2006 Graduate Catalog and 2005 Annual R & D Report - Sirindhorn ...

2006 Graduate Catalog and 2005 Annual R & D Report
Sirindhorn International Institute of Technology (SIIT)
Medical Image Processing
Taking advantage of the high capability of computers,
offering advantages over film based systems, several
image processing techniques are of interest,
especially for medical purposes in order to get most
of the information out of the given medical images.
Essentially, medical imaging can make use of texture
information, texture feature classification or texture
segmentation because of the nature of the medical
image itself. Medical assessment can then be made
fully automated later on and this will lead to a
reduction of human errors, increasing of consistency
and repeatability. This can be distributed to the
remote areas or hospitals that lack sophisticated
treatment facilities or trained experts.
Dr. Cholwich Nattee
Lecturer
B.Eng. in Computer Engineering, Chulalongkorn University, Thailand
M.Eng. in Computer Science, Tokyo Institute of Technology, Japan
D.Eng. in Computer Science, Tokyo Institute of Technology, Japan
Research Areas: Artificial intelligence, Machine learning, Knowledge discovery and Data mining, Artifcial
Intelligence applications in distance learning and pattern recognition.
Research Interest:
Inductive Logic Programming for Structure-
Activity Relationship Studies
Nowadays, a vast amount of chemical compound
structure information can be produced due to
advances in High Throughput Screening technology
that automates compound screening using the
combination of robotics, image processing and
pattern recognition. From these data, knowledge
describing compound activities and characteristics
from their structures is essential, since it can be used
for predicting characteristics of unknown compounds
for developing new drugs. Machine learning and data
mining techniques have been applied in order to
automatically obtain models describing the relations
between structure and activity. However, traditional
data mining algorithms have limitations on knowledge
representations. Thus, complicated structures of
chemical compounds cannot be handled efficiently.
Extended from traditional machine learning
techniques, Inductive Logic Programming (ILP)
applies first-order logic for representing data. This
allows complicated structures or relations among
training examples to be denoted without losing any
information. Moreover, learning results in the form of
first-order rules, are comprehensible. The knowledge
obtained can be easily explained to domain experts.
Dr. Ekawit Nantajeewarawat
Associate Professor
B.Eng. in Computer Engineering, Chulalongkorn University, Thailand
M.Eng. & D.Eng. in Computer Science, Asian Institute of Technology (AIT), Thailand
Areas of Specialization: Knowledge representations, Computational logics, Computation theory, Programming
paradigms, Object-oriented system analysis and design.
Research Interests:
Semantic Web
Expectedly, Semantic Web technology will bring
about large-scale heterogeneous Web knowledge
bases with a qualitatively new level of service. The
concept of ontology (domain theory) will play a key
role as a formal, explicit specification of shared
conceptualizations that describe the semantics of
data on the Web. Grounded upon Description logics
(DLs), the theory of XML declarative descriptions
(XDD) and Resource Description Framework (RDF),
formal ontology languages as well as meta-level
representation of Web resources are investigated.
The possibility of developing automated reasoning
systems for Semantic Web is explored from both
theoretical and practical viewpoints, e.g., a hybrid
reasoning system comprising a DL-based reasoning
component and a rule-based backward chaining
component. Realization of the Semantic Web vision
demands further research work on several other
knowledge-representation-related issues.
Reasoning with UML Diagrams
The Unified Modeling Language (UML) is a graphical
language, adopted as a standard by the Object
Management Group (OMG), for visualizing,
specifying, constructing, and documenting the
artifacts of a software-intensive system. As reported
by recent works on the formal semantics of UML,
there exist inherent interrelationships between
components of a UML model. Such interrelationships
constitute part of general knowledge about the
domain of UML, which may be used, e.g., for deriving
implicit properties and verifying the consistency of the
model. A framework for knowledge representation
and reasoning in the domain of UML is proposed, in
which a UML model is represented as textual XML
data, and the general knowledge about the UML
domain as an XML declarative description.
Development of an inference engine for automatic
refinement of the encoded UML diagrams and
derivation of implicit model properties is underway.
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