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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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A hybrid convolutional neural network-recurrent neural network approach for breast cancer detection through Mask R-CNN and ARI-TFMOA optimization Sreekala, Keshetti; Yalamati, Srilatha; Lakshmanarao, Annemneedi; Kumari, Gubbala; Kumari, Tanapaneni Muni; Desanamukula, Venkata Subbaiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3084-3094

Abstract

This paper presents a novel hybrid deep learning-based approach for breast cancer detection, addressing critical challenges such as overfitting and performance degradation in varying data conditions. Unlike traditional methods that struggle with detection accuracy, this work integrates a unique combination of advanced segmentation and classification techniques. The segmentation phase leverages Mask region-based convolutional neural network (R-CNN), enhanced by the adaptive random increment-based tomtit flock metaheuristic optimization algorithm (ARI-TFMOA), a novel algorithm inspired by natural flocking behavior. ARI-TFMOA fine-tunes Mask R-CNN parameters, achieving improved feature extraction and segmentation precision while ensuring adaptability to diverse datasets. For classification, a hybrid convolutional neural network-recurrent neural network (CNN-RNN) model is introduced, combining spatial feature extraction by CNNs with temporal pattern recognition by RNNs, resulting in a more nuanced and comprehensive analysis of breast cancer images. The proposed framework achieved significant advancements over existing methods, demonstrating improved performance. This hybrid integration of ARI-TFMOA and Hybrid CNN-RNN models represents a unique contribution, enabling robust, accurate, and efficient breast cancer detection.
Genetic algorithm-adapted activation function optimization of deep learning framework for breast mass cancer classification in mammogram images Razali, Noor Fadzilah; Isa, Iza Sazanita; Sulaiman, Siti Noraini; Osman, Muhammad Khusairi; Karim, Noor Khairiah A.; Damit, Dayang Suhaida Awang
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2820-2833

Abstract

The convolutional neural network (CNN) has been explored for mammogram cancer classification to aid radiologists. CNNs require multiple convolution and non-linearity repetitions to learn data sparsity, but deeper networks often face the vanishing gradient effect, which hinders effective learning. The rectified linear unit (ReLU) activation function activates neurons only when the output exceeds zero, limiting activation and potentially lowering performance. This study proposes an adaptive ReLU based on a genetic algorithm (GA) to determine the optimal threshold for neuron activation, thus improving the restrictive nature of the original ReLU. We compared performances on the INbreast and IPPT-mammo mammogram datasets using ReLU and leakyReLU activation functions. Results show accuracy improvements from 95.0% to 97.01% for INbreast and 84.9% to 87.4% for IPPT-mammo with ReLU and from 93.03% to 99.0% for INbreast and 84.03% to 91.06% for IPPT-mammo with leakyReLU. Significant accuracy improvements were observed for breast cancer classification in mammograms, demonstrating its potential to aid radiologists with more robust and reliable diagnostic tools.
Monkey detection using deep learning for monkey-repellent Azyze, Nur Latif Azyze Mohd Shaari; Quan, Teow Khimi; Isa, Ida Syafiza Md; Husman, Muhammad Afif
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3238-3245

Abstract

Animal intrusion has caused many issues for human beings, especially monkeys. Monkeys have caused many problems such as yield crop damage, damage to human facilities and assets and stealing food. This study aims to investigate the use of deep learning to detect monkey presence accurately and use a proper repellent system to scare them away. A deep learning algorithm is constructed with supervised learning, which includes the monkey dataset with appropriate labelling of the object of interest. The detection of the monkey comes with a bounding box with respective confidence of detection. The result is then used to evaluate the accuracy of monkey detection. The accuracy of the trained model is assessed by evaluating its performance under varying conditions of camera quality and distances. The study focuses on proving the reliability of deep learning to detect monkeys and automatically perform corresponding actions like repelling monkeys. Hence this may reduce the reliance of manpower to secure a large space and improve safety issues.
Exploring the effectiveness of hybrid artificial bee PyCaret classifier in delay tolerant network against intrusions Chaudhari, Rajashri; Deshpande, Manoj
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3149-3161

