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 6,301 Documents
 590   591   592   593   594 
A secure and cloud-based patient management system using attribute-based encryption algorithm Kalarani, Senthilkumar; Shobana, Mahalingam; Shankari, Edamakanti Uma; Praveena, Bolly Joshi; Shanthi, Subramaniam; Ramadevi, Rathinasabapathy; Sandiri, Rajendar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2445-2454

Abstract

Using attribute-based encryption (ABE), cloud-based patient management systems may be made more secure and efficient. The goal is to provide a scalable encryption infrastructure with dynamic attribute handling and context-aware access control for safe data access. Encryption procedures should directly comply with regulatory criteria to secure healthcare data and ensure data privacy and integrity. Secure attribute issuance and revocation are achieved using advanced key management and real-time auditing and monitoring to identify and react to unauthorised access. To help healthcare providers handle data, user-centric security measures including extensive training and adaptive security procedures are used. The encryption system is implemented and maintained using cost-effective cloud and open-source methodologies to ensure seamless integration and operational effectiveness in healthcare contexts. First, secure patient management system dataset results reveal ABE algorithm encryption. The encrypted values are 8F5D6A..., 7C4A3B..., 6E3B2C..., 9D8A7B..., 5E4D3C.... in the second instance, derived from role-based access control of ABE. The patients are 25-60 years old, have medical codes 101-105, 201-205, and 301-305. For roles from different fields, attribute code is 401-406, level code is 501-505.
Enhancing engineering education through virtual reality: a systematic study on immersive engineering education practices Riaji, Tarek; Hassani, Sanae El; Alaoui, Fatima Ezzahrae M'hamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1889-1899

Abstract

This article explores the integration of virtual reality (VR) and associated technologies in engineering education, focusing on the pedagogical approaches adopted in this integration, which we refer to as immersive engineering education. This study considers the application possibilities and the transformative impact of VR on engineering education. The article addresses the critical collection and analysis of VR applications in engineering education. It covers main VR-related papers published from 2015 to February 2024 and indexed in Scopus, Web of Sciences, or both, and discussing design, development challenges, and collaborative tools. Empirical evidence showcases improved engagement, motivation, and learning outcomes. The findings offer modern insights for educators and researchers on leveraging VR for impactful learning experiences, while also noting the need for further research in this evolving field.
Classification of brain stroke based on susceptibility-weighted imaging using machine learning Kandaya, Shaarmila; Saad, Norhashimah Mohd; Abdullah, Abdul Rahim; Shair, Ezreen Farina; Muda, Ahmad Sobri; Sabri, Muhammad Izzat Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1602-1611

Abstract

Magnetic resonance imaging (MRI) is used to identify brain disorders, particularly strokes. Rapid treatment, often referred to as "time is brain," is emphasized in recent studies, stressing the significance of early intervention within six hours of stroke onset to save lives and enhance outcomes. The traditional manual diagnosis of brain strokes by neuroradiologists is both subjective and time-intensive. To tackle this challenge, this study introduces an automated method for classify brain stroke from MRI images based on pre- and post-stroke patients. The technique employs machine learning, with a focus on susceptibility weighted imaging (SWI) sequences, and involves four stages: preprocessing, segmentation, feature extraction, classification and performance evaluation. The paper proposes classification and performance evaluation to determine stroke region according to three types of categories, those are poor improvement, moderate improvement and good improvement stroke patients based on pre and post patients. Then, performance evaluation is verified using accuracy, sensitivity and specificity. Results indicate that the hybrid support vector machine and bagged tree (SVMBT) yields the best performance for stroke lesion classification, achieving the highest accuracy which is 99% and showing significant improvement for stroke patients. In conclusion, the proposed stroke classification technique demonstrates promising potential for brain stroke diagnosis, offering an efficient and automated tool to assist medical professionals in timely and accurate assessments.
Vehicle side control of a wireless power transfer charger using optimized artificial neural network Ancary, Marouane El; Lassioui, Abdellah; Fadil, Hassan El; Hasni, Anwar; Asri, Yassine El; Idrissi, Zakariae El
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1487-1498

