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Tropical cyclones (TC) are often associated with severe weather conditions which cause great losses to lives and property. The precise classification of cyclone tracks is significantly important in the field of weather forecasting. In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine (SVM) to classify the tropical cyclone tracks into four types of classes namely straight, quasi-straight, curving and sinuous based on the track shape. Tropical Cyclone TRacks Ontology (TCTRO) described in this paper is a knowledge base which comprises of classes, objects and data properties that represent the interaction among the TC characteristics. A set of SWRL (Semantic Web Rule Language) rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology. Furthermore, we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset. According to experiments on the IBTrACS dataset, the proposed ontology based SVM classifier achieves an accuracy of 98.3% with reduced classification error rates. [ABSTRACT FROM AUTHOR]

Cochlear implantation is a surgical procedure by which an electronic medical device, namely a cochlear implant, is fitted to the individual who has the challenge of hearing. It takes the function of the damaged inner ear, the cochlea. The hearing impaired may not benefit from this procedure because of cross-modal plasticity. This work aims to study the implantee's visual modal adaptation by analyzing the visual evoked potential. The proposed methodology analyses the existence of cross-modal reorganization in the auditory cortex of bilateral prelingually deaf children after cochlear implantation using visual evoked potential. Fifty healthy, 50 prelingually, deaf children 50 cochlear implanted were considered as a cohort of the Visual evoked potential. The evoked potential recorded using pattern reversal stimulation. The amplitude and latency of N75, P100, and N145 components show a significant difference in normal, cochlear, and deaf. The early diagnosis of hearing impairment demands the patients and doctors to make a series of decisions for the betterment of the implantee in an accelerated manner through traditional database methodology implemented for making decisions, analyzing, interpreting, processing of data is so difficult in the conventional system. Medical knowledge represented for the computers to analyze the inferred data and to make the decisions. Ontology is the most potent tool to encode medical data semantically. A fuzzy ontology-based decision support system built to predict the cochlear implantation outcome. The ontology created using Protégé software tool and the decision taken using Jena and pellet reasoner. A fuzzy-based prediction model designed using a fuzzy interface system to estimate the categories of auditory performance (CAP) test reliable indicators of cochlear implantation success. [ABSTRACT FROM AUTHOR]