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
 560   561   562   563   564 
A review of object detection approaches for traffic surveillance systems El-Alami, Ayoub; Nadir, Younes; Mansouri, Khalifa
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.pp5221-5233

Abstract

With the decreasing cost of traffic cameras and rapid advancement in computer vision and artificial intelligence, developing robust traffic surveillance systems has become more feasible and practical. These systems can easily outperform traditional human monitoring systems, as they can collect and analyze traffic data coming from multiple cameras efficiently. A good understanding of this data allows the detection easily road anomalies in real time and in an autonomous way. Therefore, an intelligent traffic system typically consists of three components: object detection, object tracking, and behavior analysis components. In this paper, we present a review of some of the well-known object detection techniques used in traffic video surveillance. The review begins with a brief introduction to the history of object detection and the evolution of its techniques. Then we review separately the two main approaches of detection, which are traditional and deep learning approaches of detection. Finally, an experimental analysis has been conducted to evaluate and compare the performance of some of the recent relevant detection methods in terms of speed and precision, in detecting vehicles in a traffic scenario.
Energy demand forecasting of remote areas using linear regression and inverse matrix analysis Sarker, Md. Tanjil; Jaber Alam, Mohammed; Ramasamy, Gobbi; Nasir Uddin, Mohammed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp129-139

Abstract

Efficient energy demand forecasting is pivotal for addressing energy challenges in remote areas of Bangladesh, where reliable access to energy resources remains a concern. This study proposes an innovative approach that combines linear regression analysis (LRA) and inverse matrix calculation (IMC) to forecast energy demand accurately in these underserved regions. By leveraging historical energy consumption data and pertinent predictors, such as meteorological conditions, population dynamics, economic indicators, and seasonal patterns, the model provides reliable forecasts. The application of the proposed methodology is demonstrated through a case study focused on remote regions of Bangladesh. The results showcase the approach's effectiveness in capturing the intricate dynamics of energy demand and its potential to inform sustainable energy management strategies in these remote areas. This research contributes to the advancement of energy planning and resource allocation in regions facing energy scarcity, fostering a path towards improved energy efficiency and development. These techniques can be applied to estimate short-term electricity demand for any rural or isolated region worldwide.
An overview of hand gesture recognition based on computer vision Tasfia, Rifa; Izzah Mohd Yusoh, Zeratul; Binte Habib, Adria; Mohaimen, Tousif
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4636-4645

Abstract

Hand gesture recognition emerges as one of the foremost sectors which has gone through several developments within pattern recognition. Numerous studies and research endeavors have explored methodologies grounded in computer vision within this domain. Despite extensive research endeavors, there is still a need for a more thorough evaluation of the efficiency of various methods in different environments along with the challenges encountered during the application of these methods. The focal point of this paper is the comparison of different research in the domain of vision-based hand gesture recognition. The objective is to find out the most prominent methods by reviewing efficiency. Concurrently, the paper delves into presenting potential solutions for challenges faced in different research. A comparative analysis particularly centered around traditional methods and convolutional neural networks like random forest, long short-term memory (LSTM), heatmap, and you only look once (YOLO). considering their efficacy. Where convolutional neural network-based algorithms performed best for recognizing the gestures and gave effective solutions for the challenges faced by the researchers. In essence, the findings of this review paper aim to contribute to future implementations and the discovery of more efficient approaches in the gesture recognition sector.
A novel and optimized computational framework for energy efficient data dissemination in wireless sensor network Mathew Kavanathottahil, Deepa; Anita Jones Mary Pushpa, Thomas
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3045-3054

Abstract

Wireless sensor network (WSN) is an integral part of internet-of-things (IoT), where a large scale of data transmission and various complex services are identified to be delivered. In order to facilitate these services, energy efficiency is one critical demand for resource-constraint sensor nodes. Carrier sense multiple access (CSMA) has been considered for effective traffic management for high-end data delivery services. A review of existing literature on CSMA-based schemes shows that it has not yet achieved an optimal case of energy efficiency. Hence, the proposed study presents a novel computational framework in order to address this research gap. The prime contribution of the proposed study is towards presenting an optimal computational model for maximized fairness in data dissemination services in WSN especially focusing on energy efficiency. The presented study model also contributes towards optimizing route buffer and buffer power, which facilitates towards availability of energy-efficient path information. The study also introduces a mobile auxiliary node that aggregates the data from sensor nodes and delivers it to the sink node considering dynamic location updates for seamless transmission. Scripted in MATLAB, the proposed scheme exhibited 70% energy saving compared to conventional schemes of CSMA in WSN.
Implementation of innovative approach for detecting brain tumors in magnetic resonance imaging using NeuroFusionNet model Kotte, Arpitha; Ahmad, Syed Shabbeer
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6628-6641

