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Fuzzy Logic Inference system for Managing Intensive Care Unit Resources Based on Knowledge Graph

Computers, Materials & Continua 2023年第12期77卷 3801-3816页

作者： Ahmad F Subahi Areej Athama Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare *** systems can manage and manipulate this massive amount of health-related data and encourage different decision-making *** can also provide various sustainable health services such as medical error reduction,diagnosis acceleration,and clinical services quality *** intensive care unit(ICU)is one of the most important hospital ***,there are limited rooms and resources in most *** times of seasonal diseases and pandemics,ICUs face high admission *** line with this increasing number of admissions,determining health risk levels has become an essential and imperative *** creates a heightened demand for the implementation of an expert decision support system,enabling doctors to accurately and swiftly determine the risk level of ***,this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs,as a proof-of-concept,for tackling this healthcare-related *** system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to *** proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.

关键词： Fuzzy logic role-based expert system decision-support system knowledge graph Internet of Things ICU resource management Neo4J graph database

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Challenges of Renewable Forest Fuels for Green Electricity Market

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Smart Grid and Renewable Energy 2016年第2期7卷 83-93页

作者： Teijo Palander Kalle Kärhä Department of Forestry University of Eastern Finland Kuopio Finland Stora Enso Oyj Metsä Helsinki Finland

In this study, strategic electricity market scenarios are considered in a grid of Scandinavia. This multiple-objective decision environment includes the allocation of a number of renewable forest fuel procurement chains to a combined heat and power plant in Finland. The decision environment includes also electricity procurement from Sweden and Russia. The environment is further complicated by sequence-dependent operations of the local procurement chains during different periods. Due to the complex nature of the environment, multiple-objective methods cannot be directly used to solve the electricity production problem in a manner that is techno-economically relevant to the forest energy industry. Therefore, local and time-varying parameters were measured in local wood procurement conditions to improve the solution method. Using these measurements the smart decision-support system automatically adjusted the multiple-objective methodology to better describe the combinatorial complexity of the production sector. The properties of this methodology are discussed and three scenarios of how the system works based on local real-world data and optional feed-in tariff of green electricity are presented. The Finnish electricity market is subject to policy decisions regarding green energy production regulations. These decisions should be made on the basis of local techno-economic analysis presented in this study accounting for the effects of forest operations on the electricity production and import.

关键词： decision-support system Distributed Electricity Production Feed-In Tariff Green Electricity Local Measure Smart system

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