Research on Evaluation Model of Hospital Informatization Level Based on Decision Tree Algorithm

In order to improve the weight calculation accuracy of hospital informatization level evaluation and shorten the evaluation time, a research method of hospital informatization level evaluation model based on the decision tree algorithm is proposed. Using the decision tree algorithm combining fuzzy theory and ID3, the decision tree is constructed to analyze the hospital information data. By means of questionnaire survey, expert experience, mathematical statistics, and in-depth interview, information facilities construction, information resources construction, information scientific research application, management information, and information guarantee are selected as the nodes of the decision tree to evaluate the hospital information level. Construct the structural equation model, standardize the data, extract the weight of each evaluation index, and complete the evaluation of hospital informatization level. The experimental results show that the weight calculation results of this method are basically consistent with the actual results, and the evaluation efficiency is improved.

Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.

Research on Crude Oil Trade Procurement Model Based on DEA-Malmquist Algorithm

To retain valuable information to the maximum extent and enhance the ability to mine the crude oil trade purchase price demand, this paper proposes a crude oil trade purchase model based on the DEA-Malmquist algorithm. The intranet of the management and control platform shall share the same database, and the intranet shall only allow managers to access and manage the system and only allow all registered users to access and realize data exchange between the intranet and the intranet through two-dimensional code scanning; moreover, due to the resource sharing between the intranet and the intranet for crude oil trade procurement, suppliers and other registered users can immediately grasp the procurement trends of enterprises. Under the DEA-Malmquist algorithm, the uncertainty of procurement management is analyzed by fuzzy theory, and the refined procurement decision model with fuzzy parameters is established. The optimal order time and purchase quantity are determined through the symbol distance and the method of the center of gravity. Experimental results show that the method can effectively retain valuable information in the initial sequence and has better practical application value of material procurement demand intelligent mining. The proposed model obtained the highest accuracy of 98.62%.

Inverse Result of Approximation for the Max-Product Neural Network Operators of the Kantorovich Type and Their Saturation Order

In this paper, we consider the max-product neural network operators of the Kantorovich type based on certain linear combinations of sigmoidal and ReLU activation functions. In general, it is well-known that max-product type operators have applications in problems related to probability and fuzzy theory, involving both real and interval/set valued functions. In particular, here we face inverse approximation problems for the above family of sub-linear operators. We first establish their saturation order for a certain class of functions; i.e., we show that if a continuous and non-decreasing function f can be approximated by a rate of convergence higher than 1/n, as n goes to +∞, then f must be a constant. Furthermore, we prove a local inverse theorem of approximation; i.e., assuming that f can be approximated with a rate of convergence of 1/n, then f turns out to be a Lipschitz continuous function.

Research on risk management of medical and health care integration projects based on fuzzy WINGS-G1

PurposeThis study establishes a risk management system for medical and health care integration projects to address the problem of high-risk potential and a strong correlation between risk factors.Design/methodology/approachA new fuzzy WINGS-G1 model for identifying key risk factors in medical and health care integration projects is proposed by introducing the fuzzy theory and the concept of risk incidence into the Weighted Influence Non-linear Gauge System (WINGS) method.FindingsThe authors analyze the fluidity of project risk factors through complex networks to control direct risks and cut off risk transmission paths to provide a reference for risk control and prevention of medical and health care integration projects.Originality/value(1) The integration of fuzzy theory into the WINGS method solves the problem of strong subjectivity of expert scoring in the traditional WINGS method; (2) By the different probabilities of risk factors, the concept of risk incidence is introduced in the WINGS model, which is more conducive to the identification of the critical risk factors and the rational allocation and utilization of organizational resources; (3) The use of the complex network for risk interactivity analysis fully reflects the dynamic nature of risk factors in medical and health care integration projects.

A Fuzzy Multi-Criteria Model for Municipal Waste Treatment Systems Evaluation including Energy Recovery

One of the recent problems on waste sorting systems is their performance evaluation for proper decision making and management. For this purpose, multi-criteria methods can be used to evaluate the sorting system from both operational and financial perspectives. According to a recent literature review, there are no solutions for evaluating waste sorting systems that take into account: sorting point utilisation, sorting efficiency, waste stream irregularity, and technical system availability. In addition, the problem of data uncertainty and the need to use expert judgements indicate the need for the implementation of methods adjusted to the qualitative and quantitative assessment, such as the fuzzy approach. Following this, in order to overcome the presented limitations, the authors introduced the new assessment method for waste sorting systems based on multi-criteria model implementation and fuzzy theory use. Therefore, the developed model was based on a hierarchical fuzzy logic model for which appropriate membership function parameters and inference rules were defined. The specificity of the chosen assessment criteria and their justification was provided. The model has been implemented to evaluate one of the waste sorting plants in Wroclaw, Poland. Tests have been conducted for seven different configurations of waste sorting lines (with variable input parameters). The study focuses on analysing the amount of selected waste at each station in relation to the total stream size of each fraction. Efficiency was measured by the mass of the collected waste and the number of pieces of waste in each fraction. Based on the obtained results, estimations of particular parameters of the model were made, and the results were presented and commented on. It was shown that there is a significant relationship between the level of system evaluation and sorting efficiency and an inverse relationship with the level of RDF obtained. The analysis was based on Pearson’s linear correlation coefficient estimation and linear regression implementation.

Study of Fuzzy Fractional Nonlinear Equal Width Equation in the Sense of Novel Operator

In this paper, we designed an algorithm by applying the Laplace transform to calculate some approximate solutions for fuzzy fractional-order nonlinear equal width equations in the sense of Atangana-Baleanu-Caputo derivatives. By analyzing the fuzzy theory, the suggested technique helps the solution of the fuzzy nonlinear equal width equations be investigated as a series of expressions in which the components can be effectively recognised and produce a pair of numerical results with the uncertainty parameters. Several numerical examples are analyzed to validate convergence outcomes for the given problem to show the proposed method’s utility and capability. The simulation outcomes reveal that the fuzzy iterative transform method is an effective method for accurately and precisely studying the behavior of suggested problems. We test the developed algorithm by three different problems. The analytical analysis provided that the results of the models converge to their actual solutions at the integer-order. Furthermore, we find that the fractional derivative produces a wide range of fuzzy results.