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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 113 Documents
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Search results for , issue "Vol 13, No 6: December 2023" : 113 Documents clear
Towards fostering the role of 5G networks in the field of digital health Turab, Nidal M.; Al-Nabulsi, Jamal Ibrahim; Abu-Alhaija, Mwaffaq; Owida, Hamza Abu; Alsharaiah, Mohammad; Abuthawabeh, Ala
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.pp6595-6608

Abstract

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future.
Best-worst northern goshawk optimizer: a new stochastic optimization method Kusuma, Purba Daru; Hasibuan, Faisal Candrasyah
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.pp7016-7026

Abstract

This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions.
A 1.8 V 25 Mbps CMOS single-phase, phase-locked loop-based BPSK, QPSK demodulator Chaichomnan, Chutpipat; Khumsat, Phanumas
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.pp6102-6117

Abstract

A single-phase binary/quadrature phase-shift keying (BPSK/QPSK) demodulator basing on a phase-locked loop (PLL) is described. The demodulator relies on a linear characteristic a rising-edge RESET/SET flip-flop (RSFF) employed as a phase detector. The phase controller takes the average output from the RSFF and performs a sub-ranging/re-scaling operation to provide an input signal to a voltage-controlled oscillator (VCO). The demodulator is truly modular which theoretically can be extended for a multiple-PSK (m-PSK) signal. Symbol-error rate analysis has also been extensively carried out. The proposed BPSK and QPSK demodulators have been fabricated in a 0.18-mm digital complementary metal–oxide–semiconductor (CMOS) process where they operate from a single supply of 1.8 V. At a carrier frequency of 60 MHz, the BPSK and QPSK demodulators achieved maximum symbol rates of 25 and 12.5 Msymb/s while consuming 0.68 and 0.79 mW, respectively. At these maximum symbol rates, the BPSK and QPSK demodulators deliver symbol-error rates less than 7.9×10-10 and 9.8×10-10, respectively where their corresponding energy per bit figures were at 27.2 and 31.7 pJ.
Optimized stacking ensemble for early-stage diabetes mellitus prediction Aman, Aman; Chhillar, Rajender Singh
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.pp7048-7055

Abstract

This paper presents an optimized stacking-based hybrid machine learning approach for predicting early-stage diabetes mellitus (DM) using the PIMA Indian diabetes (PID) dataset and early-stage diabetes risk prediction (ESDRP) dataset. The methodology involves handling missing values through mean imputation, balancing the dataset using the synthetic minority over-sampling technique (SMOTE), normalizing features, and employing a stratified train-test split. Logistic regression (LR), naïve Bayes (NB), AdaBoost with support vector machines (AdaBoost+SVM), artificial neural network (ANN), and k-nearest neighbors (k-NN) are used as base learners (level 0), while random forest (RF) meta-classifier serves as the level 1 model to combine their predictions. The proposed model achieves impressive accuracy rates of 99.7222% for the ESDRP dataset and 94.2085% for the PID dataset, surpassing existing literature by absolute differences ranging from 10.2085% to 16.7222%. The stacking-based hybrid model offers advantages for early-stage DM prediction by leveraging multiple base learners and a meta-classifier. SMOTE addresses class imbalance, while feature normalization ensures fair treatment of features during training. The findings suggest that the proposed approach holds promise for early-stage DM prediction, enabling timely interventions and preventive measures.
Performance evaluation of transfer learning based deep convolutional neural network with limited fused spectro-temporal data for land cover classification Hasanat, Muhammad; Khan, Waleed; Minallah, Nasru; Aziz, Najam; Durrani, Awab-Ur-Rashid
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.pp6882-6890

Abstract

Deep learning (DL) techniques are effective in various applications, such as parameter estimation, image classification, recognition, and anomaly detection. They excel with abundant training data but struggle with limited data. To overcome this, transfer learning is commonly used, leveraging complex learning abilities, saving time, and handling limited labeled data. This study assesses a transfer learning (TL)-based pre-trained “deep convolutional neural network (DCNN)” for classifying land use land cover using a limited and imbalanced dataset of fused spectro-temporal data. It compares the performance of shallow artificial neural networks (ANNs) and deep convolutional neural networks, utilizing multi-spectral sentinel-2 and high-resolution planet scope data. Both machine learning and deep learning algorithms successfully classified the fused data, but the transfer learning-based deep convolutional neural network outperformed the artificial neural network. The evaluation considered a weighted average of F1-score and overall classification accuracy. The transfer learning-based convolutional neural network achieved a weighted average F1-score of 0.92 and a classification accuracy of 0.93, while the artificial neural network achieved a weighted average F1-score of 0.87 and a classification accuracy of 0.89. These results highlight the superior performance of the transfer learned convolutional neural network on a limited and imbalanced dataset compared to the traditional artificial neural network algorithm.
Design and characterization of a circularly polarized microstrip-line-fed slot array antenna for S-band applications Das, Debprosad; Hossain, Md. Farhad; Hossain, Md. Azad
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.pp6399-6409

