2017 11th International Conference on Intelligent Systems and Control (ISCO)最新文献_第4页

Significance of entropy correlation coefficient over symmetric uncertainty on FAST clustering feature selection algorithm 熵相关系数对对称不确定性在FAST聚类特征选择算法中的意义

2017 11th International Conference on Intelligent Systems and Control (ISCO) Pub Date : 1900-01-01 DOI: 10.1109/ISCO.2017.7856035

Pallavi Malji, S. Sakhare

{"title":"Significance of entropy correlation coefficient over symmetric uncertainty on FAST clustering feature selection algorithm","authors":"Pallavi Malji, S. Sakhare","doi":"10.1109/ISCO.2017.7856035","DOIUrl":"https://doi.org/10.1109/ISCO.2017.7856035","url":null,"abstract":"Feature selection is an essential method in which we identify a subset of most useful ones from the original set of features. On comparing results with original set and identified subset, we observe that the results are compatible. The feature selection algorithm is evaluated based on the components of efficiency and effectiveness, where the time required and the optimality of the subset of the feature is considered. Based on this, we are modifying the fast clustering feature selection algorithm, to check the impact of entropy correlation coefficient on it in this paper. In the algorithm, the correlation between the features is calculated using entropy correlation coefficient instead of symmetric uncertainty and then they are divided into clusters using clustering methods based on the graph. Then, the representative features i.e. those who are strongly related to the target class are selected from them. For ensuring the algorithm's efficiency, we have adopted the Kruskal minimum spanning tree (MST) clustering method. We have compared our proposed algorithm with FAST clustering feature selection algorithm on well-known classifier namely the probability-based Naive Bayes Classifier before and after feature selection. The results, on two publicly available real-world high dimensional text data, demonstrate that our proposed algorithm produces smaller and optimal features subset and also improves classifiers performance. The processing time required for the algorithm is far less than that of the FAST clustering algorithm.","PeriodicalId":321113,"journal":{"name":"2017 11th International Conference on Intelligent Systems and Control (ISCO)","volume":"os-1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127847650","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

How diverse is the network information obtained from the nodes of a biological neural network? 从生物神经网络的节点中获得的网络信息有多多样?

2017 11th International Conference on Intelligent Systems and Control (ISCO) Pub Date : 1900-01-01 DOI: 10.1109/ISCO.2017.7855970

Sayan Biswas

引用次数: 2

Cloud-Based monitoring and measurement of pressure and temperature using CC3200 基于云的压力和温度监测和测量使用CC3200

2017 11th International Conference on Intelligent Systems and Control (ISCO) Pub Date : 1900-01-01 DOI: 10.1109/ISCO.2017.7856024

Divyavani Palle, Raghavendra Rao Kanchi

引用次数: 1

Immunization strategy based on discrete particle swarm optimization algorithm in BBV network 基于离散粒子群优化算法的BBV网络免疫策略

2017 11th International Conference on Intelligent Systems and Control (ISCO) Pub Date : 1900-01-01 DOI: 10.1109/ISCO.2017.7855982

Yang Min, Zhang Jiayue, Zhang Damin

引用次数: 3

A new chaotic attractor from Rucklidge system and its application in secured communication using OFDM Rucklidge系统中一种新的混沌吸引子及其在OFDM保密通信中的应用

2017 11th International Conference on Intelligent Systems and Control (ISCO) Pub Date : 1900-01-01 DOI: 10.1109/ISCO.2017.7855989

Ramanathan C., A. R, K. Sriharsha, S. Hamsavaahini, A. R., J. Saranya, R. Subhathira, Kalaiselvan K., N. R. Raajan

引用次数: 2

Sample review comments 评审意见样本

2017 11th International Conference on Intelligent Systems and Control (ISCO) Pub Date : 1900-01-01 DOI: 10.1109/isco.2017.7856048

引用次数: 0

Buck-boost converter using Fuzzy logic for low voltage solar energy harvesting application 基于模糊逻辑的Buck-boost变换器在低压太阳能收集中的应用

2017 11th International Conference on Intelligent Systems and Control (ISCO) Pub Date : 1900-01-01 DOI: 10.1109/ISCO.2017.7856029

Chetan P. Ugale, V. Dixit

引用次数: 16

Conceptual view of low-cost sensory Evaporimeter based on Internet of Things (IoT) 基于物联网(IoT)的低成本感测蒸发器概念图

2017 11th International Conference on Intelligent Systems and Control (ISCO) Pub Date : 1900-01-01 DOI: 10.1109/ISCO.2017.7856018

Kirtan Gopal Panda, N. Kumar, A. Hossain

引用次数: 1

Modelling, simulation & comparison of BLDC motor and induction motor based condenser in a chiller cooler system using CFD 基于CFD的无刷直流电机和感应电机的冷凝器在冷水机组冷却器系统中的建模、仿真和比较

2017 11th International Conference on Intelligent Systems and Control (ISCO) Pub Date : 1900-01-01 DOI: 10.1109/ISCO.2017.7855980

P. A. Michael, Suji Vinayak A., J. A., Viswa Hariharan A., Sharon G.

引用次数: 6

Image segmentation using clustering with fireworks algorithm 利用烟花聚类算法进行图像分割

2017 11th International Conference on Intelligent Systems and Control (ISCO) Pub Date : 1900-01-01 DOI: 10.1109/ISCO.2017.7855961

P. Misra, Tapas Si

{"title":"Image segmentation using clustering with fireworks algorithm","authors":"P. Misra, Tapas Si","doi":"10.1109/ISCO.2017.7855961","DOIUrl":"https://doi.org/10.1109/ISCO.2017.7855961","url":null,"abstract":"This paper presents a hard clustering technique using fireworks algorithm with adaptive transfer function (FWAATF) for image segmentation. The fireworks algorithm (FWA) is a recently developed new Swarm Intelligence (SI) algorithm for function optimization. This algorithm simulates the process of fireworks explosion in the night sky. The main characteristic of FWA is the good balance between exploration and exploitation during the search process. The exploitation is done using good fireworks whereas the bad fireworks are responsible for exploration. FWA shows its efficiency and effectiveness in numerical function optimization over other SI algorithm like particle swarm optimization (PSO). FWA-ATF is a modified version of basic FWA and in this work, it is used in hard clustering technique to segment the image. FWA-ATF is used to find the optimal cluster centroids corresponding to different regions in the image. The proposed clustering technique is applied to segment four benchmark images and the well-known cluster validity index-Dunn's Index is used to measure the performance of the proposed clustering technique quantitatively. The performance of the proposed method is compared with clustering using K-means, PSO and basic FWA. The experimental results demonstrates that the proposed clustering technique with FWA-ATF performs better than other methods in segmentation for most of the images.","PeriodicalId":321113,"journal":{"name":"2017 11th International Conference on Intelligent Systems and Control (ISCO)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132288988","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