issue "Vol 33, No 2: February 2024" : 58 Documents clear
Unveiling visionary frontiers: a survey of cutting-edge techniques in deep learning for retinal disease diagnosis Rajatha Rajatha; Ashoka Davanageri Virupakshappa
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.pp1261-1272

Abstract

Retinal disorders impact millions of people globally. These disorders can be detected and diagnosed early enough to not only cure but also avoid permanent blindness. Manual identification of these diseases has always been tedious, time-consuming, and inconsistent. For ophthalmologists, retinal fundus images are a valuable source of information in diagnosing retinal diseases. Automatic identification of eye disorders using artificial intelligence (AI) based learning models has seen substantial development in the computer vision sector recently. Various models, particularly deep learning (DL) models are incredible in identifying and classifying diseases. In the presented review, we have performed an in-depth analysis of various existing DL models, involving preprocessing, classification, segmentation, and techniques to deal with data imbalance. We have also endeavored to gauge the effectiveness of these models by evaluating their performance using the metrics employed in their assessment. In addition, we explored various challenges along with the potential future scope in this domain. 
Optimized in-loop filtering in versatile video coding using improved fast guided filter Lakshmi Amrutha Valli Pamidi; Purnachand Nalluri
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.pp911-919

Abstract

Devices with varying display capabilities from a common source may face degradation in video quality because of the limitation in transmission bandwidth and storage. The solution to overcome this challenge is to enrich the video quality. For the mentioned purpose, this paper introduces an improved fast guided filter (IFGF) for the contemporary video coding standard H.266/VVC (versatile video coding), a continuation of H.265/HEVC (high efficiency video coding). VVC includes several types of coding techniques to enhance video coding efficiency over existing video coding standards. Despite that, blocking artifacts are still present in the images. Hence, the proposed method focuses on denoising the image and the increase of video quality, which is measured in terms of peak signal-to-noise (PSNR). The objective is achieved by using an IFGF for in-loop filtering in VVC to denoise the reconstructed images. VTM (VVC test model)-17.2 is used to simulate the various video sequences with the proposed filter. This method achieves a 0.67% Bjontegaard delta (BD)-rate reduction in low-delay configuration accompanied by an encoder run time increase of 4%.
Spark-MLlib intrusion detection mechanism using machine learning models Asra Sarwath; Raafiya Gulmeher; Zeenath Sultana
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.pp1235-1242

Abstract

Typically, a single method is employed in machine learning (ML) based intrusion detection to identify intrusion information. However, this approach lacks flexibility, has a low detection rate, and struggles to handle high-dimensional data. Consequently, it is not efficient in addressing these challenges. This study proposes a new intrusion detection architecture that utilizes Spark and ensures resilient data dissemination across the platform to improve its effectiveness. It consists of preprocessing module, a label encoder module, a feature extraction module, a classification module and a database module. The preprocessing module compresses information by utilizing the module for label encoding. This generates a lower-dimensional reconstruction and classification characteristic. The database module has the capability to store the compressed characteristics of all traffic. This enables the classifier to be tested and then returns these features back into the original traffic, facilitating retraining. In order to evaluate the efficacy of the framework, simulations were conducted using the CICIDS 2017 dataset to accurately replicate the network traffic. Based on the test findings, the accuracy of both multiclass and binary classification surpasses that of earlier studies. High precision was achieved for the traffic that was restored. The possible application of the proposed architecture for edge/fog networks is discussed in the conclusion.
Proposed algorithm base optimisation plan for feature selection-based intrusion detection in cloud computing Imane Laassar; Moulay Youssef Hadi; Arifullah Arifullah; Hassnae Remmach; Fawad Salam Khan
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.pp1140-1149

Abstract

A crucial element in detecting unusual network system behavior is the network intrusion detection system (NIDS), which also helps to stop network attacks from happening. Despite the fact that a great deal of machine learning techniques has been used in intrusion detection, current solutions still struggle to provide accurate classification results. Furthermore, when dealing with imbalanced multi-category traffic data, a single classifier may not be able to produce a superior. Particularly, internet of things (IoT) gadgets is now a commonplace aspect of life. On the other hand, some problems are becoming worse and lack clear remedies. Convergence, communication speed, and security between various IoT devices are among the primary concerns. In order to achieve this goal, an enhanced artificial bee colony technique utilizing binary search equations and neural networks—known as the (BABCN) algorithm for intrusion detection in terms of convergence and communication speed—is presented in this study. The artificial bee is improved by the depth-first search framework and binary search equations upon which the BABCN method is based. The suggested approach has a good ability to detect intrusions in the network and enhances categorization, according to the findings obtained by using the NSL-KDD dataset.
Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things Sampoorna Bhimshetty; Agughasi Victor Ikechukwu
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.pp971-980

Abstract

The internet of things (IoT) underscores pivotal real-world applications ranging from security systems to smart infrastructure and traffic management. However, contemporary IoT devices grapple with significant challenges pertaining to battery longevity and energy efficiency, constraining the assurance of prolonged network lifetimes and expansive sensor coverage. Many existing solutions, although promising on paper, are intricate and often impractical for real-world implementations. Addressing this gap, we introduce an energy-efficient routing protocol leveraging reinforcement learning (RL) tailored for wireless sensor networks (WSNs). This protocol harnesses RL to discern the optimal transmission route from the source to the sink node, factoring in the energy profile of each intermediary node. Training of the RL algorithm is facilitated through a reward function that includes energy outflow and data transmission efficacy. The model was compared against two prevalent routing protocols, LEACH and fuzzy C-means (FCM), for a comprehensive assessment. Simulation results highlight our protocol’s superiority with respect to the active node count, energy conservation, network longevity, and data delivery efficiency.
Quality of services in software defined networking: challenges and controller placement problems Siham Aouad; Issam El meghrouni; Yassine Sabri; Adil Hilmani; Abderrahim Maizate
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.pp951-959

