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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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Enhancing energy efficiency and reliability in wireless sensor networks using BioGAT optimization Shareef, D. K.; Jyothsna, Veeramreddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp601-612

Abstract

The BioGAT model, as proposed, presents a novel methodology for enhancing the efficiency of wireless sensor networks (WSNs), which are essential elements of contemporary communication and sensing systems. For real-time monitoring and data analysis, WSNs are comprised of autonomous sensor nodes that are outfitted with processing, wireless communication, and sensing capabilities. These nodes are deployed in a variety of environments. By means of an advanced optimization model, this work aims to address critical challenges in WSNs, specifically in the areas of node placement, energy efficiency, and network reliability. By utilizing biogeography-based optimization (BBO) and graph attention networks (GAT), the BioGAT model endeavors to dynamically adapt to network changes while achieving a balance between efficient coverage and energy consumption. Cluster heads (CHs), which are essential for the aggregation of data, have a significant impact on improvements in energy efficiency and the longevity of networks. By means of comprehensive simulations and evaluation, this study presents exceptional outcomes. The BioGAT model outperforms prior approaches by attaining a 95% packet delivery ratio and an enhanced throughput. In addition, the model effectively decreases mean energy consumption, underscoring its capacity to improve the sustainability and dependability of networks in a variety of WSN applications.
Advancing chronic pain relief cloud-based remote management with machine learning in healthcare Mohankumar, Nagarajan; Reddy Narani, Sandeep; Asha, Soundararajan; Arivazhagan, Selvam; Rajanarayanan, Subramanian; Padmanaban, Kuppan; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1042-1052

Abstract

Healthcare providers face a significant challenge in the treatment of chronic pain, requiring creative responses to enhance patient outcomes and streamline healthcare delivery. It suggests using cloud-based remote management with machine learning (ML) to alleviate chronic pain. Wearable device data, electronic health record (EHR) data, and patient-reported outcomes are all inputs into the suggested system’s data analysis pipeline, which combines support vector machines (SVM) with recurrent neural networks (RNN). SVM’s powerful classification skills make it possible to classify patients’ risks and predict how they will react to therapy. RNNs are very good at processing sequential data, which means they may identify trends in patient symptoms and drug adherence over time. By integrating these algorithms, healthcare professionals may create individualized treatment programs that consider each patient’s preferences and specific requirements. Early intervention and proactive treatment of pain symptoms are made possible by the system’s ability to monitor patients in real-time remotely. The system is further improved by using predictive analytics to identify patients who could benefit from extra support services and to forecast when they will have acute pain episodes. The proposed approach can change the game regarding managing chronic pain. It provides data-driven, individualized treatment that improves patient outcomes while cutting healthcare expenses.
Comparative analysis of machine and deep learning algorithms for semantic analysis in Iraqi dialect Almufti, Modher; Elamine, Maryam; Belguith, Lamia Hadrich
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1225-1233

Abstract

Text analytics, an essential component of artificial intelligence (AI) applications, plays a pivotal role in analyzing qualitative sentiments and responses in questionnaires, particularly for governmental and private organizations. Utilizing sentiment analysis enables a comprehensive understanding of people’s opinions, especially when expressed in lengthy texts in their native language, with minimal constraints. This study aims to identify the determinants of electronic service adoption among Iraqi citizens. A set of 1,695 questionnaires were distributed to Iraqi citizens; obtained 1,234 responses that were increased via data augmentation to 1,393 comments. Four machine learning (ML) and three deep learning (DL) algorithms Na¨ıve Bayes (NB), K-nearest neighboror machine (SVM), random forest (RF), as well as two variants of long-shortterm memory (LSTM) networks and convolutional neural networks (CNN) were employed to classify qualitative feedback. Following rigorous training and testing, the NB classification algorithm exhibited the highest accuracy, achieving 82.89%.
Efficient deep learning approach for brain tumor detection and segmentation based on advanced CNN and U-Net Baali, Mehdi; Bourbia, Nadjla; Messaoudi, Kamel; Bourennane, El-Bay
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1365-1375

