International Journal of Electrical and Computer Engineering最新文献_第8页

Analysis of code-based digital signature schemes 基于代码的数字签名方案分析

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5534-5541

Rupali Khurana, E. Narwal

引用次数: 1

Fuzzy logic-based controller of the bidirectional direct current to direct current converter in microgrid 基于模糊逻辑的微电网双向直流-直流变换器控制器

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp4789-4797

Younes Boujoudar, M. Azeroual, Lahcen Eliysaouy, F. Z. Bassine, Aiman J. Albarakati, Ayman Aljarbouh, A. Knyazkov, Hassan El Moussaoui, T. Lamhamdi

{"title":"Fuzzy logic-based controller of the bidirectional direct current to direct current converter in microgrid","authors":"Younes Boujoudar, M. Azeroual, Lahcen Eliysaouy, F. Z. Bassine, Aiman J. Albarakati, Ayman Aljarbouh, A. Knyazkov, Hassan El Moussaoui, T. Lamhamdi","doi":"10.11591/ijece.v13i5.pp4789-4797","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp4789-4797","url":null,"abstract":"Microgrids are small-scale power networks that include renewable energy sources, load, energy storage systems, and energy management systems (EMS). Lithium-ion batteries are the most used battery for energy storage in microgrids due to their advantages over other types of batteries. However, to protect the battery from the explosion and to manage to charge and discharge based on state-of-charge (SoC) value, this type of battery requires the use of an energy management system. The main objective of this paper is to propose an intelligent control strategy for energy management in the microgrid to control the charge and discharge of Li-ion batteries to stabilize the system and reduce the cost of electricity due to the high cost of grid electricity. The proposed technique is based on a fuzzy logic controller (FLC) for voltage control. The FLC is based on the measured voltage of the direct current (DC) bus and the fixed reference voltage to generate buck/boost converter signal control. The proposed technique has been simulated and tested using MATLAB/Simulink software which illustrates the tracking of desired power and DC bus voltage regulation. The simulation results confirm that the proposed systems can diminish the deviations of the system's voltage.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43618545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Trust based multi objective honey badger algorithm to secure routing in vehicular ad-hoc networks 基于信任的多目标蜜獾算法在车载自组织网络中的安全路由

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5190-5197

P. Mutalik, Venkangouda C. Patil

{"title":"Trust based multi objective honey badger algorithm to secure routing in vehicular ad-hoc networks","authors":"P. Mutalik, Venkangouda C. Patil","doi":"10.11591/ijece.v13i5.pp5190-5197","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5190-5197","url":null,"abstract":"A vehicular ad-hoc network (VANET) is a set of intelligent vehicles that interact without any fixed infrastructure. Data transmission between each transmitter/receiver pair is accomplished using routing protocols. However, communication over the VANET is vulnerable to malicious attacks, because of the unavailability of fixed infrastructure and wireless communication. In this paper, the trust based multi objective honey badger algorithm (TMOHBA) is proposed to achieve secure routing over the VANET. The TMOHBA is optimized by incorporating different cost functions, namely, trust, end to end delay (EED), routing overhead, energy, and distance. The developed secure route discovery using the TMOHBA is used to improve the robustness against the malicious attacks, for increasing the data delivery. Moreover, the shortest path discovery is used to minimize the delay while improving the security of VANET. The TMOHBA method is evaluated using the packet delivery ratio (PDR), throughput and EED. Existing researches such as hybrid enhanced glowworm swarm optimization (HEGSO) and ad-hoc on-demand distance vector based secure protocol (AODV-SP) are used to evaluate the TMOHBA method. The PDR of the TMOHBA method for 10 malicious attacks is 90.6446% which is higher when compared to the HEGSO and AODV-SP.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43556868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging 基于灰度共现矩阵特征的肺癌计算机断层成像分类技术

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5135-5146

Shankara Chikkalingaiah, Subbarao Anantha Padmanabha Rao Hari Prasad, Latha Dabbegatta Uggregowda

