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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 38, No 3: June 2025" : 65 Documents clear
Design and simulation of a double boost switched capacitor multilevel inverter Khadar, Shaik Abdul; Shuaib, Yassin Mohamed; Arun, Vijayakumar; Karakilic, Murat; Rahul, Jammy Ramesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1450-1462

Abstract

This article presents an innovative boost inverter configuration that produces a nine-level double voltage augmentation waveform. A significant drawback of conventional multilevel inverters (MLIs) lies in their dependence on conversion for elevating the voltage, particularly applied in conjunction with renewable energy sources. The proposed methodology, characterized by its double voltage boosting capacity, mitigates this challenge by automatically enhancing the input voltage. Switched-capacitor multilevel inverters (SCMLIs) represent a prevalent category within the realm of MLIs. This paper presents a double boost switched capacitor inverter (DB-SCI) designed to address critical issues involving the increased use of semiconductor switches, DC sources, and capacitors. The proposed DB-SCI achieves a nine-level resultant voltage utilizing a single DC source, 8 switches, and 3 capacitors. It can amplify the output voltage with a gain of two. Moreover, the DB-SCI employs a level-shifted pulse width modulation (PWM) approach to augment the resultant voltage and enhance the output voltage's quality. The article assesses the effectiveness and feasibility of the DB-SCI under various modulation indices using MATLAB/Simulink. The comparison study of MLI topologies is presented.
Plagiarism detection in verilog and textual content using linguistic features V., Sathya; C., Nalayini; Kumar, M. Kiran; G., Kumar; Babu M., Dinesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1924-1935

Abstract

The illicit act of appropriating programming code has long been an appealing notion due to the immediate time and effort savings it affords perpetrators. However, it is universally acknowledged that concerted efforts are imperative to identify and rectify such transgressions. This is particularly crucial as academic institutions, including universities, may inadvertently confer degrees for work tainted by this form of plagiarism. Consequently, the primary objective of this research is to scrutinize the feasibility of identifying plagiarism within pairs of Verilog algorithms and texts. this study aims to detect plagiarism in textual content and Verilog code by leveraging diverse linguistic characteristics from the WordNet lexical database. The primary objective is to achieve optimal accuracy in identifying instances of plagiarism, incorporating features such as modifications to text structure, synonym substitution, and simultaneous application of these strategies. The system's architecture is intricately designed to unveil instances of plagiarism in both textual content and Verilog code by extracting nuanced characteristics. The systematic process includes preprocessing, detailed analysis, and post-processing, supported by a feature-rich database. Each entry in the database represents a distinctive similarity case, contributing to a thorough and comprehensive approach to plagiarism detection.
Novel prostate cancer detection and classification model using support vector machine Sujata, Kandukuri; Sridhar, Bokka; Prasad, Avala Mallikarjuna
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1681-1689

Abstract

Prostate cancer (PCa) is one of the most common and deadliest cancers that kill men worldwide with high mortality and prevalence especially in developed countries. PCa is regarded as one of the most prevalent cancers and is one of the main causes of deaths worldwide. Early detection of PCa diseases helps in making decisions about the progressions that should have occurred in high-risk patients decrease their risks. The recent developments in technology and methods have given rise to computer aided diagnosis (CAD). Early cancer detection can greatly increase the chance of survival through the administration of the proper treatment. Due to the emerging trends and available datasets in state-of-art machine learning (ML) and deep learning (DL) techniques, there has been significant growth in recent disease prediction and classification publications. This paper presents a unique support vector machine-based model for PCa detection and classification. This analysis aims to classify the PCa using ML algorithm and to determine the risk factors. Support vector machines (SVM) is used to identify and classify the PCa. Accuracy, sensitivity, specificity, precision, and F1-score are the measurements used to evaluate the performance of the presented method. This model will achieve accuracy, sensitivity, specificity, precision, and F1-score than earlier models.
SVM algorithm-based anomaly detection in network logs and firewall logs Jesudasan Peter, John Benito; Rakesh, Nitin; Rekha, Puttaswamy; Sreelatha, Tammineni; Sujatha, Velusamy; Muthumarilakshmi, Surulivelu; Sujatha, Shanmugam
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1642-1651

Abstract

The purpose of many advanced forms of cyberattack is to deceive the monitors, and as a result, these attacks often involve several kinds, levels, and stages. Existing anomaly detection systems often examine logs or traffic for indications of attacks, ignoring any additional analysis regarding attack procedures. This is done to save time. For example, traffic detection technologies can only identify the attack flows in a general sense. Still, they cannot reconstruct the attack event process or expose the present condition of the network node. In addition, the logs kept by the firewall are significant sources of evidence; nevertheless, they are still challenging to decipher. This paper introduces support vector machine algorithm-based Anomaly detection (SVMA) in network logs and firewall logs to provide robust security against cyberattacks. This mechanism consists of three modules: preprocessing, feature selection and anomaly detection. The genetic algorithm (GA) selects the better feature from the input. Finally, the support vector machine (SVM) isolates an anomaly powerfully. The investigational outcomes illustrate that the SVMA minimizes the required time to select the features and enhances the detection accuracy.
Skin cancer disease analysis using classification mechanism based on 3D feature extraction Srikanteswara, Ramya; A. C., Ramachandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2012-2019

Abstract

Dermoscopic image analysis is essential for effective skin cancer diagnosis and classification. Extensive research work has been carried out on dermoscopic image classification for the early detection of skin cancer. However, most of the research works are concentrated on 2D features. Therefore, a 3D lesion establishment mechanism is presented in this work to generate 3D features from the obtained 3D lesions. The objective of this work is to reconstruct 3D lesion image from 2D lesion images and a multispectral reference IR light image. The 3D lesion establishment is achieved by designing an efficient convolutional neural network (CNN) architecture. Details of CNN design architecture are discussed. After reconstruction of 3D lesions, 2D and 3D features are extracted and classification is performed on the obtained 2D and 3D features. Classification performance is evaluated using the images from PH2 database. The mean classification accuracy using K-nearest neighbors (KNN) classifier based on the 3D lesion establishment using the CNN architecture is 98.70%. The performance results are compared against varied classification methods in terms of accuracy, sensitivity, specificity and are proved to be better.
Empowering microgrids: harnessing electric vehicle potential through vehicle-to-grid integration Mishra, Debani Prasad; Senapati, Rudranarayan; Samal, Sarita; Rai, Niti Rani; Behera, Niharika; Salkuti, Surender Reddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1422-1430

Abstract

Electric vehicles (EVs) can potentially be integrated into microgrids via vehicle-to-grid (V2G) technology, which enhances the energy system's stability and durability. This paper provides an in-depth examination and evaluation of V2G integration in microgrid systems. It analyses the present state of research as well as possible uses, challenges, and directions for V2G technology in the future. This paper addresses the technological, economic, and regulatory aspects of implementing V2G and provides case studies and pilot projects to shed light on potential benefits and barriers associated with its adoption. The research highlights how V2G contributes to more efficient integration of renewable energy sources, grid stabilization, and cost savings for EV owners. It also addresses the latest developments in technology and proposed laws aimed at encouraging growing applications of V2G.
Identification and segmentation of tumor using deep learning and image segmentation algorithms Chippalakatti, Shilpa; Chodavarapu, Renu Madhavi; Pallavi, Andhe
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1782-1792

