cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
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
 823   824   825   826   827 
HybridCSF model for magnetic resonance image based brain tumor segmentation Kataria, Jyoti; Panda, Supriya P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1845-1852

Abstract

The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hybrid convolutional-SVM-fuzzy C-means model combining convolutional neural network (CNN) with the classifier support vector machine (SVM) and clustering technique fuzzy C-means (FCM). The proposed model was implemented on Br35H, BraTs 2020 and BraTs2021 datasets. The suggested model outperformed the existing methods by achieving 98.6% of accuracy on Br35H dataset and dice score of 0.63, 0.87, 0.81 on BraTs 2020 dataset for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively. The achieved dice scores on the BraTs 2021datasets are 0.89, 0.95, and 0.89 for ET, WT, and TC, respectively. The results show that the suggested model HybriCSF outperforms the other CNN-based models in terms of accuracy.
Applying inductive logic programming to automate the function of an intelligent natural language interfaces for databases Bais, Hanane; Machkour, Mustapha
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp983-993

Abstract

One of the foundational subjects in both artificial intelligence (AI) and database technologies is natural language interfaces for databases (NLIDB). The primary goal of NLIDB is to enable users to interact with databases using natural languages such as English, Arabic, and French. While many existing NLIDBs rely on linguistic operations to meet the challenges of user’s ambiguity existing in natural language queries (NLQ), there is currently a growing emphasis on utilizing inductive logic programming (ILP) to develop natural language processing (NLP) applications. This is because ILP reduces the requirement for linguistic expertise in building NLP systems. This paper outlines a methodology for automating the construction of NLIDB. This method utilizes ILP to derive transfer rules that directly translate NLQ into a clear and unambiguous logical query, which subsequently translatable into database query languages (DQL). To acquire these rules, our system was trained within a corpus consisting of parallel examples of NLQs and their logical interpretations. The experimental results demonstrate the promise of this approach, as it enables the direct translation of all NLQs with grammatical structures similar to those already present in the trained corpus into a logical query.
Development of image extraction using the centerline method in the identification of appendicitis in ultrasonography Rizki, Syafrika Deni; Yuhandri, Yuhandri; Fitri, Iskandar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1750-1758

Abstract

Appendicitis is a disease that refers to inflammation of the appendix caused by obstruction, or blockage, in the lumen of the appendix. We investigated that this disease can be detected early through medical imaging such as ultrasonography (USG). However, the role of ultrasound in these cases is still limited due to the low visualization rate of the visible appendix. Based on this, this research aims to develop an image extraction process using the Centerline method in the process of identifying appendicitis in ultrasound images. The development of the extraction process is presented in the performance of the centerline and boundary extraction (CBE) algorithm which can represent image objects as boundaries that limit and separate one area from other areas. The research dataset used was 2097 ultrasound images sourced from 90 patients at the West Sumatra Lung Hospital. Based on the tests that have been carried out, it has been proven that it can reduce the width of the image object iteratively until the object is represented as a center line or the thinnest representation. The performance of the CBE algorithm in the identification process is sufficient to provide accuracy results of 92%. These results can be a new extraction concept that can provide accuracy in the identification process.
Clinical named entity extraction for extracting information from medical data Kuttaiyapillai, Dhanasekaran; Madasamy, Anand; Ayyavu, Shobanadevi; Sayeed, Md Shohel
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1722-1731

Abstract

Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine learning approaches rely on the time-consuming process of creating corpora and cannot extract information for specific needs and locations in certain situations. This paper presents a clinical NER approach based on a convolutional neural network (CNN) for better use of specific features around medical entities and analyzes the performance of the proposed approach through fine-tuning NER with effective pre-training on the BC5CDR dataset. The proposed method uses annotation of entities for various medical concepts. The second stage develops a clinically NER method. This proposed method shows interesting results on the performance measures achieving a precision of 92.57%, recall of 92.22%, and F1- measure of 91.6%.
Design and performance analysis of a long-stroke electromagnetic double-reel hammer Alkasassbeh, Jawdat S.; Pavlov, Vlademer Е.; Al-Zyoud, Khalaf Y.; Al-Awneh, Tareq A.; Alkasassbeh, Osamah; Al-Rawashdeh, Ayman Y.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp137-152

Abstract

This paper comprehensively investigates the performance characteristics of a long-stroke electromagnetic double-reel hammer compared to a conventional hammer. Quantitative analysis indicates that the long-stroke hammer shows a significant increase in striker speed and impact energy. The impact energy has increased by 255%, and energy losses in copper windings have decreased by 124% per operating cycle. Additionally, the long-stroke hammer demonstrates a 105% reduction in energy consumption and a 52% improvement in overall efficiency per cycle compared to the conventional hammer. This study examines the operational characteristics of the long-stroke hammer throughout its cycle using field theory methods, MATLAB simulations, and experimental tests. Results indicate higher impact energy and speed, lower energy losses in copper windings, and higher efficiency per cycle for the long-stroke hammer. Furthermore, a mathematical model of the long-stroke hammer is developed, incorporating static parameters and oscillograms of striker movement and current flow. A comprehensive comparison of the performance indicators of both hammers reveals significant improvements in lifting height, cycle duration, impact frequency, and striker speed for the long-stroke hammer. Overall, these findings suggest that the long-stroke operating mode can significantly enhance the efficiency and performance of conventional hammers while simultaneously reducing impact frequency and machine heating.
Plant disease classification using novel integration of deep learning CNN and graph convolutional networks Maheswara Rao, Saka Uma; Sreekala, Keshetti; Rao, Pulluri Srinivas; Shirisha, Nalla; Srinivas, Gunnam; Sreedevi, Erry
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1721-1730

