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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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Performance analysis of breast cancer histopathology image classification using transfer learning models Ramasamy, Meena Prakash; Subburaj, Thayammal; Krishnasamy, Valarmathi; Mannarsamy, Vimala
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp6006-6015

Abstract

Convolutional neural networks (CNN) which are deep learning-based methods are being currently successfully deployed and have gained much popularity in medical image analysis. CNN can handle enormous amounts of medical data which makes it possible for accurate detection and classification of breast cancer from histopathological images. In the proposed method, we have implemented transfer learning-based classification of breast cancer histopathological images using DenseNet121, DenseNet201, VGG16, VGG19, InceptionV3, and MobileNetV2 and made a performance analysis of the different models on the publicly available dataset of BreakHis. These networks were pre-trained on the ImageNet database and initialized with weights which are fine-tuned by training with input histopathological images. These models are trained with images of the BreakHis dataset with multiple image magnifications. From the comparative study of these pre-trained models on histopathology images, it is inferred that DenseNet121 achieves the highest breast cancer classification accuracy of 0.965 compared to other models and contemporary methods.
Human-machine interactions based on hand gesture recognition using deep learning methods Zholshiyeva, Lazzat; Manbetova, Zhanat; Kaibassova, Dinara; Kassymova, Akmaral; Tashenova, Zhuldyz; Baizhumanov, Saduakas; Yerzhanova, Akbota; Aikhynbay, Kulaisha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp741-748

Abstract

Human interaction with computers and other machines is becoming an increasingly important and relevant topic in the modern world. Hand gesture recognition technology is an innovative approach to managing computers and electronic devices that allows users to interact with technology through gestures and hand movements. This article presents deep learning methods that allow you to efficiently process and classify hand gestures and hand gesture recognition technologies for interacting with computers. This paper discusses modern deep learning methods such as convolutional neural networks (CNN) and recurrent neural networks (RNN), which show excellent results in gesture recognition tasks. Next, the development and implementation of a human-machine interaction system based on hand gesture recognition is discussed. System architectures are described, as well as technical and practical aspects of their application. In conclusion, the article summarizes the research results and outlines the prospects for the development of hand gesture recognition technology to improve human-machine interaction. The advantages and limitations of the technology are analyzed, as well as possible areas of its application in the future.
Comparing hyperparameter optimized support vector machine, multi-layer perceptron and bagging classifiers for diabetes mellitus prediction Yatoo, Nuzhat Ahmad; Ali, Ishok Sathik; Mirza, Imran
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5834-5847

Abstract

Diabetes Mellitus (DM) is a chronic metabolic disorder that affects the way body processes blood glucose levels. Within the medical field, Machine Learning (ML) has significant potential for accurately forecasting and diagnosing a range of chronic conditions. If an accurate prognosis is achieved early, the risk to health and intensity of DM can be significantly mitigated. In this study, a robust methodology for DM prognosis was proposed, which included anomaly replacement, data normalization, feature extraction, and K-fold cross-validation. Three machine learning methods, Support Vector Machine, Multilayer Perceptron and Bagging, were employed to predict Diabetes Mellitus using the National Health and Nutritional Examination Survey (NHANES) 2011-2012 dataset. Accuracy, AUC and Recall were chosen as the evaluation metrics and subsequently optimized during hyperparameter tweaking. From all the comprehensive tests, Bagging outperformed the other two models with an Accuracy of 96.67, AUC score of 99.2 and Recall of 97.0. The proposed methodology surpasses other approaches for forecasting DM.
Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach Saifullah, Shoffan; Dreżewski, Rafał
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2583-2591

Abstract

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.
Bone fracture classification using convolutional neural network architecture for high-accuracy image classification Solikhun, Solikhun; Windarto, Agus Perdana; Alkhairi, Putrama
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6466-6477

Abstract

This research introduces an innovative method for fracture classification using convolutional neural networks (CNN) for high-accuracy image classification. The study addresses the need to improve the subjectivity and limited accuracy of traditional methods. By harnessing the capability of CNNs to autonomously extract hierarchical features from medical images, this research surpasses the limitations of manual interpretation and existing automated systems. The goal is to create a robust CNN-based methodology for precise and reliable fracture classification, potentially revolutionizing current diagnostic practices. The dataset for this research is sourced from Kaggle's public medical image repository, ensuring a diverse range of fracture images. This study highlights CNNs' potential to significantly enhance diagnostic precision, leading to more effective treatments and improved patient care in orthopedics. The novelty lies in the unique application of CNN architecture for fracture classification, an area not extensively explored before. Testing results show a significant improvement in classification accuracy, with the proposed model achieving an accuracy rate of 0.9922 compared to ResNet50's 0.9844. The research suggests that adopting CNN-based systems in medical practice can enhance diagnostic accuracy, optimize treatment plans, and improve patient outcomes.
Rapid detection of diabetic retinopathy in retinal images: a new approach using transfer learning and synthetic minority over-sampling technique Mustafa, Hiri; Mohamed, Chrayah; Nabil, Ourdani; Noura, Aknin
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1091-1101