Abstract

Quality of service (QoS) is pivotal for ensuring effective and reliable network performance, yet achieving end-to-end QoS within current network architectures remains a persistent challenge. The emergence of software defined networking (SDN) addresses limitations in traditional networking by offering a centralized control plane. This allows dynamic resource management and efficient enforcement of QoS policies by network administrators. However, the controller placement problem (CPP) within SDN poses a significant challenge, as identifying the optimal placement of controllers is a non-deterministic polynomial-time hardness (NP-hard) problem. Researchers are actively working on solutions to address this challenge, especially in large-scale networks where deploying controllers becomes complex. Additionally, maintaining QoS in terms of controller management presents another hurdle. This paper explores these challenges, delving into the literature and providing a comprehensive analysis of controller performance metrics related to QoS parameters such as load balancing, reliability, consistency, and scalability. By addressing these challenges, the research aims to enhance QoS within the SDN framework.
Enhanced low voltage ride-through control of multilevel flying capacitor inverter based wind generation Younes El Khlifi; Abdelmounime El Magri; Adil Mansouri; Rachid Lajouad
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.pp854-861

Abstract

This paper introduces a cost-effective control method to enhance the low voltage ride-through (LVRT) capability and smooth the output power of a three-phase multilevel flying capacitor inverter (FCI) in wind turbine-based permanent magnet synchronous generator (PMSG). The proposed approach utilizes the energy storage capability of flying capacitors to mitigate wind power fluctuations and address short-duration outages and deep voltage sags. Additionally, a nonlinear controller based Lyapunov theory is developed to regulate capacitor voltages, improve power factors, and balance DC-link voltage. Numerical simulations are conducted in MATLAB/SimPower systems environment to validate the effectiveness of this comprehensive control strategy across different grid operation scenarios.
Multimodal approach for early prediction of COVID-19 disease using convolutional neural network Milind Ankleshwar; Pramod Chavan; Pratibha Chavan; Sushil K. Ambhore
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.pp1196-1204

Abstract

The latest human coronavirus is COVID-19. Chest radiography imaging is essential for screening, early detection, and monitoring COVID-19 infections since the virus resides in the lungs. Classical real time reverse transcriptase polymerase chain reaction (RT-PCR) data and chest X-ray pictures will become more important for COVID-19 identification as the pandemic spreads due to their affordability, wide availability, and infection control benefits, which reduce cross-contamination. This work presents multi-modal hybrid automated approaches to classify COVID-19 illness into three clinical categories: normal, pathogenic, and COVID-19 utilising RT-PCR test data and online chest X-ray datasets. The RT-PCR and chest X-ray image datasets were processed using supervised machine learning and convolutional neural networks (CNN). Together, these measures help us separate COVID-19 patients, those with similar symptoms, and healthy persons. The author improved detection times and classification accuracy with extra tree classifier’s feature selection and openCV’s image sharpening. The proposed approaches were tested using a research dataset. The proposed methods allowed reliable COVID-19 disease categorization for clinical decision-making, with random forest (RF) classifier global precision values of 91.58% on the RT-PCR dataset and CNN model accuracy of 95.46% on improved sharpened images.
Fetal electrocardiogram prediction using machine learning: a random forest-based approach mohammed moutaib; Mohammed Fattah; Yousef Farhaoui; Badraddine Aghoutane; Moulhime El Bekkali
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.pp1076-1083

Abstract

Monitoring fetal health during pregnancy ensures safe delivery and the newborn’s well-being. The fetal electrocardiogram (fetal ECG) is a valuable tool for assessing fetal cardiac health, but interpretation of ECG data can be challenging due to its complexity and variability. In this work, we explore the application of machine learning, particularly random forest, to predict and analyze fetal ECGs. With its ability to manage large datasets and provide precise insights, random forest is a promising solution for this challenge. By comparing our random forest-based approach with other standard machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and recurrent neural networks (RNN), we observed that our solution outperformed these methods in accuracy, robustness, and reliability. This article details the methodology used, the implementation of the algorithm, as well as the comparative results obtained. Emphasis is placed on the benefits of random forest in this specific medical context, highlighting its potential as a future tool for fetal ECG prediction. Ultimately, our research suggests a shift toward random forest-based solutions for more efficient and accurate analysis of fetal ECGs, with direct implications for clinical practice and fetal well-being.
Real-time forest fire detection, monitoring, and alert system using Arduino Afiq Ikhwan Mohd Anuar; Roslina Mohamad; Arni Munira Markom; Ronnie Concepcion II
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.pp942-950

Abstract

Early fire detection is critical to protecting forests from wildfires and enabling rapid responses to minimize fire spread. Existing forest fire detection methods cannot quickly detect forest fires and evaluate the fire risk of these sensitive areas. Hence, this research aims to develop a real-time forest fire detection, monitoring, and alert system. The development of the system started with assembling temperature and humidity sensors, a smoke sensor, an Arduino microcontroller, and a wireless fidelity module. Then, a fire monitoring and alert system was developed using Blynk. From the sensitivity flame sensor analysis with the fire, the flame sensor detected the presence of fire up to 60 cm. The sensor also indicated high temperature (45 °C) and low humidity (53.4%) at noon. Low temperature (29 ℃) and high humidity (88.4%) were identified in the morning. Moreover, the highest carbon dioxide (CO2) concentration of 1,800 ppm was recorded when the smoke from the fire was detected. The global positioning system module shows the accurate real-time location of the system displayed in the Blynk application. In conclusion, this system can detect and monitor early forest fires in real-time and can alert the authorities to protect forests from wildfires.

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