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Ontology-based Design of Knowledge Processing MachinesThe work is devoted to the development of intelligent systems knowledge processing machines design
Troussov, A., Jundge, J., Alexandrov, M., and Levner, E. Social Context as Machine-Processable Knowledge. Proceedings of the International Conference on Intelligent Information and Engineering Systems INFOS 2011, Rzeszów - Polańczyk, Poland, pTroussov, A., Jundge, J., Alexandrov, M., and Levner, E. Social Context as Machine-Processable
Modelling societal knowledge in the health sector: Machine learning and google trends computational model of the social cognitive processes related to the acquisition of new knowledge in the medical
Модели и средства компонентного проектирования машин обработки знаний на основе семантических сетей methods, means and technologies of
knowledge processing machines design. The article
describes
Thinking lifecycle as an implementation of machine understanding in software maintenance automation domain the feasibility study of automation of incident processing in Infrastructure as Service domain to optimize
Decision-making process analysis and semantic explanation extraction from trained supervised machine learning models in medical expert systems-making process of a trained model is very hard, especially considering the probabilistic nature of machine
Evolution of thinking models in automatic incident processing systems of the application of thinking models in automatically processing a user’s incidents in natural language, starting
Thinking lifecycle as an implementation of machine understanding in software maintenance automation domain the feasibility study of automation of incident processing in Infrastructure as Service domain to optimize
Evolution of thinking models in automatic incident processing systems of the application of thinking models in automatically processing a user’s incidents in natural language, starting
Multiple features for clinical relation extraction: A machine learning approach propose a machine learning model with a novel set of knowledge-based and BioSentVec embedding features. We
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