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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 4: August 2024" : 111 Documents clear
Secure aware software development life cycle on medical datasets by using firefly optimization and machine learning techniques Obulesu, Ooruchintala; Suneel, Sajja; Jangili, Sudhakar; Ledalla, Sukanya; Swetha, Ballepu; Borra, Subba Reddy
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.pp4195-4203

Abstract

The abstract highlights the critical need for securing sensitive medical data, emphasizing the challenges in the medical domain due to the confidentiality of patient, disease, doctor, and staff information. The proposed study introduces a novel approach using machine learning, specifically integrating the firefly optimization technique with the random forest algorithm, to classify medical data in a secure manner. The significance lies in addressing the security concerns associated with medical datasets, offering a solution that prioritizes confidentiality throughout the software development life cycle (SDLC). The proposed algorithm achieves an impressive accuracy of 96%, showcasing its efficacy in providing a robust and secure framework for the development of applications involving medical data. This research contributes to advancing the field of medical data security, offering a practical solution for safeguarding sensitive information in healthcare applications.
Building extraction from remote sensing imagery: advanced squeeze-and-excitation residual network based methodology Ait El Asri, Smail; Negabi, Ismail; El Adib, Samir; Raissouni, Naoufal
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.pp4531-4541

Abstract

Extracting buildings from remote sensing imagery (RSI) is an essential task in a wide range of applications, such as urban and monitoring. Deep learning has emerged as a powerful tool for this purpose, and in this research, we propose an advanced building extraction method based on SE-ResNet18 and SE-ResNet34 architectures. These models were selected through a rigorous comparative analysis of various deep learning models, including variations of residual networks (ResNet), squeeze-and-excitation residual networks (SE-ResNet), and visual geometry group (VGG), for their high performance in all metrics and their computational efficiency. Our proposed methodology outperformed all other models under consideration by a significant margin, demonstrating its robustness and efficiency. It achieved superior results with less computational effort and time, a testament to its potential as a powerful tool for semantic segmentation tasks in remote sensing applications. An extensive comparative evaluation involving a wide range of state-of-the-art works further validated our method’s effectiveness. Our method achieved an unparalleled intersection over union (IoU) score of 88.51%, indicative of its exceptional accuracy in identifying and segmenting buildings within the Wuhan University (WHU) building dataset. The overall performance of our method, which offers an excellent balance between high performance and computational efficiency, makes it a compelling choice for researchers and practitioners in the field.
Crypto-steganographic model using chaos and coding based in deoxyribonucleic acid López Torres, Edison Andrés; Alvarado-Nieto, Deicy; Amaya-Barrera, Isabel; Suárez Parra, César Augusto
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.pp4239-4247

Abstract

Given the increase of information circulating through public channels, it is essential to create robust schemes to ensure the security of such information. The results presented here were part of the research project entitled computer security models based on mathematical tools and artificial intelligence. An algorithm focused on the encryption of images carrying steganographed texts is proposed, using chaos, artificial vision and coding based in deoxyribonucleic acid (DNA). The process consists of steganographic and cryptographic steps. In the steganographic stage, a color image was taken, the combined Canny and Sobel filters were applied to achieve its dilated edges, using Chen's chaotic attractor, the positions of the edges were selected, to hide a text in binary ASCII code using the least significant bit technique. In the encryption stage, Chen's chaotic system was used to permute the stego-image and to create a chaotic image used in the diffusion process. These two images were divided into blocks represented in DNA coding, selecting the rule to apply through the three-dimensional Logistics system, and finally applying the XOR operation by layers, obtaining a single encrypted image. To validate the proposed model, safety and performance tests were applied, obtaining comparable indicators with some current scientific references.
Message steganography using separate locations and blocks Rasras, Rashad J.; Abu Sara, Mutaz Rasmi; Alqadi, Ziad
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.pp4055-4067

Abstract

A novel method of message steganography is introduced to solve the disadvantages of traditional least significant bit (LSB) based methods by dividing the covering-stego image into a secret number of blocks. A chaotic logistic map model was performed using the chaotic parameters and the number of image blocks for generating a chaotic key. This key was then sorted, and the locations of blocks 1 to 8 were used to select the required blocks to be used as covering-stego blocks. The introduced method simplifies the process of message bits hiding and extracting by adopting a batch method of bits hiding and extracting. A comparative analysis was conducted between the outcomes of proposed method and those of prevalent approaches to outline the enhancements in both speed and quality of message steganography.
A hybrid approach using convolutional neural networks and genetic algorithm to improve of sensing brain tumor prediction Ettakifi, Hamza; Tkatek, Said
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.pp4325-4335