Abstract

This paper investigates a new approach to control a wireless power transfer (WPT) charger for electric vehicles (EVs) employing an optimized artificial neural network (ANN). Enhancing the efficiency and robustness of such systems is crucial, and integrating artificial intelligence (AI)-based solutions has introduced innovative approaches in this field. The proposed method enables precise regulation of battery charging voltage even under challenging conditions, such as coil misalignment or shared grounding assemblies for multiple EVs. To assess the stability and robustness of the proposed controller, its performance was evaluated under scenarios of coil misalignment and shared grounding assemblies for EVs with varying battery voltages. The controller effectively eliminated overshoot and significantly reduced residual output voltage ripple by 4.33% compared to a conventional proportional-integral (PI) controller, demonstrating the superior performance and reliability of the ANN-based control approach.
Android-based smart digital marketplace application on agricultural commodities using a new variant recommendation system Subiyanto, Subiyanto; Prajanti, Sucihatiningsih Dian Wisika; Salim, Nur Azis; Prabowo, Setya Budi Arif; Sutrisno, Deyndrawan; Anantyo, Andika; Anggriani, Dewi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1968-1977

Abstract

In the marketing of agricultural products, addressing the challenges associated with extensive distribution chains is essential, as these directly affect sellers. Additionally, the vast array of available product options often overwhelms customers, complicating their efforts to identify and purchase items that align with their preferences. This work aims to develop a smart e-commerce application for agribusiness, specifically designed for agricultural products on the Android platform. The application integrates a recommendation system that utilizes geolocation-aware neural graph collaborative filtering (GA-NGCF), which facilitates product marketing for farmers and streamlines the product search and selection process for users based on personalized preferences. The development process encompassed various stages, from planning to rigorous testing. The application’s recommendation system, which implements GA-NGCF, operates based on three primary elements: the creation of a geolocation graph of user-item data, the integration of information between neighboring nodes, and the prediction of user preferences. The resulting smart agribusiness e-commerce application, enhanced by GA-NGCF, demonstrated marked improvements in recommendation accuracy and overall application performance during testing. Empirical results indicated substantial enhancements in recommendation metrics, with GA-NGCF achieving a recall of 0.34, a precision of 0.36, and normalized discounted cumulative gain of 0.37, thereby outperforming existing models.
Two-scale decomposition and deep learning fusion for visible and infrared images Azad, Ruhan Bevi; Unnikrishnan, Hari; Gopinath, Lokesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1593-1601

Abstract

The paper focuses on the fusion of visible and infrared images to generate composite images that preserve both the thermal radiation information from the infrared spectrum and the detailed texture from the visible spectrum. The proposed approach combines traditional methods, such as two-scale decomposition, with deep learning techniques, specifically employing an autoencoder architecture. The source images are subjected to two-scale decomposition, which extracts high-frequency detail and low-frequency base information. Additionally, an algorithmic unravelling technique establishes a logical connection between deep neural networks and traditional signal processing algorithms. The model consists of two encoders for decomposition and a decoder after the unravelling operation. During testing, a fusion layer merges the decomposed feature maps, and the decoder generates the fused image. Evaluation metrics including entropy, average gradient, spatial frequency and standard deviation are employed to subjectively assess fusion quality. The proposed approach demonstrates promise for effectively combining visible and infrared imagery for various applications.
The role of disruptive technologies in the metaverse worlds: state of the art survey Al-Karaki, Jamal N.; Gawanmeh, Amjad; Awad, Ahmed; Zerai Teklesenbet, Natnael
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2211-2223

Abstract

The metaverse has emerged as an immersive and interactive virtual world that has the potential to revolutionize various industries. The use of disruptive technologies, such as blockchain, artificial intelligence (AI), digital twin, internet of things (IoT), cloud, big data, and cybersecurity, has and will play a significant role in enhancing the capabilities of the metaverse. This paper provides a state-of-the-art survey on the role of disruptive technologies in the metaverse. The paper presents a taxonomy of the use of disruptive technologies in the metaverse and a comprehensive literature review on the application areas of the metaverse in education, healthcare, tourism, gaming, and smart cities. The paper compares the adoption of technologies in the metaverse and identifies current and future research directions. The paper contributes to understanding disruptive technologies’ potential in the metaverse. It provides insights for researchers, practitioners, and policymakers to explore the opportunities and challenges of the metaverse.
Enhancing plant disease detection using machine learning approaches for improved agricultural productivity Piska, Ganga; Janaskar, Swarali; Chandgadkar, Ojas; Bhawsar, Paras; Vishwakarma, Pinki Prakash
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2081-2088

