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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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An enhanced fletcher-reeves-like conjugate gradient methods for image restoration Hassan, Basim A.; Jabbar, Hawraz N.; Laylani, Yoksal A.; Rahman Moghrabi, Issam Abdul; Alissa, Ali Joma
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6268-6276

Abstract

Noise is an unavoidable aspect of modern camera technology, causing a decline in the overall visual quality of the images. Efforts are underway to diminish noise without compromising essential image features like edges, corners, and other intricate structures. Numerous techniques have already been suggested by many researchers for noise reduction, each with its unique set of benefits and drawbacks. Denoising images is a basic challenge in image processing. We describe a two-phase approach for removing impulse noise in this study. The adaptive median filter (AMF) for salt-and-pepper noise identifies noise candidates in the first phase. The second step minimizes an edge-preserving regularization function using a novel hybrid conjugate gradient approach. To generate the new improved search direction, the new algorithm takes advantage of two well-known successful conjugate gradient techniques. The descent property and global convergence are proven for the new methods. The obtained numerical results reveal that, when applied to image restoration, the new algorithms are superior to the classical fletcher reeves (FR) method in the same domain in terms of maintaining image quality and efficiency.
Image reconstruction through compressive sampling matching pursuit and curvelet transform Seetharamaswamy, Shashi Kiran; Veeranna, Suresh Kaggere
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6277-6284

Abstract

An interesting area of research is image reconstruction, which uses algorithms and techniques to transform a degraded image into a good one. The quality of the reconstructed image plays a vital role in the field of image processing. Compressive Sampling is an innovative and rapidly growing method for reconstructing signals. It is extensively used in image reconstruction. The literature uses a variety of matching pursuits for image reconstruction. In this paper, we propose a modified method named compressive sampling matching pursuit (CoSaMP) for image reconstruction that promises to sample sparse signals from far fewer observations than the signal’s dimension. The main advantage of CoSaMP is that it has an excellent theoretical guarantee for convergence. The proposed technique combines CoSaMP with curvelet transform for better reconstruction of image. Experiments are carried out to evaluate the proposed technique on different test images. The results indicate that qualitative and quantitative performance is better compared to existing methods.
An efficient hydro-crop growth prediction system for nutrient analysis using machine learning algorithm Chikkasiddaiah, Chandana; Govindaswamy, Parthasarathy; Srikantaswamy, Mallikarjunaswamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6681-6690

Abstract

The hydro nutrient management (HNM) for crop yield is effectively improved using proposed system. A hydro-crop growth prediction system (HCGPS) is designed using machine learning. The reconfigurable nutrients uptake crop yield prediction rate is enhanced. This proposed HCGPS is used to predict the crop yield by considering input parameters such as nutrient index (NI), electric conductivity limit (ECL), ion concentration factors (ICF) and dry weight of the crop and crop yield rate (CYR) to analyze the positive and negative correlation with crop growth. The proposed system is used to find correlation Index of input and output parameters to determine the prediction rate of crop yield. The proposed design improves smart prediction rate and efficiency of crop growth rate with optimal utilization of input variables. This proposed HCGPS is very helpful to achieve good quality yield with optimal utilization of input parameters.
A brief study on rice diseases recognition and image classification: fusion deep belief network and S-particle swarm optimization algorithm Jayaram, Miryabbelli; Kalpana, Gudikandhula; Borra, Subba Reddy; Bhavani, Battu Durga
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6302-6311

Abstract

In the regions of southern Andhra Pradesh, rice brown spot, rice blast, and rice sheath blight have emerged as the most prevalent diseases. The goal of this research is to increase the precision and effectiveness of disease diagnosis by proposing a framework for the automated recognition and classification of rice diseases. Therefore, this work proposes a hybrid approach with multiple stages. Initially, the region of interest (ROI) is extracted from the dataset and test images. Then, the multiple features are extracted, such as color-moment-based features, grey-level cooccurrence matrix (GLCM)-based texture, and shape features. Then, the S-particle swarm optimization (SPSO) model selects the best features from the extracted features. Moreover, the deep belief network (DBN) model trained by SPSO is based on optimal features, which classify the different types of rice diseases. The SPSO algorithm also optimized the losses generated in the DBN model. The suggested model achieves a hit rate of 94.85% and an accuracy of 97.48% with the 10-fold cross-validation approach. The traditional machine learning (ML) model is significantly less accurate than the area under the receiver operating characteristic curve (AUC), which has an accuracy of 97.48%.
Homogenous and interoperable signaling computer interlocking through IEC 61499 standard Abourahim, Ikram; Eleuldj, Mohsine; Amghar, Mustapha
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6228-6239

