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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
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Search results for , issue "Vol 14, No 3: June 2024" : 111 Documents clear
Framework for multiple person identification using YOLOv8 detector: a transfer learning approach Jayaram, Dileep; Vedagiri, Supriya; Ramachandra, Manjunath
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.pp2790-2802

Abstract

Video surveillance extensively uses person detection and tracking technology based on video. The majority of person detection and classification techniques currently in use encounter challenges in video sequences brought on by occlusion, ambient lighting, and variations in human facial position. This paper proposed an effective person identification and classification system based on deep learning, which comprises a you only look once at version 8 (YOLOv8) detection and classification model, to classify human faces in video sequences accurately. This work proposes a new staff-detection and classification (S-DEC) dataset for comprehensive performance evaluation. visual tracker benchmark (VTB) standard database is used for performance comparison with the proposed S-DEC dataset. The proposed technique achieved 98.67% precision accuracy. For the S-DEC dataset, the system gave 94.67% accuracy in identifying facial images from a video sequence of 38 people addressing the pose variation occlusion challenge. Earlier methods used to provide approximately 85% to 90% results taking more execution time. Many existing techniques were successful in detecting people only-identification of the detected person has been done in limited papers. The proposed method uses the cross-stage partial connections (CSPDarknet53) model, integrated with YOLOv8, to achieve faster results. The proposed framework took 35 minutes to train a deep learning model. A testing time of 2 minutes ensured that the proposed framework outplayed other existing methodologies and successfully identified extra information about the detected person.
Overbounding Ifree errors based on bayesian Gaussian mixture model for ground-based augmentation system Banu, Sheher; Shanavas, Hameem
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.pp2834-2842

Abstract

A dual-frequency measurement is employed in conjunction with an innovative Ifree filtering technique for mitigating the primary sources of Ifree influence on ground-based augmentation systems (GBAS) to safeguard the reliability of GBAS. The protective level achieved through the conventional Gaussian overbounding approach that are considered as much conventional technique. This adherence to tradition results in decreased reliability and a higher likelihood of false alarms. In contrast, the utilization of the Ifree algorithm contributes to reducing errors associated with dual-frequency measurements. This paper proposes the overbounding process according to Bayesian Gaussian mixture model (GMM) for maintaining Ifree-based GBAS range error. The Bayesian GMM is utilized for single-frequency model errors to examine the ambiguity estimations. The Monte Carlo (MC) simulation is established for defining estimated GMM assurance level accuracy which is attained through the general estimation method. Then, the last Bayesian GMM which is utilized for overbounding Ifree error distribution is investigated. According to the property of convolution invariance, the vertical protection in position field is determined without presenting difficult numerical calculations.
Study of the characteristics of broadband matching antennas for fifth-generation mobile communications based on new composite materials Nakisbekova, Balausa; Yerzhan, Assel; Boykachev, Pavel; Manbetova, Zhanat; Imankul, Manat; Shener, Anar; Yermekbaev, Muratbek; Dunayev, Pavel
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.pp2885-2895

Abstract

The presented research aims to analyze in detail the characteristics of broadband matching antennas specifically designed for 5G mobile communications applications, with an emphasis on innovative composite materials. The study focuses on a compact planar loop antenna designed for use on smartphones, covering the LTE/WWAN frequency bands 824 to 960 MHz, 1,710 to 2,690 MHz, and 3,300 to 3,600 MHz for full coverage of modern 5G networks. Experimental and numerical methods are used to broadly analyze the frequency range associated with 5G networks. The features of the use of composite materials in the implementation of antenna devices in 5G technologies are noted. A broadband matching circuit (BMC) with elements with lumped parameters and a reduced sensitivity invariant has been synthesized. A 3D model of the adaptive selective surface controller (SSC) was developed using CST Studio. The study results highlight the benefits of new composite materials in improving the performance of 5G antennas. This research makes a significant contribution to the development of 5G technologies by optimizing antenna design for efficient data transmission in modern mobile networks and can be a valuable resource for engineers and designers working in this field.
Classification of tea leaf disease using convolutional neural network approach Hairah, Ummul; Septiarini, Anindita; Puspitasari, Novianti; Tejawati, Andi; Hamdani, Hamdani; Eka Priyatna, Surya
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.pp3287-3294

Abstract

Leaf diseases on tea plants affect the quality of tea. This issue must be overcome since preparing tea drinks requires high-quality tea leaves. Various automatic models for identifying disease in tea leaves have been developed; however, their performance is typically low since the extracted features are not selective enough. This work presents a classification model for tea leaf disease that distinguishes six leaf classes: algal spot, brown, blight, grey blight, helopeltis, red spot, and healthy. Deep learning using a convolutional neural network (CNN) builds an effective model for detecting tea leaf illness. The Kaggle public dataset contains 5,980 tea leaf images on a white background. Pre-processing was performed to reduce computing time, which involved shrinking and normalizing the image prior to augmentation. Augmentation techniques included rotation, shear, flip horizontal, and flip vertical. The CNN model was used to classify tea leaf disease using the MobileNetV2 backbone, Adam optimizer, and rectified linear unit (ReLU) activation function with 224×224 input data. The proposed model attained the highest performance, as evidenced by the accuracy value 0.9455.
A review on features and methods of potential fishing zone Ya’acob, Norsuzila; Nik Dzulkefli, Nik Nur Shaadah; Abdul Aziz, Mohd Azri; Yusof, Azita Laily; Umar, Roslan
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.pp2508-2521

