2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks最新文献_第5页

A Non-linear Function Approximation from Small Samples Based on Nadaraya-Watson Kernel Regression 基于Nadaraya-Watson核回归的小样本非线性函数逼近

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI: 10.1109/CICSyN.2010.10

M. I. Shapiai, Z. Ibrahim, M. Khalid, W. Lee, V. Pavlovic

引用次数: 19

An Intelligent Framework for the Classification of Premium and Regular Gasoline for Arson and Fuel Spill Investigation Based on Extreme Learning Machines 基于极限学习机的高级汽油和普通汽油纵火和燃油泄漏调查分类智能框架

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI: 10.1109/CICSyN.2010.37

S. Olatunji, I. Adeleke

引用次数: 4

Energy-Efficient Gradient Time Synchronization for Wireless Sensor Networks 无线传感器网络的高能效梯度时间同步

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI: 10.1109/CICSyN.2010.14

Kittipat Apicharttrisorn, S. Choochaisri, C. Intanagonwiwat

引用次数: 14

A Straight Moving Path Planner for Mobile Robots in Static Environments Using Cellular Automata 基于元胞自动机的静态环境下移动机器人直线运动路径规划

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI: 10.1109/CICSYN.2010.28

Faranak Rahimi Soofiyani, A. Rahmani, M. Mohsenzadeh

引用次数: 12

Digital Imaging in Pathology towards Detection and Analysis of Human Breast Cancer 数字成像在人类乳腺癌病理检测与分析中的应用

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI: 10.1109/CICSyN.2010.43

S. Bandyopadhyay, I. Maitra, Souvik Banerjee

引用次数: 9

Cluster Based Peers Configuration Using HCNP in Peer-to-Peer Overlay Networks 点对点覆盖网络中基于HCNP的集群对等体配置

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI: 10.1109/CICSyN.2010.30

Irum Kazmi, Saira Aslam, M. Y. Javed

引用次数: 3

Spectral Entropy for Epileptic Seizures Detection 用于癫痫发作检测的光谱熵

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI: 10.1109/CICSyN.2010.84

Ahmad Mirzaei, A. Ayatollahi, P. Gifani, L. Salehi

引用次数: 9

An Intrusion Detection Architecture for Clustered Wireless Ad Hoc Networks 集群无线自组织网络的入侵检测体系结构

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI: 10.1109/CICSyN.2010.51

Jaydip Sen

{"title":"An Intrusion Detection Architecture for Clustered Wireless Ad Hoc Networks","authors":"Jaydip Sen","doi":"10.1109/CICSyN.2010.51","DOIUrl":"https://doi.org/10.1109/CICSyN.2010.51","url":null,"abstract":"Intrusion detection in wireless ad hoc networks is a challenging task because these networks change their topologies dynamically, lack concentration points where aggregated traffic can be analyzed, utilize infrastructure protocols that are susceptible to manipulation, and rely on noisy, intermittent wireless communications. Security remains a major challenge for these networks due their features of open medium, dynamically changing topologies, reliance on co-operative algorithms, absence of centralized monitoring points, and lack of clear lines of defense. In this paper, we present a cooperative, distributed intrusion detection architecture based on clustering of the nodes that addresses the security vulnerabilities of the network and facilitates accurate detection of attacks. The architecture is organized as a dynamic hierarchy in which the intrusion data is acquired by the nodes and is incrementally aggregated, reduced in volume and analyzed as it flows upwards to the cluster-head. The cluster-heads of adjacent clusters communicate with each other in case of cooperative intrusion detection. For intrusion related message communication, mobile agents are used for their efficiency in lightweight computation and suitability in cooperative intrusion detection. Simulation results show effectiveness and efficiency of the proposed architecture.","PeriodicalId":358023,"journal":{"name":"2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127172449","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: 30

A Modified Artificial Neural Network Learning Algorithm for Imbalanced Data Set Problem 不平衡数据集问题的改进人工神经网络学习算法

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI: 10.1109/CICSyN.2010.9

Asrul Adam, M. I. Shapiai, Z. Ibrahim, M. Khalid, L. C. Chew, W. Lee, J. Watada

{"title":"A Modified Artificial Neural Network Learning Algorithm for Imbalanced Data Set Problem","authors":"Asrul Adam, M. I. Shapiai, Z. Ibrahim, M. Khalid, L. C. Chew, W. Lee, J. Watada","doi":"10.1109/CICSyN.2010.9","DOIUrl":"https://doi.org/10.1109/CICSyN.2010.9","url":null,"abstract":"A modified learning algorithm of Artificial Neural Networks (ANN) is introduced in this paper to solve imbalanced data set problems. In solving imbalanced data set, it is critical to predict the minority class due to their imbalanced nature. In order to improve the standard ANN classifier prediction performance, this paper focuses on optimizing the decision boundary of the step function at the output layer of ANN using particle swarm optimization (PSO). A feedforward ANN is chosen in this study. Firstly, a conventional back propagation algorithm is employed to train the ANN. PSO is then applied to train the real predicted output of training data from this trained network. As the result, the optimum value of decision boundary is found and applied to the classifier. Prediction performance is assessed by G-mean, which is a measure to indicate the efficiency of classifiers for imbalanced data sets. Based on experimental results, the proposed model is able to solve imbalanced data sets problem with better performance compared to the standard ANN.","PeriodicalId":358023,"journal":{"name":"2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127457545","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: 14

An Evolutionary Approach to Image Noise Cancellation Using Adaptive Particle Swarm Optimization (APSO) 基于自适应粒子群优化(APSO)的图像消噪进化方法

2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks Pub Date : 2010-07-28 DOI: 10.1109/CICSyN.2010.20

P. Sree, Ravikant Verma, P. Kumar, Siddavatam Rajesh, S. P. Ghrera

引用次数: 2