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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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A 2.45/5.8 GHz high-efficiency dual-band rectifier for low radio frequency input power Sara El Mattar; Abdennaceur Baghdad; Abdelhakim Ballouk
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2169-2176

Abstract

This article proposes a concurrent rectifier for radio frequency (RF) energy harvesting from the popular ambient RF sources wireless fidelity (WiFi) 2.45 and 5.8 GHz bands. A voltage doubler-based converter circuit with the Schottky SMS7630 diode is used, this chosen diode has shown good results for low power levels. To ameliorate the resulting circuit, we used an interdigital capacitor (IDC) instead of a lumped component; and then we added a filter to reject the 3rd harmonics of each operating frequency. A dual-band impedance transformer with a direct current (DC) block function is used and optimized at low input power points for more harvested DC power. The final circuit was, therefore, more efficient and more reliable. The maximum conversion efficiencies obtained from the resulting circuit are about 60.321% for 2.45 GHz and 47.175% for 5.8 GHz at 2 dBm of input power. Compared to other previous rectifiers presented in the literature, our proposed circuit presents high efficiencies at low power levels and at these operating frequencies.
A hybrid approach of artificial neural network-particle swarm optimization algorithm for optimal load shedding strategy Trong Le, Nghia; Trieu Phung, Tan; Huy Quyen, Anh.; Phung Nguyen, Bao Long; Ngoc Nguyen, Au
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.pp4253-4263

Abstract

This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PSO focuses on 2 main goals: determine whether process shedding plan or not and the distribution of the minimum of shedding power on each demand load bus in order to restore system’s frequency back to acceptable values. In the hybrid algorithm ANN-PSO, the PSO algorithm takes responsible for searching the optimal weights in the neural network structure, which can help to optimize the network training in terms of training speed and accuracy. The distribution of shedding power at each node considering the primary control and secondary control of the generators’ unit and the phase electrical distance between the outage generators and load nodes. The effectiveness of the proposed method is experimented with multiple generators outage cases at various load levels in the IEEE-37 Bus scheme where load shedding cases are considered compared with other traditional technique.
Internet of things based fall detection and heart rate monitoring system for senior citizens Md. Hasib Sarowar; Md. Fazlul karim Khondakar; Himaddri Shakhar Roy; Habib Ullah; Riaj Ahmed; Quazi Delwar Hossain
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3204-3216

Abstract

Falls cause the maximum number of injuries, deaths, and hospitalizations due to injury for senior citizens worldwide. So, fall detection is essential in the health care of senior citizens. Present methods lack either accuracy or comfortability. The design of fall detection and heart rate monitoring system for senior citizens has been presented in this paper. The hardware interface includes wearable monitoring devices based on a tri-axial accelerometer and Bluetooth module that makes a wireless connection by software interface (mobile application) to the caregiver. Global positioning system (GPS) can also track the location of the elder. For detecting falls accurately, an effective fall detection algorithm is developed and used. The performance parameters of the fall detection system are accuracy (97.6%), sensitivity (92.8%), and specificity (100%). A pulse sensor is used for monitoring the heart rate of the elder. The device is put on the hips to increase comfortability. Whenever the elder's fall is detected, the device can send information on fall data and heart rate with location to the respective caregiver successfully. So, this device can minimize the injury and health cost of a fallen person as a victim can get help within a short time.
Peanut leaf spot disease identification using pre-trained deep convolutional neural network Urbano B. Patayon; Renato V. Crisostomo
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3005-3012

Abstract

Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group–16 (VGG16), visual geometry group–19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants.
Efficient addressing schemes for internet of things Venkatesh Thamarai Kannan; Rekha Chakravarthi
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.pp4415-4429

Abstract

The internet of things (IoT) defines the connectivity of physical devices to provide the machine to machine communication. This communication is achieved through various wireless standards for sensor node connectivity. The IoT calls from the formation of various wireless sensor nodes (WSNs) in a network. The existing neighborhood discovery method had the disadvantage of time complexity to calculate the cluster distance. Our proposed method rectifies this issue and gives accurate execution time. This paper proposed mobility management system based on proxy mobile IPv6 as distributed PMIPv6 with constrained application protocol (CoAP-DPMIP) and PMIPv6 with constrained application protocol (CoAP-PMIP). It also provides the optimized transmission path to reduce the delay handover in IoT network. The PMIPv6 described the IPv6 address of mobile sensor device for efficient mobility management. The network architecture explains three protocol layers of open systems interconnection model (OSI model). The OSI layers are data link layer, network layer and transport layer. We have proposed the distance estimation algorithm for efficient data frames transmission. This paper mainly focuses the secure data transmission with minimum loss of error. The evaluation result proved that proposed technique performance with delay, energy, throughput and packet delivery ratio (PDR). Also, it measures the computational time very effectively.
Performance evaluation of dual backhaul links RF/FSO for small cells of 5G cellular system A. Aldhaibani, Jaafar; Naser Jurn, Yaseen; Ibrahim Abdulkhaleq, Nadhir
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.pp3922-3931

