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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 6,301 Documents
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Ad hoc wireless network implementing BEE-LEACH Kumar, Arun; Chakravarthy, Sumit; Gaur, Nishant; Nanthaamornphong, Aziz
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.pp2945-2954

Abstract

Adaptations have been key to the development and advancement of the low energy adaptive clustering hierarchy (LEACH) protocol. Presented is an alteration to the traditional LEACH, low energy adaptive clustering hierarchy, algorithm. This algorithm focuses on the battery life optimization of sensors within a wireless sensor network (WSN). These sensors will be grouped into clusters with the aim of maximizing the battery life of the overall system by sorting each sensor by residual energy and assigning the highest residual energy the role of cluster head. The protocol will then assign sensors to cluster heads based on distance relative to the head. This algorithm achieves the goal of extending battery life and offers itself as a promising alternative to standard LEACH algorithms. The algorithm is tested by comparing sensor battery life, total sensors communicating at a given time, and sensors with residual energy. This paper addresses the strengths and vulnerabilities of the algorithm, as well as proposed work for further implementation for the following groups looking to create their own LEACH protocol.
A fusion of cross-shaped window attention block and enhanced 3D U-Net for brain tumor segmentation Polaki, Ramya; Rangarajan, Prasanna Kumar; Pallavi, Gundala; Rajasekhar, Elakkiya; Altalbe, Ali
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.pp7103-7115

Abstract

Brain tumor diagnosis and treatment are primarily reliant on medical imaging, necessitating precise segmentation methodologies for practical clinical solutions. Tumor boundaries are difficult to consistently identify, even with breakthroughs in deep learning. To address this challenge, we propose a novel approach that combines an upgraded 3D U-Net architecture for brain tumor segmentation with cross-shaped window attention (CSWA-U-Net). Current segmentation techniques have limitations, particularly in capturing amorphous tumor shapes and fuzzy boundaries. Our strategy aims to overcome these constraints by combining the complementary capabilities of the expanded 3D U-Net, which is efficient at managing volumetric data and maintaining spatial features, with the cross-shaped window attention, which is well-known for capturing long-range relationships and contextual information. We evaluate our method's efficacy using a variety of performance measures, including specificity, sensitivity, and the Dice score. Our results demonstrate increased performance, with Dice scores of 94.7% for the whole tumor, 93.4% for the enhanced tumor region, and 90.5% for the tumor core. Furthermore, our technique has high sensitivity and specificity, highlighting its potential for improving medical imaging analysis.
Optimized parameter extraction techniques for enhanced performance evaluation of organic solar cells Uvais, Mohd; Ansari, Asif Jamil; Asim, Mohammed; Manzar, Mohammad Saood
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.pp1263-1273

Abstract

The global energy landscape is in the midst of a transformative shift, compelled by the urgent need to reduce our reliance on fossil fuels and embrace eco-friendly alternatives. Organic photovoltaics (OPVs) have emerged as a promising alternative, offering the distinct advantage of performing well in low-light conditions, including indoor environments. Extensive research and development efforts are dedicated to realizing the full potential of OPVs as adaptable, cost-effective, and environmentally friendly solar energy solutions. This paper conducts a thorough examination of the intricate characterization of organic solar cells, with a specific emphasis on crucial parameters like power conversion efficiency, open-circuit voltage, and fill factor. The study utilizes a single diode model to simulate these cells' behavior, employing a meticulous process for parameter extraction. This method leverages Origin software and Python programming, incorporating open-source packages to ensure robust validation. This systematic and rigorous approach significantly enhances our comprehension of OPVs and plays a substantial role in optimizing their performance. In essence, this research represents a significant step forward in advancing sustainable energy technologies, laying a foundation for a greener and more environmentally conscious future.
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.
Enhancing cryptographic protection, authentication, and authorization in cellular networks: a comprehensive research study Moldamurat, Khuralay; Seitkulov, Yerzhan; Atanov, Sabyrzhan; Bakyt, Makhabbat; Yergaliyeva, Banu
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.pp479-487

Abstract

This research article provides an extensive analysis of novel methods of cryptographic protection as well as advancements in authentication and authorization techniques within cellular networks. The aim is to explore recent literature and identify effective authentication and authorization methods, including high-speed data encryption. The significance of this study lies in the growing need for enhanced data security in scientific research. Therefore, the focus is on identifying suitable authentication and authorization schemes, including blockchain-based approaches for distributed mobile cloud computing. The research methodology includes observation, comparison, and abstraction, allowing for a comprehensive examination of advanced encryption schemes and algorithms. Topics covered in this article include multi-factor authentication, continuous authentication, identity-based cryptography for vehicle-to-vehicle (V2V) communication, secure blockchain-based authentication for fog computing, internet of things (IoT) device mutual authentication, authentication for wireless sensor networks based on blockchain, new secure authentication schemes for standard wireless telecommunications networks, and the security aspects of 4G and 5G cellular networks. Additionally, in the paper a differentiated authentication mechanism for heterogeneous 6G networks blockchain-based is discussed. The findings presented in this article hold practical value for organizations involved in scientific research and information security, particularly in encryption and protection of sensitive data.
Internet behavioral models for improving internet quality of service or user profiling: a systematic literature review Lei, Zhang; Kamal Bashah, Nor Shahniza
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.pp4352-4364

