Authors:
Augustin Prodan
1
;
Mădălina Rusu
1
;
Remus Câmpean
1
and
Rodica Prodan
2
Affiliations:
1
Iuliu Haţieganu University, Romania
;
2
MedFam Group, Romania
Keyword(s):
Web-based Education, e-Learning Scenario, Java and XML Technologies, Artificial Intelligence, Intelligent Tutoring Systems, Wound Image Understanding, Wound Healing Simulation.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Applications
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bayesian Networks
;
Case-Based Reasoning
;
Computer-Supported Education
;
Databases and Information Systems Integration
;
e-Business
;
Education/Learning
;
e-Learning
;
e-Learning and e-Teaching
;
Enterprise Information Systems
;
Human-Computer Interaction
;
Intelligent Agents
;
Internet Technology
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Multimedia Database Applications
;
Pattern Recognition
;
Soft Computing
;
Software Agents and Internet Computing
;
Symbolic Systems
;
Theory and Methods
;
Web Databases
;
Web Information Systems and Technologies
Abstract:
This paper presents an e-learning framework for analyzing, processing and understanding wound images, to be used in teaching, learning and research activities. We intend to promote e-learning technologies in medical, pharmaceutical and health care domains. Our approach to e-learning is so called blended learning, which combines traditional face-to-face and Web-based on-line learning, with focus on principles of active learning. Using Java and XML technologies, we build models for various categories of wounds, due to various aetiologies. Based on colour and texture analysis, we identify the main barriers to wound healing, such as tissue non-viable, infection, inflammation, moisture imbalance, or edge non-advancing. This framework provides the infrastructure for preparing e-learning scenarios based on practice and real world experiences. We make experiments for wound healing simulation using various treatments and compare the results with experimental observations. Our experiments are
supported by XML based databases containing knowledge extracted from previous wound healing experiences and from medical experts knowledge. Also, we rely on new paradigms of the Artificial Intelligence for creating e-learning scenarios to be used in a context of active learning, for wound image understanding. To implement the e-learning tools, we use Java technologies for dynamic processes and XML technologies for dynamic content.
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