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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 65 Documents
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Search results for , issue "Vol 37, No 2: February 2025" : 65 Documents clear
A joint learning classification for intent detection and slot filling with domain-adapted embeddings Muhammad, Yusuf Idris; Salim, Naomie; Zainal, Anazida
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.pp1306-1316

Abstract

For dialogue systems to function effectively, accurate natural language understanding is vital, relying on precise intent recognition and slot filling to ensure smooth and meaningful interactions. Previous studies have primarily focused on addressing each subtask individually. However, it has been discovered that these subtasks are interconnected and achieving better results requires solving them together. One drawback of the joint learning model is its inability to apply learned patterns to unseen data, which stems from a lack of large, annotated data. Recent approaches have shown that using pretrained embeddings for effective text representation can help address the issue of generalization. However, pretrained embeddings are merely trained on corpus that typically consist of commonly discussed matters, which might not necessarily contain domain specific vocabularies for the task at hand. To address this issue, the paper presents a joint model for intent detection and slot filling, harnessing pretrained embeddings and domain specific embeddings using canonical correlation analysis to enhance the model performance. The proposed model consists of convolutional neural network along with bidirectional long short-term memory (BiLSTM) for efficient joint learning classification. The results of the experiment show that the proposed model performs better than the baseline models.
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.
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.
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.
Machine learning based prediction of production using real time data of a point bottom sealing and cutting machine Mary Diana, Fathima Rani Irudaya; Rajendran, Subha; Muthusamy, Selvadass
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.pp1376-1386

Abstract

The packaging sector utilizes polypropylene based flexible materials for diverse product packaging with customization options in size and design achieved through advanced flexographic printing and point bottom sealing and cutting machines. Accurately estimating production time and quantity is vital for efficient planning and cost estimation, with factors like material dimensions, thickness, and cutting machine speed influencing production output. Understanding the intricate relationship between these parameters is essential for comprehending their impact on production time and quantity. Predicting production quantity before production begins helps in determining machine runtime and associated costs. In large-scale production systems, machine learning (ML) has proven to be a useful tool for resource allocation and predictive scheduling. An attempt has been made in this paper to develop an intelligent model for predicting the yield of a cutting machine using artificial neural network (ANN), support vector regression (SVR), regression tree ensemble (RTE) and gaussian process regression (GPR). The most crucial features for prediction were identified and the hyperparameters of the ML models were optimized to create efficient models for prediction. A comparative analysis of the four models revealed that the GPR model was simple and effective with least training time and prediction error.
Lung cancer prediction with advanced graph neural networks Moozhippurath, Bineesh; Natarajan, Jayapandian
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.pp1077-1084

Abstract

This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions.
Effective autism spectrum disorder sensory and behavior data collection using internet of things Kumar, Vittalraju Chetan; Umesh, Dadadahalli Ramu
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.pp1274-1283

Abstract

Wireless body area networks (WBANs) connected with wearable internet of things (WIoT) offer useful features including sensory information collection, analysis, and transmission for continuous behavior monitoring of autism spectrum disorder (ASD) patients. Due to users’ mobility and time-driven sensed data, data collection becomes very difficult. The current approach employs cluster-based multi-objective path-optimized data collection mechanisms that have experienced hotspot issues leading to loss of energy and coverage problems near the base stations. This work presents the high energy and reliable sensory and behavior data collection (HERSBDC) mechanism to address the research difficulties. To ensure network coverage, the HERSBDC initially provides a new uneven clustering mechanism. Next, multi-objective-based cluster head (CH) selection metrics are proposed. The final step is the creation of a multi-objective routing path to gather vital ASD data more reliably and energy-efficiently. Comparing the proposed HERSBDC algorithm to the low energy adaptive cluster-hierarchy (LEACH)-based, and distributed energy-efficient clustering and routing (DECR) methods, the simulation results demonstrate that the HERSBDC mechanism achieves a much better lifetime by 62.28% and 11.89%, the delivery ratio by 15.04% and 9.51%, with minimal delay by 52.65%, and 9.65%, and routing overhead by 32.05%, and 42.65%, respectively.

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