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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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Utilizing metaheuristic optimization with transfer learning for efficient colorectal carcinoma detection in biomedical imaging Babu Ramisetti, Lova Naga; Reddy, Desidi Narsimha; Pathipati, Harikrishna; Srividya, Yenumula; Pesaru, Swetha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1693-1703

Abstract

Colorectal cancer (CRC) is the third most popular cancer across the world. Its morbidity and death are reduced by early screening and detection. The screening outcomes are enhanced by computer-aided detection (CAD) and artificial intelligence (AI) in screening models. Contemporary imaging technologies such as near-infrared (NIR) fluorescence and optical coherence tomography (OCT) are implemented to identify the early-phase CRC of the gastrointestinal tract (GI tract) via the identification of morphological and microvasculature changes. Most recently, deep learning (DL)-based approaches have been used directly on raw data. Nevertheless, they are hampered by biomedical data deficiency. These studies can enhance metaheuristic optimization using the transfer learning to detect colorectal cancer successfully (MHOTL-ECRCD). The MHOTL-ECRCD method concentrates on biomedical imaging of CRC categorization and detection. MHOTL-ECRCD minimizes noise through the process of adaptive bilateral filtering (ABF). In MHOTL-ECRCD methodology, Inception-ResNet-V2 is adopted to learn the inherent and complicated image preprocessing features thus used during feature extraction. To classify CRC and detect it, the gated recurrent unit (GRU) approach is applied. Lastly, parameters of the GRU model are optimized with a human evolutionary algorithm. Good classification results of MHOTL-ECRCD are demonstrated by a number of benchmark dataset trials. MHOTL-ECRCD technology superseded the recent techniques as large volumes of comparison were made.
Modelling and simulation of maximum power point tracking on partial shaded PV based-on a physical phenomenon-inspired metaheuristic algorithm Megantoro, Prisma; Dona Saya, Joy Sefine; Syahbani, Muhammad Akbar; Fadhilah, Marwan; Vigneshwaran, Pandi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1923-1937

Abstract

Maximum power point tracking (MPPT) is a technique to optimize the photovoltaic (PV) current generation, so it can improve the efficiency of solar energy harvesting. MPPT works by searching the voltage which generates the maximum power, called the maximum power point (MPP). MPP value changes by the fluctuance of ambient temperature and solar insolation level depicted by the I-V curve. Searching the MPP will be more complex if the partial shading is happened. The effect of partial shading will rise to more than one local MPPs. In this research, an optimization algorithm is modeled and simulated the MPPT technique in partial shading. The optimization uses the new metaheuristic algorithm which inspired from a physical phenomenon, called Archimedes optimization algorithm (AOA). The AOA uses mathematical modeling which has convergence capabilities, balanced exploration, and exploitation and is suitable for solving complex optimization technique, like MPPT. The research used varies partial insolation percentage. The implementation of MPPT-AOA compared to other metaheuristic algorithms to analysis its performance in the aspect of PV system parameters and tracking process parameters. The simulation result shows that the AOA can enrich the MPPT technique and improve the solar energy harvesting which is superior to other algorithms.
Smartphone-based fingerprint authentication using siamese neural networks with ridge flow attention mechanism Imane, Benchergui Malika; Abdelkader, Ghazli; Benaoumeur, Senouci M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1622-1632

Abstract

Authenticating finger photo images captured using a smartphone camera provides a good alternative solution in place of the traditional method-based sensors. This paper introduces a novel approach to enhancing fingerprint authentication by leveraging images captured via a mobile camera. The method employs a siamese neural network (SNN) combined with a ridge flow attention mechanism and convolutional neural networks (CNN). Our approach begins with collecting a dataset consisting of finger images from two individuals then we apply multiple preprocessing techniques to extract fingerprint images, followed by generating augmented data to improve model robustness, scaling, and normalizing them to form images suitable for model training. Next, we generate positive and negative pairs for training a SNN. We used the SNN with CNN for feature extraction, combined with an attention mechanism that focuses on the ridge flow pattern of fingerprints to improve feature relevance which significantly contributed to the performance enhancement. As for the testing performance, our model has an accuracy of 90%, precision of 89%, recall of 83%, F1 score of 86%, area under the curve (AUC) 95 %, and 13% of equal error rate (EER) when using smartphone-captured images for fingerprint recognition.
Performance evaluation of distribution network with change of load by connecting wind DG Sankepally, Swathi; Kumar Bali, Sravana
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1459-1466

