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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 novel k-means powered algorithm for an efficient clustering in vehicular ad-hoc networks Khalid Kandali; Lamyae Bennis; Hanan Halaq; Hamid Bennis
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3140-3148

Abstract

Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%.
A new approach to design line start permanent magnet synchronous motors Karol Swierczynski; Maciej Antal; Marcin Habrych; Bartosz Brusilowicz
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2508-2516

Abstract

The article describes a new approach to line start permanent magnet synchronous motors (LSPMSM) design process. This different, novel approach is based on the modification of mass-produced induction motors. The presented method facilitates and expedites the electrical motor design process. A field-circuit model of the motor has been created to expound the methodology of the process. Then, the machine's geometry was changed to apply permanent magnets. The problems and benefits associated with the use of permanent magnets were described. The created model was tested. The authors examined the operation of the LSPMSM motor in different states, such as starting, no-load, and blocked-rotor tests. Electromechanical characteristics have been plotted. The simulation results were compared with the parameters and characteristics of the induction motor. The conducted tests proved the correctness of the design process. The operational properties of the motor have been improved. Moreover, the validity of using LSPMSM motors instead of induction motors has also been demonstrated.
Glottic lesion segmentation of computed tomography images using deep learning Divya Rao; Prakashini Koteshwara; Rohit Singh; Vijayananda Jagannatha
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3432-3439

Abstract

The larynx, a common site for head and neck cancers, is often overlooked in automated contouring due to its small size and anatomically complex nature. More than 75% of laryngeal tumors originate in the glottis. This paper proposes a method to automatically delineate the glottic tumors present contrast computed tomography (CT) images of the head and neck. A novel dataset of 340 images with glottic tumors was acquired and pre-processed, and a senior radiologist created a detailed, manual slice-by-slice tumor annotation. An efficient deep-learning architecture, the U-Net, was modified and trained on our novel dataset to segment the glottic tumor automatically. The tumor was then visualized with the corresponding ground truth. Using a combined metric of dice score and binary cross-entropy, we obtained an overlap of 86.68% for the train set and 82.67% for the test set. The results are comparable to the limited work done in this area. This paper’s novelty lies in the compiled dataset and impressive results obtained with the size of the data. Limited research has been done on the automated detection and diagnosis of laryngeal cancers. Automating the segmentation process while ensuring malignancies are not overlooked is essential to saving the clinician’s time.
Black spots identification on rural roads based on extreme learning machine Abdelilah Mbarek; Mouna Jiber; Ali Yahyaouy; Abdelouahed Sabri
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3149-3160

Abstract

Accident black spots are usually defined as road locations with a high risk of fatal accidents. A thorough analysis of these areas is essential to determine the real causes of mortality due to these accidents and can thus help anticipate the necessary decisions to be made to mitigate their effects. In this context, this study aims to develop a model for the identification, classification and analysis of black spots on roads in Morocco. These areas are first identified using extreme learning machine (ELM) algorithm, and then the infrastructure factors are analyzed by ordinal regression. The XGBoost model is adopted for weighted severity index (WSI) generation, which in turn generates the severity scores to be assigned to individual road segments. The latter are then classified into four classes by using a categorization approach (high, medium, low and safe). Finally, the bagging extreme learning machine is used to classify the severity of road segments according to infrastructures and environmental factors. Simulation results show that the proposed framework accurately and efficiently identified the black spots and outperformed the reputable competing models, especially in terms of accuracy 98.6%. In conclusion, the ordinal analysis revealed that pavement width, road curve type, shoulder width and position were the significant factors contributing to accidents on rural roads.
A genetic algorithm coupled with tree-based pruning for mining closed association rules Jashma Suresh Ponmudiyan Poovan; Dinesh Acharya Udupi; Nandanvana Veerappareddy Subba Reddy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2876-2890

Abstract

Due to the voluminous amount of itemsets that are generated, the association rules extracted from these itemsets contain redundancy, and designing an effective approach to address this issue is of paramount importance. Although multiple algorithms were proposed in recent years for mining closed association rules most of them underperform in terms of run time or memory. Another issue that remains challenging is the nature of the dataset. While some of the existing algorithms perform well on dense datasets others perform well on sparse datasets. This paper aims to handle these drawbacks by using a genetic algorithm for mining closed association rules. Recent studies have shown that genetic algorithms perform better than conventional algorithms due to their bitwise operations of crossover and mutation. Bitwise operations are predominantly faster than conventional approaches and bits consume lesser memory thereby improving the overall performance of the algorithm. To address the redundancy in the mined association rules a tree-based pruning algorithm has been designed here. This works on the principle of minimal antecedent and maximal consequent. Experiments have shown that the proposed approach works well on both dense and sparse datasets while surpassing existing techniques with regard to run time and memory.
Modeling of magnetic sensitivity of the metal-oxide-semiconductor field-effect transistor with double gates Ghanim Thiab Hasan; Ali Hlal Mutlaq; Kamil Jadu Ali; Mohammed Ayad Saad
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2632-2639