Abstract

Brain tumor is the most constantly diagnosed cancer, and the opinion of the brain is veritably sensitive and complex, which is the subject of numerous studies and inquiries. In computer vision, deep literacy ways, similar as the convolutional neural network (CNN), are employed due to their bracket capabilities using learned point styles and their capability to work with complex images. still, their performance is largely dependent on the network structure and the named optimization system for tuning the network parameters. In this paper, we present new yet effective styles for training convolutional neural networks. The maturity of current state-of-the-art literacy styles for convolutional neural networks are grounded on grade descent. In discrepancy to traditional convolutional neural network training styles, we propose an enhancement by incorporating the inheritable algorithm for brain tumor prediction. Our work involves designing a convolutional neural network model to grease the bracket process, training the model using different optimizers (Adam and the inheritable algorithm), and assessing the model through colorful trials on the brain magnetic resonance imaging (MRI) dataset. We demonstrate that the convolutional neural network model trained using the inheritable algorithm performs as well as the Adam optimizer, achieving a bracket delicacy of 99.5.
Design of a perturb and observe and neural network algorithms-based maximum power point tracking for a grid-connected photovoltaic system Salem, Ahmed Ali; Ismail, Mohamed Mahmoud; Zedan, Honey Ahmed; Elnaghi, Basem Elhady
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.pp3674-3687

Abstract

Integrating photovoltaic systems (PV) into the grid has garnered significant attention due to increasing interest in renewable energy sources. Maximizing the PV systems output power is crucial for improving energy efficiency and reducing operating costs. This paper presents a comparative analysis of two different techniques of maximum power point tracking (MPPT): perturb and observe (P&O) and artificial neural network (ANN) MPPT, focusing on their application in grid-connected PV systems. The paper evaluates their performance under various operating conditions, including changes in irradiance and temperature, that are discussed in three cases. The comparative analysis includes metrics such as voltage regulation and powerloss. MATLAB Simulink is utilized to implement P&O and ANN MPPT methods, which include a PV cell connected to an MPPT-controlled boost converter. The simulation demonstrates the power loss of the PV model as well as the voltage regulation in the three cases for the two methods. The results obtained in simulation and implementations show that the ANN method outperforms the P&O in the three cases discussed in terms of powerloss, voltage regulation, and efficiency. The results also show that the change in output power from PV is noticeable when compared to changes in radiation, while the change is slight when temperatures change.
Socio-technical factors influencing big data analytics adoption in healthcare Wolseley, Nik Nurdini; Salahuddin, Lizawati; Mohd Aboobaider, Burhanuddin; Raja Ikram, Raja Rina; Hashim, Ummi Rabaah; Abdul Rahim, Fiza
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.pp4745-4758

Abstract

The purpose of this study is to determine the key socio-technical factors influencing big data analytics adoption in healthcare services. A systematic literature review was conducted using peer-reviewed scholarly publications spanning from 2013 to 2023 to illuminate the influencing factors. Twelve papers focused on the factors influencing big data analytics (BDA) adoption in healthcare services were included for review. The factors were divided into four major groups namely i) person, ii) technology, iii) organization, and iv) environment. Analytical skills define a person, whereas technology is characterized by system quality and information quality. Organization support, organization resources, training, data governance, and evidence-based decision-making are all associated with the organization. Finally, government regulations are allocated to the environment. This review presents evidence of the socio-technical factors that influence big data analytics adoption in healthcare services. The findings from this review recommend future big data analytics adoption in healthcare services to carefully evaluate the factors identified in this study.
Accelerating real-time deterministic discovery through single instruction multiple data graphical processor unit for executing distributed event logs Fauzan, Hermawan; Sarno, Riyanarto; Saikhu, Ahmad
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.pp4214-4227

Abstract

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery.
Enhancing efficiency in capacitive power transfer: exploring gap distance and load robustness Yusop, Yusmarnita; Cheok, Yan Qi; Saat, Shakir; Husin, Huzaimah
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.pp3649-3662

Abstract

In this paper, the capacitive power transfer (CPT) technology is used as an alternative to inductive power transfer (IPT). CPT relies on electric fields that are not sensitive to the presence of any metals, utilizes metal electrodes for power transfer, and is less bulky compared to IPT. The proposed CPT system utilizes a Class-E resonant inverter with a double-sided inductor-capacitor (LC) matching circuit which operates at an optimum load, with a duty cycle, D=0.5 to gain an output power, W and efficiency, η=84.6%. The proposed CPT system enhances the system’s efficiency as compared to the past research while preserving the zero-voltage switching (ZVS) condition within a wider load range from 50 Ω to 1,316 Ω. This paper also shows that the proposed CPT system is less sensitive to load and coupling variations. Finally, the rate of power dissipated at varied load resistances, has been derived successfully to determine the sensitivity level of the proposed CPT systems toward load variations. These equations are then validated by plotting the efficiency graphs based on load and coupling variations.
Analysis of an operational trans-conductance amplifier with positive feedback Park, Sung Sik
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.pp3820-3829

Abstract

In this paper, we present an analysis of an operational trans-conductance amplifier (OTA) with two positive feedback. The direct current (DC) transfer function is obtained by analyzing the OTA using the drain current in the weak inversion region. The analysis results were verified through comparison with SPICE simulations, and the characteristics of the DC transfer function analysis for the OTA design are well matched with the simulation results. The designed OTA dissipates a low power of 41.4 nW, and features the slew rate is improved by 436% compared to a conventional OTA without two positive feedback. Additionally, a DC gain and a unity-gain bandwidth is improved by 36 dB and 6.7 times, respectively. The OTAs are designed for the 0.18 μm complementary metal–oxide–semiconductor (CMOS) process.

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