Abstract

India's agricultural sector faces persistent challenges due to the prevalence of plant diseases, which severely impact crop quality and productivity, exacerbating the ongoing food supply crisis. Traditional methods of diagnosing plant diseases are often time-consuming, labor-intensive, and prone to inaccuracies, making it difficult for farmers to implement timely interventions. To address these issues, a forward-looking strategy utilizing artificial intelligence (AI) and machine learning (ML) has been proposed, aiming to revolutionize disease detection and management in agriculture. This involves the development of a comprehensive novel dataset named Leafsnap, which is uniquely sourced directly from real-world agricultural environments. This dataset ensures the authenticity and relevance of the data, reflecting the actual conditions faced by farmers. Leafsnap serves as a foundation for training advanced algorithmic models designed to identify patterns and symptoms indicative of various leaf diseases. The proposed system leverages a combination of cutting-edge AI and ML techniques, including convolutional neural networks (CNN), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost) and logistic regression (LR). By integrating these advanced computational techniques into agricultural practices, the system aims to provide farmers with an efficient, reliable, and scalable solution for disease management. The ultimate goal is to foster agricultural sustainability by minimizing crop losses due to disease, thereby bolstering food security and supporting the livelihoods of farmers across India.
Evaluating tumor heterogeneity in oncology with genomic-imaging and cloud-based genomic algorithms Gurulakshmanan, Gurumoorthi; Amarnath, Raveendra N.; Lebaka, Sivaprasad; Reddy, Munnangi Koti; Mohankumar, Nagarajan; Muthumarilakshmi, Surulivelu; Srinivasan, Chelliah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2427-2435

Abstract

The goal of this initiative is to rethink how oncology is traditionally practiced by integrating novel approaches to genomic imaging with cloud-based genomic algorithms. The research intends to give a thorough knowledge of cancer biology by focusing on the decoding of tumor heterogeneity as its primary objective. It is possible to get a more nuanced understanding of the intricacy of tumors via the integration of high-resolution imaging tools and sophisticated genetic analysis. It is a pioneering use of cloud computing, which enables the quick analysis of large genomic information. The major goal is to decipher the complex genetic variants that are present inside tumors in order to direct the creation of individualized treatment strategies. This discovery marks a significant step forward, since it successfully bridges the gap between genetics and imaging. Diagnostic accuracy and treatment effectiveness have both been improved. This innovative technique permits real-time analysis, which in turn enables treatment tactics to be adjusted in a timely manner. It makes a significant contribution to the continuous development of oncological research as well as its translation into better clinical outcomes for cancer patients.
A novel one-dimensional chaotic map with improved sine map dynamics Htiti, Mohamed; Akharraz, Ismail; Ahaitouf, Abdelaziz
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2128-2137

Abstract

These days, keeping information safe from people who should not have access to it is very important. Chaos maps are a critical component of encryption and security systems. The classical one-dimensional maps, such as logistic, sine, and tent, have many weaknesses. For example, these classical maps may exhibit chaotic behavior within the narrow range of the rate variable between 0 and 1and the small interval's rate variable. In recent years, several researchers have tried to overcome these problems. In this paper, we propose a new one-dimensional chaotic map that improves the sine map. We introduce an additional parameter and modify the mathematical structure to enhance the chaotic behavior and expand the interval's rate variable. We evaluate the effectiveness of our map using specific tests, including fixed points and stability analysis, Lyapunov exponent analysis, diagram bifurcation, sensitivity to initial conditions, the cobweb diagram, sample entropy and the 0-1 test.

Page 592 of 631 | Total Record : 6301

 590   591   592   593   594 

Filter by Year

2011 2026


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