{"title":"Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging","authors":"Shankara Chikkalingaiah, Subbarao Anantha Padmanabha Rao Hari Prasad, Latha Dabbegatta Uggregowda","doi":"10.11591/ijece.v13i5.pp5135-5146","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5135-5146","url":null,"abstract":"Lung cancer, which causes the majority of fatalities worldwide each year, is one of the deadliest diseases. The survival rate of cancer patients could be improved with better cancer detection methods. Image processing and machine learning have both been used to aid in lung cancer detection, but a method that both increase accuracy and increases a patient’s survival rate has yet to be identified. In an effort to find the most effective method for the accurate lung cancer recognition, this paper analyses and compares several classification algorithms. Lung computed tomography (CT) images are enhanced by removing noise using a median filter. For filtered image, threshold segmentation is used to segment it into distinct parts. From the segmented image different features are extracted using the grey level co-occurrence matrix (GLCM). several classification strategies, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods, are used to classify lung images as malignant or normal based on the extracted features. Methods are evaluated based on a number of various performance measures, like accuracy, a precision, the recall, and the F1-Score. Based on the experimental outcomes, SVM outperforms other classification methods in accurately detecting lung cancer with an accuracy of 99.32%.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45724970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region 摩洛哥-加勒布地区马铃薯作物的机器学习预测施肥模型

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5942-5950

Said Tkatek, Samar Amassmir, Amine Belmzoukia, J. Abouchabaka

{"title":"Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region","authors":"Said Tkatek, Samar Amassmir, Amine Belmzoukia, J. Abouchabaka","doi":"10.11591/ijece.v13i5.pp5942-5950","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5942-5950","url":null,"abstract":"Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R2Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R2, MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45923610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Triple-layer remote phosphor geometry: an excellent selection to improve the optical properties of white light-emitted diodes 三层远端荧光粉几何结构:改善白光发射二极管光学性能的极佳选择

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5118-5125

T. Bui, My Hanh Nguyen Thi, N. D. Quoc Anh, Nguyen Le Thai

引用次数: 0

Adaptive traffic lights based on traffic flow prediction using machine learning models 基于机器学习模型的交通流量预测的自适应交通灯

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5813-5823

Idriss Moumen, J. Abouchabaka, N. Rafalia

{"title":"Adaptive traffic lights based on traffic flow prediction using machine learning models","authors":"Idriss Moumen, J. Abouchabaka, N. Rafalia","doi":"10.11591/ijece.v13i5.pp5813-5823","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5813-5823","url":null,"abstract":"Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42219666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Characterization of electricity demand based on energy consumption data from Colombia 基于哥伦比亚能源消耗数据的电力需求特征分析

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp4798-4809

Santiago Toledo-Cortés, Juan S. Lara, Alvaro Zambrano, F. G. González Osorio, J. Rosero García

{"title":"Characterization of electricity demand based on energy consumption data from Colombia","authors":"Santiago Toledo-Cortés, Juan S. Lara, Alvaro Zambrano, F. G. González Osorio, J. Rosero García","doi":"10.11591/ijece.v13i5.pp4798-4809","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp4798-4809","url":null,"abstract":"The development of dynamic energy distribution grids to optimize energy resources has become very important at the international level in recent years. A very important step in this development is to be able to characterize the population based on their consumption behaviour. However, traditional consumption meters that report information at a monthly rate provide little information for in-depth analysis. In Colombia, this has changed in recent years due to the implementation and integration of advanced metering infrastructure (AMI). This infrastructure allows to record consumption values in small time intervals, and the available data then allows for the execution of many analysis mechanisms. In this paper we present an analysis of the electricity demand profile from a new dataset of energy consumption in Colombia. A characterization of the users demand profiles is presented using a k-means clustering procedure. Whit this customer segmentation technique we show that is possible identify customer consumption patterns and to identify anomalies in the system. In addition, this type of analysis also allows to assess changes in the consumption pattern of users due to social measures such as those resulting from the coronavirus disease (COVID-19) pandemic.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46723864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Arduino based 74-series integrated circuits testing system at gate level 基于Arduino的74系列集成电路门级测试系统

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp4950-4957

Y. Hashim, Marwa Awni, Abdullah Mufeed

引用次数: 1

Machine learning in drug supply chain management during disease outbreaks: a systematic review 疾病暴发期间药品供应链管理中的机器学习:系统综述

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5517-5533

Gunadi Emmanuel, Arief Ramadhan, Muhammad Zarlis, E. Abdurachman, A. Trisetyarso

{"title":"Machine learning in drug supply chain management during disease outbreaks: a systematic review","authors":"Gunadi Emmanuel, Arief Ramadhan, Muhammad Zarlis, E. Abdurachman, A. Trisetyarso","doi":"10.11591/ijece.v13i5.pp5517-5533","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5517-5533","url":null,"abstract":"The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks.","PeriodicalId":38060,"journal":{"name":"International Journal of Electrical and Computer Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44396114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1