Abstract

The aim of this research is to determine the optimal location and size of a minimum number of distributed generators (DGs) needed to maintain the stable operation of an IEEE 85-bus distributed network. The main objective is to ensure the stability of the distribution network by optimizing the placement and capacity of DGs. This is accomplished through the utilization of particle swarm optimization (PSO). The stability of the distribution network is checked by evaluating the voltages and power losses using load flow. The stability of the distribution network is assessed using boundary criteria that are not altered by more than 5% of the nominal voltage value. The distribution network voltage stability is assessed using various case studies, one of that involves a change in load driven by connecting WDG and the other by a change in power supply from wind DGs due to varying wind speed. The PSO is implemented in IEEE-85 bus distribution network using MATLAB software.
Comprehensive secure code review analysis of web application security vulnerabilities Aziz, Azlinda Abdul; Mohd Suradi, Nur Razia; Handan, Rahayu; Rizal Arbain, Mohd Noor
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1807-1814

Abstract

A secure code review is a process of software development involves systematic examination of application code. However, web applications evolving of cyber threats makes it challenging to conduct adequate security. Therefore, this paper conducts a comprehensive secure code review analysis to protect any crucial aspect of web security from potential threats and vulnerabilities. The application code is scanned for security issues during the real review and the results are classified according to the areas of vulnerability. As a result, the application code risk level and list of risk categories were defined. This result assists in prioritizing issues for resolution, beginning with the most critical problems to lower risk levels. Next, list of risk categories that give the most significant security vulnerabilities affect to application codes are defined. SQL injection, weak password handling, insecure direct object reference, information exposure, improper session management, missing input validation, deprecated functions, and lack of comments are defined as a risk category. Moreover, the result of application code weakness in the security of the application code is determined based on the level of risk and categories. Thus, analysis result offers the developers a clear perspective on protects the web applications from threats and vulnerabilities.
Development of an automatic processing system for predicting the earthquake signals using machine learning techniques Gupta, Mukesh Kumar; Kumar, Brijesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp2023-2031

Abstract

Earthquake signals are crucial for minimizing the impact of seismic activities. Current algorithms face difficulties in correctly identifying P-waves and assessing magnitudes, which affects the amount of advance warning given. It is crucial to establish standardized methods for the effective selection and integration of multiple algorithms. Machine learning techniques could considerably enhance detection reliability. The research seeks to rectify this shortfall and strengthen automated detection as well as prediction capabilities. The model's performance is assessed using real earthquake data in simulations compared to individual algorithms. The objective of this research is to develop an optimized multi-algorithm framework that enhances the warning lead times and overall reliability. This framework underpinning this method is shaped by the operational demands inherent in early warning systems. The objective of the work is to contribute to the betterment of seismic risk reduction. An ML methodology, merging several distinct detection algorithms, will be deployed along with a tailored prioritization system. The intention is to strengthen the model's dependability and its overall level of consistency. The ML-based multi-algorithm framework significantly boosts the performance of Early Earthquake Warning Systems, providing a scalable approach to enhance automated detection and public safety, ultimately advancing the effectiveness of seismic hazard reduction through quicker and more accurate warnings.
A smart wearable posture correcting device based on spine curvature and vibration measurement Christhudass, Jerome; Perumal, Manimegalai; Balachandran, Kowsalya; Chella Muthu, Sella Dharshini; Balasubramanian, Keerthana
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1514-1524

