IEEE EUROCON 2021 - 19th International Conference on Smart Technologies最新文献_第8页

Sensitivity Analysis of Oil Shale Retorting Process through Sobol and Fourier Amplitude Sensitivity Test (FAST) 利用Sobol和傅立叶振幅灵敏度试验(FAST)分析油页岩重整过程的敏感性

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535609

Hasan Qayyum Chohan, I. Ahmad

引用次数: 0

Brassicaceae Leaf Disease Detection using Image Segmentation Technique 基于图像分割技术的十字花科叶片病害检测

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535574

Jasten K. D. Treceñe

{"title":"Brassicaceae Leaf Disease Detection using Image Segmentation Technique","authors":"Jasten K. D. Treceñe","doi":"10.1109/EUROCON52738.2021.9535574","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535574","url":null,"abstract":"In the present time, leaf disease is one of the major problems of Brassicaceae vegetables in the agriculture domain as it affects the quality and quantity of the vegetables. The most common leaf disease of these vegetables is downy mildew, blight leaf disease, and leaf spot. This paper mainly considers identifying the diseased region of the leaves using the image segmentation technique and presents experimentation of the desired number of clusters k. To address the objectives, image acquisition, pre-processing, segmentation, and emphasizing the affected portion of the leaves are all part of the process of the proposed method. The images were transformed into grayscale and removed from the background using Otsu’s thresholding method. K-means clustering algorithm was applied to segment the different regions of the sample images. Finally, the clustered images were then analyzed using a median filter to emphasize the region of interest of the affected leaves. With the different number of clusters k used, k = 4 was successfully segmented the diseased portion, and it was confirmed by the elbow method. Further, the infected area of the sample images was presented in different colors. Also, the proposed method provides a 96.90% accuracy compared to other image segmentation techniques. Image segmentation has become an effective tool in various applications in the agricultural sector.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116054315","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

Comparison of Motor Imagery EEG Classification using Feedforward and Convolutional Neural Network 前馈与卷积神经网络在运动意象脑电分类中的比较

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535592

T. Majoros, S. Oniga

引用次数: 3

Optimal Allocation of RDG in Distribution System Considering the Seasonal Uncertainties of Load Demand and Solar-Wind Generation Systems 考虑负荷需求和太阳能-风力发电系统季节性不确定性的配电系统RDG优化配置

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535617

M. Zellagui, N. Belbachir, C. El‐Bayeh

{"title":"Optimal Allocation of RDG in Distribution System Considering the Seasonal Uncertainties of Load Demand and Solar-Wind Generation Systems","authors":"M. Zellagui, N. Belbachir, C. El‐Bayeh","doi":"10.1109/EUROCON52738.2021.9535617","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535617","url":null,"abstract":"Recently, the installation of Renewable Distributed Generation (RDG) into the Electrical Distribution System (EDS) became one of the best solutions that guarantee the balance between electric energy consumption and production, also show various advantages. In addition to delivering clean energy, they contribute to minimizing power losses, as well as enhancing the voltage profiles. In this paper, the metaheuristic optimization algorithm of the Grey Wolf Optimizer (GWO) is utilized to optimally allocate the RDG based multiple PV and WT units into EDS considering the uncertainty of electrical output energy from the RDGs as well as load demand variation during all seasons. The Multi-Objective Functions (MOF) developed in this paper is considered to minimize simultaneous the indices of the total of Active Power Loss Index (APLI), the Reactive Power Loss Index (RPLI), the Voltage Deviation Index (VDI), the Operation Time Index (OTI) of the overcurrent relay (OCR), and enhance the Coordination Time Interval Index (CTII) of the overcurrent relays installed in the test system which is the IEEE 33-bus EDS.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122854419","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: 5

The Electrification of Kharkiv City at the End of ХIX – at the Beginning of ХX Century 在ХIX末- ХX世纪初哈尔科夫市的电气化

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535540

M. Gutnyk, E. Tverytnykova, S. Radohuz, I. Krylenko, Svitlana Tkachenko

引用次数: 2

Comparative Analysis of Next Generation Aircraft Data Networks 下一代飞机数据网络的比较分析

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535577

Osman Rasit Kultur, H. Ş. Bilge

引用次数: 1

Design and Implementation of IoT Network Prototype to Facilitate the Food Production Process in Agriculture 促进农业食品生产过程的物联网网络原型设计与实现

