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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 111 Documents
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Search results for , issue "Vol 14, No 5: October 2024" : 111 Documents clear
Fuzzy-proportional-integral-derivative-based controller for stable control of unmanned aerial vehicles with external payloads Tiep, Do Khac; Tien, Nguyen 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.pp5094-5106

Abstract

In the paper, a proportional derivative (PD) controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize an unmanned aerial vehicle (UAV), to control the attitude. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of quadcopter and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which make pitch, roll, yaw and height values. The fuzzy-PD control algorithm is real-time applied to the quadcopter in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and fuzzy-PD controller have accomplished to track a defined trajectory with the quadcopter.
Stochastic agent-based models optimization applied to the problem of rebalancing bike-share systems Soto, Daniel Anderson; Ceballos, Yony
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.pp5641-5651

Abstract

This paper presents an agent-based model employing a stochastic optimization search that attempts to find an optimal solution to the online bicycle rebalancing problem for general bike-sharing systems. The algorithm receives the initial and final global state configuration of the system. The main objective of the study is to find the minimum cost path from the initial to the final state. Each agent of the model has four behavioral options that search the optimal configuration; at each iteration, it selects one of these options based on random thresholds and shares the temporary solution found with neighboring agents to improve their search process. The algorithm presents a high exploratory behavior of the search space, which helps to find an approximation away from the local optimal configuration. Additionally, the exchanges between agents allow a consensus on the solutions found. The algorithm has been tested with two different generated configurations using as a basis a real dataset extracted from a functional bike-sharing system collected in 2019. The results show a positive evolution originating from the emerging effect of stochastic selection and interaction between agents.
A review of object detection approaches for traffic surveillance systems El-Alami, Ayoub; Nadir, Younes; Mansouri, Khalifa
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.pp5221-5233

Abstract

With the decreasing cost of traffic cameras and rapid advancement in computer vision and artificial intelligence, developing robust traffic surveillance systems has become more feasible and practical. These systems can easily outperform traditional human monitoring systems, as they can collect and analyze traffic data coming from multiple cameras efficiently. A good understanding of this data allows the detection easily road anomalies in real time and in an autonomous way. Therefore, an intelligent traffic system typically consists of three components: object detection, object tracking, and behavior analysis components. In this paper, we present a review of some of the well-known object detection techniques used in traffic video surveillance. The review begins with a brief introduction to the history of object detection and the evolution of its techniques. Then we review separately the two main approaches of detection, which are traditional and deep learning approaches of detection. Finally, an experimental analysis has been conducted to evaluate and compare the performance of some of the recent relevant detection methods in terms of speed and precision, in detecting vehicles in a traffic scenario.
Optimal control design of the COVID-19 model based on Lyapunov function and genetic algorithm Sa'adah, Aminatus; Saragih, Roberd; Handayani, Dewi
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.pp5117-5130

Abstract

Millions of people died worldwide as a result of the coronavirus disease 2019 (COVID-19) pandemic that started in early 2020. Examining the COVID-19 susceptible-exposed-infected-recovery (SEIR) mathematical model is one approach to developing the best control scenario for this disease. The study utilized two control variables, vaccination, and therapy, to construct a control function that relied on the quadratic Lyapunov function. The control objective was to lower the number of COVID-19 infections while maintaining system stability. A genetic algorithm (GA) is used as a novel method to estimate controller parameter value to replace the previously used parameter tuning procedure. Then, a numerical simulation was carried out implementing three control scenarios, namely vaccination control only, treatment control only, and vaccination and treatment control simultaneously. Based on the results, scenario 3 (vaccination and treatment simultaneously) showed the most significant decrease: the average decrease in the exposed human population was 98.29%, and the infected human population was 98.18%.
Estimation of kernel density function using Kapur entropy Chawla, Leena; Kumar, Vijay; Saxena, Arti
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.pp6016-6022

Abstract

Information-theoretic measures play a vital role in training learning systems. Many researchers proposed non-parametric entropy estimators that have applications in adaptive systems. In this work, a kernel density estimator using Kapur entropy of order α and type β has been proposed and discussed with the help of theorems and properties. From the results, it has been observed that the proposed density measure is consistent, minimum, and smooth for the probability density function (PDF) underlying given conditions and validated with the help of theorems and properties. The objective of the paper is to understand the theoretical viewpoint behind the underlying concept.
A semantic-based approach for domain specific language development Negm, Eman; Salah, Akram; Makady, Soha
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.pp5366-5380

Abstract

A domain specific language (DSL) ties the business and technical models, by letting technical developers write programs with the business domain properties. Yet, DSLs are not used due to the cost of developing them. Such cost stems from the needed expertise within both the domain knowledge and language development technicalities for any DSL engineer who would design such a language. This paper proposes a semantic-based DSL development approach that utilizes an ontology as a formal way for domain representation. The domain ontology is semi-automatically transformed into a DSL. Then, an ontology reasoning algorithm provides reasoning services on the DSL structure and the programs developed using such DSL by application developers. Such reasoning services can automatically detect flaws in the DSL design like possible inconsistency or the presence of unsatisfiable or redundant classes thus serving the DSL engineer. The reasoning services can also discover inconsistency or redundant classes in programs built using the designed DSL, thus serving the application developer. The proposed approach was implemented within a language workbench using projectional-editing and was evaluated on two different ontologies from varied domains. The results show correct transformation of the input ontology, valid instantiation of designed application, and efficient reasoning services.
Relationship between features volatility and bug occurrence rate to support software evolution Hadiningrum, Tiara Rahmania; Mardiana, Bella Dwi; Rochimah, Siti
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.pp5381-5389