Abstract

In this paper, we investigated the effect of magnetic field on the carrier transport phenomenon in metal-oxide-semiconductor field-effect transistor (MOSFET) with double gates by examining the behavior of the semiconductor under the Lorentz force and a constant magnetic field. Various behaviors within the channel have been simulated including the potential distribution, conduction and valence bands, total current density, total charge density and the magnetic field. The results obtained indicate that this modulation affects the electrical characteristics of the device such as on-state current (ION), subthreshold leakage current (IOF), threshold voltage (VTh), and the Hall voltage (VH) is induced by the magnetic field. The change in threshold voltage caused by the magnetic field has been observed to affect the switching characteristics of the device, such as speed and power loss, as well as the threshold voltage VTh and (ION/IOF) ratio. Note that it is reduced by 10-3 V. 102 for magnetic fields of ±6 and ±5.5 tesla respectively.
Waist-to-height ratio assessment device Ertie Abana; Mycah Accad; Marvin James Pagauisan; Patrick Taguiam; Mary Ronalie Ferrer
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2686-2694

Abstract

Many diseases are associated with excess abdominal fat like cardiovascular diseases. Monitoring and controlling abdominal fat led to one of the many factors that can change the status of a person’s health. Awareness of the waist-to-height ratio (WHtR) can be a guide to adjusting to a person’s lifestyle and maintaining a normal WHtR value. This study developed the WHtR assessment device that automatically calculates the WHtR value, displays the health status, and suggests the ideal waist circumference. The device is composed of a microcontroller that interconnects the other components of the device. A touchscreen liquid crystal display component was used as an input and output unit at the same time. The several testing that was conducted revealed accurate WHtR value calculation. The device is effective in assessing the health status of all age groups. The ideal waist circumference from the device was compared to manual computation and found that the success rate is one hundred percent (100%).
Survey on data aggregation based security attacks in wireless sensor network Nikhath Tabassum; Geetha D. Devanagavi; Rajashekhar C. Biradar; Chaya Ravindra
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3131-3139

Abstract

Wireless sensor network (WSN) has applications in military, health care, environmental monitoring, infrastructure, industrial and commercial applications. The WSN is expected to maintain data integrity in all its network operations. However, due to the nature of wireless connectivity, WSN is prone to various attacks that alter or steal the data exchanged between the nodes. These attacks can disrupt the network processes and also the accuracy of its results. In this survey paper, we have reviewed various attacks available in the literature till date. We have also listed existing methods that focus on data aggregation based security mechanisms in WSN to counter the attacks. We have classified and compared these methods owing to their encryption techniques. This paper intends to support researchers to understand the basic attacks prevalent in WSN and schemes to counter such attacks.
Design of the electric propulsion system for dumper trucks Walter Naranjo Lourido; Luis Ariel Riaño Ocampo; Gustavo Andres Gallego Chipiaje; Javier Eduardo Martinez Baquero; Luis Alfredo Rodriguez Umaña
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2546-2554

Abstract

This article designs a high-efficiency electric propulsion system for industrial trucks, such as dumper trucks. This design proposes using an alternative energy storage system of green H2 hydrogen to reduce emissions. This design determines the propulsion systems' technical and power requirements, starting with each vehicle's driving and duty cycles. For this analysis, a longitudinal dynamic model is created, with which the behavior of the energy conversion chain of the propulsion system is established. The evolutionary methodology analyzes the dynamic forces of vehicle interaction to size the propulsion system's components and the storage system. Using green H2 as fuel allows an energy yield three times higher than diesel. In addition, using this green hydrogen prevents the emission of 264,172 kg of CO₂, which the dumper emits when consuming 1,000 daily gallons of diesel within its working day.
Ultrasound renal stone diagnosis based on convolutional neural network and VGG16 features Noor Hamzah Alkurdy; Hadeel K. Aljobouri; Zainab Kassim Wadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3440-3448

Abstract

This paper deals with the classification of the kidneys for renal stones on ultrasound images. Convolutional neural network (CNN) and pre-trained CNN (VGG16) models are used to extract features from ultrasound images. Extreme gradient boosting (XGBoost) classifiers and random forests are used for classification. The features extracted from CNN and VGG16 are used to compare the performance of XGBoost and random forest. An image with normal and renal stones was classified. This work uses 630 real ultrasound images from Al-Diwaniyah General Teaching Hospital (a lithotripsy center) in Iraq. Classifier performance is evaluated using its accuracy, recall, and F1 score. With an accuracy of 99.47%, CNN-XGBoost is the most accurate model.

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