Abstract-This paper presents an automated ontology framework for service robots. The framework is designed to automatically create an ontology and an instance of concept in dynamic environment. Ontology learning from text is applied to build a concept hierarchy using WordNet which provides a rich semantic processing for physical objects. The Automated Ontology is composed of four modules: Concept Creation, Property Creation, Relationship Creation and Instance of Concept Creation. The automated ontology algorithm was implemented in order to create the concept hierarchy in the Robot Ontology. The Semantic Knowledge Acquisition represents knowledge of physical objects in dynamic environments. In simulation experiments, the list of object names and property names was identified. The result shows the concept hierarchy which represents explicit terms and the semantic knowledge of physical objects for performing everyday manipulation tasks.
Early diagnosis of colorectal cancer focuses on detecting polyps in the colon as early as possible so that patients can have the best chances for success- ful treatment. This research presents the optimized parameters for polyp detection using a deep learning technique. Polyp and non-polyp images are trained on the InceptionResnetV2 model by the Faster Region Con- volutional Neural Networks (Faster R-CNN) framework to identify polyps within the colon images. The proposed method revealed more remarkable results than previous works, precision: 92.9 %, recall: 82.3%, F1-Measure: 87.3%, and F2-Measure: 54.6% on public ETIS-LARIB data set. This detection technique can reduce the chances of missing polyps during a pro- longed clinical inspection and can improve the chances of detecting multiple polyps in colon images.
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