2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)最新文献_第5页

A validity index method for clusters with different degrees of dispersion and overlap 一种不同离散度和重叠度聚类的有效性指标方法

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449829

P. Lin, P. Huang, Che-Yu Li

{"title":"A validity index method for clusters with different degrees of dispersion and overlap","authors":"P. Lin, P. Huang, Che-Yu Li","doi":"10.1109/ICACI.2016.7449829","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449829","url":null,"abstract":"Cluster validity index Is used for estimating the quality of partitions to a dataset by clustering algorithms, and finding the optimal number of clusters to be partitioned. In this paper, we propose a new validity index, which is based on a dispersion measure and an overlap measure. The dispersion measure estimates the overall data density of the clusters in the dataset; whereas the overlap measure estimates the degree of isolation among all clusters. Low degree of dispersion means that the overall clusters are densely distributed and hence are compact; and low degree of overlap means that clusters are overall well separated. Thus, a good clustering result is expected to have a lower dispersion measure and a lower overlap measure. We conducted several experiments to validate the effectiveness of our validity indexing method, including artificial datasets and public real datasets. Experimental results show that our validity indexing method has superior effectiveness and reliability for estimating the optimal number of clusters that widely differ in degrees of dispersion and overlap, when compared to nine other indices proposed in the literature.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124280017","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

A reasoning system about knowledge acquisition in bi-agent interaction 双智能体交互中知识获取的推理系统

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449861

Jinsheng Gao, Changle Zhou

{"title":"A reasoning system about knowledge acquisition in bi-agent interaction","authors":"Jinsheng Gao, Changle Zhou","doi":"10.1109/ICACI.2016.7449861","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449861","url":null,"abstract":"In artificial intelligence, knowledge acquisition and reasoning are the most basic key elements for intelligent agent to obtain the ability of the simulation thinking which capacitate intelligent agent to carry out a series of actions. At present, Dynamic Epistemic Logic (DEL) has been a primary technique for modelling knowledge acquisition and reasoning. This is mainly due to the dynamic epistemic logic has a stronger expression ability to deal with many problems about agent's cognitive reasoning in computer science. In this paper, we distinguish the major sorts of knowledge that the cognitive agent will hold in their interaction in the beginning, and then establish a Knowledge Acquisition System (KAS) which is added a new operator of `knowledge acquisition' to extend the basic epistemic language for bi-agent interaction. Other than that, we prove the soundness and completeness of this system, and analyze some properties about it. By this system, it is clear to explain how the agent acquires knowledge that will be transformed into common knowledge by observing the other agent's speech act. The acquired knowledge is further transformed into new private knowledge that provides the strategy options in agent's mind in interaction.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122111105","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 autocratic decision making method using group recommendations based on ranking interval type-2 fuzzy sets 基于排序区间2型模糊集的群推荐专制决策方法

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449808

Shou-Hsiung Cheng, Shyi-Ming Chen, Zhi-Cheng Huang

引用次数: 2

A novel multiple attribute decision making method based on interval-valued intuitionistic fuzzy geometric averaging operators 一种基于区间值直觉模糊几何平均算子的多属性决策方法

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449807

Shyi-Ming Chen, Shou-Hsiung Cheng, Wei-Hsiang Tsai

引用次数: 7

A combined KFDA method and GUI realized for face recognition 实现了KFDA与GUI相结合的人脸识别方法

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449815

Xuan Li, Dehua Li

{"title":"A combined KFDA method and GUI realized for face recognition","authors":"Xuan Li, Dehua Li","doi":"10.1109/ICACI.2016.7449815","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449815","url":null,"abstract":"Traditional face recognition methods such as Principal Components Analysis(PCA), Independent Component Analysis(ICA) and Linear Discriminant Analysis(LDA) are linear discriminant methods, but in the real situation, a lot of problems can't be linear discriminated; therefore, researchers proposed face recognition method based on kernel techniques which can transform the nonlinear problem of inputting space into the linear problem of high dimensional space. In this paper, we propose a recognition method based on kernel function which combines kernel Fisher Discriminant Analysis(KFDA) with kernel Principle Components Analysis(KPCA) and use typical ORL(Olivetti Research Laboratory) face database as our experimental database. There are four key steps: constructing feature subspace, image projection, feature extraction and image recognition. We found that the recognition accuracy has been greatly improved by using nonlinear identification method and combined feature extraction methods. We use MATLAB software as the platform, and use the GUI to demonstrate the process of face recognition in order to achieving human-computer interaction and making the process and result more intuitive.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130414149","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

