Applications and Innovations in Intelligent Systems XVI: Proceedings of AI-2008, The Twenty-eighth SGAI International Conference on Innovative Techniques and Applications of Artificial IntelligenceTony Allen, Richard Ellis, Miltos Petridis Swallowing sound recognition is an important task in bioengineering that could be employed in systems for automated swallowing assessment and diagnosis of abnormally high rate of swallowing (aerophagia) [1], which is the primary mode of ingesting excessive amounts of air, and swallowing dysfunction (dysphagia) [2]-[5], that may lead to aspiration, choking, and even death. Dysphagia represents a major problem in rehabilitation of stroke and head injury patients. In current clinical practice videofluoroscopic swallow study (VFSS) is the gold standard for diagnosis of swallowing disorders. However, VFSS is a ti- consuming procedure performed only in a clinical setting. VFSS also results in some radiation exposure. Therefore, various non-invasive methods are proposed for swallowing assessment based on evaluation of swallowing signals, recorded by microphones and/or accelerometers and analyzed by digital signal processing techniques [2]-[5]. Swallowing sounds are caused by a bolus passing through pharynx. It is possible to use swallowing sounds to determine pharyngeal phase of the swallow and characteristics of the bolus [2]. |
Contents
| 3 | |
| 18 | |
| 19 | |
Recognition of Swallowing Sounds Using TimeFrequency Decomposition and LimitedReceptive Area Neural Classifier | 33 |
Visualization of Agriculture Data UsingSelfOrganizing Maps | 47 |
MACHINE LEARNING 2 | 61 |
Graphbased Image Classification by Weighting Scheme
| 63 |
A Machine Learning Application for Classification of Chemical Spectra
| 77 |
Speech Input Logon | 147 |
An Electronic Tree Inventory for Arboriculture Management
| 157 |
Conversational Agents in ELearning | 169 |
AI IN HEALTHCARE | 184 |
Breast cancer diagnosis based on evolvable fuzzy classifiers and feature selection
| 185 |
Towards Next Generation Healthcare | 197 |
A Hybrid Constraint Programming Approach for Nurse Rostering Problems
| 211 |
Using Evolved Fuzzy Neural Networks for Injury Detection from Isokinetic Curves
| 225 |
Learning to rank order a distancebased approach
| 91 |
WEB TECHNOLOGIES | 102 |
Deploying Embodied AI into Virtual Worlds | 103 |
Using Ontology Search Engines to support Users and Intelligent Systems solving a range of tasks
| 117 |
Combining DLReasoning with PublishSubscribe
| 131 |
INTELLIGENT SYSTEMS | 146 |
SHORT PAPERS | 240 |
An evolutionary approach to simulated football free kick optimisation
| 241 |
An Application of Artificial Intelligence to the Implementation of Electronic Commerce
| 247 |
Hybrid System for the Inventory of the Cultural Heritage using Voice Interface for Knowledge acquisition
| 253 |
Other editions - View all
Applications and Innovations in Intelligent Systems XVI: Proceedings of AI ... Tony Allen,Richard Ellis,Miltos Petridis No preview available - 2008 |
Applications and Innovations in Intelligent Systems XVI: Proceedings of AI ... Tony Allen,Richard Ellis,Miltos Petridis No preview available - 2008 |
Common terms and phrases
Accessed achieved agent algorithm allows analysis application approach assigned associated average classification clinical Computer concepts Conference connected constraints contains conversational agents corresponds create dataset defined described descriptors developed distance domain Engineering evaluation example existing experiments extracted Figure Framework frequent function further fuzzy genetic given graph IEEE implementation improve individual initial input instances Intelligence International knowledge language layer learning LIRA load machine means method MicroGA mining neural network neuron nurse objects obtained ontologies patterns performance population position possible presented problem Proceedings proposed range ranking recognition represents rosters rules sample selection sequence shift shows solutions strategies structure sub-graph Table task techniques tree types University values variables vector virtual world weighted