Search results for: Anchana Sooksomchitra

Authors: Anchana Sooksomchitra

Abstract:

This study aims to explore and compare the current condition of community radio stations in Phutthamonthon district, Nakhon Pathom province, Thailand, as well as the challenges they are facing. Qualitative research tools including in-depth interviews; documentary analysis; focus group interviews; and observation, are used to examine the content, programming, and management structure of three community radio stations currently in operation within the district. Research findings indicate that the management and operational approaches adopted by the two non-profit stations included in the study, Salaya Pattana and Voice of Dhamma, are more structured and effective than that of the for-profit Tune Radio. Salaya Pattana – backed by the Faculty of Engineering, Mahidol University, and the charity-funded Voice of Dhamma, are comparatively free from political and commercial influence, and able to provide more relevant and consistent community-oriented content to meet the real demand of the audience. Tune Radio, on the other hand, has to rely solely on financial support from political factions and business groups, which heavily influence its content.

Keywords: Radio broadcasting, programming, management, community radio, Thailand.

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Authors: Anchana Khemphila, Veera Boonjing

Abstract:

In this study, the Multi-Layer Perceptron (MLP)with Back-Propagation learning algorithm are used to classify to effective diagnosis Parkinsons disease(PD).It-s a challenging problem for medical community.Typically characterized by tremor, PD occurs due to the loss of dopamine in the brains thalamic region that results in involuntary or oscillatory movement in the body. A feature selection algorithm along with biomedical test values to diagnose Parkinson disease.Clinical diagnosis is done mostly by doctor-s expertise and experience.But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests for diagnosis.In many cases,not all the tests contribute towards effective diagnosis of a disease.Our work is to classify the presence of Parkinson disease with reduced number of attributes.Original,22 attributes are involved in classify.We use Information Gain to determine the attributes which reduced the number of attributes which is need to be taken from patients.The Artificial neural networks is used to classify the diagnosis of patients.Twenty-Two attributes are reduced to sixteen attributes.The accuracy is in training data set is 82.051% and in the validation data set is 83.333%.

Keywords: Data mining, classification, Parkinson disease, artificial neural networks, feature selection, information gain.

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