cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
ijece@iaesjournal.com
Phone
-
Journal Mail Official
ijece@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 3   4   5   6   7 
Search results for , issue "Vol 14, No 5: October 2024" : 111 Documents clear
Cloud based prediction of epileptic seizures using real-time electroencephalograms analysis Thahniyath, Gousia; Yadav, Chelluboina Subbarayudu Gangaiah; Senkamalavalli, Rajagopalan; Priya, Shanmugam Sathiya; Aghalya, Stalin; Reddy, Kuppireddy Narsimha; Murugan, Subbiah
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.pp6047-6056

Abstract

This study aims to improve the accuracy of epileptic seizure prediction using cloud-based, real-time electroencephalogram analysis. The goal is to build a strong framework that can quickly process electroencephalogram (EEG) data, extract relevant features, and use advanced machine learning algorithms to predict seizures with high accuracy and low latency by taking advantage of cloud platforms' computing power and scalability. The main objective is to provide patients and their caregivers with timely notifications so that they may control epilepsy episodes proactively. The goal of this project is to improve the lives of people with epilepsy by reducing the impact of seizures and improving treatment results via real-time analysis of EEG data. Cloud computing also allows the suggested seizure prediction system to be more accessible and scalable, meaning more people worldwide could benefit from it. This section discusses the results from five separate datasets of patients with epileptic seizures who underwent EEG analysis with the following details as frontopolar (FP1, FP2), frontal (F3, F4), frontotemporal (F7, F8), central (C3, C4), temporal (T3, T4), parieto-temporal (T5, T6), parietal (P3, P4), occipital (O1, O2), time (HH:MM:SS).
Reddit social media text analysis for depression prediction: using logistic regression with enhanced term frequency-inverse document frequency features Ayyalasomayajula, Madan Mohan Tito; Agarwal, Akshay; Khan, Shahnawaz
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.pp5998-6005

Abstract

Language provides significant insights into an individual’s emotional state, social status, and personality traits. This research aims to enhance depression detection through the analysis of linguistic features and various dataset attributes. The dataset, sourced from the social networking platform Reddit, comprises posts and comments from individuals diagnosed with depression. Logistic regression with term frequency-inverse document frequency (TF-IDF) is employed as the primary model for text classification. To improve model performance, a novel feature—the average time interval between consecutive posts or comments—is introduced, contributing to a marginal but noteworthy improvement in accuracy. The proposed model demonstrates superior F1 scores compared to other models applied to the same dataset. Given the increasing recognition of mental health’s significance, accurately diagnosing mental disorders is of paramount importance. This study underscores the potential of leveraging linguistic analysis and advanced machine learning techniques to identify depressive symptoms, thereby contributing to more effective mental health diagnostics and interventions.
13-level modular multilevel inverter application for the exhaust fan drive control of Thu Thiem Road tunnel Anh, An Thi Hoai Thu; Cuong, Tran Hung
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.pp5008-5017

Abstract

The ventilation system plays a vital role in ensuring the safety of people and means of transportation. Fresh air is created in the tunnel mainly thanks to the exhaust fans arranged at the top of the tunnels. The drive motor for the exhaust fan in the Thu Thiem Road tunnel has a power of 560 kW and operates at a voltage of 6 kV. The paper proposes a 13-level modular multilevel inverter (MMC) with the improved nearest level modulation (NLM) method to ensure the quality of voltage output from the voltage source inverter-fed exhaust fan drive motor. This is a novel combination aimed at transforming electrical power at high voltage levels, high power, and enhancing operational efficiency and the lifespan of semiconductor components within the inverter when operating continuously and over extended durations. The theoretical research results verified through MATLAB/Simulink software with simulation parameters collected from the exhaust fan motor of Thu Thiem Road tunnel, Vietnam show total harmonic distortion of the current in operation with 13 levels is 1.23%, while that of the current in operation with 7 levels is 10.1%; total harmonic distortion (THD) of the voltage with 13 levels is 5.33%, while that of the voltage with 7 levels is 11.37%.
The comparison of several cryptosystems using the elliptic curve: a report Trung, Mai Manh; Do, Le Phe; Tuan, Do Trung; Trieu, Thu Thuy; Tanh, Nguyen Van; Tri, Ngo Quang; Cong, Bui Van
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.pp5319-5329

Abstract

The elliptic curve cryptosystem (ECC) has several applications in Information Security, especially in cryptography with two main activities including encrypting and decrypting. There were several solutions of different research teams which propose various forms of the elliptic curve cryptosystem on cryptographic sector. In the paper, we proposed a solution for applying the elliptic curve on cryptography which is based on these proposals as well as basic idea about the elliptic curve cryptosystem. We also make comparison between our proposal and other listed solution in the same application of the elliptic curve for designing encryption and decryption algorithms. The comparison results are based on parameters such as time consumption (t), RAM consumption (MB), source code size (Bytes), and computational complexity.
Single phase robustness variable structure load frequency controller for multi-region interconnected power systems with communication delays Nguyen, Phan-Thanh; Nguyen, Cong-Trang
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.pp5064-5071