Abstract

Internet behavior models have found applications across diverse domains, notably in internet addiction, customer satisfaction analysis, user purchasing behavior prediction, and optimizing internet of things (IoT) sensor performance. However, a notable gap exists in exploring these models in enhancing internet quality of service (QoS), specifically in campus settings, intricately linked to the nuances of students' online behavior. This study elucidates the strategic utilization of internet behavioral models for augmenting internet QoS and facilitating user behavior analysis. Creating datasets grounded in internet users' access behavior represents a pivotal phase, with explicit, implicit, and mixed methods emerging as the prevailing approaches. In this comprehensive literature review, we systematically scrutinized the methods, techniques, and inherent characteristics of constructing internet behavior models according to a systematic literature review process. The qualitative findings extracted from the systematic review encapsulated 1,046 articles, meticulously classified according to predefined inclusion and exclusion criteria. Subsequently, 35 articles were judiciously selected for in-depth analysis. This study culminated in identifying the most pertinent methodologies and salient features pivotal to construct robust internet behavior model for improving internet QoS and user experience.
Decoding sarcasm: unveiling nuances in newspaper headlines D, Suma; M, Raviraja Holla; M, Darshan Holla
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.pp3011-3020

Abstract

This study navigates the intricate landscape of sarcasm detection within the condensed confines of newspaper titles, addressing the nuanced challenge of decoding layered meanings. Leveraging natural language processing (NLP) techniques, we explore the efficacy of various machine learning models—linear regression, support vector machines (SVM), random forest, na¨ıve Bayes multinomial, and gaussian na¨ıve Bayes—tailored for sarcasm detection. Our investigation aims to provide insights into sarcasm within the succinct framework of newspaper titles, offering a comparative analysis of the selected models. We highlight the varied strengths and weaknesses of these models. Random forest exhibits superior performance, achieving a remarkable 94% accuracy in accurately identifying sarcasm in text. It is closely trailed by SVM with 90% accuracy and logistic regression with 83% accuracy.
Optimal control strategies based on extended Kalman filter in mathematical models of COVID-19 Suhika, Dewi; Saragih, Roberd; Handayani, Dewi; Apri, Mochamad
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.pp6300-6312

Abstract

The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is an extremely contagious variant that has garnered global attention due to its potential for rapid spread and its impact on the effectiveness of vaccines and non-pharmacological measures. In this paper, we investigate mathematical models involving vaccinated individuals and control functions to analyze how the spread of coronavirus disease 2019 (COVID-19) infection evolves over time. In the process of constructing a mathematical model for COVID-19, there are many parameters whose values are not yet known with certainty. Therefore, the extended Kalman filter method is used as a tool to estimate these parameters in an effort to better understand the dynamics of the spread and evolution of this disease. This method helps align the mathematical model with existing empirical data, allowing us to make more accurate predictions about the course of the COVID-19 pandemic and plan more precise actions to address the situation. Furthermore, an optimal control design is applied to reduce the number of infected individuals by implementing seven strategies involving a combination of health education, vaccination, and isolation controls. The simulation results we conducted indicate that the use of optimal control strategies can lead to a significant decrease in the number of individuals infected with COVID-19.
Phase delay through slot-line beam switching microstrip patch array antenna design for sub-6 GHz 5G band applications Das, Debprosad; Hossain, Md. Farhad; Hossain, Md. Azad; Rahman, Muhammad Asad; Hossain, Md. Motahar; Hossam-E-Haider, Md.
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.pp1625-1633

Abstract

Two, four, eight, and sixteen-element patch array antennas for beam switching are presented in this study. For a 1×2 array, an aperture-coupled feeding mechanism is used to feed patches while a slot line on the ground plane provides the phase delay between antenna elements. The 1×2 array is used to create the 2×2, 4×2, and 8×2 arrays, and an equal power divider provides the signal for each. For applications in the 5G sub-6 GHz frequency spectrum, the antennas are modeled. With -37.14 dB, -17.85 dB, -21.51 dB, and -26.03 dB return loss for two, four, eight, and sixteen-element array antennas respectively the simulation demonstrates that the antennas are properly matched at the resonant frequency. The antennas can switch its radiated beam to ±24o, ±24o, ±28o, and ±26o with gains of 8.97 dBi, 11.19 dBi, 13.23 dBi, and 16.24 dBi, respectively at the resonance frequency. The directivity of the proposed antenna is found to be 9.17 dBi, 11.20 dBi, 13.40 dBi, and 16.45 dBi respectively. The antennas are constructed with two 0.8 mm-thick Teflon substrate layers. The ground plane between the two substrate layers contains the aperture and the slot line that generates the phase delay.
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.

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