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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 112 Documents
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Search results for , issue "Vol 12, No 4: August 2022" : 112 Documents clear
A new compact grounded coplanar waveguide slotted multiband planar antenna for radio frequency identification data applications Dakir, Rachid; Mouhsen, Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3800-3807

Abstract

This research presents the development and conception of a new compact grounded coplanar waveguide fed slotted rectangular planar antenna with a multi-frequency band for radio frequency identification data (RFID) reader applications which is based on the antenna mono-band frequency to use for a various applications RFID to support a different operating range. The optimized of the final prototype designing operates a multiple frequency bands ranging from 0.7-1.1 GHz, 2.2-2.5 GHz and 5.4-6 GHz for 0.9/2.4 GHz and 5.8 GHz RFID operation bands which is adapted from ultra-high frequency band (0.9 GHz) to microwave frequency band (2.4-5.8 GHz) RFID systems. This antenna is implemented and printed on a FR4 substrate with a size of 30×50×1.6 mm3. The novel prototype includes of a radiator rectangular patch with a symmetrical slot and a U-slot with I-stub on ground plan. The principles parameters of the antenna have been studied optimized and miniaturized by using a two simulators CST Microwave Studio and advanced design system (ADS) to validate the simulation results before the planar antenna realization. The final structure is achieved and validated of the results measurement. Experimental results show that the proposed antenna with a small size has good and stable radiation and thus promising for a various RFID applications.
A classification model based on depthwise separable convolutional neural network to identify rice plant diseases Md. Sazzadul Islam Prottasha; Sayed Mohsin Salim Reza
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3642-3654

Abstract

Every year a number of rice diseases cause major damage to crop around the world. Early and accurate prediction of various rice plant diseases has been a major challenge for farmers and researchers. Recent developments in the convolutional neural networks (CNNs) have made image processing techniques more convenient and precise. Motivated from that in this research, a depthwise separable convolutional neural network based classification model has been proposed for identifying 12 types of rice plant diseases. Also, 8 different state-of-the-art convolution neural network model has been fine-tuned specifically for identifying the rice plant diseases and their performance has been evaluated. The proposed model performs considerably well in contrast to existing state-of-the-art CNN architectures. The experimental analysis indicates that the proposed model can correctly diagnose rice plant diseases with a validation and testing accuracy of 96.5% and 95.3% respectively while having a substantially smaller model size.
Health monitoring catalogue based on human activity classification using machine learning Ansam A. Abdulhussien; Oday A. Hassen; Charu Gupta; Deepali Virmani; Akshara Nair; Prachi Rani
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3970-3980

Abstract

In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
Fuzzy logic based authentication in cognitive radio networks Nasir Abdulhussien, Israa; Abdulridha Abduljaleel, Safa
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4327-4334

Abstract

Security is a critical issue in cognitive radio networks because the cognitive node enters and variably leaves the spectrum, so it is difficult to process communication secretly. We suggested a fuzzy logic-based implicit authentication mechanism to be a solution for the confusion if there were any cognitive node doubts it to be unauthentic, and to improve user privacy in cognitive radio networks. Using a fuzzy logic technique, the proposed scheme computed certification based on proposed feedback. When a cognitive node needs to join the network, it is verified by using fuzzy logic if the node was authenticated or not. Our proposed fuzzy logic's results implicit authentication proved that it was a very successful and applicable scheme on cognitive radio networks, and it will be able to make an effective final decision in the context of incompleteness, ambiguity, and heterogeneity
Evolution of wireless communication networks: from 1G to 6G and future perspective Ahmed Amin Ahmed Solyman; Khalid Yahya
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3943-3950

Abstract

Since about 1980, a new generation has appeared approximately every decade. Mobile phones started with first-generation (1G), then the successful second generation (2G), and then mixed successful auctions since the launch of 3G. According to business terms, 1G and 2G were providing voice and gradually include data (3G is unsuccessful, 4G is very successful). Today, we are seeing a stir over what 5G will provide. Key expectations currently being discussed include an ultra-high 20 Gb/s bit rate, an ultra-low latency of just 1 millisecond, and a very high capacity. Given the enormous potential of 5G communication networks and their expected evolution, what should 6G include that is not part of 5G or its long-term evolution? 6G communication networks should deliver improved range and data speeds, as well as the ability to connect users from anywhere. This article details possible 6G communication networks. More specifically, the primary influence of this research is to deliver a complete synopsis of the development of wireless communication networks from 1G to 6G.
Tifinagh handwritten character recognition using optimized convolutional neural network Niharmine, Lahcen; Outtaj, Benaceur; Azouaoui, Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4164-4171