Abstract

Radio frequency (RF) backhaul links between the wireless stations especially between small cells stations such as relay stations (RSs) are insufficient with high capacities and huge number of users for 5G cellular networks. An alternative solution is using free space optics (FSO) communications, however, there are limitations in this system. In this paper we proposed a mixed of RF link together with FSO link. Using hardselector between them and programmatically controlled according of quality of links to overcome the obstacles faced the transfer of high data. Based on the outage probability of each links an algorithm is proposed to select the optimal link and provide the power consumption with guaranteed high quality of the link. The analytical expressions for the outage probability and ergodic capacity are derived. The numerical results show the effectiveness of proposed model in terms of connectivity and capacity compared to other links.
Hybrid model in machine learning–robust regression applied for sustainability agriculture and food security Mukhtar Mukhtar; Majid Khan Majahar Ali; Mohd. Tahir Ismail; Ferdinand Murni Hamundu; Alimuddin Alimuddin; Naseem Akhtar; Ahmad Fudholi
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.pp4457-4468

Abstract

A dataset containing 1924 observations used in this study to evaluate the effect of 435 different independent variables on one dependent variable. Big data has some issues such as irrelevant variables and outliers. Therefore, this study focused on analysing and comparing the impact of three different variable selection based on machine learning techniques, including random forest (RF), support vector machines (SVM), and Boosting. Further, the M robust regression was applied to address the outliers using M–bi square, M–Hampel, and M–Huber. Random forest and M-Hampel results revealed the significant comparing from the other methods such as mean absolute error (MAE) 175.33995, mean square error (MSE) 31.8608, mean average percentage error (MAPE) 9.16091, sum of square error (SSE) 89270.45, R–square 0.829511, and R–square adjusted 0.82670. Also, these techniques indicated that the 8 selection criteria were lower than the other techniques including Akaike information criterion (AIC) 47.25915, generalized cross validation (GCV) 47.27169, hannan-quinn (HQ) 47.60351, RICE (47.2845), SCHWARZ 51.7099, sigma square (SGMASQ) 46.50605, SHIBATA 47.23489, and final prediction error (FPE) 47.25929. Therefore, the study recommended that the best random forest and M-Hampel models are helpful to show the minimum issues and efficient validation for analysing and comparing big data.
Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method Dina Mohammed Sherif El-Torky; Mohamed Ismail Roushdy; Maryam Nabil Al-Berry; Mohammed Abd El-Mageed Salem
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2553-2563

Abstract

3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using a wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images.
Application of optimization algorithms for classification problem Alaa Eleyan; Mohammad Shukri Salman; Bahaa Al-Sheikh
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.pp4373-4379

Abstract

The work presented in this paper investigates the use of metaheuristic optimization algorithms for the face recognition problem. In the first setup, a face recognition system is implemented using particle swarm optimization (PSO) and firefly optimization algorithms, separately. PSO and firefly are used for forming the feature vectors in the feature selection stage. These feature vectors serve as the new representation for the face images that will be fed to the classifier. In the second setup, selected features from both PSO and firefly algorithms are fused to form one single feature vector for each face image before the classification stage. Extensive simulations are conducted using Poznan University of Technology (PUT) and face recognition technology (FERET) face databases. Optimal values for population size and maximum iterations number were selected before conducting the experiments. The effect of using different numbers of selected features on the performance is investigated for feature selection using PSO, firefly, and feature fusion of both.
An authenticated key management scheme for securing big data environment Thoyazan Sultan Algaradi; Boddireddy Rama
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3238-3248

Abstract

If data security issues in a big data environment are considered, then the distribution of keys, their management, and the ability to transfer them between server users in a public channel will be one of the most critical issues that must consider on. In which the importance of keys management may outweigh the importance of the encryption algorithm strength. Therefore, this paper raised a new proposed scheme called (AKMS) that works through two levels of security. First, to concerns how the user communicates with the server with preventing any attempt to penetrate senders/receivers. Second, to make the data sent vague by encrypting it, and unreadable by others except for the concerned receiver, thus the server function be limited only as a passageway for communication between the sender and receiver. In the presented work some concepts discussed related to analysis and evaluation as keys security, data security, public channel transmission, and security isolation inquiry which demonstrated the rich value that AKMS scheme carried. As well, AKMS scheme achieved very satisfactory results about computation cost, communication cost, storage overhead which proved that AKMS scheme is appropriate, secure, and practical to use and protect the user's private data in big data environments.

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