How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic

Intuitionistic fuzzy logic is the main tool in the recently developed step-wise “cross-evaluation” procedure that aims at the assessment of different optimization algorithms. In this investigation, the procedure previously applied to compare the effectiveness of two or three algorithms has been significantly upgraded to evaluate the performance of a set of four algorithms. For the first time, the procedure applied here has been tested in the evaluation of the effectiveness of genetic algorithms (GAs), which are proven as very promising and successful optimization techniques for solving hard non-linear optimization tasks. As a case study exemplified with the parameter identification of a S. cerevisiae fed-batch fermentation process model, the cross-evaluation procedure has been executed to compare four different types of GAs, and more specifically, multi-population genetic algorithms (MGAs), which differ in the order of application of the three genetic operators: Selection, crossover and mutation. The results obtained from the implementation of the upgraded intuitionistic fuzzy logic-based procedure for MGA performance assessment have been analyzed, and the standard MGA has been outlined as the fastest and most reliable one among the four investigated algorithms.

Development process of multiagent system for glycemic control of intensive care unit patients

This article presents the entire process of developing an agent-based system for the glycemic control of patients in the Intensive Care Unit (ICU). The agent’s goal is to monitor and recommend treatment to keep the patient’s blood glucose within the target range, avoiding complications in the health of patients and even decreasing rates of morbidity and mortality in the ICU. The process of developing the agent-based solution was presented, starting from the understanding of the problem, including a brief review of the literature, going through the pre-project and modelling through the Tropos methodology, until the implementation. The agent inference mechanism is based on production rules and intuitionistic fuzzy logic. An illustration of use, with the collaboration of a specialist intensive care physician, shows how agents behave in a real situation of monitoring and controlling the blood glucose of patients admitted to the ICU, interacting with all elements of the proposed architecture. Finally, feedback from health professionals indicate the system can assist in the glycemic control of patients in the ICU having advantages over traditional monitoring systems.

Intuitionistic fuzzy Logic System and its Application to Global Carbon Dioxide Emissions Prediction

In this study, a Takagi-Sugeno-Kang based intuitionistic fuzzy logic system is proposed for the prediction of global carbon dioxide emission for the first time. The intuitionistic fuzzy logic system is an integration of artificial neural network learning and intuitionistic fuzzy logic reasoning. The gradient descent back propagation is applied in the optimization of the parameters of the proposed model. The model is evaluated based on some performance metrics. Results of evaluation revealed that the intuitionistic fuzzy logic system outperforms other existing models in the literature in terms of prediction accuracy

Prioritization of Medical Errors in Patient Safety Management: Framework Using Interval-Valued Intuitionistic Fuzzy Sets

Medical errors negatively affect patients, healthcare professionals, and healthcare establishments. Therefore, all healthcare service members should be attentive to medical errors. Research has revealed that most medical errors are caused by the system, rather than individuals. In this context, guaranteeing patient safety and preventing medical faults appear to be basic elements of quality in healthcare services. Healthcare institutions can create internal regulations and follow-up plans for patient safety. While this is beneficial for the dissemination of patient safety culture, it poses difficulties in terms of auditing. On the other hand, the lack of a standard patient safety management system, has led to great variation in the quality of the provided service among hospitals. Therefore, this study aims to create an index system to create a standard system for patient safety by classifying medical errors. Due to the complex nature of healthcare and its processes, interval-valued intuitionistic fuzzy logic is used in the proposed index system. Medical errors are prioritized, based on the index scores that are generated by the proposed model. Because of this systematic study, not only can the awareness of patient safety perception be increased in health institutions, but their present situation can also be displayed, on the basis of each indicator. It is expected that the outcomes of this study will motivate institutions to identify and prioritize their future improvements in the patient safety context.