Browsing by Author "Lin, Ting-Lan"

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A Convolutional Neural Network Approach for Dental Panoramic Radiographs Classification
(2017-12-01)
Kuo, Yu-Fang
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Lin, Szu-Yin
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Wu, Calvin H.
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Chen, Shih-Lun
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Lin, Ting-Lan
Dental Panoramic Radiograph (DPR) has been widely accepted in the dental industry in recent years for providing valuable information to dental practitioners. Conventional fashion of analyzing this information has always been relying on the professional individuals. Nonetheless, the recent technology breakthrough with computer vision is promising in reducing effort and labor costs. The purpose of this study is to propose an automated DPR classification method that could be used to identify condition such as having undergone endodontic therapy. This new method used a combination of unique preprocessing procedure with highly developed image recognition algorithm known as Convolutional Neural Network (CNN). The new method firstly preprocesses and normalizes the DPR images as the training dataset, then trains the network model using the training data. Finally, testing datasets will then be used for generating evaluation results. Based from the experimental results, the trained network with preprocessed training datasets performed an accuracy rate of 85.3%. In addition, the performance of the network could still be improved by increasing the number of training datasets and using more preprocessing methods. In conclusion, computer visions can provide confident better performance in DPR classification in practices such as DPR classification.
Automated knowledge discovery and semantic annotation for network and web services
(2016-07-01)
Lin, Szu-Yin
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Chung, Chia-Chen
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Hu, Wei-Che
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Hung, Chihli
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Chen, Shih-Lun
With the rise of the Internet of things, the smart environmental issue is becoming increasingly important. Sensor web is one of the best solutions to this issue and provides the advantages of sensor networks and web services. Ontology web language for services (OWL-S) is an OWL-based web services ontology, which provides the ability to describe the semantics of web services and their capabilities in a formal and machine-processable manner. Moreover, it aids semantic service matching, selection and composition. However, automatically annotating semantic web services is a highly complicated and tedious task. In this study, we propose a methodology to uncover information in the history data and profiles of web services and then semantically annotate them. With the proposed approach, semantic relationships between web services could be extracted via a combination of association rules and input/ output matching. Our results show that this hybrid automated knowledge-discovery approach works better than traditional approaches do. We also provide a scenario to explain how the proposed methodology works.