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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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Identification of ocular disease from fundus images using CNN with transfer learning Berrichi, Fatima Zohra; Belmadani, Abderrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp613-621

Abstract

Eye diseases are one of the serious health problems affecting human life. Detecting and diagnosing them early is critical to prompt treatment and preventing vision loss. However, all studies in the field of eye disease classification using machine learning models are limited to the detection of single diseases, and the accuracy rate is still low in multi-class systems. In this study, we propose a multi-class classification model using four pre-trained CNNs (DenseNet121, ResNet50, EfficientNetB3 and VGG16). The model classified eye diseases into four categories: diabetic retinopathy, cataract, glaucoma, and normal. To improve the training process, another data augmentation technique is applied to increase the amount of data. The performance metrics of the system are calculated using the confusion matrix. DenseNet-121 shows excellent performance in retinal disease classification in 30 epochs of training, with training and test accuracy reaching 99.97% and 96.21% respectively. The implementation of this system should be considered as a very useful means to help ophthalmologists to rapid and precision detection of various eye diseases in the future.
Simulation of ray behavior in biconvex converging lenses using machine learning algorithms Carlos-Chullo, Juan Deyby; Vilca-Quispe, Marielena; Fernandez-Granda, Whinders Joel; Castro-Gutierrez, Eveling
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp357-366

Abstract

This study used machine learning (ML) algorithms to investigate the simulation of light ray behavior in biconvex converging lenses. While earlier studies have focused on lens image formation and ray tracing, they have not applied reinforcement learning (RL) algorithms like proximal policy optimization (PPO) and soft actor-critic (SAC), to model light refraction through 3D lens models. This study addresses that gap by assessing and contrasting the performance of these two algorithms in an optical simulation context. The findings of this study suggest that the PPO algorithm achieves superior ray convergence, surpassing SAC in terms of stability and accuracy in optical simulation. Consequently, PPO offers a promising avenue for optimizing optical ray simulators. It allows for a representation that closely aligns with the behavior in biconvex converging lenses, which holds significant potential for application in more complex optical scenarios.
A hybrid combination of improved mayfly optimization based modified perturb and observe for solar based water pumping system Sawant, Dattatray Surykant; Srinivasa Rao, Yerramreddy; Sawant, Rajendra Ramchandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp50-62

Abstract

In recent years, solar water pumping systems (WPS) have been fuel-free and environmentally beneficial because they have gained a lot of attention in the agricultural and industrial sectors. Traditional water pumps consume higher amount of energy which make it as frequently unreliable, low efficiency and needs high maintenance. For WPS applications, Brushless DC (BLDC) motors are far superior options than other induction motors because of their high efficiency, high dependability, and low maintenance needs. Thus, in this research, the major goal is to develop a more efficient, reliable, and maintenance-free solar WPS solution. This paper describes a sensorless control strategy that reduces the need for hall sensors and increases system’s overall reliability. Solar system power is typically impacted by partial shadowing and cannot reach the maximum available power because the traditional perturbed and observe (P&O) algorithm fails. This paper integrates the modified P&O (MP&O) algorithm with an improved mayfly optimization (IMO) name called IMO-MP&O to address these issues by efficiently extracts the maximum power from solar. From the results, it clearly shows that IMO-MP&O achieved higher efficiency of 99.58% than the existing P&O MPPT which is analyzed the MATLAB sim-power-system toolboxes.
Enhancing patient navigation and referral through tele-referral system with geographical information systems Domingo, Winston G.; Gonzales, Virdi C.; Gamay, Jennifer A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp281-291

Abstract

A tele-referral system with a geographic information system (GIS) integrates telehealth services with spatial data to enhance healthcare delivery. Resource constraints can significantly impact the effectiveness of a tele-referral system with GIS. Addressing delayed or missed referrals is critical to ensuring timely patient care and improving health outcomes. Implementing a tele-referral system with GIS can significantly enhance healthcare delivery by leveraging spatial data and telehealth technologies to improve access, efficiency, and outcomes. One major issue is the lack of access to specialists, particularly in underprivileged communities. Patients face accessing specialized care due to a cumbersome referral process or long wait times, as well as the lack of patient engagement. The results showed that the GIS-enabled tele-referral system significantly reduced patient waiting times and improved the coordination of care. By incorporating these functionalities and strategies, the tele-referral system with GIS can effectively address issues related to delayed or missed referrals, ensuring timely patient care and improving overall health outcomes. By incorporating these strategies and functionalities, the tele-referral system with GIS can effectively address limited access to specialists, ensuring timely patient care and optimal use of available resources.
Enhancing BEMD decomposition using adaptive support size for CSRBF functions Arrazaki, Mohammed; El Ouahabi, Othman; Zohry, Mohamed; Babbah, Adel
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp172-181

Abstract

Despite their widespread development, the Fourier transform and wavelet transform are still unsuitable for analyzing non-stationary and non-linear signals. To address this limitation, bidimensional empirical mode decomposition (BEMD) has emerged as a promising technique. BEMD effectively extracts structures at various scales and frequencies but faces significant computational complexity, primarily during the extremum interpolation phase. To mitigate this, different interpolation functions were presented and suggested, with BEMD using compactly supported radial basis functions (BEMD-CSRBF) showing promising results in reducing computational cost while maintaining decomposition quality. However, the choice of support size for CSRBF functions significantly impacts the quality of BEMD. This article presents an enhancement to the BEMD-CSRBF algorithm by adjusting the CSRBF support size based on the extrema distribution of the image. Our method’s results show a significant improvement in the BEMD-CSRBF algorithm’s quality. Furthermore, when compared to the other two approaches to BEMD, it shows higher accuracy in terms of both intrinsic mode function (IMF) quality and computational efficiency.
Recognition of plant leaf diseases based on deep learning and the chemical reaction optimization algorithm Ba, Nghien Nguyen; Thi, Nhung Nguyen; Quoc, Dung Vuong; Cong, Cuong Nguyen
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp447-458

