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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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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.
Intelligent intrusion detection through deep autoencoder and stacked long short-term memory Moukhafi, Mehdi; Tantaoui, Mouad; Chana, Idriss; Bouazi, 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.pp2908-2917

Abstract

In the realm of network intrusion detection, the escalating complexity and diversity of cyber threats necessitate innovative approaches to enhance detection accuracy. This study introduces an integrated solution leveraging deep learning techniques for improved intrusion detection. The proposed framework consists on a deep autoencoder for feature extraction, and a stacked long short-term memory (LSTM) network ensemble for classification. The deep autoencoder compresses raw network data, extracting salient features and mitigating noise. Subsequently, the stacked LSTM ensemble captures intricate temporal dependencies, correcting anomaly detection precision. Experiments conducted on the UNSW-NB15 dataset, and a benchmark in intrusion detection validate the effectiveness of the approach. The solution achieves an accuracy of 90.59%, with precision, recall, and F1-Score metrics reaching 90.65, 90.59, and 90.57, respectively. Notably, the framework outperforms standalone models and demonstrates the advantage of synergizing deep autoencoder-driven feature extraction with the stacked LSTM ensemble. Furthermore, a binary classification experiment attains an accuracy of about 90.59%, surpassing the multiclass classification and affirming the model's potential for binary threat identification. Comparative analyses highlight the pivotal role of feature extraction, while experimentation illustrates the enhancement achieved by incorporating the synergistic deep autoencoder-Stacked LSTM approach.
Wheelchair safety system using fuzzy logic controller to avoid obstruction Yulianto, Endro; Salwa, Umaimah Mitsalia Ummi; Triwiyanto, Triwiyanto; Indarto, Tri Bowo
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.pp7001-7012

Abstract

A wheelchair is the primary means of mobility for individuals unable to walk. This study aimed to develop a safety system for electric wheelchairs to help people with tetraplegia avoid obstructions. The main contribution of this study is the implementation of a sensor with a wider reflection angle and the adjustment of the wheelchair's speed based on the distance to the obstruction, eliminating the need for manual speed selection. The safety system utilizes LV-MaxSonarEZ1 ultrasonic sensors, which function as reflectance distance readers placed on the front, rear, right, and left sides of the wheelchair. The output from the sensors is input into an Arduino, which functions as the controller. The safety system employs adaptive speed control based on distance through a fuzzy logic controller. The wheelchair was tested with obstruction distances of 1, 1.8, 3, and 10 m. The wheelchair could stop at a distance of 34.06 cm for forward movement and 45.16 cm for reverse movement. The results of this study successfully demonstrate the creation of a safety system on a wheelchair using ultrasonic sensors to avoid obstructions and detect areas, with more adaptive speed control based on distance through a fuzzy logic controller.
Control energy management system for photovoltaic with bidirectional converter using deep neural network Widjonarko, Widjonarko; Utomo, Wahyu Mulyo; Omar, Saodah; Baskara, Fatah Ridha; Rosyadi, Marwan
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.pp1437-1447

Abstract

Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS.
Performance evaluation of machine learning algorithms for meat freshness assessment Arsalane, Assia; Klilou, Abdessamad; Barbri, Noureddine El
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.pp5858-5865

Abstract

In meat industry, a non-destructive evaluation and prediction of meat quality attributes is highly required. Artificial vision technology is a powerful and widely used tool for meat quality evaluation because of reliability, reproducibility, non-invasiveness, and non-destructiveness. Machine learning methods are a fundamental and crucial part of artificial vision technology. Their choice is critical in determining successfully the quality of meat. The goal of this paper was to compare the performance of three artificial intelligence-based methods to evaluate the beef meat freshness. In this research, a dataset of beef meat samples images was used to extract the color and texture features. Different methods including the support vector machines (SVM), k-nearest neighbor (KNN), and naïve Bayes (NB) algorithms were applied to determine the freshness of samples. The accuracy rates of KNN, SVM and NB algorithms were obtained about 92.59%, 90.12% and 87.65%, respectively. The results show that the KNN provides the highest classification rates against SVM and NB algorithms.
Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study Labd, Zakia; Bahassine, Said; Housni, Khalid; Hamou Aadi, Fatima Zahrae Ait; Benabbes, Khalid
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.pp589-599