Observer design for single-link robot arm systems based on sampled and delayed output 基于采样和延迟输出的单连杆机械臂观测器设计

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449801

Yanjun Shen, Shiqi Fu, Zhengping Wu

引用次数: 2

Spam filtering by semantics-based text classification 基于语义的文本分类垃圾邮件过滤

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449809

Wei Hu, Jinglong Du, Yongkang Xing

{"title":"Spam filtering by semantics-based text classification","authors":"Wei Hu, Jinglong Du, Yongkang Xing","doi":"10.1109/ICACI.2016.7449809","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449809","url":null,"abstract":"Spam has been a serious and annoying problem for decades. Even though plenty of solutions have been put forward, there still remains a lot to be promoted in filtering spam emails more efficiently. Nowadays a major problem in spam filtering as well as text classification in natural language processing is the huge size of vector space due to the numerous feature terms, which is usually the cause of extensive calculation and slow classification. Extracting semantic meanings from the content of texts and using these as feature terms to build up the vector space, instead of using words as feature terms in tradition ways, could reduce the dimension of vectors effectively and promote the classification at the same time. In this paper, a novel Chinese spam filtering approach with semantics-based text classification technology was proposed and the related feature terms were selected from the semantic meanings of the text content. Both the extraction of semantic meanings and the selection of feature terms are implemented through attaching annotations on the texts layer-by-layer. This filter performed well when experimented on a public Chinese spam corpus.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126176615","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: 20

Brain storm optimization algorithm for full area coverage of wireless sensor networks 无线传感器网络全区域覆盖的头脑风暴优化算法

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449796

Haoyu Zhu, Yuhui Shi

{"title":"Brain storm optimization algorithm for full area coverage of wireless sensor networks","authors":"Haoyu Zhu, Yuhui Shi","doi":"10.1109/ICACI.2016.7449796","DOIUrl":"https://doi.org/10.1109/ICACI.2016.7449796","url":null,"abstract":"Coverage problem is a fundamental issue in designing efficient wireless sensor networks, in which both coverage rate and energy consumption should be considered. A brain storm optimization algorithm is a swarm intelligence algorithm which is inspired by the human brainstorming process. This paper will focus on the application of the brain storm optimization algorithm in full coverage problems of wireless sensor networks. The full coverage problems are divided into two types: problems either with fixed or flexible number of activated sensor nodes. Binary detection model and grid based strategy will be used in describing the mathematic model of the full coverage problem, which will be applied to test the effectiveness of the brain storm optimization algorithm for solving coverage problems of wireless sensor networks in different areas. Experimental results on irregular areas even with obstacles illustrate the efficiency and effectiveness of the brain storm optimization algorithm for solving full coverage problems of wireless sensor networks. In addition, if the number of activated sensor nodes is flexible, with an appropriate weight coefficient, the brain storm optimization algorithm can obtain a reasonable number of sensor nodes to realize full coverage.","PeriodicalId":211040,"journal":{"name":"2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125303548","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 adaptive neuro fuzzy inference system for prediction of anxiety of students 基于自适应神经模糊推理系统的学生焦虑预测

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449795

S. Devi, Sanjay Kumar, G. Kushwaha

引用次数: 19

Spatio-temporal cuboid pyramid for action recognition using depth motion sequences 基于深度运动序列的时空长方体金字塔动作识别

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2016-02-01 DOI: 10.1109/ICACI.2016.7449827

Xiaopeng Ji, Jun Cheng, Wei Feng

引用次数: 11