Abstract

Plant diseases present substantial challenges to global agriculture, significantly affecting crop yields and jeopardizing food security. Accurate and timely detection of these diseases is paramount for mitigating their adverse effects. This paper proposes a novel approach for plant disease classification by integrating convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The model aims to enhance classification accuracy by leveraging both visual features extracted by CNNs and relational information captured by GCNs. Using a Kaggle dataset containing images of diseased and healthy plant leaves from 31 classes, including apple, corn, grape, peach, pepper bell, potato, strawberry, and tomato. Standalone CNN models were trained on image data from each plant type, while standalone GCN models utilized graph-structured data representing plant relationships within each subset. The proposed integrated CNN-GCN model capitalizes on the complementary strengths of CNNs and GCNs to achieve improved classification performance. Through rigorous experimentation and comparative analysis, the effectiveness of the integrated CNN-GCN approach was evaluated alongside standalone CNN and GCN models across all plant types. Results demonstrated the superiority of the integrated model, highlighting its potential for enhancing plant disease classification accuracy.
Novel modified Chernobyl disaster optimizer for controlling DC motor Aribowo, Widi; Shehadeh, Hisham A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1361-1369

Abstract

This article presents the modified Chernobyl disaster optimizer (CDO) method for DC motor control to find the optimal proportional integral derivative (PID) settings. DC motors are widely used machinery. DC motors are also simple to use. The detonation of the Chernobyl nuclear reactor core served as the inspiration for the idea and guiding principles of the CDO. CDO has limitations in the stability of exploration and exploitation areas. This research aims to obtain a new balance of exploration and exploitation. This study suggests incorporating the levy flight and chaotic algorithm (CA) techniques to enhance the CDO method. This study was conducted with the MATLAB/Simulink software. A comparative technique, which included the marine predator algorithm (MPA), golden jackal optimization (GJO), and CDO, was utilized to determine the performance of the MCDO method. According to the study’s findings, the MCDO method’s overshoot value outperformed all other approaches.
Eye disease detection using transfer learning based on retinal fundus image data Imaduddin, Helmi; Sakina, Alivia Rahma
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp509-516

Abstract

The escalating global prevalence of blindness remains a pressing concern, with eye diseases representing the primary culprits behind this issue. Vision is integral to various aspects of human life, underscoring the significance of effective eye disease detection. Presently, disease detection relies largely on manual methods, which are susceptible to misdiagnosis. However, the advent of technology has paved the way for disease detection through the application of deep learning methodologies. Deep learning exhibits substantial potential in disease detection, particularly when applied to image data, as attested by its accuracy in algorithmic assessments. This research introduces a novel approach to disease detection, specifically transfer learning-based deep learning. The study seeks to evaluate and compare the performance of various models, including EfficientNetB3, DenseNet-121, VGG-16, and ResNet-152, in identifying three prevalent eye diseases: cataract, diabetic retinopathy, and glaucoma, utilizing retinal fundus image data. Extensive experimentation reveals that the DenseNet-121 model achieves the highest accuracy levels, boasting precision, recall, F1-score, and accuracy values of 96.5%, 96%, 96.25%, and 96.20%, respectively. These results demonstrate the superior performance of the employed transfer learning model, signifying its efficacy in detecting eye diseases.
No-reference image quality assessment based on visual explanation images and deep transfer learning Ahmed, Basma; Omer, Osama A.; Singh, Vivek Kumar; Rashed, Amal; Abdel-Nasser, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1521-1531

Abstract

Quantifying image quality in the absence of a reference image continues to be a challenge despite the introduction of numerous no-reference image quality assessments (NR-IQA) in recent years. Unlike most existing NRIQA methods, this paper proposes an efficient NR-IQA method based on deep visual interpretations. Specifically, the main components of the proposed method are: i) generating a pseudo-reference image (PRI) for the input distorted images, ii) employing a pretrained convolutional network to extract feature maps from the distorted image and the corresponding PRI, iii) producing visual explanation images (VEIs) by using the feature maps of the distorted image and the corresponding PRI, iv) measuring the similarity between the two VEIs using an image similarity metric, and v) employing a non-linear mapping function for quality score alignment. In our experiments, we evaluated the efficacy of the proposed method across various forms of distortion using four benchmark datasets (LIVE, SIQAD, CSIQ, and TID2013). The proposed approach demonstrates parity with the latest methods, as evidenced by comparisons with both hand-crafted NR-IQA and deep learning-based approaches.
An intelligent approach to detect and predict online fraud transaction using XGBoost algorithm Bala, Bala Santhosh; Yadav, Pasupula Praveen; Reddy, Mogathala Raghavendra
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1491-1498

Abstract

The most popular payment method in recent years is the credit card. Due to the E-commerce industry’s explosive growth, the usage of credit cards for online purchases have been greatly increased as a result frauds has increased. Banks have been facing challenges to detect the credit card system fraud in recent years. Credit card fraud happens when the card was stolen for any unauthorized purposes or if the fraudster utilizes the credit card information for his own use. In order to prevent credit card fraud, it is essential to build detection measures. While detecting credit card theft with machine learning (ML), the features of credit card frauds play an important and they must be carefully selected. A fraud detection algorithm must be created in order to correctly locate and stop fraudulent activity as technology advances along with the amount of fraud cases. ML methods are essential for identifying fraudulent transactions. The implementation of fraud detection models is particularly difficult because of the sensitive nature of the data, the unbalanced class distributions, and the lack of data. In this work, an intelligent approach to detect and predict online fraud transaction using extreme gradient boosting (XGBoost) algorithm is described. The XGBoost model predicts whether a transaction is fraud or not. This model will achieve better performance interarm of recall, precision, accuracy and F1-score for credit card fraud detection.

Page 825 of 914 | Total Record : 9138

 823   824   825   826   827 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue