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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 5: October 2022" : 112 Documents clear
Computer-aided diagnostic system kinds and pulmonary nodule detection efficacy Omar Raad Kadhim; Hassan Jassim Motlak; Kasim Karam Abdalla
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4734-4745

Abstract

This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.
A computationally efficient learning model to classify audio signal attributes Maha Veera Vara Prasad Kantipud; Satish Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4926-4934

Abstract

The era of machine learning has opened up groundbreaking realities and opportunities in the field of medical diagnosis. However, it is also observed that faster and proper diagnosis of any diseases/medical conditions require proper analysis and classification of digital signal data. It indicates the proper identification of tumors in the brain. Brain magnetic resonance imaging (MRI) data has to be appropriately classified, and similarly, pulse signal analysis is required to evaluate the human heart operating condition. Several studies have used machine learning (ML) modeling to classify speech signals, but very few studies have explored the classification of audio signal attributes in the context of intelligent healthcare monitoring. The study thereby aims to introduce novel mathematical modeling to analyze and classify synthetic pulse audio signal attributes with cost-effective computation. The numerical modeling is composed of several functional blocks where deep neural network-based learning (DNNL) plays a crucial role during the training phase, and also it is further combined with a recurrent structure of long-short term memory (R-LSTM) feedback connections (FCs). The design approaches further experiment in a numerical computing environment in terms of accuracy and computational aspects. The classification outcome of the proposed approach shows that it attains approximately 85% accuracy, which is comparable to the baseline approaches and execution time.
Evolutionary reinforcement learning multi-agents system for intelligent traffic light control: new approach and case of study Mohamed Amine Basmassi; Sidina Boudaakat; Jihane Alami Chentoufi; Lamia Benameur; Ahmed Rebbani; Omar Bouattane
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5519-5530

Abstract

Due to the rapid growth of urban vehicles, traffic congestion has become more serious. The signalized intersections are used all over the world and still established in the new construction. This paper proposes a self-adapted approach, called evolutionary reinforcement learning multi-agents system (ERL-MA), which combines computational intelligence and machine learning. The concept of this work is to build an intelligent agent capable of developing senior skills to manage the traffic light control system at any type of junction, using two powerful tools: learning from the confronted experience and the assumption using the randomization concept. The ERL-MA is an independent multi-agents system composed of two layers: the modeling and the decision layers. The modeling layer uses the intersection modeling using generalized fuzzy graph technique. The decision layer uses two methods: the novel greedy genetic algorithm (NGGA), and the Q-learning. In the Q-learning method, a multi Q-tables strategy and a new reward formula are proposed. The experiments used in this work relied on a real case of study with a simulation of one-hour scenario at Pasubio area in Italy. The obtained results show that the ERL-MA system succeeds to achieve competitive results comparing to urban traffic optimization by integrated automation (UTOPIA) system using different metrics.
Sensored speed control of brushless DC motor based salp swarm algorithm Wisam Najm Al-Din Abed; Omar Abbood Imran; Ali Najim Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4832-4840

Abstract

This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time.
Performance analysis of compressive sensing recovery algorithms for image processing using block processing Mathiyalakendran Aarthi Elaveini; Deepa Thangavel
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5063-5072

Abstract

The modern digital world comprises of transmitting media files like image, audio, and video which leads to usage of large memory storage, high data transmission rate, and a lot of sensory devices. Compressive sensing (CS) is a sampling theory that compresses the signal at the time of acquiring it. Compressive sensing samples the signal efficiently below the Nyquist rate to minimize storage and recoveries back the signal significantly minimizing the data rate and few sensors. The proposed paper proceeds with three phases. The first phase describes various measurement matrices like Gaussian matrix, circulant matrix, and special random matrices which are the basic foundation of compressive sensing technique that finds its application in various fields like wireless sensors networks (WSN), internet of things (IoT), video processing, biomedical applications, and many. Finally, the paper analyses the performance of the various reconstruction algorithms of compressive sensing like basis pursuit (BP), compressive sampling matching pursuit (CoSaMP), iteratively reweighted least square (IRLS), iterative hard thresholding (IHT), block processing-based basis pursuit (BP-BP) based onmean square error (MSE), and peak signal to noise ratio (PSNR) and then concludes with future works.
Remote sensing and geographic information systems technics for spatial-based development planning and policy I Wayan Gede Krisna Arimjaya; Muhammad Dimyati
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5073-5083

Abstract

Indonesia's land-use and land-cover change (LULCC) is a global concern. The relocation plan of the capital city of Indonesia to East Kalimantan will be becoming an environmental issue. Knowing the latest land cover change modeling and prediction research is essential for fundamental knowledge in spatial planning and policies for regional development. Five articles related to integrated technology of geographic information systems (GIS) and remote sensing for spatial modeling were reviewed and compared using nine variables: title, journal (ranks), keywords, objectives, data sources, variables, location, method, and main findings. The results show that the variables that significantly affect LULCC are height, slope, distance from the road, and distance from the built-up area. The artificial neural network-based cellular automata (ANN-CA) method could be the best approach to model the LULCC. Furthermore, by the current availability of global multi-temporal and multi-sensor remote sensing data, the LULCC modeling study can be limitless
Multi-objective tasks scheduling using artificial bee colony algorithm based on cellular automata in cloud computing environment Mehdi Salehi Babadi; Mohammad Ebrahim Shiri; Mohammad Reza Moazami Goudarzi; Hamid Haj Seyyed Javadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5657-5666

Abstract

Due to the development of communication device technology and the need to use up-to-date infrastructure ready to respond quickly and in a timely manner to computational needs, the competition for the use of processing resources is increasing nowadays. The scheduling tasks in the cloud computing environment have been remained a challenge to access a quick and efficient solution. In this paper, the aim is to present a new tactic for allocating the available processing resources based on the artificial bee colony (ABC) algorithm and cellular automata for solving the task scheduling problem in the cloud computing network. The results show the performance of the proposed method is better than its counterparts.
Proactive depression detection from Facebook text and behavior data Siranuch Hemtanon; Saifon Aekwarangkoon; Nichnan Kittiphattanabawon
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5027-5035

Abstract

This paper proposes a proactive method to detect the clinical depression affected person from post and behavior data from Facebook, called text-based and behavior-based models, respectively. For a text-based model, the words that make up the posts are separated and converted into vectors of terms. A machine learning classification applies the term frequency- inverse document frequency technique to identify important or rare words in the posts. For the behavior-based model, the statistical values of the behavioral data were designed to capture depressive symptoms. The results showed that the behavior-based model was able to detect depressive symptoms better than the text-based model. Regarding performance, a detection model using behaviors yields significantly higher F1 scores than those using words in the post. The K-nearest neighbor (KNN) classifier is the best model with the highest F1 score of 1.0, while the highest F1 score of the behavior-based model is 0.88. An analysis of the predominant features influencing depression signifies that posted messages could detect feelings of self-hatred and suicidal thoughts. At the same time, behavioral manifestations identified depressed people who manifested as restlessness, insomnia, decreased concentration.
Appling tracking game system to measure user behavior toward cybersecurity policies Khalid Adnan Alissa; Bashar Abedalmohdi Aldeeb; Hanan Abdullah Alshehri; Shahad Abdulaziz Dahdouh; Basstaa Mohammad Alsubaie; Afnan Mohammad Alghamdi; Moath Khaireddin Alkenani; Mutasem Khalil Alsmadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5164-5175

Abstract

Institutions wrestle to protect their information from threats and cybercrime. Therefore, it is dedicating a great deal of their concern to improving the information security infrastructure. Users’ behaviors were explored by applying traditional questionnaire as a research instrument in data collocate process. But researchers usually suffer from a lack of respondents' credibility when asking someone to fill out a questionnaire, and the credibility may decline further if the research topic relates to aspects of the use and implementation of information security policies. Therefore, there is insufficient reliability of the respondent's answers to the questionnaire’s questions, and the responses might not reflect the actual behavior based on the human bias when facing the problems theoretically. The current study creates a new idea to track and study the behavior of the respondents by building a tracking game system aligned with the questionnaire whose results are required to be known. The system will allow the respondent to answer the survey questions related to the compliance with the information security policies by tracking their behavior while using the system.
Risk assessment of power system transient instability incorporating renewable energy sources Ayman Hoballah; Salah Kamal EL-Sayed; Sattam Al Otaibi; Essam Hendawi; Nagy Elkalashy; Yasser Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4649-4660

Abstract

Transient stability affected by renewable energy sources integration due to reductions of system inertia and uncertainties associated with the expected generation. The ability to manage relation between the available big data and transient stability assessment (TSA) enables fast and accurate monitoring of TSA to prepare the required actions for secure operation. This work aims to build a predictive model using Gaussian process regression for online TSA utilizing selected features. The critical fault clearing time (CCT) is used as TSA index. The selected features map the system dynamics to reduce the burden of data collection and the computation time. The required data were collected offline from power flow calculations at different operating conditions. Therefore, CCT was calculated using electromagnetic transientsimulation at each operating point by applying self-clearance three phase short circuit at prespecified locations. The features selection was implemented using the neighborhood component analysis, the Minimum Redundancy Maximum Relevance algorithm, and K-means clustering algorithm. The vulnerability of selected features tends to result great variation on the best features from the three methods. Hybrid collection of the best common features was used to enhance the TSA by refining the final selected features. The proposed model was investigated over 66-bus system.

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