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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 111 Documents
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Search results for , issue "Vol 14, No 5: October 2024" : 111 Documents clear
A detailed analysis of deep learning-based techniques for automated radiology report generation Dhamanskar, Prajakta; Thacker, Chintan
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.pp5906-5915

Abstract

The automated creation of medical reports from images of chest X-rays has the potential to significantly reduce workloads for healthcare providers and accelerate patient care, especially in environments with limited resources. This study provides an extensive overview of deep learning-based techniques designed for radiology report generation from chest X-ray pictures automatically. By examining recent research, we delve into various deep learning architectures and techniques used for this task, including transformer-based approaches, attention mechanisms, sequence-to-sequence models, adversarial training methods, and hybrid models. We also discuss about the datasets used for evaluation and training, as well as future directions and research problems in this area. The significance of deep learning in revolutionizing radiology reporting is further emphasized by our review, which also highlights the need for additional research to address challenges such data accessibility, image quality variability, interpretation of complex findings, and contextual integration. The objective of this research is to present a comparative analysis of cutting-edge methods for developing automated medical report generation to enhance patient outcomes and healthcare delivery.
An efficient approximate method for solving Bratu’s boundary value problem Al-Khaled, Kamel; Ajeel, Mahmood Shareef; Abu-Irwaq, Issam; Al-Khalid, Hala
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.pp5738-5743

Abstract

We compute the numerical solution of Bratu’s boundary value problem (BVP). To achieve this, we apply a new and useful approach to solve Bratu’s boundary value problem by using Green’s function and a new integral operator, along with a modified version of the Adomian decomposition method. This process produces solutions that call for the boundary conditions to be applied explicitly. Statistical results demonstrating the robustness and efficiency of the proposed scheme are included. An exact and approximate solution comparison is made with known results. The quantitative outcomes showcase our novel approach’s high numerical precision and consistency across a range of parameter configurations.
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.
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.
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.
Digitalization of educational plays for quality education Soulimani, Younes Alaoui; Elaachak, Lotfi; Bouhorma, Mohammed
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.pp5443-5457

Abstract

Repetitive tests on a learning material help schoolchildren to memorize and to learn this material. Psychologists call this phenomenon the testing effect. Skilled teachers use learning plays to embed routine tests in an engaging way. To widespread this practice, we propose a framework to digitize learning plays embedding routine tests into educational videogames. We have identified the smallest set of game design elements required to build an educational videogame out of a learning play. We have used the self-determination theory to group game design elements, and to define a breakdown structure for engagement engineering. This structure helps select the appropriate design elements for an engagement driver. We have applied the framework to digitize a learning play. We have tested the digital play with 238 schoolchildren who considered it as a video game. The video game tested a proposed pattern to create challenges allowing an engaging flow experience. The pattern increased responses (9%) and created time distortion (24%). Delivering rewards following variable schedules reduced errors (49%) and increased time distortion (16%). This research explores how to digitize learning plays into engaging educational video games and how to design engaging video games to remediate missed learning.
Forecasting stock market prices using deep learning methods Ismailova, Aisulu; Beldeubayeva, Zhanar; Kadirkulov, Kuanysh; Doumcharieva, Zhanagul; Konyrkhanova, Assem; Ussipbekova, Dinara; Aripbayeva, Ainura; Yesmukhanova, Dariga
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.pp5601-5611

Abstract

The article focuses on enhancing stock market price prediction through artificial neural networks and machine learning. It underscores the significance of improving forecast accuracy by incorporating historical stock prices, macroeconomic indicators, news events, and technical indicators. Exploring deep learning principles, it delves into convolutional neural networks (CNN), recurrent neural networks (RNN), including long short-term memory (LSTM), and gated recurrent unit (GRU) modifications. This financial time series processing study covers data preprocessing, creating training/test sets, and selecting evaluation metrics. Results suggest promising applications for the developed forecasting models in stock markets, stressing the importance of considering various factors for precise forecasts in dynamic financial environments. Historical reserve data serves as the model foundation. Integration of macroeconomic, news, and technical indicators offers a holistic approach, aiding trend and anomaly identification for enhanced forecasts. The article recommends suitable deep learning architectures, highlighting LSTM and GRU's effectiveness in adapting to intricate data dependencies. Experimental outcomes showcase these architectures' benefits in predicting stock market prices, offering valuable insights for finance and asset management professionals in financial analysis and machine learning realms.
Enhancing internet of things security: evaluating machine learning classifiers for attack prediction Arabiat, Areen; Altayeb, Muneera
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.pp6036-6046