Abstract

In challenging environments with intermittent connectivity and the absence of end-to-end paths, delay tolerant networks (DTNs) require robust security measures to safeguard against potential threats. This study addresses these issues by implementing an intrusion detection system (IDS) enhanced with machine learning techniques. Common threats such as distributed denial-of-service (DDoS) and flood attacks are tackled using datasets like network intrusion detection (NID) and flood attack datasets. Multiple machines learning methods, including k-nearest neighbors (K-NN), decision trees (DT), logistic regression (LR), and others, are utilized to improve detection accuracy. A PyCaret-based approach is developed to increase efficiency while preserving attack detection accuracy in DTNs. Comparative research demonstrates that PyCaret outperforms Scikit-learn models, and the artificial bee PyCaret classifier (ABPC) optimizes hyperparameters to improve model performance. NS2 simulation shows the system's ability to thwart attacks, offering useful insights into DTN security and improving communication reliability in various situations.
Leukemia detection using SegNet and faster region-based convolutional neural network Valiaveetil, Della Reasa; Kanimozhi, T.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3028-3038

Abstract

Prevention of cancer is mostly attained by surveillance of the transformation zones. White blood cells (WBCs) are established in the bone marrow and intemperate growth of WBC leads to leukemia. Hematologists examine the microscopic images in manual method for predicting leukemia, but it is very complex process and without any guaranteed for accurate. In this proposed study, deep learning techniques involved to segment and classify the three types of leukemia like acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML) and chronic lymphocytic leukemia (CLL) using the BioGps dataset. The purpose of deep learning in medical science enhances the accuracy and precision of determining leukemia in early stages. In this study, introducing a sigmoid stretching (SS) in pixel enhancement for preprocessing; SegNet (St) is comfort to extract the structural features of the leukocytes and to segment the normal and blast cells for a clear classification; faster region-based convolutional neural network (faster R- CNN) carried under the process of classification and optimization done by dragon fly algorithm. The result of this work achieves best accuracy related to the existing techniques of convolutional neural network (CNN) such as support vector machine (SVM), k-nearest neighbors (kNN) and Bayesian model. This study achieves the accuracy rate of 97%, precision rate of 94% and sensitivity rate of 90% respectively with low complexity.
The implementation of Archimedes optimization algorithm for solar charge controller-maximum power point tracking in partial shading condition Perkasa, Sigit Dani; Megantoro, Prisma; Hidayah, Nayu Nurrohma; Vigneshwaran, Pandi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2769-2785

Abstract

Maximum power point tracking (MPPT) enhances the efficiency of solar photovoltaic (PV) systems by ensuring optimal power extraction under varying conditions. MPPT is implemented in solar charge controllers or hybrid inverters connected to PV arrays. The current-voltage (IV) curve, influenced by temperature and irradiance fluctuations, becomes more complex under partial shading, causing multiple local maxima and reducing efficiency. This study proposes an MPPT technique using the Archimedes optimization algorithm (AOA), a novel metaheuristic inspired by Archimedes' principle. The AOA-based MPPT integrates a DC/DC buck converter controlled by an STM32 microcontroller to address challenges in complex shading conditions. Comparative analysis demonstrates the AOA's superiority in achieving high efficiency and fast convergence. The AOA-based MPPT achieved an average efficiency of 93.17% across shading scenarios, outperforming PSO (87.04%) and non-MPPT systems (84.56%). It also exhibited faster average tracking times of 90.5 ms compared to PSO's 100.5 ms, ensuring robust and reliable performance. These results confirm the effectiveness of the AOA-based method in maximizing energy harvesting in real-world PV applications.
Cucumber leaf disease identification in real-time via deep learning based algorithms Rahman, Md Mizanur; Nadim, Mahimul Islam; Akther, Mahinur; Ullah, Ahad; Ahmed, Jakaria; Ahmed, Muhammad Jalal Uddin; Jahan, Israt
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3127-3138

