Journal of Artificial Intelligence and Data Mining最新文献_第10页

Interval Type-II Fuzzy H∞ Frequency Control for an Island Microgrid 海岛微电网区间ⅱ型模糊H∞频率控制

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-01-01 DOI: 10.22044/JADM.2019.6982.1825

F. Sabahi

{"title":"Interval Type-II Fuzzy H∞ Frequency Control for an Island Microgrid","authors":"F. Sabahi","doi":"10.22044/JADM.2019.6982.1825","DOIUrl":"https://doi.org/10.22044/JADM.2019.6982.1825","url":null,"abstract":"Frequency control is one of the key parts for the arrangement of the performance of a microgrid (MG) system. Theoretically, model-based controllers may be the ideal control mechanisms; however, they are highly sensitive to model uncertainties and have difficulty with preserving robustness. The presence of serious disturbances, the increasing number of MG, varying voltage supplies of MGs, and both independent operations of MGs and their interaction with the main grid makes the design of model-based frequency controllers for MGs become inherently challenging and problematic. This paper proposes an approach that takes advantage of interval Type II fuzzy logic for modeling an MG system in the process of its robust H∞ frequency control. Specifically, the main contribution of this paper is that the parameters of the MG system are modeled by interval Type-II fuzzy system (IT2FS), and simultaneously MG deals with perturbation using H∞ index to control its frequency. The performance of the microgrid equipped with the proposed modeling and controller is then compared with the other controllers such as H2 and μ-synthesis during changes in the microgrid parameters and occurring perturbations. The comparison shows the superiority and effectiveness of the proposed approach in terms of robustness against uncertainties in the modeling parameters and perturbations.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68375074","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

Capturing Outlines of Planar Generic Images by Simultaneous Curve Fitting and Sub-division 基于曲线拟合和细分的平面通用图像轮廓捕获方法

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-01-01 DOI: 10.22044/JADM.2019.6727.1788

A. Ebrahimi, G. B. Loghmani, M. Sarfraz

引用次数: 3

Salt and Pepper Noise Removal using Pixon-based Segmentation and Adaptive Median Filter 基于像素分割和自适应中值滤波的椒盐噪声去除

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-01-01 DOI: 10.22044/JADM.2019.7921.1930

S. A. Amiri

引用次数: 2

Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing 基于改进的帝国竞争算法和简单后处理的图像分割

Journal of Artificial Intelligence and Data Mining Pub Date : 2019-11-01 DOI: 10.22044/JADM.2019.3935.1464

V. Naghashi, S. Lotfi

引用次数: 0

Morphological Exudate Detection in Retinal Images using PCA-based Optic Disc Removal 基于pca的视盘去除视网膜图像形态学渗出物检测

Journal of Artificial Intelligence and Data Mining Pub Date : 2019-11-01 DOI: 10.22044/JADM.2019.1488

J. Darvish, M. Ezoji

{"title":"Morphological Exudate Detection in Retinal Images using PCA-based Optic Disc Removal","authors":"J. Darvish, M. Ezoji","doi":"10.22044/JADM.2019.1488","DOIUrl":"https://doi.org/10.22044/JADM.2019.1488","url":null,"abstract":"Diabetic retinopathy lesion detection such as exudate in fundus image of retina can lead to early diagnosis of the disease. Retinal image includes dark areas such as main blood vessels and retinal tissue and also bright areas such as optic disk, optical fibers and lesions e.g. exudate. In this paper, a multistage algorithm for the detection of exudate in foreground is proposed. The algorithm segments the background dark areas in the proper channels of RGB color space using morphological processing such as closing, opening and top-hat operations. Then an appropriate edge detector discriminates between exudates and cotton-like spots or other artificial effects. To tackle the problem of optical fibers and to discriminate between these brightness and exudates, in the first stage, main vessels are detected from the green channel of RGB color space. Then the optical fiber areas around the vessels are marked up. An algorithm which uses PCA-based reconstruction error is proposed to discard another fundus bright structure named optic disk. Several experiments have been performed with HEI-MED standard database and evaluated by comparing with ground truth images. These results show that the proposed algorithm has a detection accuracy of 96%.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45488710","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: 4

Depth Improvement for FTV Systems Based on the Gradual Omission of Outliers 基于渐隐异常值的FTV系统深度改进

Journal of Artificial Intelligence and Data Mining Pub Date : 2019-11-01 DOI: 10.22044/JADM.2019.7278.1864

H. Hosseinpour, Seyed A. Moosavie nia, M. Pourmina

引用次数: 0

Prediction and Diagnosis of Diabetes Mellitus Using a Water Wave Optimization Algorithm 用水波优化算法预测和诊断糖尿病

Journal of Artificial Intelligence and Data Mining Pub Date : 2019-11-01 DOI: 10.22044/JADM.2018.6446.1758

