Interval Type-2 Fuzzy Logic Systems for Evaluating Students’ Academic Performance

Hameed, Ibrahim A.; Elhoushy, Mohanad; Osen, Ottar L.

doi:10.1007/978-3-319-63184-4_22

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Massod [27] designed a smart washing machine using fuzzy logic. Hameed et al [17], estimated students' academic performance using interval type-2 fuzzy logic systems. Jana et al [18], made a comparative analysis on lead removal using micellar-enhanced ultrafiltration based on type-2 fuzzy logic and response surface methodology.…”

Section: Introductionmentioning

confidence: 99%

Interval type-2 fuzzy aggregation operator in decision making and its application

Lathamaheswari

Nagarajan

Kavikumar

2021

Complex Intell. Syst.

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Type-2 fuzzy sets (T2FSs) can deal with higher modeling and uncertainties which exist in the real-world application, specifically in the control systems. Particularly the climate changes are always uncertain and thus, the type-2 fuzzy controller is an effective system to handle those situations. Polyhouse is a methodology used to cultivate the plants. It breaks the seasonal hurdle of the formulation and it is also suitable for the conflictive climate conditions. Controlling and directing internal parameters of the polyhouse play an essential role in the growth of the plant. Among those, humidity is an important element when one deals with the growth of the plant in polyhouse. It affects the weather, as well as the global change of the climate and hence, the inner climate of the polyhouse will be disturbed. In this paper, operational laws for triangular interval type-2 fuzzy numbers and derived triangular interval type-2 weighted geometric (TIT2WG) operator with their desired mathematical properties using Dombi triangular norms. Also, humidity control is analyzed using interval type-2 fuzzy controller (IT2FC) with the use of derived aggregation operator which is the aim of the paper. Further stability of the system has been analyzed by applying four different defuzzification methods and the method is recommended which gives a better response.

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Section: Introductionmentioning

confidence: 99%

Interval type-2 fuzzy aggregation operator in decision making and its application

Lathamaheswari

Nagarajan

Kavikumar

2021

Complex Intell. Syst.

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Optimized type-2 fuzzy controller based on IoMT for stabilizing the glucose level in type-1 diabetic patients

Sayed,

Zalam,

Elhoushy

et al. 2023

Sci Rep

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Due to advancements in existing Internet of Medical Things (IoMT) systems and devices, the blood glucose level (BGL) for type-1 diabetic patients (T1DPs) is effectively and continually monitored and controlled by Artificial Pancreas. Because the regulation of BGL is a very complex process, many efforts have been conducted to design a powerful and effective controller for the exogenous insulin infusion system. The main objective of this study is to propose an optimized interval type-2 fuzzy (IT2F) based controller of artificial pancreas for regulation BGL of T1DP based on IoMT. The proposed controller should avoid the risk of hyperglycemia and hypoglycemia situations that T1DP faces during the infusion of exogenous insulin. The main contribution of this work is using meta-heuristic method called grey wolf optimizer (GWO) to tune the footprint of uncertainty for IT2F’s membership functions to inject the proper dose of insulin under different conditions. The nonlinear extended Bergman minimal model (EBMM) with uncertainty is used to represent the blood glucose regulation and represent the dynamics of meal disturbance in T1DP. The effectiveness and the performance of the proposed controller are investigated using MATLAB/Simulink platform. Simulation results show that the proposed controller can avoid both severe hypoglycemia and hyperglycemia for nominal parameters of the model, in addition to model under the presence of both parametric uncertainty and uncertain meal disturbance.

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Management of the Success of Student’s Learning Based on the Markov Model

Noskov

Somova

Fedotova

2018

Informatika i obrazovanie

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The article proposes a model for forecasting the success of student’s learning. The model is a Markov process with continuous time, such as the process of “death and reproduction”. As the parameters of the process, the intensities of the processes of obtaining and assimilating information are offered, and the intensity of the process of assimilating information takes into account the attitude of the student to the subject being studied. As a result of applying the model, it is possible for each student to determine the probability of a given formation of ownership of the material being studied in the near future. Thus, in the presence of an automated information system of the university, the implementation of the model is an element of the decision support system by all participants in the educational process. The examples given in the article are the results of an experiment conducted at the Institute of Space and Information Technologies of Siberian Federal University under conditions of blended learning, that is, under conditions when classroom work is accompanied by independent work with electronic resources.

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Interval Type-2 Fuzzy Logic Systems for Evaluating Students’ Academic Performance

Cited by 4 publications

References 27 publications

Interval type-2 fuzzy aggregation operator in decision making and its application

Interval type-2 fuzzy aggregation operator in decision making and its application

Optimized type-2 fuzzy controller based on IoMT for stabilizing the glucose level in type-1 diabetic patients

Management of the Success of Student’s Learning Based on the Markov Model

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