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Toplam kayıt 20, listelenen: 11-20
Type-1 possibilistic fuzzy forecasting functions
(Elsevier, 2020)
Type-1 Fuzzy Functions (T1FFs) were developed by Turksen as an alternative fuzzy inference system (FIS) and have been commonly used in forecasting problems. The main advantages of T1FFs are that they are free of rules and ...
A fuzzy ID3 induction for linguistic data sets
(Wiley, 2018)
In real life, humans communicate by means of words. Computing with words enables flexibility via fuzzy logic to reach more informative results for the classification and decision-making. Fuzzy logic handles the imprecise ...
Meta fuzzy functions: Application of recurrent type-1 fuzzy functions
(2018-12)
The main objective of meta-analysis is to aggregate the results of multiple scientific studies on a specific topic. Instead of aggregating the results of different studies, different methods are aggregated with the help ...
Machine Learning to Identify Dialysis Patients at High Death Risk
(Elsevier Science Inc, 2019)
Introduction: Given the high mortality rate within the first year of dialysis initiation, an accurate estimation of postdialysis mortality could help patients and clinicians in decision making about initiation of dialysis. ...
Application of machine learning to the prediction of postoperative sepsis after appendectomy
(Mosby-Elsevier, 2021)
Background: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with ...
J-curve in Turkish bilateral trade: A nonlinear approach
(Taylor and Francis Inc., 2019)
This study aims to bring further evidence on recent developments of the J-curve literature by employing linear and nonlinear autoregressive distributed lag (ARDL) approaches for Turkish bilateral trade data with respect ...
A Novel Learning Algorithm to Optimize Deep Neural Networks: Evolved Gradient Direction Optimizer (EVGO)
(Ieee-Inst Electrical Electronics Engineers Inc, 2021)
Gradient-based algorithms have been widely used in optimizing parameters of deep neural networks' (DNNs) architectures. However, the vanishing gradient remains as one of the common issues in the parameter optimization of ...
Gradient boosting for Parkinson's disease diagnosis from voice recordings
(Bmc, 2020)
Background Parkinson's Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often ...
GREY WOLF OPTIMIZER BASED RECURRENT FUZZY REGRESSION FUNCTIONS FOR FINANCIAL DATASETS
(2020)
Time series models are used extensively in many fields, such as medicine, engineering, business, economics, and finance, with the aim of making forecasts through the help of observation values from previous periods. ...
An adaptive forecast combination approach based on meta intuitionistic fuzzy functions
(Ios Press, 2021)
Intuitionistic meta fuzzy forecast combination functions are introduced in the paper. There are two challenges in the forecast combination literature, determining the optimum weights and the methods to combine. Although ...