Abstract

Brain tumor is a typical mass of tissue that develops when cells proliferate and divide excessively. Brain tumor perception requires a great deal of work and experience from the medical professional in order to identify the tumor's precise location. If a brain tumor is not discovered in a timely manner, it affects a person's ability to function normally and raises the death rate. This study focuses on tumor segmentation and tumor detection using magnetic resonance imaging (MRI) images. This work helps the medical professional to precisely identify the tumor location and segmentation process provides cost effective data storage. The YOLOv8s model is utilized for tumor identification, while the image segmentation technique is employed for tumor segmentation. The images come from an open-source dataset used for research, and Roboflow 100 transforms them into .yaml files that are congenial with YOLOv8s. To train the model the dataset is split into training, validation and testing. Proposed model consist of dataset which comprises 639 images, of which 453 are utilized for training, 122 for validation, and 64 for testing, resulting in a ratio of 71:19:10. The dataset is subjected to preprocessing and augmentation. The suggested model performance is assessed depending on the parameters like precision, recall, mAP50 and mAP50-95.
A new modified B4 inverter using SRF controller with SVPWM technique for grid-connected PV system Anitha, Golkonda; Kondreddi, Krishnaveni; Yesuratnam, Guduri
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1411-1421

Abstract

The integration of renewable power sources into the grid presents a complex challenge, as the grid operates at AC voltage, while photovoltaic (PV) arrays generate DC power. A 3-phase inverter synchronizes with the grid’s voltage and frequency for efficient energy integration. In conventional technique, a 3-ph 6-switch (B6) inverter is used for sharing the power to the grid. In this paper reduced switch count 3-ph 4-switch (B4) inverter topology is introduced with reduced power losses. This topology has 4 insulated gate bipolar transistor (IGBT) switches and two capacitors replacing the other 2 switches positioned in one leg of the inverter, which connects to a grid connected PV system. A grid synchronization method called synchronous reference frame (SRF) based proportional integral (PI) is used to track the phase angle of the grid and subsequently inject current into the grid. A B4 inverter is operated by a novel space vector pulse width modulation (SVPWM) control technique which operates in 4 possible switching states. A comparative analysis is carried out with the PV array grid integration connected through B4 and B6 inverter topologies with SRF control. The modeling and design are carried out in a MATLAB/Simulink environment with graphs plotted according to the conditions. The comparative analysis validates the importance of SRF controllers for the grid integration of any renewable source.
Deep learning approaches, platforms, datasets for behaviorbased recognition: a survey Jeddah, Yunusa Mohammed; Abdallah Hashim, Aisha Hassan; Khalifa, Othman Omran
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1880-1895

Abstract

Video surveillance is an extensively used tool due to the high rate of atypical behavior and many cameras that enable video capture and storage. Unfortunately, most of these cameras are operator dependent for stored content analysis. This limitation necessitates the provision of an automatic behavior identification system. This behavior identification can be achieved using unsupervised (generative) computer vision methods. Deep learning makes it possible to model human behavior regardless of where they could be. We attempt to classify current research work to report the ongoing trends in human behavior recognition using deep learning algorithms. This paper reviews various aspects, like the ones associated with machine learning and deep learning models, human activity recognition (HAR), deep learning frameworks/tools, abnormal behavior datasets, and a variety of other current trends in the field of automatic learning. All these are to give the researcher a sense of direction in this area.
Design and implementation of an automatic irrigation system for plants in Lima-Perú Astuhuaman-Medina, Jean Piere; Granados-Zárate, Adrian Humberto; Marcatinco-Gonzales, Jhonel Wilfredo; Segura-Viteri, Martin Fernando; Morante-Medina, Aldhair; Castro-Vargas, Cristian
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1580-1590

Abstract

In many regions of the world, water used in agriculture becomes a scarce and costly resource over time. It is necessary to make efficient use of this vital resource. For this reason, we opted for an innovative project that can be of great use for agriculture, incorporating information and communication technologies such as the internet of things (IoT), databases, and smartphone applications. The research proposes an IoT system to control and monitor crops in a specific area based on the ESP32 microcontroller, using the DHT11 sensor to collect temperature and relative humidity data. The sensors send the information to the central node for the wireless communication part. The central node activates the actuators to control and store the information in a database for corresponding monitoring. The mobile application displays the results from the database and causes them to be turned on and off manually. The system was implemented for home plant cultivation but can be used for other types of cultivation due to its flexibility.

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