Abstract

The internet of things (IoT) has contributed to improving the quality of service and operational efficiency in many areas, such as smart cities, but this technology has faced a major dilemma: the problem of cyber-attacks of various types. In this study, we relied on the use of machine learning (ML) and deep learning (DL) techniques to present a proposed model of an intrusion detection system (IDS) for detecting different types of IoT attacks that include ARP_poisoning, DOS_SYN_Hping, MQTT_Publish, NMAP_FIN_SCAN, NMAP_OS_DETECTION, and Thing_Speak. However, the proposed model is built using Orange3 data mining tools. The model consists of random forest (RF), artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM) classifiers. On the other hand, the data set that is used was obtained from the Kaggle platform's real-time IoT infrastructure data set, called RT-IoT2022. The data set consists of a huge number of records, which are processed and then reduced to 7,481 records using linear discriminant analysis. In the next stage, the data set is fed to the Orange3 data mining tool, which is divided into 70% of the training dataset and 30% of the test dataset, in addition to using fold-cross validation to increase accuracy and avoid overfitting. Thus, the experimental results showed the superiority of RF with a classification accuracy of (99.9%), while the accuracy in ANN reached (99.8%), (97.8%) in LR, and finally, for SVM, the accuracy reached (92.9%).
A cost-effective, reliable and accurate framework for multiple-target tracking by detection approach using deep neural network Divyaprabha, Divyaprabha; Seebaiah, Guruprsad
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.pp5681-5690

Abstract

Over the years the area of object tracking and detection has emerged and become ubiquitous owing to its potential contribution towards video surveillance applications. Multiple object tracking (MOT) estimates the trajectory of several objects of interest simultaneously over time in a series of video frames. Even though various research proposals have encouraged the use of machine learning techniques in designing multi-object trackers, the existing solutions need to be more practicable for online tracking due to more complicated algorithms, The study, therefore, introduces a cost-effective tracking solution for multiple–target tracking by detection where it incorporates the you only look once version 4 (YOLOv4) and person re-identification network, which are further integrated with the proposed tracking model, which considers both bounding box and appearance features to handle the motion prediction and data association respectively. The novelty of this approach lies in considering appearance features, which not only help predict tracks through allocations problem solving but also handle the cost of computation problems. Here, the system utilizes a pre-trained association metric with which the occlusion challenges are also handled, whereas the target tracking has taken place even in more extended periods of occlusion, making it suitable with the existing efficient tracking algorithms.
A novel comprehensive investigation for enhancing cluster analysis accuracy through ensemble learning methods Lakshmi, H. N.; Ramana, Thaduri Venkata; K, LNC Prakash; Reddy, L. Kiran Kumar; Raju, Kachapuram Basava
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.pp5802-5812

Abstract

Ensemble learning stands out as a widely embraced technique in machine learning. This research explores the application of ensemble learning, including ensemble clustering, to enhance the precision of cluster analysis for datasets with multiple attributes and unclear correlations. Employing a majority voting-based ensemble clustering approach, specific techniques such as k-means clustering, affinity propagation, mean shift, BIRCH clustering, and others are applied to defined datasets, leading to improved clustering results. The study involves a comprehensive comparative analysis, contrasting ensemble clustering outcomes with those of individual techniques. The process of improving cluster identification accuracy encompasses data collection, pre-processing to exclude irrelevant elements, and the application of standard clustering algorithms. The task includes defining the optimal number of groups before comparing clustering models. Additionally, a combined model is constructed by merging BIRCH clustering and mean shift clustering, leveraging their advantages to enhance overall clustering strength and accuracy. This research contributes to advancing ensemble learning and ensemble clustering methodologies, offering improved accuracy, and uncovering hidden patterns in complex datasets.

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