Abstract

The goal of this study is to create a strong system that can quickly detect and precisely classify brain tumors, which is essential for improving treatment results. The study uses advanced image processing techniques and the NeuroFusionNet deep learning model to accurately segment data from the brain tumor segmentation (BRATS) dataset, presenting a detailed methodology. The objective is to create a high-precision system that surpasses current methods in key performance metrics. NeuroFusionNet demonstrates outstanding accuracy of 99.21%, as well as impressive specificity and sensitivity rates of 99.17% and 99.383%, respectively, exceeding previous benchmarks. The findings emphasize the system's ability to greatly enhance the diagnostic process, enabling early intervention and ultimately improving patient care in brain tumor detection and classification.
Classification of pathologies on digital chest radiographs using machine learning methods Aitimov, Murat; Shekerbek, Ainur; Pestunov, Igor; Bakanov, Galitdin; Ostayeva, Aiymkhan; Ziyatbekova, Gulzat; Mediyeva, Saule; Omarova, Gulmira
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1899-1905

Abstract

This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using pre-processed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
Optimal control design of the COVID-19 model based on Lyapunov function and genetic algorithm Sa'adah, Aminatus; Saragih, Roberd; Handayani, Dewi
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.pp5117-5130

Abstract

Millions of people died worldwide as a result of the coronavirus disease 2019 (COVID-19) pandemic that started in early 2020. Examining the COVID-19 susceptible-exposed-infected-recovery (SEIR) mathematical model is one approach to developing the best control scenario for this disease. The study utilized two control variables, vaccination, and therapy, to construct a control function that relied on the quadratic Lyapunov function. The control objective was to lower the number of COVID-19 infections while maintaining system stability. A genetic algorithm (GA) is used as a novel method to estimate controller parameter value to replace the previously used parameter tuning procedure. Then, a numerical simulation was carried out implementing three control scenarios, namely vaccination control only, treatment control only, and vaccination and treatment control simultaneously. Based on the results, scenario 3 (vaccination and treatment simultaneously) showed the most significant decrease: the average decrease in the exposed human population was 98.29%, and the infected human population was 98.18%.
Compact 3D monolithic microwave integrated circuit bandpass filter based on meander resonator for 5G millimeter-wave Sinulingga, Emerson Pascawira; Nasution, Abdul Risyal
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp157-165

Abstract

Bandpass filters for millimeter-wave band applications are typically designed using resonators. However, the design of a multilayer coplanar waveguide (CPW) monolithic microwave integrated circuit (MMIC) bandpass filter for 5G millimeter-wave band, n257 with operating frequencies from 26.5 to 29.5 GHz is still not available. Therefore, in this work, a compact bandpass filter for 5G millimeter-wave application was designed with multilayer CPW MMIC bandpass filter based on a meander resonator. The meander resonator of the bandpass filter was designed using low-loss multilayer CPW lines. In designing the bandpass filter, the resonator length and perturbation was utilized to optimize the resonance and bandwidth, and meander resonator was used to miniaturize the bandpass filter. As result, a compact bandpass filter with size of 0.75×0.75 mm2 for 5G millimeter-wave band n257 was achieved. It has bandwidth of 3 GHz, an insertion loss of -2.87 dB and a return loss of -11.1 dB at frequency 28 GHz.
Deep learning based multi disease classification of plant leaves using light weight residual architecture Sadhasivam, Muniyandi; Geetha, Manoharan Kalaiselvi; Maria Britto, James Gladson
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4646-4654

Abstract

Plant diseases can severely impact crop yields, posing a major risk to worldwide food stability. Prompt and precise identification of these diseases is crucial for early intervention and efficient crop administration. This paper introduces an innovative method for detecting plant leaf diseases using residual networks (ResNets) and the PlantVillage dataset. To develop light weight residual (LWR) architecture, five convolutional layers are interleaved with five max-pooling layers, making up the architecture of ten layers. The number of filters in the convolutional layers is gradually increased from 32 to 64 and up to 512 with a 3×3 kernel. A fully connected layer is the last layer of the network which provides the classification of leaf diseases The LWR architecture is trained and evaluated using the PlantVillage dataset, a broad collection of annotated images. This dataset serves as the basis for the system. The findings of the experiments provide evidence that the suggested system has higher accuracy, sensitivity, and specificity measures. The use of residual networks in LWR architecture improves the capability of the model to acquire complicated representations, which in turn enables a more precise differentiation between healthy and unhealthy plant leaves.
Deep learning based Arabic short answer grading in serious games Soulimani, Younes Alaoui; El Achaak, Lotfi; Bouhorma, Mohammed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp841-853

Abstract

Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research.

Page 562 of 631 | Total Record : 6301

 560   561   562   563   564 

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