Abstract

Cucumber is a cash crop in Bangladesh as it is a side dish grown commercially in cultivable lands year-round. The early prediction of disease-prone crops could save grooming time and minimize losses. The conventional method of examining leaves just through observation of the human eye could only detect the diseases at an advanced stage without a concrete decision of which disease it might be and regular inspection is labour intensive, inaccurate and often unreliable. This study evaluates machine learning-based image analysis for classifying healthy and diseased cucumber leaves by training deep learning models to detect and identify observable traits. Total 1,629 images use as primary dataset and all the data collected from the cucumber field of Bangladesh. To fulfill this purpose, convolutional neural network (CNN), InceptionV3, and EfficientNetB4 are the models implemented in this paper to improve the classification of objects. The dataset was optimized by pre-processing techniques and the leaves are classified into four categories, namely angular leaf spot, downy mildew, powdery mildew, and good leaf. The EfficienNetB4 model achieved the highest train and test accuracy respectively 95% and 87%. A comparative examination of the available models was conducted in this paper to reach a solid decision.
Classification of morphologically similar Indian rice variety using machine learning algorithms Shadaksharappa, Harini; Chakrasali, Saritha; Ningappa, Krishnamurthy Gorappa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3202-3211

Abstract

India, among the agriculture-based economy grows wide variety of rice along with other crops. These varieties have different commercial values as they are different in their features. It becomes extremely challenging to classify rice varieties which have similar features but are different in their quality. This study considers four varieties of similar looking rice which conform to be Sona-Masuri. A total of 4180 images are considered to extract 56 features including textural, red, green, blue (RGB) and hue, saturation, value (HSV) color and wavelet decomposition. Machine learning (ML) models like support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT) and voting classifiers are developed for feature dataset and convolutional neural network (CNN) model for image dataset. The results obtained for every model are obtained using statistical methods and the results are expressed in a table for accuracy, precision, recall and F1-score. A classification accuracy of 72.48% is obtained for SVM using polynomial kernel trick by considering all 56 features. The customized CNN model is designed with three convolution layers has resulted in 97.13% of training accuracy and 87.5% of validation accuracy. Based on the results obtained, it is witnessed that the ML models employed in this study to classify rice types with similar appearances have practical applications.
Application of satisfiability problem solvers for assessing the strength of hash algorithms Algazy, Kunbolat; Sakan, Kairat; Varennikov, Andrey; Kapalova, Nursulu
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3191-3201

Abstract

This article presents a methodology for assessing the strength of cryptographic algorithms and provides experimental data obtained from studying the cryptographic strength of the developed hash function HBC-256 using modern satisfiability problem (SAT) solvers. Various SAT solvers implementing the conflict-driven clause learning (CDCL) algorithm, based on the Davis-Putnam-Logemann-Loveland (DPLL) algorithm, were used to conduct the cryptanalysis of the HBC-256 hash function. The most effective was the parallel SAT solver Parkissat, and thus it was used for more in-depth research. A series of experiments were conducted to determine how resistant the HBC-256 hashing algorithm is to preimage attacks for one, two, three, and four rounds. For this purpose, four sets of files were prepared using special propositional encoding tools, each set including 30 files in the standard of center for discrete mathematics and theoretical computer sciences (DIMACS) format. These files contain Boolean formulas in conjunctive normal form (CNF), used as input for modern SAT solvers. To obtain more accurate time measurements, the same experiment was repeated multiple times, after which the average time was determined. The results of this study show that SAT solvers encounter significant difficulties when attempting to solve the preimage search problem for the full-round version of the HBC-256 hash function, even when only 30 bits of the original message are unknown.
Tracking control of uncertain third order jerk equation Genesio-Tesi using adaptive backstepping Mu'tamar, Khozin; Naiborhu, Janson; Saragih, Roberd; Handayani, Dewi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2758-2768

Abstract

This article presents the uncertain Genesio-Tesi, a third-order Jerk equation in the form of an ordinary differential equation, with the potential to exhibit chaos under certain conditions. The main focus of this article is to design a control function for the uncertain Genesio-Tesi, which has uncertain parameters with unknown values. The adaptive backstepping method designs the control function, demonstrating its ability to stabilize the system output towards a given trajectory using Lyapunov stability. To test the robustness of the proposed control method, simulations were conducted with various scenarios, including disturbances to the steady-state system. Simulation results show that the controller successfully drove the system output along a desired trajectory, whether constant or a function, and maintained system stability even with significant disturbances.

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