Abstract

This paper proposes an estimator-based single phase robustness variable structure load frequency controller (SPRVSLFC) for the multi-region interconnected power systems (MRIPS) with communication delays. The key attainments of this research consist of two missions: i) a global stability of the power systems is guaranteed by removing the reaching phase in traditional variable structure control (TVSC) technique; and ii) a novel output feedback load frequency controller is established based on the estimator tool and output information only. Initially, a single-phase switching function is constructed to disregard the reaching phase in TVSC. Then, an unmeasurable state variable of the MRIPS is estimated by using the proposed estimator tool. Next, a new SPRVSLFC for the MRIPS is suggested based on the support of the estimator tool and output data only. Furthermore, a sufficient constraint is constructed by retaining the linear matrix inequality (LMI) procedure for ensuring the robust stability of motion dynamics in sliding mode. Finally, the performance of interconnected power plant under changed multi-constraints is imitated with the novel control technique to validate the practicability of the plant.
Real-time business intelligence development using machine learning to increase the potential of the dairy goat milk business Primawati, Alusyanti; Sitanggang, Imas Sukaesih; Annisa, Annisa; Astuti, Dewi Apri
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.pp5612-5625

Abstract

The development of big data and real-time data warehouse (RTDW) technologies has transformed traditional business intelligence (BI) into real-time business intelligence (RTBI). The RTBI framework is developed in this study by incorporating machine learning-based real-time prediction features. The complexity of layer integration in the RTBI framework is a challenge in building RTBI. The development of RTBI was carried out in business areas that did not have RTBI from the beginning, such as the dairy goat milk business in Probolinggo, East Java. Another main reason is that the dairy goat milk business is a food alternative to cow's milk in Indonesia. The results of this study can contribute to increasing the potential value of the goat milk business. The research method was developed by adapting to the Kimball method and unified modeling language (UML). The real-time prediction feature with the long short-term memory (LSTM) algorithm is the main feature in the RTBI framework developed in the research. The calculation results of real-time predictive analysis latency successfully approached 0 milliseconds (ms), namely 9.35×10-5 ms. The application of RTBI in the dairy goat milk business was successfully built but the real data is very limited, so RTBI is less able to describe the movement of the business.
Detection of fungal diseases of plants from leaf images based on neural network technologies Fedorchenko, Ievgen; Yusof, Mohd Faizal; Oliinyk, Andrii; Chornobuk, Maksym; Khokhlov, Mykola; Alsayaydeh, Jamil Abedalrahim Jamil
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.pp5866-5873

Abstract

The paper addresses the issue of automating the detection of fungal diseases in plants using digital images of their leaves. The spread of diseases among agricultural and horticultural crops causes significant economic losses worldwide, making the development of an effective and affordable solution to this problem highly valuable. Literature analysis suggests the viability of employing a convolutional neural network (CNN) to tackle this issue. The 'Fungus recognition' model was developed based on a custom CNN architecture using the TensorFlow library. The model underwent training and testing on a publicly available dataset. Test results show that 'Fungus recognition' achieves a classification accuracy level of 90%, surpassing similar models considered. The developed model can be adapted for deployment on mobile computing devices, paving the way for its practical implementation in agriculture and horticulture.
Integrating hetero-core fiber optics sensor in intelligent technological textiles Arif, Noor Azie Azura Mohd; Jiun, Chong Chee; Sen, Yong Wei; Ehsan, Abang Annuar
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.pp4987-4995

Abstract

In the context of the emerging Industry 4.0 paradigm, smart fabric sensors have been representing a novel addition to the textile industry. The proposed sensors utilize macro-bending techniques with varying fiber optic core sizes. The study involved the construction and testing of macro-bending sensors using single-mode (9 μm) and hetero-core (50–9–50 μm) fibers, configured into seven sinusoidal loops. The experiment was further extended to different types of elastic textiles. Spandex demonstrated superior linearity compared with jersey and rubber bands. The integration with the DOIT ESP32 DevKit facilitated real-time monitoring of respiratory rates. The results from the experiment indicated that the macro-bending sensor, fabricated using hetero-core optical fiber, exhibited superior sensitivity in comparison to the sensor assembled from single-mode optical fiber, with respective sensitivity values of 1.72 and 1.30 dB/cm. The designed sensors displayed closely aligned behavior during forward and reverse loading, indicating the reversibility of the fiber optic sensor. Given its simplistic design and low fabrication cost, the proposed sensor holds significant potential for practical applications.
Enhanced accuracy estimation model energy import in on-grid rooftop solar photovoltaic Sahrin, Alfin; Abadi, Imam; Musyafa, Ali
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.pp5970-5983