Abstract

Agriculture plays a crucial role in developing countries such as Vietnam, where 70 percent of the population is employed in agriculture, and 57 percent of the social labor force works in the agricultural sector. Therefore, crop productivity directly affects the lives of many people. One of the primary reasons for reduced crop yields is plant leaf diseases caused by bacteria, fungi, and viruses. Hence, there is a need for a method to help farmers identify leaf diseases early to take appropriate action to protect crops and shift to smart agricultural production. This paper proposes lightweight deep learning (DL) models combined with a support vector machine (SVM), with hyperparameters fine-tuned by chemical reaction optimization (CRO), for detecting plant leaf diseases. The main advantage of the method is the simplicity of the architecture and optimization of the DL model’s hyperparameters, making it easily deployable on low hardware devices. To test the performance of the proposed method, experiments are performed on the PlantVillage dataset using Python. The superiority of the proposed method over the well-known visual geometry group-16 (VGG-16) and MobileNetV2 models is demonstrated by a 10% increase in accuracy prediction and a decrease of 5% and 66% in training time, respectively.
Automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis Dhumal, Yashomati R.; Shinde, Arundhati A.; Sapkal, Roshnadevi Jaising; Bhairannawar, Satish
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp649-656

Abstract

Handwritten document analysis is a method used in academia that examines the patterns and strokes of a person’s handwriting in order to get a deeper understanding of that person’s personality and character. In spite of the fact that there are a number of models and methods that may be used in the investigation of automated graphology, there are a few challenges that need to be solved. Among these challenges is the identification of efficient classification techniques that provide the highest possible degree of accuracy. Within the scope of this study, we propose automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis (MRA) where the data is preprocessed using histogram equalization and the spurious line segment section is attached to the genuine line segment portion in order to segment the succeeding line from the authentic picture of the document. A deep dense network is combined with self-attention MRA in order to provide a novel approach to the investigation of authentic handwritten text. Using the most recent and cutting-edge standards that are currently in use, an evaluation is performed to determine whether or not the proposed strategy is feasible. It is observed that the proposed method obtained nearly 98% accuracy with precision of 99%.
Exploring diverse prediction models in intelligent traffic control Vilakkumadathil, Sahira; Thiyagarajan, Velumani
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp393-402

Abstract

Traffic congestion is a major challenge that affects excellence of life for numerous people across world. The fast growth in many vehicles contributes to congestion during peak and non-peak hours. The vehicle traffic resulted in many issues like accidents and inefficiency in traffic flow. Many traffic light control systems operate on fixed time intervals leads to inefficiency. The fixed-time signals cause unnecessary delays on roads with minimum number of quantity vehicles. Intelligent transport systems (ITS) introduce new comprehensive framework that combine the advanced technologies to improve the transportation network efficiency and to optimize the traffic management. The high-traffic routes are forced to wait excessively. Machine learning (ML) methods have designed to examine the traffic control. However, the accurate detection and vehicle tracking are essential one for effective ITS. In order to mention these problems, ML and deep learning (DL) methods are introduced to improve prediction performance.
Implementation of a prototype to prevent childhood accidents in dangerous domestic environments using ESP 32 Wi-Fi module Lavalle-Sandoval, Jenner; Córdova-Cardenas, Paul; Rivera-Quispe, Sheyla; Andrade-Arenas, Laberiano
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp88-98

Abstract

Robotics has significantly advanced human evolution by optimizing tasks in fields such as medicine, engineering, and mechanics, enhancing daily life through various robotic prototypes. These innovations help prevent accidents and injuries, whether at home or in hazardous environments. For instance, sensors can detect gas leaks, fires, and other potential disasters. This research aims to design a prototype adaptable to any home environment that poses risks to infants, such as kitchens, bathrooms, or stairs. The proposed prototype incorporates gas, motion, and sound sensors connected to a Wi-Fi ESP 32 module, which alerts parents to any potential danger to their children. The research is developed in six phases: component selection, circuit simulation, prototype design, three-dimensional (3D) printing, code programming, and final testing. The results demonstrate a positive impact, improving the control and care of infants by alerting parents to hazards such as gas leaks, crying, or movement in risky areas. The conclusion confirms the effectiveness of the prototype in providing timely alerts to safeguard infants in potentially dangerous situations.
An ensemble image augmentation approach to enhance granular parakeratosis dataset Janthakal, Sheetal; Hosalli, Girisha
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp312-320

Abstract

The study discusses the revolutionizing impact of deep convolutional neural network (CNN) techniques on medical image classification, particularly in identifying skin lesions. It addresses the challenge of limited datasets for granular parakeratosis (GP) and paraneoplastic pemphigus (PNP) by employing traditional and advanced ensemble data augmentation techniques. These techniques include geometric transformations, generative adversarial networks (GANs), Cutout, and keep augment. GP affects keratinization in the groin and other regions, while PNP is associated with malignancies. The study’s relevance is enhanced by the shared imaging characteristics of the chosen conditions. By utilizing tools like U-net for segmentation, region props for feature extraction, and a support vector machine (SVM) 10-fold cross-validation model for classification, the study achieved impressive performance metrics, including 95% accuracy, 100% sensitivity, and 100% specificity when evaluated on the DermnetNZ skin lesion dataset. These findings underscore the effectiveness of augmentation in enhancing the precision of medical image classifiers and signify a substantial improvement over traditional method. Thus, the research showcases the critical role of data augmentation in overcoming data scarcity challenges and advances medical image analysis.

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