Semantic annotation represents a metadata of the document based on domain ontology. The purpose of this paper is to develop a ranking algorithm for semantic document annotation and to evaluate its performance in the Semantic Web (SW) application. The evaluation is to compare the ranking algorithm against other algorithms. For the evaluation purpose, all the algorithms are applied into the SW application. The SW application is a research prototype retrieval engine, PicoDoc. The system framework of PicoDoc is based on OCAS2008 ontology. During the experimentation stage, a real-life dataset from news article corpus of ABC and BBC websites are selected. The experiment shows promising results in retrieving related information using the ranking algorithm.

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Tongue inspection is a complementary diagnosis method that widely used in Traditional Chinese Medicine (TCM) by inspecting the tongue body constitution to decide the physiological and pathological functions of the human body. Since tongue manifestation is done by practitioner’s observation using naked eye, many limitations can affect the diagnosis result including environment condition and experiences of the practitioner. Lately, tongue diagnosis has been widely studied in order to solve these limitations via digital system. However, most of recent digital system are bulky and not equipped with intelligent diagnosis system that can finally predict the health status of the patient. In this research, digital tongue diagnosis system that uses intelligent diagnosis consisted of image segmentation analysis, tongue coating recognition analysis, and tongue color classification has been embedded on Raspberry Pi. Tongue segmentation implements Hue, Saturation and Value (HSV) color space with Brightness Conformable Multiplier (BCM) for adaptive brightness filtering to recognized tongue body accurately while eliminating perioral area. Tongue Coating Recognition uses threshold method to detect tongue coating and eliminate the unwanted features including shadow. Tongue color classification uses hybrid method consisted of k-means clustering and Support Vector Machine (SVM) to classify between red, light red and deep red tongue and further gave diagnosis based on color. This experiment concluded that it is feasible to embed the algorithm on Raspberry Pi to promote system portability while attaining similar accuracy for future telemedicine.

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