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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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Enhancing resource management in fog-cloud internet of things systems with deep learning-based task allocation Venkatesan, Vijayalakshmi; Murugan, Saravanan
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.pp7244-7253

Abstract

A fog-cloud internet of things (IoT) system integrates fog computing with cloud infrastructure to efficiently manage processing data closer to the source, reducing latency and bandwidth usage. Efficient task scheduling in fog-cloud system is crucial for optimizing resource utilization and minimizing energy consumption. Even though many authors proposed energy efficient algorithms, failed to provide efficient method to decide the task placement between fog nodes and cloud nodes. The proposed hybrid approach is used to distinguish the task placement between fog and cloud nodes. The hybrid approach comprises the parametric task categorization algorithm (PTCA) for task categorization and the multi metric forecasting model (MMFM) based on deep deterministic policy gradient (DDPG) recurrent neural networks for scheduling decisions. PTCA classifies tasks based on priority, quality of service (QoS) demands, and computational needs, facilitating informed decisions on task execution locations. MMFM enhances scheduling by optimizing energy efficiency and task completion time. The experimental evaluation outperforms the existing models, including random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN). The proposed result shows an accuracy rate of 89%, and energy is consumed 50% lesser than the existing models. The proposed research advances energy-efficient task scheduling, enabling intelligent resource management in fog-cloud IoT environments.
Effective driver distraction warning system incorporating fast image recognition methods Nguyen, Van Binh; Trinh, Phu Duy
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.pp1572-1582

Abstract

Modern cars are equipped with advanced automatic technology featuring various safety measures for car occupants. However, the growing density of vehicles, especially in areas where infrastructure development lags, poses potential dangers, particularly accidents caused by driver subjectivity. These incidents may occur due to driver distraction or the presence of high-risk obstacles on the road. This article presents a comprehensive solution to assist drivers in mitigating these risks. Firstly, the study introduces a novel method to enhance the recognition of a driver's facial features by analyzing benchmarks and the whites of the eyes to assess the distraction level. Secondly, a domain division method is proposed to identify obstacles and lanes in front of the vehicle, enabling the assessment of the danger level. This information is promptly relayed to the driver and relevant individuals, such as the driver's manager or supervisor. An experimental device has also been developed to evaluate the effectiveness of the algorithms, solutions, and processing capabilities of the system.
Sliding mode control for the speed loop combined with adaptive coefficients for urban trains’ load variations of Nhon – Hanoi Station Metro line Anh, An Thi Hoai Thu; Cuong, Tran Hung; Dinh, Ha Van
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.pp5030-5037

Abstract

Electric trains are becoming increasingly popular due to their environmental protection and ability to transport a large number of passengers. Alongside this trend, traction motors for electric trains have become diverse thanks to the rapid development of power electronics. Among them, the permanent magnet synchronous motor (PMSM) stands out with advantages such as high efficiency, high torque-to-current ratio, and compactness compared to other motors of the same power, making it the top choice. However, PMSM motors are nonlinear objects, so the nonlinear control technique of sliding mode control has been applied to the speed loop in this paper. Additionally, electric trains' inertial torque and load torque vary due to changes in the number of passengers during peak and off-peak hours and weather conditions. Therefore, this paper introduces two adaptive coefficients to account for these variations. Simulation results show that the sliding mode control technique for the speed loop circuit provides a faster and more accurate speed response. Meanwhile, the two parameters also adapt to the inertial and load torque variations. This ensures the safety and efficiency of the electric train system, contributing to the advantages of this mode of transportation.
A novel optimized deep learning method for protein-protein prediction in bioinformatics Thareja, Preeti; Chillar, Rajender Singh
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.pp749-758

Abstract

Proteins have been shown to perform critical activities in cellular processes and are required for the organism's existence and proliferation. On complicated protein-protein interaction (PPI) networks, conventional centrality approaches perform poorly. Machine learning algorithms based on enormous amounts of data do not make use of biological information's temporal and spatial dimensions. As a result, we developed a sequence-dependent PPI prediction model using an Aquila and shark noses-based hybrid prediction technique. This model operates in two stages: feature extraction and prediction. The features are acquired using the semantic similarity technique for good results. The acquired features are utilized to predict the PPI using hybrid deep networks long short-term memory (LSTM) networks and restricted Boltzmann machines (RBMs). The weighting parameters of these neural networks (NNs) were changed using a novel optimization approach hybrid of aquila and shark noses (ASN), and the results revealed that our proposed ASN-based PPI prediction is more accurate and efficient than other existing techniques.
Multi-temporal assessment of wind, solar, and hydropower resources for off-grid microgrid Odetoye, Oyinlolu Ayomidotun; Kehinde Olulope, Paul; Olabisi Olanrewaju, Matthew; Olusola Alimi, Adeleke
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.pp3755-3767

