Abstract: Clustering is a well known data mining technique used in pattern recognition and information retrieval. The initial dataset to be clustered can either contain categorical or numeric data. Each type of data has its own specific clustering algorithm. In this context, two algorithms are proposed: the k-means for clustering numeric datasets and the k-modes for categorical datasets. The main encountered problem in data mining applications is clustering categorical dataset so relevant in the datasets. One main issue to achieve the clustering process on categorical values is to transform the categorical attributes into numeric measures and directly apply the k-means algorithm instead the k-modes. In this paper, it is proposed to experiment an approach based on the previous issue by transforming the categorical values into numeric ones using the relative frequency of each modality in the attributes. The proposed approach is compared with a previously method based on transforming the categorical datasets into binary values. The scalability and accuracy of the two methods are experimented. The obtained results show that our proposed method outperforms the binary method in all cases.
Abstract: This research work is aimed at speech recognition
using scaly neural networks. A small vocabulary of 11 words were
established first, these words are “word, file, open, print, exit, edit,
cut, copy, paste, doc1, doc2". These chosen words involved with
executing some computer functions such as opening a file, print
certain text document, cutting, copying, pasting, editing and exit.
It introduced to the computer then subjected to feature extraction
process using LPC (linear prediction coefficients). These features are
used as input to an artificial neural network in speaker dependent
mode. Half of the words are used for training the artificial neural
network and the other half are used for testing the system; those are
used for information retrieval.
The system components are consist of three parts, speech
processing and feature extraction, training and testing by using neural
networks and information retrieval.
The retrieve process proved to be 79.5-88% successful, which is
quite acceptable, considering the variation to surrounding, state of
the person, and the microphone type.
Abstract: Thailand-s health system is challenged by the rising
number of patients and decreasing ratio of medical
practitioners/patients, especially in rural areas. This may tempt
inexperienced GPs to rush through the process of anamnesis with the
risk of incorrect diagnosis. Patients have to travel far to the hospital
and wait for a long time presenting their case. Many patients try to
cure themselves with traditional Thai medicine. Many countries are
making use of the Internet for medical information gathering,
distribution and storage. Telemedicine applications are a relatively
new field of study in Thailand; the infrastructure of ICT had
hampered widespread use of the Internet for using medical
information. With recent improvements made health and technology
professionals can work out novel applications and systems to help
advance telemedicine for the benefit of the people. Here we explore
the use of telemedicine for people with health problems in rural areas
in Thailand and present a Telemedicine Diagnosis System for Rural
Thailand (TEDIST) for diagnosing certain conditions that people
with Internet access can use to establish contact with Community
Health Centers, e.g. by mobile phone. The system uses a Web-based
input method for individual patients- symptoms, which are taken by
an expert system for the analysis of conditions and appropriate
diseases. The analysis harnesses a knowledge base and a backward
chaining component to find out, which health professionals should be
presented with the case. Doctors have the opportunity to exchange
emails or chat with the patients they are responsible for or other
specialists. Patients- data are then stored in a Personal Health Record.