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535556

Arturs Kempelis, A. Romānovs, A. Patlins

引用次数: 5

Skin Disease Classification using Dermoscopy Images through Deep Feature Learning Models and Machine Learning Classifiers 通过深度特征学习模型和机器学习分类器使用皮肤镜图像进行皮肤病分类

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535552

Siddharth Gupta, A. Panwar, Kishor Mishra

{"title":"Skin Disease Classification using Dermoscopy Images through Deep Feature Learning Models and Machine Learning Classifiers","authors":"Siddharth Gupta, A. Panwar, Kishor Mishra","doi":"10.1109/EUROCON52738.2021.9535552","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535552","url":null,"abstract":"Skin is one of the very important and largest organs of the human body that helps in regulating the body temperature and is responsible for sensations such as feel, touch, hot and cold. These days, skin problems are very common in day-to-day life. There may be many reasons for skin diseases: unbalanced and impure diet, several types of pollutions, or maybe family heredity. However, if skin disease after a long treatment does not show any sign of improvement or the skin cells grow abnormally, this may lead to skin cancer. There are many forms of skin cancer. For early and timely diagnosis of skin cancer, an efficient technique is required at utmost importance. Many people across the globe lost their lives due to the late diagnosis. Therefore, a technique that is cost-effective, quicker, and easily accessible needs a higher demand. These days for the classification of images, machine learning, and deep learning techniques proved to be the most efficient approach. In this paper, the dataset of several images of a benign and malignant tumor was taken and pre-processed. Once all the images were pre-processed, they are ready to fed in several CNN models. These models extract the features and pass the images to several machine learning classifiers for the classification of moles as benign or malignant. The results verify by using the classification approach it becomes very much easy for the dermatologist to easily detect the lesions and provide the appropriate treatment to the patient to save the life.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117193278","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: 12

A Performance Comparison of Pre-trained Deep Learning Models to Classify Brain Tumor 预训练深度学习模型在脑肿瘤分类中的性能比较

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535636

A. Diker

引用次数: 1

An Epidemiological-based Regression Analysis of Alzheimer’s disease and Mild Cognitive Impairment Converts in the Female Population 女性人群中阿尔茨海默病和轻度认知障碍转换的流行病学回归分析

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies Pub Date : 2021-07-06 DOI: 10.1109/EUROCON52738.2021.9535564

A. Khan, S. Zubair, Samreen Khan

{"title":"An Epidemiological-based Regression Analysis of Alzheimer’s disease and Mild Cognitive Impairment Converts in the Female Population","authors":"A. Khan, S. Zubair, Samreen Khan","doi":"10.1109/EUROCON52738.2021.9535564","DOIUrl":"https://doi.org/10.1109/EUROCON52738.2021.9535564","url":null,"abstract":"Detection and prediction of Alzheimer's Disease (AD) conversion from the stage of Mild Cognitive Impairment (MCI) have remained a challenging task. Regression analysis is a method that sorts those essential features/biomarkers that have a strong impact on the overall prediction. This study centres on delivering an individualized regression analysis of cognitively normal and MCI converts over the twenty independent biomarkers that leverage clinical data. Out of the 1713 male and female subjects, 768 female subjects were studied to investigate the prevalence of AD and MCI, those diagnosed with AD and MCI and their associated risk factors. The study data were gathered from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Twenty potential clinical features were included; comprised of a combination of demographic, cerebral spinal fluid, cognitive, diffusion tensor imaging, electroencephalography, genetic, magnetic resonance imaging, and positron emission tomography testing variables. The regression analysis metrics R-squared, F-statistic, Omnibus, Durbin-Watson, Coefficient and Standard error, were used to evaluate the model. Our results showed that cognitive assessment metrics were highly significant among the other testing biomarkers. Additionally, we determined the significance of each clinical variable. Our performed analysis could impact the clinical setting as a means to further develop a machine learning model in predicting the conversion of MCI to AD or to detect principle subjects for clinical trials.","PeriodicalId":328338,"journal":{"name":"IEEE EUROCON 2021 - 19th International Conference on Smart Technologies","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132871637","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