Abstract

The challenge of early detection of diabetic retinopathy (DR), a leading cause of vision loss in working-age individuals in developed nations, was addressed in this study. Current manual analysis of digital color fundus photographs by clinicians, although thorough, suffers from slow result turnaround, delaying necessary treatment. To expedite detection and improve treatment timeliness, a novel automated detection system for DR was developed. This system utilized convolutional neural networks. Visual geometry group 16-layer network (VGG16), a pre-trained deep learning model, for feature extraction from retinal images and the synthetic minority over-sampling technique (SMOTE) to handle class imbalance in the dataset. The system was designed to classify images into five categories: normal, mild DR, moderate DR, severe DR, and proliferative DR (PDR). Assessment of the system using the Kaggle diabetic retinopathy dataset resulted in a promising 93.94% accuracy during the training phase and 88.19% during validation. These results highlight the system's potential to enhance DR diagnosis speed and efficiency, leading to improved patient outcomes. The study concluded that automation and artificial intelligence (AI) could play a significant role in timely and efficient disease detection and management.
Satellite image encryption using 2D standard map and advanced encryption standard with scrambling Benchikh, Omar; Bentoutou, Youcef; Taleb, Nasreddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5153-5171

Abstract

In today’s world, the need for higher levels of security in storing and transferring data has become a key concern. It is essential to safeguard data from any potential information leaks to prevent threats that may compromise data confidentiality. Therefore, to protect critical and confidential satellite imagery, this paper proposes a novel encryption method based on the combination of image bands scrambling with chaos and the advanced encryption standard (AES). The proposed approach aims to enhance the security of satellite imagery while maintaining efficiency and robustness against various attacks. It possesses several appealing technical characteristics, notably a high level of security, a large key space, and resilience to single event upsets (SEUs) and transmission errors. To evaluate the performance of the proposed encryption technique, extensive experiments have been conducted by considering factors such as security level, resistance to SEUs, and computational efficiency. Our results demonstrate that the proposed method achieves a high level of security and a large key space, ensuring the confidentiality and integrity of satellite imagery data. Furthermore, the method exhibits resilience against SEUs and transmission errors, and offers efficient processing, making it suitable for real-world applications.
Wicked node detection in wireless ad-hoc network by applying supervised learning Ranganathan, Chitra Sabapathy; Sampathrajan, Rajeshkumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4120-4127

Abstract

A wireless ad-hoc network (WANET) is a decentralized network supported by wireless connections without a pre-existing architecture. However, the mobility of nodes is a defining characteristic of WANETs, and the speed with which nodes may act poses several security risks. As a result of these wicked nodes, more data packets are lost, which might cause a significant delay. Thus, it is very important to identify wicked nodes in WANET. This work provides a support vector machine approach for detecting (SVMD) wicked nodes in the internet of things. The number of characteristics is reduced using the linear correlation coefficient (LCC) technique. With the LCC technique, we can precisely measure the strength of the connection between any two nodes while clearing the field of irrelevant information. Further, the support vector machine (SVM) algorithm may identify the wicked nodes by analyzing metrics such as the packet received ratio, packet delay ratio, and remaining energy ratio. The next step is to reach a verdict in which the wicked nodes are punished by being rendered inoperable. The simulation results show that the network latency is minimized, and the chance of missing detection is decreased using this method in WANET.
Design of higher gain linearly polarized 2×2 microstrip patch array antenna for wireless communication Gani, Prakash G.; Hegde, Shriram P.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2695-2707

Abstract

The fifth-generation technology is popular and best as far as data rates are concerned for wireless applications. To meet the increased demand of the modern world for faithful communication, this research work is carried out. In this approach, a single patch, 1×2 array, and 2×2 array are designed using a low-cost FR4 dielectric substrate (Є????=4.4) covering the 3.3–3.8 GHz frequency band. Due to the lack of gain from arrays in the present research, an attempt is made to achieve excellent gain from arrays with a minimum number of patches. First, the gain of a single inset-fed antenna is compared with another normal single patch of the same thickness but with an additional air dielectric medium. Next, the second single patch is extended to obtain a rectangular 1×2 array and a 2×2 array antenna. A second single patch measuring 50×32.5×0.8 mm is designed assuming an infinite ground plane. It has 3 mm air gap between the patch strip and the ground. Air acts as a second dielectric layer that reduces power loss. This allows a maximum gain of 15.2 dB with a return loss of -22 dB. Also, antenna efficiency and bandwidth are 91% and 267.69 MHz, respectively.
The effect of distance learning on student learning achievement: a meta-analysis Nusantara, Bayuk; Hadi, Samsul; Retnawati, Heri; Sumaryanto, Sumaryanto; Prasojo, Lantip Diat; Sotlikova, Rimajon; Arlinwibowo, Janu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6486-6497

Abstract

Distance learning has been an option during the last few years due to the coronavirus disease 2019 (COVID-19) pandemic. The aim of this research is to identify the effectiveness of distance learning compared to conventional learning in terms of student learning achievement. This research is meta- analysis of random group contrast design (experiment-control) models. The data selection process refers to the inclusion criteria of year, theme, data type and data completeness. Based on these criteria, 10 articles were selected. The analysis process begins with testing the homogeneity assumption using three methods, namely ????2, ????2, and Q which shows heterogeneous data so that random model selection is appropriate, testing freedom of publication bias with Egger's test and funnel plot which shows that the data collected is free from publication bias, identifying the effect size and standard error, as well as conducting moderator variable analysis which considers domain, continent, subject, education level and year variables. The results of this study show that although distance learning has a positive influence on student learning achievement, the difference is not significant when compared with conventional learning. In addition, these results can be moderated by achievement domain variables, type of subject, level of education, and year.

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