Abstract

The technological evolution of signaling systems has created a dependency from infrastructure managers to suppliers and industrials dominating the market. Indeed, for each deployed computer interlocking, the modification of field equipment is required to allow an adaptation with the new interlocking in terms of communication protocols and logical interface. In addition, to ensure safe traffic of trains, the communication of railway signaling data is necessary between interlockings. However, delayed deployments from one station to another make the establishment of communication channels costly and difficult, or even impossible, since each supplier keeps confidential its communication protocols and usually opts for interfacing based on wired logic. This paper presents our approach to a homogeneous architecture of interlocking meeting modularity requirements, interoperability, and logical interfacing between interlockings. This approach relies on a classification of internal functions of the computer interlocking, a distribution of the execution of those functions and making useful information available for interfaces between adjacent interlockings through the IEC 61499 standard coupled with service-oriented architecture (SOA).
An image steganography using improved hyper-chaotic Henon map and fractal Tromino Nagarajegowda, Shyla; Krishnan, Kalimuthu
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6836-6846

Abstract

Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixel’s image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively.
Encountering social engineering activities with a novel honeypot mechanism Abualhija, Mwaffaq; Al-Shaf’i, Nid’a; Turab, Nidal M.; Hussein, Abdelrahman
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7056-7064

Abstract

Communication and conducting businesses have eventually transformed to be performed through information and communication technology (ICT). While computer network security challenges have become increasingly significant, the world is facing a new era of crimes that can be conducted easily, quickly, and, on top of all, anonymously. Because system penetration is primarily dependent on human psychology and awareness, 80% of network cyberattacks use some form of social engineering tactics to deceive the target, exposing systems at risk, regardless of the security system's robustness. This study highlights the significance of technological solutions in making users more safe and secure. Throughout this paper, a novel approach to detecting and preventing social engineering attacks will be proposed, combining multiple security systems, and utilizing the concept of Honeypots to provide an automated prevention mechanism employing artificial intelligence (AI). This study aims to merge AI and honeypot with intrusion prevention system (IPS) to detect social engineering attacks, threaten the attacker, and restrict his session to keep users away from these manipulation tactics.
Thai culture image classification with transfer learning Rattaphun, Munlika; Songsri-in, Kritaphat
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6259-6267

Abstract

Classifying images of Thai culture is important for a variety of applications, such as tourism, education, and cultural preservation. However, building a Machine learning model from scratch to classify Thai cultural images can be challenging due to the limited availability of annotated data. In this study, we investigate the use of transfer learning for the task of image classification on a dataset of Thai cultural images. We utilize three popular convolutional neural network models, namely MobileNet, EfficientNet, and residual network (ResNet) as baseline pre-trained models. Their performances were evaluated when they were trained from random initialization, used as a feature extractor, and fully fine-tuned. The results showed that all three models performed better in terms of accuracy and training time when they were used as a feature extractor, with EfficientNet achieving the highest accuracy of 95.87% while maintaining the training time of 24 ms/iteration. To better understand the reasoning behind the predictions made by the models, we deployed the gradient-weighted class activation mapping (Grad-CAM) visualization technique to generate heatmaps that the models attend to when making predictions. Both our quantitative and qualitative experiments demonstrated that transfer learning is an effective approach to image classification on Thai cultural images.
Ontology engineering of automatic text processing methods Sadirmekova, Zhanna; Tussupov, Jamalbek; Murzakhmetov, Aslanbek; Zhidekulova, Gulkiz; Tungatarova, Aigul; Tulenbayev, Murat; Akhmetzhanova, Shynar; Altynbekova, Zhanar; Borankulova, Gauhar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6620-6628

Abstract

Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.
Capacitor current analysis of a three-level neutral point clamped converter under unbalanced loading conditions Thahab, Riyadh Toman; Al-Mhana, Tahani Hamodi; Thahab, Ahmed Toman
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6156-6173

Abstract

A neutral point clamped (NPC) converter is considered a forefront in industrial applications. Supplying a typically balanced stand-alone load is one of those applications. However, the loading may become unbalanced which can impact the capacitors’ current and voltage ripple. In this work, an approach is proposed that analyze the capacitor current under unbalanced loading. The proposed method is based on a combination of two-dimensional Fourier series and symmetrical components. Since, two-dimensional Fourier series sectorize the spectrum into harmonics that are either defined by multiples of, fundamental, carrier or both frequencies, the method derives the Fourier coefficients for each sector, corresponding to a sequence current. Therefore, based on the presented approach, each harmonic amplitude in the spectrum sector is defined by three Fourier coefficients reflecting zero, positive and negative sequence current. The capacitor current spectrum is obtained by vector summing sequence coefficients. The method is tested on unbalanced load conditions with (out) a ground path. Results verify feasibility of the proposed method in deriving Fourier coefficients of capacitor currents that accurately reflects the loading status of the NPC. This is vital for converter design in terms of proper sizing of DC capacitor and can help in avoiding components failure.

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