S. T. Dehkordi, A. K. Bardsiri, M. Zahedi

{"title":"Prediction and Diagnosis of Diabetes Mellitus Using a Water Wave Optimization Algorithm","authors":"S. T. Dehkordi, A. K. Bardsiri, M. Zahedi","doi":"10.22044/JADM.2018.6446.1758","DOIUrl":"https://doi.org/10.22044/JADM.2018.6446.1758","url":null,"abstract":"Data mining is an appropriate way to discover information and hidden patterns in large amounts of data, where the hidden patterns cannot be easily discovered in normal ways. One of the most interesting applications of data mining is the discovery of diseases and disease patterns through investigating patients' records. Early diagnosis of diabetes can reduce the effects of this devastating disease. A common way to diagnose this disease is performing a blood test, which, despite its high precision, has some disadvantages such as: pain, cost, patient stress, lack of access to a laboratory, and so on. Diabetic patients’ information has hidden patterns, which can help you investigate the risk of diabetes in individuals, without performing any blood tests. Use of neural networks, as powerful data mining tools, is an appropriate method to discover hidden patterns in diabetic patients’ information. In this paper, in order to discover the hidden patterns and diagnose diabetes, a water wave optimization(WWO) algorithm; as a precise metaheuristic algorithm, was used along with a neural network to increase the precision of diabetes prediction. The results of our implementation in the MATLAB programming environment, using the dataset related to diabetes, indicated that the proposed method diagnosed diabetes at a precision of 94.73%,sensitivity of 94.20%, specificity of 93.34%, and accuracy of 95.46%, and was more sensitive than methods such as: support vector machines, artificial neural networks, and decision trees.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48221231","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: 9

Entropy-based Consensus for Distributed Data Clustering 基于熵的分布式数据聚类一致性

Journal of Artificial Intelligence and Data Mining Pub Date : 2019-11-01 DOI: 10.22044/JADM.2018.4237.1514

M. Owhadi-Kareshki, M. Akbarzadeh-T.

{"title":"Entropy-based Consensus for Distributed Data Clustering","authors":"M. Owhadi-Kareshki, M. Akbarzadeh-T.","doi":"10.22044/JADM.2018.4237.1514","DOIUrl":"https://doi.org/10.22044/JADM.2018.4237.1514","url":null,"abstract":"The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in the consensus process, hence no private data are transferred. With the proposed use of entropy as an internal measure of consensus clustering validation at each machine, the cluster centers of the local machines with higher expected clustering validity have more influence in the final consensus centers. We also employ relative cost function of the local Fuzzy C-Means (FCM) and the number of data points in each machine as measures of relative machine validity as compared to other machines and its reliability, respectively. The utility of the proposed consensus strategy is examined on 18 datasets from the UCI repository in terms of clustering accuracy and speed up against the centralized version of FCM. Several experiments confirm that the proposed approach yields to higher speed up and accuracy while maintaining data security due to its protected and distributed processing approach.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43215744","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

A Novel Architecture for Detecting Phishing Webpages using Cost-based Feature Selection 一种利用基于成本的特征选择检测网络钓鱼网页的新架构

Journal of Artificial Intelligence and Data Mining Pub Date : 2019-11-01 DOI: 10.22044/JADM.2019.7183.1852

A. Zangooei, V. Derhami, F. Jamshidi

{"title":"A Novel Architecture for Detecting Phishing Webpages using Cost-based Feature Selection","authors":"A. Zangooei, V. Derhami, F. Jamshidi","doi":"10.22044/JADM.2019.7183.1852","DOIUrl":"https://doi.org/10.22044/JADM.2019.7183.1852","url":null,"abstract":"Phishing is one of the luring techniques used to exploit personal information. A phishing webpage detection system (PWDS) extracts features to determine whether it is a phishing webpage or not. Selecting appropriate features improves the performance of PWDS. Performance criteria are detection accuracy and system response time. The major time consumed by PWDS arises from feature extraction that is considered as feature cost in this paper. Here, two novel features are proposed. They use semantic similarity measure to determine the relationship between the content and the URL of a page. Since suggested features don't apply third-party services such as search engines result, the features extraction time decreases dramatically. Login form pre-filer is utilized to reduce unnecessary calculations and false positive rate. In this paper, a cost-based feature selection is presented as the most effective feature. The selected features are employed in the suggested PWDS. Extreme learning machine algorithm is used to classify webpages. The experimental results demonstrate that suggested PWDS achieves high accuracy of 97.6% and short average detection time of 120.07 milliseconds.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48983466","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

Analytical evaluation of an innovative decision-making algorithm for VM live migration 虚拟机实时迁移决策创新算法的分析评价

Journal of Artificial Intelligence and Data Mining Pub Date : 2019-11-01 DOI: 10.22044/JADM.2018.7178.1847

Majid Tajamolian, M. Ghasemzadeh

引用次数: 3