2010 10th International Conference on Hybrid Intelligent Systems最新文献_第3页

Evaluating classification methods applied to multi-label tasks in different domains 评价不同领域多标签任务的分类方法

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600014

A. M. Santos, Anne M. P. Canuto, Antonino Feitosa Neto

引用次数: 7

Examination of the performance of objective reduction using correlation-based weighted-sum for many objective knapsack problems 用基于关联的加权和方法检验目标约简在许多目标背包问题中的性能

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600027

T. Murata, Akinori Taki

引用次数: 14

On the improvement of Knowledge Management status through case-based reasoning in a hybrid approach 基于案例推理的混合型知识管理现状改进研究

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600025

E. Corchado, Aitor Mata, Álvaro Herrero, Lourdes Cecilia Sáiz Bárcena

引用次数: 0

An optimization heuristic for siting observers in huge terrains stored in external memory 存储在外部存储器中的巨大地形中观测者定位的优化启发式算法

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600013

S. V. G. Magalhães, M. Andrade, W. Randolph Franklin

引用次数: 8

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties 不确定条件下的智能仿生制造调度系统

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600084

A. Madureira, I. Pereira

引用次数: 30

An improved identification technique of gene regulatory network from gene expression time series data using multi-objective differential evolution 基于多目标差分进化的基因表达时序数据基因调控网络识别技术的改进

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5601067

Debasish Datta, A. Konar, A. Nagar, Archana Bisoyi

{"title":"An improved identification technique of gene regulatory network from gene expression time series data using multi-objective differential evolution","authors":"Debasish Datta, A. Konar, A. Nagar, Archana Bisoyi","doi":"10.1109/HIS.2010.5601067","DOIUrl":"https://doi.org/10.1109/HIS.2010.5601067","url":null,"abstract":"Gene regulatory network provides the knowledge of interaction strength among the genes in living organisms. Accurate identification of gene regulatory network is of prime interest to the researchers in recent time. Different researchers applied different optimization techniques to solve this problem. Most of these optimization techniques considers the square error between reference and simulated gene expression as their objective and minimize it to get a solution for the identification problem under consideration. But these techniques do not guarantee a unique set of network parameter, because the squared error is a non-linear multimodal surface of network parameters. Therefore considering only square error as the objective function is not a good choice. An alternative way of formulation of this problem is to validate it from different perspective. In this paper, we propose a technique for identification of gene regulatory network using multiple objectives. The objectives are designed to make the identification technique more robust. Multi-objective differential evolution is used to find a set of pareto-optimal solutions with respect to the objective functions. Among those solutions, one is chosen according to some suitable criterion. Computer simulation has shown that the proposed technique can identify useful interaction information from gene expression time series data.","PeriodicalId":174618,"journal":{"name":"2010 10th International Conference on Hybrid Intelligent Systems","volume":"38 6 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":"125709395","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 comparison of positive, boundary, and possible rules using the MLEM2 rule induction algorithm 使用MLEM2规则归纳算法的正规则、边界规则和可能规则的比较

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5601064

J. Grzymala-Busse, Shantan R. Marepally, Yiyu Yao

引用次数: 6

A GRASP heuristic with Path-Relinking for a bi-objective p-median problem 双目标p中值问题的路径链接把握启发式

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600091

J. Arroyo, M. D. S. Soares, P. M. Santos

引用次数: 5

A Particle Swarm Optimization based approach for the maximum coverage problem in cellular base stations positioning 基于粒子群优化的蜂窝基站最大覆盖问题定位方法

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600087

Antônio I. S. Nascimento, C. J. A. B. Filho

引用次数: 6

AIIDA-SQL: An Adaptive Intelligent Intrusion Detector Agent for detecting SQL Injection attacks AIIDA-SQL:用于检测SQL注入攻击的自适应智能入侵检测代理

2010 10th International Conference on Hybrid Intelligent Systems Pub Date : 2010-10-18 DOI: 10.1109/HIS.2010.5600026

Cristian Pinzón, J. F. D. Paz, J. Bajo, Álvaro Herrero, E. Corchado

{"title":"AIIDA-SQL: An Adaptive Intelligent Intrusion Detector Agent for detecting SQL Injection attacks","authors":"Cristian Pinzón, J. F. D. Paz, J. Bajo, Álvaro Herrero, E. Corchado","doi":"10.1109/HIS.2010.5600026","DOIUrl":"https://doi.org/10.1109/HIS.2010.5600026","url":null,"abstract":"SQL Injection attacks on web applications have become one of the most important information security concerns over the past few years. This paper presents a hybrid approach based on the Adaptive Intelligent Intrusion Detector Agent (AIIDA-SQL) for the detection of those attacks. The AIIDA-SQL agent incorporates a Case-Based Reasoning (CBR) engine which is equipped with learning and adaptation capabilities for the classification of SQL queries and detection of malicious user requests. To carry out the tasks of attack classification and detection, the agent incorporates advanced algorithms in the reasoning cycle stages. Concretely, an innovative classification model based on a mixture of an Artificial Neuronal Network together with a Support Vector Machine is applied in the reuse stage of the CBR cycle. This strategy enables to classify the received SQL queries in a reliable way. Finally, a projection neural technique is incorporated, which notably eases the revision stage carried out by human experts in the case of suspicious queries. The experimental results obtained on a real-traffic case study show that AIIDA-SQL performs remarkably well in practice.","PeriodicalId":174618,"journal":{"name":"2010 10th International Conference on Hybrid Intelligent Systems","volume":"11 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":"114979067","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: 45