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2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)最新文献

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Robust fuzzy varying coefficient regression model based on Huber loss function 基于Huber损失函数的鲁棒模糊变系数回归模型
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238742
A. Khammar, M. Arefi, M. Akbari
{"title":"Robust fuzzy varying coefficient regression model based on Huber loss function","authors":"A. Khammar, M. Arefi, M. Akbari","doi":"10.1109/CFIS49607.2020.9238742","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238742","url":null,"abstract":"A generalized fuzzy regression model named fuzzy varying coefficient regression model is proposed by Shen et al. [14]. In this study, we introduce a fuzzy varying coefficient regression model based on Huber loss function and a kernel function. Unlike Shen et al.'s approach, the our approach is robust in the presence of outliers data. This advantage is examined by a numerical example.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116796790","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
Matching in n-th Type Intuitionistic Fuzzy Graphs n型直觉模糊图中的匹配
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238708
M. Khalili, R. Borzooei
{"title":"Matching in n-th Type Intuitionistic Fuzzy Graphs","authors":"M. Khalili, R. Borzooei","doi":"10.1109/CFIS49607.2020.9238708","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238708","url":null,"abstract":"Intuitionistic fuzzy graphs of n-th Type (IFGsnT) model is a flexible model for evaluating human information. In order to establish the relation between some concepts of crisp graphs and fuzzy graphs, this paper aims to express the concept of matching in IFGsnTs.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129019433","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
ECAT: an Enhanced Confidence-aware Trust-based recommendation system ECAT:一个增强的基于信任的推荐系统
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238759
Maryam Taherpour, Mehrdad Jalali, Hasan Shakeri
{"title":"ECAT: an Enhanced Confidence-aware Trust-based recommendation system","authors":"Maryam Taherpour, Mehrdad Jalali, Hasan Shakeri","doi":"10.1109/CFIS49607.2020.9238759","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238759","url":null,"abstract":"One of the most widely employed recommendation approaches is collaborative filtering. There are some limitations to this approach, such as the cold-start user problem. Trust-based recommendation approaches are solutions to prevent traditional collaborative filtering-based approaches generating a poor recommendation. We have introduced a unique recommendation approach in our conference paper, called Confidence-aware Trust (CAT), which considers a confidence estimate in both direct and indirect trust calculations. However, the CAT recommendation approach does not involve some vital aspects, namely: the number of neighbors of active users/ target items, consistency in rating values, and the level of confidence in the predicted rating value as well. To address these aspects, we further propose an innovative approach, called an Enhanced Confidence-aware trust (ECAT), which improves the CAT recommendation approach. The Movielens dataset was evaluated. The experimental results show an improved performance by ECAT over its counterparts with respect to the accuracy of recommendations, especially tackle the issue of cold -start users.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127549153","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
Ensembling tree-based classifiers for improving the accuracy of cyber attack detection 集成基于树的分类器以提高网络攻击检测的准确性
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238705
Ensieh Nejati, H. Shakeri, Hassan Raei Sani
{"title":"Ensembling tree-based classifiers for improving the accuracy of cyber attack detection","authors":"Ensieh Nejati, H. Shakeri, Hassan Raei Sani","doi":"10.1109/CFIS49607.2020.9238705","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238705","url":null,"abstract":"Nowadays, the new generation of technology has completely relied on the network-based services. So the wide use of the Internet gives an opportunity to cyber attackers to target the systems which process and save vital information and disrupt their functionality. According to this, the need for finding a way to prevent these attacks and make computer systems more secured is essential, and cyber security turns to a fundamental concern for researchers. A well-known technology in detecting unusual access to the network is Intrusion Detection Systems (IDS). High accuracy and low False Alarm Rate could be pivotal challenges in developing IDS. To address this issue, this paper introduced an intrusion detection system by ensembling tree-based classifiers including decision tree, random forest and Gradient Boosted tree. The model is tested by different feature selection methods, and for evaluating its performance, the NSL-KDD dataset is applied. The results obtained show an improvement in accuracy in comparison with some existing methods.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125078840","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
