2010 10th International Conference on Hybrid Intelligent Systems最新文献

Evolutionary improvement of search queries and its parameters 搜索查询及其参数的进化改进

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600018

P. Krömer, V. Snás̃el, J. Platoš, A. Abraham

引用次数: 6

Internet traffic classification using a Hidden Markov Model 使用隐马尔可夫模型的互联网流量分类

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5601068

J. Maia, R. H. Filho

{"title":"Internet traffic classification using a Hidden Markov Model","authors":"J. Maia, R. H. Filho","doi":"10.1109/HIS.2010.5601068","DOIUrl":"https://doi.org/10.1109/HIS.2010.5601068","url":null,"abstract":"This paper examines the performance of a new Hidden Markov Model (HMM) structure used as the core of an Internet traffic classsifier and compares the results against other models present in the literature. Traffic modeling and classification find importance in many areas such as bandwidth management, traffic analysis, prediction and engineering, network planning, Quality of Service provisioning and anomalous traffic detection. The new HMM structure, which takes into account the packet payload size (PS) and the inter-packet times (IPT) sequences, is obtained by concatenation of a first part which is framed with a HMM profile with another part whose structure is that of a fully-connected HMM. The first part captures the specific properties of the initial protocol packets while the second part captures the statistical properties of the whole sequence present in the flow. Models generated are found to increase the accurate in classifying different traffic classes in the analysed dataset. The average accuracy obtained by the classifier is 62.5% having seen only five packets, 80.0% after examining 13 packets and 95.5% after seeing the unidirectional entire flow.","PeriodicalId":174618,"journal":{"name":"2010 10th International Conference on Hybrid Intelligent Systems","volume":"351 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122477835","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: 13

The application of a CICA Neural Network on Farsi license plates recognition CICA神经网络在波斯语车牌识别中的应用

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600081

Mojdeh Akhtari, K. Faez

引用次数: 6

Fractal image compression based on spatial correlation and chaotic particle swarm optimization 基于空间相关和混沌粒子群优化的分形图像压缩

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600077

G. Vahdati, M. Yaghoobi, M. Akbarzadeh-Totonchi

{"title":"Fractal image compression based on spatial correlation and chaotic particle swarm optimization","authors":"G. Vahdati, M. Yaghoobi, M. Akbarzadeh-Totonchi","doi":"10.1109/HIS.2010.5600077","DOIUrl":"https://doi.org/10.1109/HIS.2010.5600077","url":null,"abstract":"Fractal image compression explores the self-similarity property of a natural image and utilizes the partitioned iterated function system (PIFS) to encode it. This technique is of great interest both in theory and application. However, it is time-consuming in the encoding process and such drawback renders it impractical for real time applications. The time is mainly spent on the search for the best-match block in a large domain pool. In order to solve the high complexity of the conventional encoding scheme for fractal image compression, a spatial correlation chaotic particle swarm optimization (SC-CPSO), based on the characteristics of fractal and partitioned iterated function system (PIFS) is proposed in this paper. There are two stages for the algorithm: (1) Make use of spatial correlation in images for both range and domain pool to exploit local optima. (2) Adopt chaotic PSO (CPSO) to explore the global optima if the local optima are not satisfied. Experiment results show that the algorithm convergent rapidly. At the premise of good quality of the reconstructed image, the algorithm saved the encoding time and obtained high compression ratio.","PeriodicalId":174618,"journal":{"name":"2010 10th International Conference on Hybrid Intelligent Systems","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116370231","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

DPF-based japanese phoneme recognition using tandem MLNs 基于dpf的串联mln日语音素识别

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600078

Mohammed Rokibul Alam Kotwal, Manoj Banik, G. M. Islam, M. Hossain, Foyzul Hassan, Mohammad Mahedi Hasan, Muhammad Ghulam, M. N. Huda

引用次数: 0

Metaheuristic techniques for Support Vector Machine model selection 支持向量机模型选择的元启发式技术

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600086

J. Blondin, A. Saad

引用次数: 16

A Gaussian Artificial Immune System for Multi-Objective optimization in continuous domains 连续域多目标优化的高斯人工免疫系统

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600022

P. Castro, F. V. Zuben

引用次数: 8

Hybrid system for a never-ending unsupervised learning 混合系统永无止境的无监督学习

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5601070

A. Dragoni, G. Vallesi, P. Baldassarri

引用次数: 0

A hybrid evolutionary direct search technique for solving Optimal Control problems 一种求解最优控制问题的混合进化直接搜索技术

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600080

A. Ghosh, Aritra Chowdhury, Ritwik Giri, Swagatam Das, A. Abraham

{"title":"A hybrid evolutionary direct search technique for solving Optimal Control problems","authors":"A. Ghosh, Aritra Chowdhury, Ritwik Giri, Swagatam Das, A. Abraham","doi":"10.1109/HIS.2010.5600080","DOIUrl":"https://doi.org/10.1109/HIS.2010.5600080","url":null,"abstract":"An Optimal Control is a set of differential equations describing the path of the control variables that minimize the cost functional (function of both state and control variables). Direct solution methods for optimal control problems treat them from the perspective of global optimization: perform a global search for the control function that optimizes the required objective. Invasive Weed Optimization (IWO) technique is used here for optimal control. However, the direct solution method operates on discrete n-dimensional vectors, not on continuous functions, and becomes computationally unmanageable for large values of n. Thus, a parameterization technique is required, which can represent control functions using a small number of real-valued parameters. Typically, direct methods using evolutionary techniques parameterize control functions with a piecewise constant approximation. This has obvious limitations, both for accuracy in representing arbitrary functions, and for optimization efficiency. In this paper a new parameterization is introduced, using Bézier curves, which can accurately represent continuous control functions with only a few parameters. It is combined with Invasive Weed Optimization into a new evolutionary direct method for optimal control. The effectiveness of the new method is demonstrated by solving a wide range of optimal control problems.","PeriodicalId":174618,"journal":{"name":"2010 10th International Conference on Hybrid Intelligent Systems","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121319418","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

Using a reinforcement-based feature selection method in Classifier Ensemble 基于增强的分类器集成特征选择方法

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600015

K. Vale, Antonino Feitosa Neto, A. Canuto

引用次数: 5