Abstract

Software evolution is an essential foundation in delivering technology that adapts to user needs and industry dynamics. In an era of rapid technological development, software evolution is not just a necessity, but a must to ensure long-term relevance. Developers are faced with major challenges in maintaining and improving software quality over time. This research aims to investigate the correlation between feature volatility and bug occurrence rate in software evolution, to understand the impact of dynamic feature changes on software quality and development process. The research method uses commit analysis on the dataset as a marker of bug presence, studying the complex relationship between feature volatility and bug occurrence rate to reveal the interplay in software development. Validated datasets are measured by metrics and correlations are measured by Pearson-product-moment analysis. This research found a strong relationship between feature volatility and bug occurrence rate, suggesting that an increase in feature changes correlates with an increase in bugs that impact software stability and quality. This research provides important insights into the correlation between feature volatility and bug occurrence rates, guiding developers and quality practitioners to develop more effective testing strategies in dynamic development environments.
Effective detection of breast pathology using machine learning methods Orazayeva, Ainur; Tussupov, Jamalbek; Shangytbayeva, Gulmira; Galymova, Assem; Zhunissova, Ulzhalgas; Tergeussizova, Aliya; Tleubayeva, Arailym; Kenzhebayeva, Zhanat
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.pp5593-5600

Abstract

This work is devoted to the research and development of methods for effectively identifying breast pathologies using modern machine learning technologies, such as you only look once (YOLOv8) and faster region-based convolutional neural network (R-CNN). The paper presents an analysis of existing approaches to the diagnosis of breast diseases and an assessment of their effectiveness. YOLOv8 and Faster R-CNN architectures are then applied to create pathology detection models in mammography images. The work analyzed and classified identified breast pathologies at six levels, taking into account different degrees of severity and characteristics of the diseases. This approach allows for more accurate determination of disease progression and provides additional data for more individualized treatment planning. Classification results at various levels can improve the quality of medical decisions and provide more accurate information to doctors, which in turn improves the overall efficiency of diagnosis and treatment of breast diseases. Experimental results demonstrate high accuracy and speed of image processing, providing fast and reliable detection of potential breast pathologies. The data obtained confirm the effectiveness of the use of machine learning algorithms in the field of medical diagnostics, providing prospects for the further development of automated systems for detecting breast diseases in order to improve early diagnosis and treatment efficiency.
Development of an algorithm for identifying the autism spectrum based on features using deep learning methods Amirbay, Aizat; Mukhanova, Ayagoz; Baigabylov, Nurlan; Kudabekov, Medet; Mukhambetova, Kuralay; Baigusheva, Kanagat; Baibulova, Makbal; Ospanova, Tleugaisha
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.pp5513-5523

Abstract

The presented scientific work describes the results of the development and evaluation of two deep learning algorithms: long short-term memory with a convolutional neural network (LSTM+CNN) and long short-term memory with an autoencoder (LSTM+AE), designed for the diagnosis of autism spectrum disorders. The study focuses on the use of eye tracking technology to collect data on participants' eye movements while interacting with animated objects. These data were saved in NumPy array format (.npy) for ease of later analysis. The algorithms were evaluated in terms of their accuracy, generalization ability, and training time, which was confirmed by experts. The main goal of the study is to improve the diagnosis of autism, making it more accurate and effective. The convolutional neural network long short-term memory and autoencoder-long short-term memory models have shown promise as tools for achieving this goal, with the autoencoder model standing out for its ability to identify internal relationships in data. The article also discusses potential clinical applications of these algorithms and directions for future research.
Advancements in ammonia gas detection: a comparative study of sensor technologies Hadi, Amran Abdul; Shaipuzaman, Nurulain Nadhirah; Aspar, Mohd Amir Shahlan Mohd; Salim, Mohd Rashidi; Manap, Hadi
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.pp5107-5116

Abstract

Ammonia gas is a colorless gas that is known for its pungent odor. It is commonly used in various industries, such as agriculture, refrigeration, and chemical manufacturing. This paper provides a comprehensive overview of various technologies employed in ammonia gas sensors. The objective is to compare and identify the optimum method to detect ammonia gas. The review encompasses catalytic gas sensors, metal oxide gas sensors, polymer conductivity gas sensors, optical gas sensors, and indirect gas sensors, detailing their respective operational principles. Additionally, the advantages and disadvantages of each technology for ammonia gas detection are outlined. All these technologies have been used for many applications and some of them have been commercialized. Some sensor characteristics suggestions are also stated in order to develop an improved optical ammonia sensor for industrial applications.

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