An Improved Method Multi-View Group Recommender System (IMVGRS) 一种改进的多视图组推荐系统(IMVGRS)
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238688
Maryam Sadeghi, S. A. Asghari, M. Pedram
{"title":"An Improved Method Multi-View Group Recommender System (IMVGRS)","authors":"Maryam Sadeghi, S. A. Asghari, M. Pedram","doi":"10.1109/CFIS49607.2020.9238688","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238688","url":null,"abstract":"Today, one of the users' issues on the web is finding their desired information from a massive amount of data. Recommender systems aid users in making decisions and choosing their suitable items by personalizing the contents for users by their interest. In the past, most of the researches has been done on individual recommender systems. But now, attention has been drawn to group recommender systems. For this reason, this paper tried to improve a group recommender system. In this article, an Improved Multi-View Group Recommender System (IMVGRS) has been proposed. This multi-view group recommender system recommends to a group of the user from two standpoints of user preferences (ratings) and social connection (trust). First, the dimension of the data has been reduced with the Singular-Value Decomposition (SVD) method. Second, the system has been clustered with the complete linkage method. Experimental results, show the effectiveness of the proposed improved method.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128189210","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 New Approach To Estimate Depth Of Cars Using A Monocular Image 利用单目图像估计车辆深度的新方法
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238702
Seyed Ali Doustdar Tousi, Javad Khorramdel, F. Lotfi, Amirhossein Nikoofard, A. Ardekani, H. Taghirad
{"title":"A New Approach To Estimate Depth Of Cars Using A Monocular Image","authors":"Seyed Ali Doustdar Tousi, Javad Khorramdel, F. Lotfi, Amirhossein Nikoofard, A. Ardekani, H. Taghirad","doi":"10.1109/CFIS49607.2020.9238702","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238702","url":null,"abstract":"Predicting scene depth from RGB images is a challenging task. Since the cameras are the most available, least restrictive and cheapest source of information for autonomous vehicles; in this work, a monocular image has been used as the only source of data to estimate the depth of the car within the frontal view. In addition to the detection of cars in the frontal image; a convolutional neural network (CNN) has been trained to detect and localize the lights corresponding to each car. This approach is less sensitive to errors due to the disposition of bounding boxes. An enhancement on the COCO dataset has also been provided by adding the car lights labels. Simulation results show that the proposed approach outperforms those who only use the height and width of bounding boxes to estimate the depth.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133639736","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
Detection of Mental Illness Risk on Social Media through Multi-level SVMs 基于多层次支持向量机的社交媒体心理疾病风险检测
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238692
Kimia Hemmatirad, H. Bagherzadeh, Ehsan Fazl-Ersi, Abedin Vahedian
{"title":"Detection of Mental Illness Risk on Social Media through Multi-level SVMs","authors":"Kimia Hemmatirad, H. Bagherzadeh, Ehsan Fazl-Ersi, Abedin Vahedian","doi":"10.1109/CFIS49607.2020.9238692","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238692","url":null,"abstract":"As shown by several previous studies, personal narratives through social media are often indicative of one's psychological state. In particular, mental illnesses such as depression were found to be associated with distinct linguistic patterns. However, many people with mental illness still do not receive full treatment. In this paper, we study mental illnesses through people's choice of words in expressing themselves on two popular social media platforms, Reddit and Twitter. Our goal is to develop an empirical model to detect and diagnose major mental disorders in individuals. We build a substantial dataset of posts made by people suffering from mental illnesses and the control ones, and in order to generate numerical feature from text we apply text cleaning and Word2Vec language modeling, and then for classification we used SVM machine which classifies posts and users with high accuracy. We achieve an accuracy of 95% on Twitter users and an accuracy of 73% on the Reddit challenge.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128263005","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: 3
