scholarly journals Improved adaptive neuro-fuzzy inference system based on modified glowworm swarm and differential evolution optimization algorithm for medical diagnosis

2020 ◽  
Author(s):  
Kishore Balasubramanian ◽  
N. P. Ananthamoorthy
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Fuzzy Inference System ◽  
Medical Diagnosis ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Differential Evolution Optimization Algorithm

scholarly journals Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing

2021 ◽  
Vol 10 (6) ◽  
pp. 382
Author(s):  
A’kif Al-Fugara ◽  
Ali Nouh Mabdeh ◽  
Mohammad Ahmadlou ◽  
Hamid Reza Pourghasemi ◽  
Rida Al-Adamat ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Wildland Fire ◽  
Hybrid Models ◽  
Support Vector ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Whale Optimization

Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic models, the whale optimization algorithm (WOA) and simulated annealing (SA) to map wildland fires in Jerash Province, Jordan. For modeling, 109 fire locations were used along with 14 relevant factors, including elevation, slope, aspect, land use, normalized difference vegetation index (NDVI), rainfall, temperature, wind speed, solar radiation, soil texture, topographic wetness index (TWI), distance to drainage, and population density, as the variables affecting the fire occurrence. The area under the receiver operating characteristic (AUROC) was used to evaluate the accuracy of the models. The findings indicated that SVR-based hybrid models yielded a higher AUROC value (0.965 and 0.949) than the ANFIS-based hybrid models (0.904 and 0.894, respectively). Wildland fire susceptibility maps can play a major role in shaping firefighting tactics.

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