2016 International Joint Conference on Neural Networks (IJCNN)最新文献_第2页

FEDD: Feature Extraction for Explicit Concept Drift Detection in time series 时间序列中显式概念漂移检测的特征提取

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-29 DOI: 10.1109/IJCNN.2016.7727274

R. C. Cavalcante, Leandro L. Minku, Adriano Oliveira

引用次数: 37

A dynamic self-structuring neural network model to combat phishing 一种对抗网络钓鱼的动态自结构神经网络模型

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-28 DOI: 10.1109/IJCNN.2016.7727750

F. Thabtah, R. Mohammad, L. Mccluskey

引用次数: 24

Complex social network partition for balanced subnetworks 复杂的社会网络分区，实现均衡的子网

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727744

H. Zhang, Jiming Liu, Chunyu Feng, C. Pang, Tongliang Li, Jing He

{"title":"Complex social network partition for balanced subnetworks","authors":"H. Zhang, Jiming Liu, Chunyu Feng, C. Pang, Tongliang Li, Jing He","doi":"10.1109/IJCNN.2016.7727744","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727744","url":null,"abstract":"Complex social network analysis methods have been applied extensively in various domains including online social media, biological complex networks, etc. Complex social networks are facing the challenge of information overload. The demands for efficient complex network analysis methods have been rising in recent years, particularly the extensive use of online social applications, such as Flickr, Facebook and LinkedIn. This paper aims to simplify the network complexity through partitioning a large complex network into a set of less complex networks. Existing social network analysis methods are mainly based on complex network theory and data mining techniques. These methods are facing the challenges while dealing with extreme large social network data sets. Particularly, the difficulties of maintaining the statistical characteristics of partitioned sub-networks have been increasing dramatically. The proposed Normal Distribution (ND) based method can balance the distribution of the partitioned sub-networks according to the original complex network. Therefore, each subnetwork can have its degree distribution similar to that of the original network. This can be very beneficial for analyzing sub-divided networks and potentially reducing the complexity in dynamic online social environment.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114677350","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

Optimization of an electro-optical representation of the C. elegans connectome through neural network cluster analysis 利用神经网络聚类分析优化秀丽隐杆线虫连接体的光电表征

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727828

A. Petrushin, L. Ferrara, A. Blau

引用次数: 0

A network-based approach to detect spammer groups 一种基于网络的方法来检测垃圾邮件发送组

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727668

Q. Do, Alexey Zhilin, Caibre Zordan Pio Junior, Gaoxiang Wang, F. Hussain

{"title":"A network-based approach to detect spammer groups","authors":"Q. Do, Alexey Zhilin, Caibre Zordan Pio Junior, Gaoxiang Wang, F. Hussain","doi":"10.1109/IJCNN.2016.7727668","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727668","url":null,"abstract":"Online reviews nowadays are an important source of information for consumers to evaluate online services and products before deciding which product and which provider to choose. Therefore, online reviews have significant power to influence consumers' purchase decisions. Being aware of this, an increasing number of companies have organized spammer review campaigns, in order to promote their products and gain an advantage over their competitors by manipulating and misleading consumers. To make sure the Internet remains a reliable source of information, we propose a method to identify both individual and group spamming reviews by assigning a suspicion score to each user. The proposed method is a network-based approach combining clustering techniques. We demonstrate the efficiency and effectiveness of our approach on a real-world and manipulated dataset that contains over 8000 restaurants and 600,000 restaurant reviews from TripAdvisor website. We tested our method in three testing scenarios. The method was able to detect all spammers in two testing scenarios, however it did not detect all in the last scenario.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116960109","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

An inductive semi-supervised learning approach for the Local and Global Consistency algorithm 局部和全局一致性算法的归纳半监督学习方法

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727722

C. A. R. Sousa

引用次数: 3

An experimental study on joint modeling of mixed-bandwidth data via deep neural networks for robust speech recognition 基于深度神经网络的混合带宽数据联合建模的鲁棒语音识别实验研究

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727253

Jianqing Gao, Jun Du, Changqing Kong, Huaifang Lu, Enhong Chen, Chin-Hui Lee

引用次数: 7

Tagging Chinese microblogger via sparse feature selection 基于稀疏特征选择的中文微博标签

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727505

R. Shang, Xinyu Dai, Shujian Huang, Yi Li, Jiajun Chen

引用次数: 0

Heterogeneous extreme learning machines 异质极限学习机

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727400

J. J. Valdés

{"title":"Heterogeneous extreme learning machines","authors":"J. J. Valdés","doi":"10.1109/IJCNN.2016.7727400","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727400","url":null,"abstract":"The developments in communication, sensor and computing technologies are generating information at increasing rates and the nature of the data is becoming highly heterogeneous. Accordingly, the objects under study are described by collections of variables of very different kinds (e.g. numeric, non-numeric, images, signals, videos, documents, etc.) with different degrees of imprecision and incompleteness. Many data mining and machine learning methods do not handle heterogeneity well, requiring variables of the same type, information completeness (or imputation), also assuming no imprecision. Extreme learning machines (ELM) are very interesting computational algorithms because of their structural simplicity, their good performance and their speed. Accordingly, extending their scope by making them capable of processing heterogeneous information may increase their attractiveness as a modeling tool for addressing complex problems. ELMs are discussed in the context of heterogeneous data and approaches to build ELMs capable of performing classification and regression tasks in such cases are presented. Their performance is illustrated with real world examples of classification and regression involving heterogeneous information with scalar data described by nominal, ordinal, interval, ratio, and fuzzy variables as well as with entire empirical probability distributions as predictor variables.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127254511","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

Nearest Neighbour Search using binary neural networks 最近邻搜索使用二进制神经网络

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727873

Demetrio Ferro, Vincent Gripon, Xiaoran Jiang

引用次数: 9