Abstract

Tifinagh handwritten character recognition has been a challenging problem due to the similarity and variability of its alphabets. This paper proposes an optimized convolutional neural network (CNN) architecture for handwritten character recognition. The suggested model of CNN has a multi-layer feed-forward neural network that gets features and properties directly from the input data images. It is based on the newest deep learning open-source Keras Python library. The novelty of the model is to optimize the optical character recognition (OCR) system in order to obtain best performance results in terms of accuracy and execution time. The new optical character recognition system is tested on a customized dataset generated from the amazigh handwritten character database. Experimental results show a good accuracy of the system (99.27%) with an optimal execution time of the classification compared to the previous works.
A typical analysis of hybrid covert channel using constructive entropy analytics Krishnamurthy Koundinya, Anjan; Hebbur Satyanarayana, Gururaja
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3820-3826

Abstract

A covert timing channel is based on modulation of the timing information in the network packets in a secured communication. The delicacy of this channel is primarily viewed as single coherent channel thwart the detection from any third-party entity or network admin. The timing covert channel is strenuous to detect under many scenarios due to the intricate complexity of the channel. The exploration of timing covert channel shed light on intrinsic design aspects which elevate understanding to an advanced level. This will effectively bring out elite literature aspects of the timing covert channel for seamless implementation. Supraliminal channels are innocuous message-based channel introduced as a trapdoor in the communication system either intentional or as vulnerability of the system. A hybrid covert channel is the existence of homogeneous or heterogeneous network covert channel variants either at same instant or at different instant of time. For instance, one of possible hybrid covert channel is the co-existence of timing covert channel in transmission control protocol (TCP) and supraliminal channel in voice over internet protocol (VoIP). This paper introduces this variant of the hybrid covert channel and their significance in network communication. The paper also refers to standard measures-entropy, covertness index to understand hybrid covert channel.
Directional movement index based machine learning strategy for predicting stock trading signals Arjun Singh Saud; Subarna Shakya
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4185-4194

Abstract

Intelligent stock trading systems are demand of the modern information age. This research paper proposed a directional movement index based machine learning (DMI-ML) strategy for predicting stock trading signals. Performance of the proposed strategy was evaluated in terms of annual rate of return (ARR), Sharpe ratio (SR), and percentage of profitable trades executed by the trading strategy. In addition, performance of the proposed model was evaluated against the strategies viz. traditional DMI, Buy-Hold. From the experimental results, we observed that the proposed strategy outperformed other strategies in terms of all three parameters. On average, the ARR obtained from the DMI-ML strategy was 52.58% higher than the ARR obtained from the Buy-Hold strategy. At the same time, the ARR of the proposed one was found 75.12% higher than the ARR obtained from the traditional DMI strategy. Furthermore, the Sharpe ratio for the DMI-ML strategy was positive for all stocks. On the other side, the percentage of profitable trades executed by the DMI-ML strategy soared in comparison to the percentage of such trades by the traditional DMI. This study also extended analysis of the proposed model with the various intelligent trading strategies proposed by authors in various literatures and concluded that the proposed DMI-ML strategy is the better strategy for stock trading.
Degraded character recognition from old Kannada documents Sridevi Tumkur Narasimhaiah; Lalitha Rangarajan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3632-3641

Abstract

This paper addresses preparation of a dataset of Kannada characters which are degraded and robust recognition of such characters. The proposed recognition algorithm extracts the histogram of oriented gradients (HOG) features of block sizes 4x4 and 8x8 followed by principal component analysis (PCA) feature reduction. Various classifiers are experimented with and fine K-nearest neighbor classifier performs best. The performance of proposed model is evaluated using 5-fold cross validation method and receiver operating characteristic curve. The dataset devised is of size 10440 characters having 156 classes (distinct characters). These characters are from 75 pages of not well preserved old books. A comparison of proposed model with other features like Haar wavelet and Geometrical features suggests that proposed model is superior. It is observed that the PCA reduced features followed by fine K-nearest neighbor classifier resulted in the best accuracy with acceptance rate of 98.6% and 97.9% for block sizes of 4x4 and 8x8 respectively. The experimental results show that HOG feature extraction has a high recognition rate and the system is robust even with extensively degraded characters.
A blind steganography approach for hiding privacy details in images of digital imaging and communications in medicine using QR code Rashad, Mahmoud; Elhadad, Ahmed; El-Saady, Kamal
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3721-3729

Abstract

This study aims to hide patient’s privacy details of digital imaging and communications in medicine (DICOM) files using the quick response (QR) code images with the same size using steganographic technique. The proposed method is based on the properties of the discrete cosine transform (DCT) of the DICOM images to embed a QR code image. The proposed method includes two parts: data embedding and extraction process. Moreover, the stego DICOM image could be blindly used to produce the embedded QR code image without the existence of the original DICOM image. The performances of proposed method were evaluated using the metrics of the peak signal to noise ratio (PSNR), the structural similarity index (SSIM), the universal quality index (UQI), the correlation coefficient (R) and the bit error rate (BER) values. The experimental results scored a high PSNR after the embedding process by embedding a QR code image into the DICOM image with the same size.

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