Abstract

In this paper, we propose an innovative deep learning methodology dedicated to tumor detection and segmentation in medical images using convolutional neural networks (CNNs) and the U-Net architecture. The study emphasizes the importance of improving the quality and relevance of these features by employing advanced preprocessing methods. The subsequent development involves training a CNN model to achieve accurate tumor classification within the medical images. Among the various deep learning techniques proposed for medical image analysis, U-net-based models have gained significant popularity for multimodal medical image segmentation. However, due to the diverse shapes, sizes, and appearances of brain tumors, simple block architectures commonly used in segmentation tasks may not adequately capture the complexity of tumor boundaries and internal structures. The experimental results provide compelling evidence of the proposed approach's efficacy in accurately detecting and segmenting brain tumors. The results highlight the successful performance of the approach and its ability to achieve accurate tumor identification and segmentation.
A novel boundary adaptive oversampling approach for intrusion detection Kaur, Ritinder; Gupta, Neha
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp518-529

Abstract

Managing unbalanced datasets is a significant challenge in intrusion detection, since uncommon assaults are often obscured by the bulk of regular network traffic. In order to mitigate the effects of class imbalance and improve intrusion detection system (IDS) performance, it is necessary to use a variety of imbalanced learning algorithms. Methods of data augmentation such as adaptive synthetic sampling (ADASYN) and synthetic minority oversampling technique (SMOTE) are useful in addressing class imbalance. This paper introduces a novel technique to data resampling where decision tree-generated decision boundaries are used to conduct ADASYN on complicated and unusual samples. When this method’s efficacy was evaluated using the standard NSL-KDD dataset, the accuracy of the unusual class u2r was increased to 42% and, for r2l it was improved to 83%, respectively. The UNSW-NB 15 dataset has been used for further validation of the method, and its statistical significance has been asserted by comparing the suggested method to other oversampling techniques.
Deportation of constant amplitude impulsive outlier (CAIO) through novel repetitive new switching-based median filtering approach Patanavijit, Vorapoj; Thakulsukanant, Kornkamol
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp789-800

Abstract

This research paper nominates a novel repetitive new switching-based median filtering approach (R-NSBMF) for outlier deportation on computer numerical pictures that are surpassingly subverted by constant amplitude impulsive outlier (CAIO) or Salt & Pepper noise. This approach reestablishes the outlier numerical pictorial feature (which has the minimum amplitude or the maximum amplitude) by the median filter of the finite impulse response (FIR) linear predictor of all the non-outlier numerical pictorial feature in the calculating numerical pictorial division under the repetitive groundwork. The proposed R-NSBMF approach is investigated on numerous computer numerical pictures (Girl, Lena, Pepper and F16) on spacious outlier percentage and the proposed R-NSBMF approach exposes admirable outlier-deportation numerical pictures than the mean filter (mf), standard median filter (SMF), adaptive median filter (AMF), weight median filter (WMF) and original NSBMF and it professes admirable peak signal-to-noise ratio (PSNR) and pictorial quality.
A three-phase model to keyword detection in Arabic corpora Namly, Driss; Bouzoubaa, Karim; Tachicart, Ridouane
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp206-213

Abstract

The exponential growth of Arabic text data in recent years has created an urgent demand for sophisticated keyword detection techniques that are specifically tailored to the nuances of the Arabic language. This study addresses the critical need for efficient tools capable of swiftly and accurately identifying keywords within a collection of Arabic documents, particularly when analyzing multiple documents in a corpus. To meet this challenge, we present a novel corpus specifically designed for keyword detection in Arabic texts, along with an innovative approach that integrates three distinct candidate keyword lists: a frequency-based list, a vector space model list, and a machine learning-based list. This hybrid methodology leverages the strengths of each technique, enabling a more comprehensive and effective keyword identification process. We conducted extensive experimental validation to assess the performance and computational efficiency of our proposed pipeline. The results demonstrate that our approach consistently achieves robust performance across a variety of domains, with evaluation metrics indicating F1-scores that consistently surpass 91%. Overall, this study contributes to the advancement of automated keyword detection in Arabic, paving the way for enhanced information retrieval and text analysis capabilities.
Optimizing 2D-to-3D image conversion for precise flat surface detection using laser triangulation and HSV masking Rahayu Purwanti, Bernadeta Siti; Akhinov, Ihsan Auditia; Mulyono, Raden Sugeng; Nurtanto, Muhammad; Hamid, Mustofa Abi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp111-122