Abstract

This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Local Fourier features for handwriting digit images classification Alain Bernard, Djimeli-Tsajio; Thierry, Noulamo; Jean-Pierre, Lienou T.; Daniel, Tchiotsop; Nagabhushan, Panduranga
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.pp2592-2601

Abstract

Multiple choice questions (MCQ) are effective in normative assessment and offline testing is still relevant due to the lack of efficient mass infrastructures and maintenance. For the automatic correction of MCQ paper form and reporting of the grade, it is generally necessary to read and recognize a handwriting digit in a box. This paper focuses on local feature extraction in the frequency domain using Fourier transform. The pre-process begins with the extraction of the fields from the entity map, followed by the application of 2D fast Fourier transform (2DFFT) and the reduction of computed coefficients to obtain the corresponding final local characteristic in the representation. The experimental results of the Modified National Institute of Standards and Technology (MNIST) handwriting digits dataset show that the local characteristics extracted in the frequency domain used as input to a support vector machine (SVM) classifier are efficient in terms of 99.51% accuracy. The proposed system successfully helped in the reporting of all the marks for seven subjects in a class of 98 students during the automatic correction of the MCQ exam papers.
A multimodal machine learning approach to generate news articles from geo-tagged images Gotmare, Abhay; Thite, Gandharva; Bewoor, Laxmi
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.pp3434-3442

Abstract

Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by leveraging cutting-edge developments in machine learning, image captioning, and advanced text generation technologies. Employing a multimodal approach that integrates machine learning and transformer algorithms, such as visual geometry group network16 (VGGNet16), convolutional neural network (CNN) and a long short-term memory (LSTM) based system, the algorithm initiates by extracting the location from exchangeable image file format (Exif) data from the image. The features are extracted from the image and corresponding news headline is generated. The headlines are used for generating a comprehensive article with contemporary large language model (LLM). Further, the algorithm generates the news article big-science large open-science open-access multilingual language model (BLOOM). The algorithm was tested on real time photographs as well as images from the internet. In both the cases the news articles generated were validated with ROUGE and BULE score. The proposed work is found to be successful attempt in journalism field.
Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset Rhouas, Sara; El Attaoui, Anas; El Hami, Norelislam
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.pp3177-3186

Abstract

In the ever-evolving landscape of global commerce, marked by the convergence of digital transformation and borderless markets, this research addresses the intricate challenges of currency exchange and risk management. Leveraging Bayesian optimization, the study fine-tunes the random forest algorithm using the extensive Klarna E-commerce dataset. Through systematic analysis, the research uncovers insights into managing currency prediction amid dynamic global markets. Emphasizing the role of Bayesian optimization parameters, the study reveals nuanced trade-offs in model performance. Notably, the optimal simulation, conducted with 14 iterations, 1 job, and a random state set to 684, exhibits a standout performance, showcasing a negative mean squared error (MSE) of approximately -0.9891 and an accuracy rate of 74.63%. The primary objective is to assess the impact of Bayesian optimization in enhancing the random forest algorithm's predictive capabilities, particularly in currency prediction within international e-commerce. These findings offer refined strategies for businesses navigating the intricate landscape of global finance, empowering decision-making through a comprehensive understanding of data, algorithms, and challenges in international commerce.
Internet of things-based digital scale to detect stunting symptoms in babies under two years of age Hutabarat, Daniel Patricko; Wijaya, Willis; Wijaya, Wilbert Devin
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.pp3467-3474

Abstract

Given the ongoing global challenge of stunting, characterized primarily by chronic underweight in infants under two years of age, a new approach leveraging digital scale and the internet of things (IoT) has been developed. This innovative system was designed to facilitate the early detection and continual monitoring of stunting symptoms caused by malnourishment. Key features include an IoT-enabled digital scale for precise weight measurement, a robust cloud platform for reliable data storage and comprehensive analysis, and an easy-to-use mobile app for user engagement. This system demonstrates its potential to simplify tracking fluctuations in baby weight and development progress related to stunting over time. Early trials demonstrated an impressive accuracy rate of 99.4% in body weight measurements and provided excellent conclusions in determining the body weight status of the infants. Overall, this IoT-based solution catalyzes the improvement of stunting detection methodologies and early intervention strategies, thus promising a better solution and a significant positive impact on global child health.
Graph neural network based human detection in videos during occlusion environments Sriram, Kusuma; Purushotham, Kiran
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.pp2616-2624

Abstract

One of the most difficult perceptual problems for many applications is accurately recognizing the human object in a variety of circumstances. This can be difficult due to obstructions, weather, complex backdrops, cast shadows, and occlusions. Occlusion is a challenging open problem where a detector can only perceive a portion of the target human because of obstacles in the surrounding. In this research, an experimental investigation was conducted using the multi object tracking (MOT17) datasets to construct a graph neural network-based solution for the detection of humans in videos while considering the possibility of occlusion. Graph neural network (GNN) is used for the construction of neural solver model for detecting human object in occlusion scenario. The results obtained shows that this proposed method offers a considerable improvement in efficiency in comparison to the ways that have been used in the past. The values obtained for the standard performance metrics are higher than the state-of-the-art methods.

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