Adv. Artif. Intell.最新文献_第4页

Selection for Reinforcement-Free Learning Ability as an Organizing Factor in the Evolution of Cognition 无强化学习能力在认知进化中的组织因素选择

Adv. Artif. Intell. Pub Date : 2013-01-01 DOI: 10.1155/2013/841646

S. Arnold, Reiji Suzuki, Takaya Arita

引用次数: 4

Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks 用偏最小二乘回归和人工神经网络预测哮喘预后

Adv. Artif. Intell. Pub Date : 2013-01-01 DOI: 10.1155/2013/435321

E. Chatzimichail, E. Paraskakis, A. Rigas

{"title":"Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks","authors":"E. Chatzimichail, E. Paraskakis, A. Rigas","doi":"10.1155/2013/435321","DOIUrl":"https://doi.org/10.1155/2013/435321","url":null,"abstract":"The long-termsolution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors to identify which of the young patients are at a high risk of asthma disease progression. Moreover, thismay lead to better treatment opportunities and hopefully better disease outcomes in adulthood.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":"75 1","pages":"435321:1-435321:7"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72831827","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: 19

A Novel Method for Training an Echo State Network with Feedback-Error Learning 一种基于反馈误差学习的回声状态网络训练新方法

Adv. Artif. Intell. Pub Date : 2013-01-01 DOI: 10.1155/2013/891501

R. A. Løvlid

引用次数: 3

A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation 多机器人任务分配的优化与市场方法比较研究

Adv. Artif. Intell. Pub Date : 2013-01-01 DOI: 10.1155/2013/256524

Mohamed Badreldin, A. Hussein, A. Khamis

引用次数: 57

A Hybrid Reasoning Model for "Whole and Part" Cardinal Direction Relations “整体与部分”基本方向关系的混合推理模型

Adv. Artif. Intell. Pub Date : 2013-01-01 DOI: 10.1155/2013/205261

A. Kor, B. Bennett

引用次数: 10

Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features 不精确归算作为求解未观测特征均值分类问题的工具

Adv. Artif. Intell. Pub Date : 2013-01-01 DOI: 10.1155/2013/176890

L. Utkin, Y. Zhuk

引用次数: 0

Adaptive Group Formation in Multirobot Systems 多机器人系统中的自适应群体形成

Adv. Artif. Intell. Pub Date : 2013-01-01 DOI: 10.1155/2013/692658

Ahmed Wagdy, A. Khamis

引用次数: 8

Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration 基于人工智能的电力系统恢复过程中开关过电压评估技术

Adv. Artif. Intell. Pub Date : 2013-01-01 DOI: 10.1155/2013/316985

I. Sadeghkhani, A. Ketabi, R. Feuillet

引用次数: 2

Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion 利用多传感器数据融合处理数据不确定性和不一致性

Adv. Artif. Intell. Pub Date : 2013-01-01 DOI: 10.1155/2013/241260

Waleed A. Abdulhafiz, A. Khamis

引用次数: 25

Ant Colony Optimisation for Backward Production Scheduling 逆向生产调度的蚁群优化

Adv. Artif. Intell. Pub Date : 2012-09-19 DOI: 10.1155/2012/312132

Leandro Pereira dos Santos, G. E. Vieira, H. V. D. R. Leite, M. T. Steiner

{"title":"Ant Colony Optimisation for Backward Production Scheduling","authors":"Leandro Pereira dos Santos, G. E. Vieira, H. V. D. R. Leite, M. T. Steiner","doi":"10.1155/2012/312132","DOIUrl":"https://doi.org/10.1155/2012/312132","url":null,"abstract":"The main objective of a production scheduling system is to assign tasks (orders or jobs) to resources and sequence them as efficiently and economically (optimised) as possible. Achieving this goal is a difficult task in complex environment where capacity is usually limited. In these scenarios, finding an optimal solution—if possible—demands a large amount of computer time. For this reason, in many cases, a good solution that is quickly found is preferred. In such situations, the use of metaheuristics is an appropriate strategy. In these last two decades, some out-of-the-shelf systems have been developed using such techniques. This paper presents and analyses the development of a shop-floor scheduling system that uses ant colony optimisation (ACO) in a backward scheduling problem in a manufacturing scenario with single-stage processing, parallel resources, and flexible routings. This scenario was found in a large food industry where the corresponding author worked as consultant for more than a year. This work demonstrates the applicability of this artificial intelligence technique. In fact, ACO proved to be as efficient as branch-and-bound, however, executing much faster.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":"65 1","pages":"312132:1-312132:12"},"PeriodicalIF":0.0,"publicationDate":"2012-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77536550","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: 8