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

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

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

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

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

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

Reliable and efficient webserver management for task scheduling in edge-cloud platform 可靠、高效的web服务器管理，用于边缘云平台的任务调度

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5922-5931

Sangeeta Sangani, Rudragouda S. Patil

{"title":"Reliable and efficient webserver management for task scheduling in edge-cloud platform","authors":"Sangeeta Sangani, Rudragouda S. Patil","doi":"10.11591/ijece.v13i5.pp5922-5931","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5922-5931","url":null,"abstract":"The development in the field of cloud webserver management for the execution of the workflow and meeting the quality-of-service (QoS) prerequisites in a distributed cloud environment has been a challenging task. Though, internet of things (IoT) of work presented for the scheduling of the workflow in a heterogeneous cloud environment. Moreover, the rapid development in the field of cloud computing like edge-cloud computing creates new methods to schedule the workflow in a heterogenous cloud environment to process different tasks like IoT, event-driven applications, and different network applications. The current methods used for workflow scheduling have failed to provide better trade-offs to meet reliable performance with minimal delay. In this paper, a novel web server resource management framework is presented namely the reliable and efficient webserver management (REWM) framework for the edge-cloud environment. The experiment is conducted on complex bioinformatic workflows; the result shows the significant reduction of cost and energy by the proposed REWM in comparison with standard webserver management methodology.","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":"44738042","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

Improved vision-based diagnosis of multi-plant disease using an ensemble of deep learning methods 使用集成的深度学习方法改进基于视觉的多植物疾病诊断

International Journal of Electrical and Computer Engineering Pub Date : 2023-10-01 DOI: 10.11591/ijece.v13i5.pp5109-5117

Rashidul Hasan Hridoy, Arindra Dey Arni, Aminul Haque

{"title":"Improved vision-based diagnosis of multi-plant disease using an ensemble of deep learning methods","authors":"Rashidul Hasan Hridoy, Arindra Dey Arni, Aminul Haque","doi":"10.11591/ijece.v13i5.pp5109-5117","DOIUrl":"https://doi.org/10.11591/ijece.v13i5.pp5109-5117","url":null,"abstract":"Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.","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":"44190288","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