Mode Optimization and Rule Management of Intellectual Property Rights Protection of Educational Resource Data Based on Machine Learning Algorithm

Educational resource data are a collection of final documents obtained by users, including full-text journals, books, dissertations, newspapers, conference papers, and other database materials. While searching for information in the educational resource database, these resources also have functions such as copying, downloading, reproduction, and dissemination, which raise the issue of expression and protection of intellectual property. Machine learning takes how computers simulate human learning behaviors as the main research content, which can independently determine learning objects, construct their characteristics, perform additional operations beyond the limitations of preset instructions, and discover value from the expression of relative works. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of intellectual property expression and protection of educational resource data; elaborated the development background, current status, and future challenges of machine learning technology; introduced the methods and principles of data classification algorithm and protection authority identification; performed the technical framework design and expression system establishment of the intellectual property expression of educational resource data based on machine learning; analyzed the mode optimization and rule management of intellectual property protection of educational resource data based on machine learning; and finally conducted a simulation experiment and its result analysis. The results show that the machine learning technology can build a subject-oriented, highly integrated, and time-changing educational resource data storage environment; the comprehensive, analysis-oriented decision-supporting system formed by machine learning can give full play to the potential role of data integration and value discovery and is therefore of great significance for the intellectual property expression and protection of integrated and complexly-related educational resource data. The study results of this paper provide a reference for further research on the intellectual property expression and protection of educational resource data based on machine learning.

A Fuzzy-Based Customisation of Healthcare Knowledge to Support Clinical Domestic Decisions for Chronically Ill Patients

The development of customised healthcare systems is becoming an important issue in the healthcare industry due to the rapid increase in the number of chronically ill patients. These systems aim to deliver effective care to patients having chronic diseases through customised services. However, knowledge bases need also to be customised since systems are confronted with huge amount of personalised and imprecise medical knowledge. Therefore, we propose in this paper a new system to customise medical knowledge according to progressive disease phases and pathological cases. A rule management process first customises rules according to the specificities of every disease phase, and then matches a private knowledge base with each enrolled patient. This base contains only the patient’s customised knowledge. After reasoning, another customisation process is carried out by the component, Result Manager, which ensures the validation of the system outcomes by the pathological case experts, before being recommended. This will better ensure the recommendation of the generated results to the non-professional users. In addition, Result Manager offers fuzzy semantic queries to the experts. In conclusion, our new decision support system makes medical aid decisions not only addressed to physicians, but also to chronically ill patients and persons regarded as caregivers.