Abstract

For a proposed multi-source all-renewable microgrid in Nigeria’s Middle-belt region, this paper presents a multi-temporal approach to the investigation of the uncertainty in the potential of renewable energy resources. The wind, solar, and hydropower resources for a proposed multi-source all-renewable off-grid community microgrid are considered using an array of probabilistic techniques. The peculiar variances in the location’s climate throughout the year make the more common method of annual models of renewable resources unsuitable for power system planning. Consequently, a more granular model of its renewable resources over time is needed. Therefore, for the chosen location, for each renewable resource, a composite multitemporal maximum-likelihood estimation-based (MLE) probabilistic model for characterization is developed. A total of 39 probabilistic models are developed. Up to 40% improvement in the accuracy of the statistical measures for renewable resource uncertainty was observed. Multi-temporal approach provides more accurate information for power system planning over time than the conventional approach of single aggregate models, especially for hydropower, which is strongly affected by the relatively sporadic occurrence of rainfall. The study shows that solar energy is promising, hydropower potential is seasonal and complementary, and wind potential is low at the location considered in this study.
Peak-to-average power ratio minimization and complexity reduction in MIMO-OFDM systems using spatial circular shifting and temporal interleaving method Ramadevi, Dubala; Trinatha Rao, Polipalli
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.pp2771-2778

Abstract

Multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) technological support for the simultaneous and frequent access by a large number of users to radio resources. For 5G cellular systems, this exhaust is not enough to provide physical layer services. An appropriate Peak-to-average power ratio (PAPR) minimization principle, which maximizes data capacity and channel utility, has been used to address this issue. In this paper, mainly focus on minimize the high PAPR of the candidate sequence of the OFDM sub-block using modified enhancement asymmetric arithmetic coding scheme (M-EAAC). According to this, circular shifting of the candidate sequence is established in the spatial circular shifting and temporal interleaving (SCS-TI) form to generated different set of conjugated phases which is multiplied with candidate sequence. Then, the transmitting antenna is identified the best lowest PAPR of the candidate sequence is chosen for entire OFDM data transmission. The simulation results conveys that the proposed SCS-TI method provide acceptable improvement in the PAPR reduction as compared with conventional selective mapping(SLM)and pseudo-random SLM(PR-SLM). Moreover, the complexity evaluation which ensure the proposed method provides better improvement at three important stages includes inverse fast Fourier transform (IFFT) operation, optimization process, and PAPR calculation at each candidate sequence.
Parallel numerical simulation of the 2D acoustic wave equation Altybay, Arshyn; Darkenbayev, Dauren; Mekebayev, Nurbapa
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.pp6519-6525

Abstract

Mathematical simulation has significantly broadened with the advancement of parallel computing, particularly in its capacity to comprehend physical phenomena across extensive temporal and spatial dimensions. High-performance parallel computing finds extensive application across diverse domains of technology and science, including the realm of acoustics. This research investigates the numerical modeling and parallel processing of the two-dimensional acoustic wave equation in both uniform and non-uniform media. Our approach employs implicit difference schemes, with the cyclic reduction algorithm used to obtain an approximate solution. We then adapt the sequential algorithm for parallel execution on a graphics processing unit (GPU). Ultimately, our findings demonstrate the effectiveness of the parallel approach in yielding favorable results.
An efficient convolutional neural network-extreme gradient boosting hybrid deep learning model for disease detection applications Bhaskar, Navaneeth; Ajithkumar, Aswathy Maruthompilli; Tupe-Waghmare, Priyanka
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.pp2035-2042

Abstract

In this paper, we present an efficient deep-learning hybrid model comprising an extreme gradient boosting (XGBoost) supervised learning algorithm and convolutional neural networks (CNN) for the automated detection of diseases. The proposed model is implemented and tested to detect type-2 diabetes by measuring the acetone concentration in the exhaled breath. Acetone will be present in much higher concentrations in type-2 diabetic patients compared to non-diabetic people. A novel sensing module is designed and implemented in our study to measure the acetone concentration in exhaled breath. The proposed approach delivered good results, with a classification accuracy of 97.14%. The findings of this study show how effectively the proposed detection module functions in disease diagnosis applications. As the detection process is simple and non-invasive, people can undergo routine checks for diabetes with the proposed detection module.
A 26 GHz rectenna based on a solar cell antenna for internet of things applications Baccouch, Chokri; Omar, Saleh; Rhaimi, Belgacem C.
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.pp5253-5262

Abstract

This paper presents a new rectenna system that combine a patch antenna with a solar cell to capture energy from both radio frequency (RF) signals and sunlight. The patch antenna collects RF signals, while the solar cell converts sunlight into electricity. This integration offers a sustainable energy solution for internet of things (IoT) sensors or drones. The antenna's performance at 26 GHz demonstrates impressive metrics, including a -68 dB S11 reflection, 700 MHz bandwidth, 6.25 dBi gain, 49.8 Ω impedance, and 42.25% RF-DC conversion efficiency. The "solar rectenna" integrates both technologies, driving technological advancement and fostering sustainability in wireless communication.
Determinant factors of mobile investment app users among generation Z Indonesia Hanif, Hidjra; Nadlifatin, Reny; Hutama, Rizal Risnanda; Ali, Achmad Holil Noor; Persada, Satria Fadil
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.pp3073-3083

Abstract

Generation Z, alternatively referred to as the digital native generation, is distinguished by its profound immersion in technological progress. This study elucidates the determinants of generation Z's technological improvement in mobile investing application usage (MIA). As the instrument for factors analysis, the modified unified theory of acceptance and use of technology-2 (UTAUT-2) technique was implemented. The presented hypotheses were validated through the application of structural equation modeling (SEM) to the data acquired from 280 respondents via online questionnaires. The research revealed that trust, habit, performance expectation, and perceived risk had a substantial impact on the behavioral intention of Generation Z to utilize MIA. Furthermore, actual usage behavior is notably influenced by habit and behavioral intention, whereas gender acts as a substantial moderator in relation to performance expectancy and price value variables.

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