Abstract

In the United States, aalmost $50 billion is expended in neck pain therapy each year. Poor posture, which affects the primary tendon responsible for reproducing finished tasks on time, has previously been recognized as a major source of upper spine discomfort. The primary objective of this study is to design and develop a device that not only detects deviations in posture but also employs vibration alerts to encourage corrective actions. The methodology involves the integration of an inertial measurement unit (IMU) sensor and a Flex Sensor to measure the angle and position of the spine, enabling real-time posture assessment. Additionally, a Piezo-electric sensor is incorporated to measure the vibration of the user's spine. The device provides real-time feedback via a mobile application to help users maintain optimal posture. Data analysis involved filtering and machine learning-based classification to assess posture deviations. The system demonstrated an accuracy of 90% in classifying posture states, with an average error of 2.7° in spine curvature measurement. This research contributes to the field of wearable technology by offering an innovative solution for posture correction, emphasizing the importance of proactive interventions in fostering healthy habits.
Comprehensive multiclass debris detection for solar panel maintenance using ANN models S. M., Renuka Devi; J., Vaishnavi; A., Gayatri; K., Ragini; K., Ramesh Reddy; B., Koti Reddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1489-1498

Abstract

Solar photovoltaic (PV) technology has emerged as a leading renewable energy solution globally. However, maintaining optimal performance remains a challenge due to the accumulation of debris, including dust, bird droppings, and other contaminants on the panels. These deposits significantly reduce the efficiency of solar panels, necessitating regular monitoring and cleaning. Automated inspection systems provide a cost-effective alternative to traditional methods by minimizing labor-intensive efforts. This study proposes a machine learning-based framework for detecting and classifying several types of debris on solar panels. The methodology utilizes gray-level co-occurrence matrix (GLCM) texture features and key statistical features extracted from RGB, HSV, and LAB color spaces. A dataset comprising 19 distinct classes, such as “Without Dust,” “Bird Droppings,” “Black Soil,” and “Sand,” was employed to train and evaluate the models. Among the tested classification techniques, artificial neural networks (ANN) achieved a notable accuracy of 93.94%, demonstrating their effectiveness in identifying and categorizing debris. This work underscores the potential of machine learning-based feature extraction and classification techniques to automate solar panel inspection and facilitate targeted cleaning interventions, thereby enhancing overall system efficiency.
Enhancing privacy in document-oriented databases using searchable encryption and fully homomorphic encryption Belhaj, Abdelilah; Ziti, Soumia; Lagmiri, Souad Najoua; El Bouchti, Karim
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1661-1672

Abstract

In cloud-based not only SQL (NoSQL) databases, maintaining data privacy and the integrity are critically challenged by the risks of unauthorized external access and potential threats from malicious insiders. This paper presents a proxy-based solution that provides privacy-preserving by combining searchable encryption and brakerski-fan-vercauteren (BFV) fully homomorphic encryption (FHE) to facilitate secure search and aggregate query execution on encrypted data. Through extensive performance evaluations and security analyses, we show that our approach offers a robust solution for privacy-preserving data operations, with performance overhead introduced by the use of FHE. This solution gives an opportunity for a robust framework for secure data management and querying in NoSQL databases, with promising implications for practical deployment and future research. This work represents a significant advancement in the secure handling of data in NoSQL oriented databases, supplying a practical solution for privacy-conscious organizations.
An improved hybrid AC to DC converter suitable for electric vehicles applications Mahafzah, Khaled A.; A. Obeidat, Mohamad; Alsalem, Hesham; Mansour, Ayman; Riva Sanseverino, Eleonora
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1499-1513

Abstract

This paper introduces a novel hybrid AC-DC converter designed for various applications like DC micro-grids, Electric Vehicle setups, and the integration of renewable energy resources into electric grids. The suggested hybrid converter involves a diode bridge rectifier, two interconnected single ended primary inductor converter (SEPIC) and Flyback converters, and two additional auxiliary controlled switches. These extra switches facilitate switching between SEPIC, Flyback, or a combination of both. The paper ex-tensively discusses the operational modes using mathematical equations, deriving specific duty cycles for each switch based on the circuit parameters. This hybrid converter aims to decrease total harmonic distortion (THD) in the line current. The findings exhibit a THD of approximately 14.51%, showcasing a 3% reduction compared to prior hybrid converters, thereby enhancing the power factor of the line current. Furthermore, at rated load conditions, the proposed converter achieves 90% efficiency. To validate the proposed hybrid converter’s functionality, a 4.5 kW converter is simulated and performed using MATLAB/Simulink after configuring the appropriate passive parameters.

Page 895 of 914 | Total Record : 9138

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