Abstract

A 2×2 slot array antenna fed by microstrip line for circular polarization operated in the S band frequency range is designed in this paper. Single cross slot with single port feed as well as dual port feed is taken into consideration for realizing circular polarization and combining these two processes, the slot array is designed with single feed for circular polarization. The antennas are designed on a Teflon glass fiber substrate of thickness 0.8 mm. The slot array dimension is 120×142×1.636 mm3. Smith chart of single cross slot antenna with single feed as well as dual feed has a dip at 2.69 and 2.53 GHz respectively indicate the capability of realizing circular polarization in the S band frequency range. The return loss of the slot array antenna is -58 dB shows good input impedance matching of the antenna. A dip in the smith chart of the slot array shows circular polarization near 2.4 GHz ensuring wireless applications as well. Axial ratio is found to be less than 1 dB in the resonance frequency. The impedance bandwidth percentage of the slot array antenna is 12.24%. The simulation is done by using keysight advanced design system (ADS) software.
A robust super twisting fractional-order sliding mode-based control of vehicle longitudinal dynamic subjected to a constant actuator fault Abzi, Imane; Kabbaj, Mohammed Nabil; Benbrahim, Mohammed
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.pp6185-6194

Abstract

This paper deals with the design and analysis of a super twisting fractional-order sliding mode controller (ST-FOSMC) to adjust the vehicle longitudinal dynamic when braking. While vehicle loading, road types, and modeling uncertainties are time-varying parameters, the control law must be robust against these disturbances. Also, the aging of the brake plate may introduce a difference between the control output and the actuator response that should be considered. The proposed control strategy has been used to enable the anti-lock braking system (ABS) to track the desired wheel slip value despite the presence of disturbances and constant actuator fault. The design of this controller is presented and the system stability is guaranteed by applying the Lyapunov theory. We carried out a simulation example that makes a comparison between our controller and the one based on the fractional-order sliding mode control to investigate which one of them outperforms the other. The results exhibit the superiority of the super twisting fractional order controller over the traditional fractional-order sliding mode controller during the braking phase.
A comparative study of steganography using watermarking and modifications pixels versus least significant bit Caballero, Hector; Muñoz, Vianney; Ramos-Corchado, Marco A.
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.pp6335-6350

Abstract

This article presents a steganography proposal based on embedding data expressed in base 10 by directly replacing the pixel values from images red, green blue (RGB) with a novel compression technique based on watermarks. The method considers a manipulation of the object to be embedded through a data compression triple process via LZ77 and base 64, watermark from low-quality images, embedded via discrete wavelet transformation-singular value decomposition (DWT-SVD), message embedded by watermark is recovered with data loss calculated, the watermark image and lost data is compressed again using LZ77 and base 64 to generate the final message. The final message is embedded in portable network graphic (PNG) images taken from the Microsoft common objects in context (COCO), ImageNet and uncompressed color image database (UCID) datasets, through a filtering process pixel of the images, where the selected pixels expressed in base 10, and the final message data is embedded by replacing units’ position of each pixel. In experimentation results an average of 40 dB in peak signal noise to ratio (PSNR) and 0.98 in the similarity structural index metric (SSIM) evaluation were obtained, and evasion steganalysis rates of up to 93% for stego-images, the data embedded average is 3.2 bpp.
Autonomous open-source electric wheelchair platform with internet-of-things and proportional-integral-derivative control Maneetham, Dechrit; Crisnapati, Padma Nyoman; Thwe, Yamin
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.pp6764-6777

Abstract

This study aims to improve the working model of autonomous wheelchair navigation for disabled patients using the internet of things (IoT). A proportional-integral-derivative (PID) control algorithm is applied to the autonomous wheelchair to control movement based on position coordinates and orientation provided by the global positioning system (GPS) and digital compass sensor. This system is controlled through the IoT system, which can be operated from a web browser. Autonomous wheelchairs are handled using a waypoint algorithm; ESP8266 is used as a microcontroller unit that acts as a bridge for transmitting data obtained by sensors and controlling the direct current (DC) motors as actuators. The proposed system and the autonomous wheelchair performance gave satisfactory results with a longitude and latitude error of 1.1 meters to 4.5 meters. This error is obtained because of the limitations of GPS with the type of Ublox Neo-M8N. As a starting point for further research, a mathematical model of a wheelchair was created, and pure pursuit control algorithm was used to simulate the movement. An open-source autonomous IoT platform for electric wheelchairs has been successfully created; this platform can help nurses and caretakers.
Android-manifest extraction and labeling method for malware compilation and dataset creation Hindarto, Djarot; Djajadi, Arko
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.pp6568-6577

Abstract

Malware is a nuisance for smartphone users. The impact is detrimental to smartphone users if the smartphone is infected by malware. Malware identification is not an easy process for ordinary users due to its deeply concealed dangers in application package kit (APK) files available in the Android Play Store. In this paper, the challenges of creating malware datasets are discussed. Long before a malware classification process and model can be built, the need for datasets with representative features for most types of malwares has to be addressed systematically. Only after a quality data set is available can a quality classification model be obtained using machine learning (ML) or deep learning (DL) algorithms. The entire malware classification process is a full pipeline process and sub processes. The authors purposefully focus on the process of building quality malware datasets, not on ML itself, because implementing ML requires another effort after the reliable dataset is fully built. The overall step in creating the malware dataset starts with the extraction of the Android Manifest from the APK file set and ends with the labeling method for all the extracted APK files. The key contribution of this paper is on how to generate datasets systematically from any APK file.

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