Disease Intelligence (DI) is based on the acquisition and aggregation of fragmented knowledge of diseases at multiple sources all over the world to provide valuable information to doctors, researchers and information seeking community. Some diseases have their own characteristics changed rapidly at different places of the world and are reported on documents as unrelated and heterogeneous information which may be going unnoticed and may not be quickly available. This research presents an Ontology based theoretical framework in the context of medical intelligence and country/region. Ontology is designed for storing information about rapidly spreading and changing diseases with incorporating existing disease taxonomies to genetic information of both humans and infectious organisms. It further maps disease symptoms to diseases and drug effects to disease symptoms. The machine understandable disease ontology represented as a website thus allows the drug effects to be evaluated on disease symptoms and exposes genetic involvements in the human diseases. Infectious agents which have no known place in an existing classification but have data on genetics would still be identified as organisms through the intelligence of this system. It will further facilitate researchers on the subject to try out different solutions for curing diseases.Comment: Disease Intelligence, Disease Ontology, Information Extraction, Semantic We

Research and Development work on Commercial Agriculture (CA) has not sufficiently been taken into account of those transformations occurring in the country’s socio-economic and political environment, especially in the lens of decision-making and adoption of eco-friendly technologies in CA. It has been debated for a long time that well-set strategic policies and guidelines incorporated and agreed upon by all stakeholders into a model possess a vast potential to facilitate such decision-making towards the establishment of an eco-friendly CA. In the light of that, an illustrative model was built of which the logical paths were characterized by the variables stand for Key Performance Drives (KPD) to exhibit the state of societal acceptance of the research outputs on the concept of ‘Research for Society’ within the realm of functional flexibility of research institutes. The outputs and outcomes from such an exploratory analysis would facilitate research institutes working on CA to systematically adopt the ‘best-fit KPDs’ in research development and researches to be conceived from societal roots. Further, it would establish the true connections and dependencies between research, social awareness of research needs, and flexibility of research institutes to enhance the performance of the sector, through collected data on Key Performance Indicators (KPI).