Abstract

Installing rooftop solar photovoltaic (PV) with an on-grid system benefits consumers because it can reduce imports of electrical energy from the grid. This study aims to model the estimation of energy imports generated from on-grid rooftop solar PV systems. This estimation model was carried out in 20 provincial capitals in Indonesia. The parameters used are weather conditions, orientation angle, and energy generated from the on-grid rooftop solar PV system. The value of imported energy is divided into three combinations based on the azimuth angle direction, which describes the type and shape of the roof of the building (one-direction, two-directions, and three-directions). Modeling was done using machine learning with neural network (NN), linear regression, and support vector machine. A comparison of the machine learning algorithm results is NN produces the smallest root mean square error (RMSE) value of the three. Model enhancement uses a grid search cross-validation (GSCV) to become the GSCV-NN model. The RMSE results were enhanced from 53.184 to 44.389 in the one-direction combination, 145.562 to 141.286 in the two-direction combination, and 81.442 to 76.313 in the three-direction combination. The imported energy estimation model on the on-grid rooftop solar PV system with GSCV-NN produces a more optimal and accurate model.
Neural network control of a nonlinear dynamic plant with a predictive model Siddikov, Isamidin; Khalmatov, Davronbek; Khushnazarova, Dilnoza; Khujanazarov, Ulugbek
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.pp5131-5138

Abstract

The paper considered the possibilities of applications of neural network technologies to control a dynamic plant with nonlinear properties. To give the control system the desired dynamic property, the use of a neural network predictive controller is proposed. The model of the control plant is in the form of a multilayer forward-directional neural network, which allows us to construct a controller using generalized equation methods with prediction. A neural network control algorithm with prediction based on minimizing the quadratic quality functional is proposed. The algorithm makes it possible to minimize the root mean square error of regulation and the control signal rate of change. To determine the sequences of optimal control impacts, the application of the Newton-Raphson method is proposed. To reduce computational costs when receiving control signals, the decomposition of the original matrix, represented as a Hess matrix, is carried out. To predict the behavior of a control plant, a formula is proposed for calculating the gradient of a neural network, discrepant by the possibility of its use in the real-time mode of the control. The proposed algorithm of the neural network control with predictive allows higher quality control of complex nonlinear dynamic plants in the real-time mode.

Page 5 of 12 | Total Record : 111

 3   4   5   6   7 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 16, No 1: February 2026 Vol 15, No 6: December 2025 Vol 15, No 5: October 2025 Vol 15, No 4: August 2025 Vol 15, No 3: June 2025 Vol 15, No 2: April 2025 Vol 15, No 1: February 2025 Vol 14, No 6: December 2024 Vol 14, No 5: October 2024 Vol 14, No 4: August 2024 Vol 14, No 3: June 2024 Vol 14, No 2: April 2024 Vol 14, No 1: February 2024 Vol 13, No 6: December 2023 Vol 13, No 5: October 2023 Vol 13, No 4: August 2023 Vol 13, No 3: June 2023 Vol 13, No 2: April 2023 Vol 13, No 1: February 2023 Vol 12, No 6: December 2022 Vol 12, No 5: October 2022 Vol 12, No 4: August 2022 Vol 12, No 3: June 2022 Vol 12, No 2: April 2022 Vol 12, No 1: February 2022 Vol 11, No 6: December 2021 Vol 11, No 5: October 2021 Vol 11, No 4: August 2021 Vol 11, No 3: June 2021 Vol 11, No 2: April 2021 Vol 11, No 1: February 2021 Vol 10, No 6: December 2020 Vol 10, No 5: October 2020 Vol 10, No 4: August 2020 Vol 10, No 3: June 2020 Vol 10, No 2: April 2020 Vol 10, No 1: February 2020 Vol 9, No 6: December 2019 Vol 9, No 5: October 2019 Vol 9, No 4: August 2019 Vol 9, No 3: June 2019 Vol 9, No 2: April 2019 Vol 9, No 1: February 2019 Vol 8, No 6: December 2018 Vol 8, No 5: October 2018 Vol 8, No 4: August 2018 Vol 8, No 3: June 2018 Vol 8, No 2: April 2018 Vol 8, No 1: February 2018 Vol 7, No 6: December 2017 Vol 7, No 5: October 2017 Vol 7, No 4: August 2017 Vol 7, No 3: June 2017 Vol 7, No 2: April 2017 Vol 7, No 1: February 2017 Vol 6, No 6: December 2016 Vol 6, No 5: October 2016 Vol 6, No 4: August 2016 Vol 6, No 3: June 2016 Vol 6, No 2: April 2016 Vol 6, No 1: February 2016 Vol 5, No 6: December 2015 Vol 5, No 5: October 2015 Vol 5, No 4: August 2015 Vol 5, No 3: June 2015 Vol 5, No 2: April 2015 Vol 5, No 1: February 2015 Vol 4, No 6: December 2014 Vol 4, No 5: October 2014 Vol 4, No 4: August 2014 Vol 4, No 3: June 2014 Vol 4, No 2: April 2014 Vol 4, No 1: February 2014 Vol 3, No 6: December 2013 Vol 3, No 5: October 2013 Vol 3, No 4: August 2013 Vol 3, No 3: June 2013 Vol 3, No 2: April 2013 Vol 3, No 1: February 2013 Vol 2, No 6: December 2012 Vol 2, No 5: October 2012 Vol 2, No 4: August 2012 Vol 2, No 3: June 2012 Vol 2, No 2: April 2012 Vol 2, No 1: February 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue