Innovations in Applied Artificial Intelligence: 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2005, Bari, Italy, June 22-24, 2005, ProceedingsFloriana Esposito “Intelligent systems are those which produce intelligent o?springs.” AI researchers have been focusing on developing and employing strong methods that are capable of solving complex real-life problems. The 18th International Conference on Industrial & Engineering Applications of Arti?cial Intelligence & Expert Systems (IEA/AIE 2005) held in Bari, Italy presented such work performed by many scientists worldwide. The Program Committee selected long papers from contributions presenting more complete work and posters from those reporting ongoing research. The Committee enforced the rule that only original and unpublished work could be considered for inclusion in these proceedings. The Program Committee selected 116 contributions from the 271 subm- ted papers which cover the following topics: arti?cial systems, search engines, intelligent interfaces, knowledge discovery, knowledge-based technologies, na- ral language processing, machine learning applications, reasoning technologies, uncertainty management, applied data mining, and technologies for knowledge management. The contributions oriented to the technological aspects of AI and the quality of the papers are witness to a research activity clearly aimed at consolidating the theoretical results that have already been achieved. The c- ference program also included two invited lectures, by Katharina Morik and Roberto Pieraccini. Manypeoplecontributedindi?erentwaystothesuccessoftheconferenceand to this volume. The authors who continue to show their enthusiastic interest in applied intelligence research are a very important part of our success. We highly appreciate the contribution of the members of the Program Committee, as well as others who reviewed all the submitted papers with e?ciency and dedication. |
From inside the book
Results 1-5 of 30
Page 17
... pixels between grey levels, and accumulating this information as a charge. This representation is also called accumulative computation, and has already been proved in applications such as moving object shape recognition in noisy ...
... pixels between grey levels, and accumulating this information as a charge. This representation is also called accumulative computation, and has already been proved in applications such as moving object shape recognition in noisy ...
Page 18
... pixel [ x , y ] of the data field is calculated . Generally , if the property is fulfilled at pixel [ x , y ] , the charge value at that pixel Ch [ x , y , t ] goes incrementing by increment charge value C up to reaching Chmax , whilst ...
... pixel [ x , y ] of the data field is calculated . Generally , if the property is fulfilled at pixel [ x , y ] , the charge value at that pixel Ch [ x , y , t ] goes incrementing by increment charge value C up to reaching Chmax , whilst ...
Page 19
... pixel enters the computation. In stereovision, methods based on local primitives as pixels and contours may be very efficient, but are too much sensitive to locally ambiguous regions, such as occlusions or uniform texture regions ...
... pixel enters the computation. In stereovision, methods based on local primitives as pixels and contours may be very efficient, but are too much sensitive to locally ambiguous regions, such as occlusions or uniform texture regions ...
Page 24
... pixels, as its output is a dense map of dispari- ties. Besides, it also takes the advantage of algorithms based on higher level primi- tives by putting into correspondence complete regions of the image – see, permanency memories - and ...
... pixels, as its output is a dense map of dispari- ties. Besides, it also takes the advantage of algorithms based on higher level primi- tives by putting into correspondence complete regions of the image – see, permanency memories - and ...
Page 27
... pixel o )} i,j=1,...,IW,IH (i,j) in the (IW: image image being width, part IH: of image height) object, where α o is its parameters and I is a photometric variable. The search window location is simply computed as follows [5, 6, 7]: x ...
... pixel o )} i,j=1,...,IW,IH (i,j) in the (IW: image image being width, part IH: of image height) object, where α o is its parameters and I is a photometric variable. The search window location is simply computed as follows [5, 6, 7]: x ...
Contents
| 1 | |
| 16 | |
| 19 | |
| 36 | |
| 55 | |
| 69 | |
Robust Character Segmentation System for Korean Printed Postal | 82 |
FeatureTableBased Automatic Question Generation for TreeBased | 95 |
Endoscopy Images Classification with Kernel Based Learning | 400 |
Minimum Spanning Trees in Hierarchical Multiclass Support Vector | 422 |
Improving the Readability of Decision Trees Using Reduced Complexity | 442 |
An Application in Public Health Care | 459 |
A DomainIndependent Approach to DiscourseLevel Knowledge | 470 |
A Meteorological Conceptual Modeling Approach Based on Spatial | 490 |
Mining Information Extraction Rules from Datasheets Without | 510 |
Genetic Algorithms | 524 |
DistanceBased Dynamic Interaction of Humanoid Robot with Multiple | 111 |
Robot Competition Using Gesture Based Interface | 131 |
Feasibility of Multiagent Simulation for the Trust and Tracing Game | 145 |
Reliable Multiagent Systems with Persistent PublishSubscribe | 165 |
Automated Teleoperation of WebBased Devices Using Semantic | 185 |
Plan Execution in Dynamic Environments | 208 |
Structural Advantages for Ant Colony Optimisation Inherent | 218 |
New Upper Bounds for the Permutation Flowshop Scheduling Problem | 232 |
Automatic Word Spacing in Korean for Small Memory Devices | 249 |
Haptic Fruition of 3D Virtual Scene by Blind People | 269 |
Towards Effective Adaptive Information Filtering Using Natural | 290 |
Discovering Learning Paths on a Domain Ontology Using Natural | 310 |
A Geometric Approach to Automatic Description of Iconic Scenes | 315 |
Reasoning | 321 |
Inferring DefiniteClause Grammars to Express Multivariate Time Series | 332 |
Obtaining a Bayesian Map for Data Fusion and Failure Detection | 342 |
Event Handling Mechanism for Retrieving Spatiotemporal Changes | 353 |
Freeway Traffic Qualitative Simulation | 360 |
PredictionBased Diagnosis and Loss Prevention Using ModelBased | 367 |
Classification of Ophthalmologic Images Using an Ensemble of Classifiers | 380 |
Application of a Genetic Algorithm to Nearest Neighbour Classification | 544 |
Hardware Architecture for Genetic Algorithms | 554 |
Selforganizing Radial Basis Function Network Modeling for Robot | 579 |
Using an Artificial Neural Network to Improve Predictions of Water | 599 |
Detecting Fraud in Mobile Telephony Using Neural Networks | 613 |
Decision Support and Heuristic Search | 619 |
A SwarmBased | 638 |
Search on Transportation Network for LocationBased Service | 657 |
A New Crowded Comparison Operator in Constrained Multiobjective | 678 |
PoseInvariant Face Detection Using EdgeLike Blob Map and Fuzzy | 695 |
A Fuzzy LogicBased Approach for Detecting Shifting Patterns | 705 |
Semantic | 722 |
Complementing Search Engines with Text Mining | 743 |
Building Intelligent | 762 |
A Case Study | 783 |
A Nurse Scheduling System Based on Dynamic Constraint Satisfaction | 799 |
CaseBased Reasoning for Financial Prediction | 839 |
A Support Method for Qualitative SimulationBased Learning System | 851 |
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Innovations in Applied Artificial Intelligence: 18th International ... Floriana Esposito Limited preview - 2005 |
Innovations in Applied Artificial Intelligence: 18th International ... Floriana Esposito No preview available - 2005 |
Common terms and phrases
Abstract agent analysis application approach Artificial Intelligence Artificial Neural Networks attributes Berlin Heidelberg 2005 cells circuit classes classifier cluster components Computer concept constraints context data mining database dataset decision decision tree defined detection domain environment error Esposito Eds evaluation Evolutionary Algorithms example experiments feature extraction function fuzzy genetic algorithm goal graph haptic hierarchies IEA/AIE IEEE implementation information extraction input knowledge layer LNAI Machine Learning MCCs measure method module multi-agent systems neural networks neurons node objects obtained ontology operator optimal output paper parameters part-numbers patterns perceptron performance pixel prediction problem Proc proposed query sequence recognition region represent representation robot Section selection semantic sensor signal simulation solution space spatial association rules speech recognition Springer-Verlag Berlin Heidelberg structure Support Vector Machines Table techniques Technology tion tracking traffic variables visualization