Abstract

Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.
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.
Hardware and software co-design for detecting hypertension from photoplethysmogram Chowdhury, Aditta; Chowdhury, Mehdi Hasan; Das, Diba; Ghosh, Sampad; Chak Chung Cheung, Ray
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.pp2647-2654

Abstract

Hypertension is one of the leading causes of cardiovascular disease morbidity in the world. If remains untreated, it may cause severe damage like heart attack or even death. Early detection is required to prevent the development of other cardiac abnormalities. Photoplethysmogram (PPG) is a bio signal that can be obtained optically by a sensor. It is studied to monitor the change of volume of blood and detect heart conditions. Previous studies have already applied PPG to detect hypertension at the software level. In this article, along with software-based detection, we have implemented a digital hardware-based system for detecting hypertension from signals recorded using PPG sensor. Xilinx ZedBoard Zynq-7000 field programmable gate array (FPGA) board is utilized for designing the embedded system. The hypertension detection accuracy is 98.02% at the software level while for the digital system, it is 96.05% consuming 0.374 W power. The study can be analyzed for other cardiac disease detection and medical equipment development.
Enhancing sentiment analysis in Kannada texts by feature selection Eshwarappa, Sunil Mugalihalli; Shivasubramanyan, Vinay
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.pp6572-6582

Abstract

In recent years, there has been a noticeable surge in research activities focused on sentiment analysis within the Kannada language domain. The existing research highlights a lack of labelled datasets and limited exploration in feature selection for Kannada sentiment analysis, hindering accurate sentiment classification. To address this gap, the study aims to introduce a novel Kannada dataset and develop an effective classifier for improved sentiment analysis in Kannada texts. The study presents a new Kannada dataset from SemEval 2014 Task4 using Google Translate. It then introduces a modified bidirectional encoder representation from transformers BERT for Kannada dataset called as Kannada-BERT (K-BERT). Further, a probability-clustering (PC) approach is presented to extract the topics and its related aspects. Both the K-BERT classifier and PC approach were merged to attain a K-BERT-PC classifier, integrating a modified BERT model and probability clustering approach for achieving better results. Experimental results demonstrate that K-BERT-PC achieves superior performance in polarity and sentiment analysis accuracy, with an impressive accuracy rate of 91%, surpassing existing classifiers. This work contributes by providing a solution to the scarcity of labelled datasets for Kannada sentiment analysis and introduces an effective classifier, K-BERT-PC, for enhanced sentiment analysis outcomes in Kannada texts.
Predicting and detecting fires on multispectral images using machine learning methods Aitimov, Murat; Kaldarova, Mira; Kassymova, Akmaral; Makulov, Kaiyrbek; Muratkhan, Raikhan; Nurakynov, Serik; Sydyk, Nurmakhambet; Bapiyev, Ideyat
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.pp1842-1850

Abstract

In today's world, fire forecasting and early detection play a critical role in preventing disasters and minimizing damage to the environment and human settlements. The main goal of the study is the development and testing of machine learning algorithms for automated detection of the initial stages of fires based on the analysis of multispectral images. Within the framework of this study, the capabilities of three popular machine learning methods: extreme gradient boosting, logistic regression, and vanilla convolutional neural network (vanilla CNN), are considered in the task of processing and interpreting multispectral images to predict and detect fires. XGBoost, as a gradient-boosted decision tree algorithm, provides high processing speed and accuracy, logistic regression stands out for its simplicity and interpretability, while vanilla CNN uses the power of deep learning to analyze spatial and spectral data. The results of the study show that the integration of these methods into monitoring systems can significantly improve the efficiency of early fire detection, as well as help in predicting potential fires.

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