scholarly journals A Neuro-Fuzzy Inference Model for Breast Cancer Recognition

2012 ◽  
Vol 4 (5) ◽  
pp. 163-173 ◽  
Author(s):  
Bekaddour Fatima
Keyword(s):  
Breast Cancer ◽  
Fuzzy Inference ◽  
Inference Model ◽  
Neuro Fuzzy

scholarly journals Adaptive Neuro-Fuzzy Inference Model for Monitoring Hypertension Risk

2021 ◽  
Vol 16 (4) ◽  
pp. 0-0
Keyword(s):  
High Risk ◽  
Healthcare Professionals ◽  
Fuzzy Inference ◽  
Teaching Hospitals ◽  
Inference Model ◽  
Inference System ◽  
Hypertension Risk ◽  
Neuro Fuzzy ◽  
Medium Risk ◽  
Very High

This study presented a model to classify risk of hypertension using Adaptive Neuro-Fuzzy Inference System (ANFIS). In order to develop the model cardiologists from teaching hospitals in Nigeria were interviewed so as to identify required variables for classification. Structured questionnaires were used to elicit information about the risk factors and the associated risk of hypertension from respondents. The MATLAB ANFIS Toolbox was used to simulate the model. The result of this study revealed that there were 33 main variables identified for monitoring hypertension risk and they were in line with the WHO/ISH classification standard. The result showed that majority of the patients selected had very high risk (57.0%) of hypertension which consisted more than 50% of the patients selected followed by 19% representing patients with high risk of hypertension, followed by patients with medium risk of hypertension. In conclusion, the model assist healthcare professionals to have accurate diagnosis, early detection and proper management of hypertension.

scholarly journals An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs

2019 ◽  
Vol 9 (4) ◽  
pp. 809 ◽  
Author(s):  
Mohammed Mashrei ◽  
Alaa Mahdi
Keyword(s):  
Shear Strength ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Input Parameter ◽  
Punching Shear ◽  
Concrete Slabs ◽  
Shear Reinforcement ◽  
Inference Model ◽  
Inference System ◽  
Neuro Fuzzy

An adaptive neuro-fuzzy inference system (ANFIS)-based model was developed to predict the punching shear strength of flat concrete slabs without shear reinforcement. The model was developed using a database collected from 207 experiments available in the existing literature. Five key input parameters were used to build the model, which were slab effective depth, concrete strength, reinforcement ratio, yield tensile strength of reinforcement, and width of square loaded area. The output parameter of the model was punching shear strength. The results from the adaptive neural fuzzy inference model were compared to those from the simplified punching shear equations of ACI, BS-8110, Model Code 2010, Euro-Code 2, and also experimental results. The root mean square error (RMSE) and the correlation coefficient (R) were used as evaluation criteria. Parametric studies were presented using ANFIS to assess the effect of each input parameter on the punching shear strength and to compare ANFIS results to those from the equations proposed in commonly used codes. The results showed that the ANFIS model is simple and provided the most accurate predictions of the punching shear strength of two-way flat concrete slabs without shear reinforcement.

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