On numerical solution of nonlinear fuzzy Urysohn-Volterra delay integral equations based on iterative method and trapezoidal quadrature rule 基于迭代法和梯形求积规则的非线性模糊Urysohn-Volterra延迟积分方程数值解
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238670
R. Ezzati, A. M. Gholam, H. Nouriani
{"title":"On numerical solution of nonlinear fuzzy Urysohn-Volterra delay integral equations based on iterative method and trapezoidal quadrature rule","authors":"R. Ezzati, A. M. Gholam, H. Nouriani","doi":"10.1109/CFIS49607.2020.9238670","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238670","url":null,"abstract":"In the present study, in the beginning, we prove the existence and uniqueness of the solution of nonlinear fuzzy Urysohn-Volterra delay integral equations (NFUVDIE). Then, we propose an iterative method and trapezoidal quadrature rule which numerically solve this equation. In addition, we prove the convergence analysis and error estimate of the proposed numerical method by theorem 3. Eventually, we conclude the efficiency of the presented method. Notice that the study of this equation is important since they have broad applications in various engineering sciences. Recently, a number of researchers suggested variant numerical methods for solving of Volterra fuzzy delay integral equations.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124106591","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
Sentiment Analysis of Informal Persian Texts Using Embedding Informal words and Attention-Based LSTM Network 基于嵌入非正式词和基于注意的LSTM网络的非正式波斯语文本情感分析
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238699
M. Karrabi, Leila Oskooie, M. Bakhtiar, Mohammad Farahani, R. Monsefi
{"title":"Sentiment Analysis of Informal Persian Texts Using Embedding Informal words and Attention-Based LSTM Network","authors":"M. Karrabi, Leila Oskooie, M. Bakhtiar, Mohammad Farahani, R. Monsefi","doi":"10.1109/CFIS49607.2020.9238699","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238699","url":null,"abstract":"The massive volume of comments on websites and social networks has made it possible to raise awareness of people's beliefs and preferences regarding products and services on a large scale. For this purpose, sentiment analysis, which refers to the determination of the sentiment of texts, has been proposed as an intelligent solution. From a methodological point of view, the recent combination of words embedding and deep neural networks (DNNs) has become an effective approach for sentiment analysis. In Persian studies, formal corpuses such as Wikipedia dumps have been used for word embedding. The fundamental difference between formal and informal texts means that the vectors derived from formal texts in informal contexts such as social networks do not result in desirable accuracy. To overcome this drawback, in this paper, we provide a large integrated text corpus of several different sources of informal comments and we also utilize the Fasttext as the word embedding algorithm. In this research, we use Attention-based LSTM, which has been shown to perform more effectively compared to the similar methods in sentiment analysis for the English language. The proposed method is evaluated on the two Persian “Taaghche” and “Filimo” datasets collected in this paper. The experiments on the two Persian datasets prove that utilizing informal vectors in sentiment analysis and applying the attention model improves the prediction accuracy of the DNN in the sentiment analysis of Persian texts.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134133361","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: 3
Multi-objective optimization algorithms in analog active filter design 模拟有源滤波器设计中的多目标优化算法
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) Pub Date : 2020-09-01 DOI: 10.1109/CFIS49607.2020.9238673
N. S. Shahraki, S. Zahiri
{"title":"Multi-objective optimization algorithms in analog active filter design","authors":"N. S. Shahraki, S. Zahiri","doi":"10.1109/CFIS49607.2020.9238673","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238673","url":null,"abstract":"In this paper, component values of analog active filter are optimized based on multi-objective optimization. For this purpose, the multi-objective inclined planes system optimization (MOIPO) algorithm is evaluated and applied as a powerful method in this field. The estimated variables values are selected based on the manufacturer's values of E12 series. By considering a fourth-order Butterworth filter, the global optimization capability of MOIPO is investigated. The performance of the proposed method is compared with the well-known algorithms, non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO). The simulation results prove that MOIPO is superior for the minimization quality factors deviation and cut-off frequency deviation compared to other methods.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123931116","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: 6
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