Abstract

This study tackles a critical challenge in converting two-dimensional (2D) images into three-dimensional (3D) representations, focusing on the precise detection of flat surfaces. The research utilizes a triangulation method involving laser and camera systems, emphasizing the optimization of laser shooting angles and camera positioning to accurately determine z-coordinates. The methodology employs hue, saturation, and value (HSV) color masking, which has proven superior to traditional red, green, blue (RGB) methods for isolating red line objects. Key findings indicate that the optimal laser angle, β1=70.65°, significantly minimizes root mean square (RMS) error, thereby enhancing the accuracy of 3D imaging. Additionally, the use of three laser lines at different angles enables a more comprehensive detection of z-coordinates by creating multiple reference points across the surface. This arrangement improves the robustness and precision of the 3D reconstruction process, as the intersecting laser lines generate detailed coordinate data that is critical for accurately mapping surface irregularities. These results not only support existing theories in digital feature extraction but also offer a robust framework for practical applications in manufacturing and quality control, particularly in surface defect detection. The study’s innovative approach advances the field of computer vision, providing new insights and methodologies for optimizing image conversion techniques.
Integrating blockchain, internet of things, and cloud for secure healthcare Kumaran, K Senthur; Khekare, Ganesh; Athitya M, Thanu; Arulmozhivarman, Aakash; Pranav M, Arvind; Chidambaram N, Hiritish
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp928-936

Abstract

This research paper shows a decentralized healthcare architecture using the integration of internet of things (IoT), blockchain, and cloud to improve speed up tuple broken security as well as scalability. Real time health information (e.g., pulse rate, sugar level) from patients is captured by IoT devices and preprocessed at the fog computing layer to securely send them to a cloud platform. Immutability and transparency Patient health records recorded by blockchain solutions are highly irreversible due to the underlying technology, while smart contracts take care of data integrity and privacy. The cloud layer delivers storage that scales and works, also including real-time analytics to access patient data from anywhere for healthcare providers while the core helps manage long-term information architecture. It does so by automating healthcare workflows and taking some of the manual interventional processes out such that care delivery becomes even more efficient. Together, these technologies provide a secure, efficient, patient-centered healthcare system whose architecture can easily support future needs in remote patient monitoring and inter-institutional collaboration, responding to emerging demands from modern healthcare systems.
Prediction of chronic diseases based on ML packages using spark MLlib Oussous, Aicha; Ez-Zahout, Abderrahmane; Ziti, Soumia
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1121-1129

Abstract

Heart disease, diabetes, and breast cancer pose significant global health challenges, and effectively addressing these chronic diseases necessitates a coordinated international effort. The integration of machine learning and predictive analytics offers promising solutions for tackling these issues. Our study presents a unified model that utilizes the random forest (RF) algorithm and SparkMLlib to predict these three diseases, testing the model on three distinct datasets and evaluating its performance using scientific metrics, including the receiver operating characteristic (ROC) curve, accuracy, precision, recall, and F1-score. Furthermore, we aim to investigate whether variations in medical data and contextual factors impact the results. The findings indicate that while the model shows strong overall performance, its effectiveness may differ for each disease due to factors such as data characteristics, disease-specific features, model behavior, and various biological and medical considerations; understanding these factors is essential for improving model performance and ensuring its appropriate use in clinical environments.

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