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Toplam kayıt 12, listelenen: 1-10
Artificial Intelligence-Assisted Prediction of Late-Onset Cardiomyopathy Among Childhood Cancer Survivors
(NLM (Medline), 2021)
PURPOSE: Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling intervention before development of heart failure. We implemented artificial ...
A Novel ARMA Type Possibilistic Fuzzy Forecasting Functions Based on Grey-Wolf Optimizer (ARMA-PFFs)
(Springer, 2021)
This study proposes a new time series forecasting method that employs possibilistic fuzzy c-means, an autoregressive moving average model (ARMA), and a grey wolf optimizer (GWO) in type-1 fuzzy functions. Type-1 fuzzy ...
Forecast combination with meta possibilistic fuzzy functions
(Elsevier Science Inc, 2021)
There are many methods to obtain accurate forecasts for time series data in the literature. It is imperative to find an appropriate method with the correct assumptions for a given data set and circumstances. However, the ...
META FUZZY INDEX FUNCTIONS
(Ankara Univ, Fac Sci, 2020)
Meta-analysis was introduced to aggregate the findings of different primary studies in statistical aspects. However, in the proposed study, the term meta is used to aggregate different models for a specific topic with the ...
Dating currency crises and designing early warning systems: Meta-possibilistic fuzzy index functions
(Wiley, 2020)
In order to analyse the currency crises in Turkey over the period of January 1990 and October 2019, we first dated currency crises with meta-possibilistic fuzzy index functions. Then, we determined the significant predictors ...
Meta fuzzy functions based feed-forward neural networks with a single hidden layer for forecasting
(Taylor & Francis Ltd, 2021)
Feed-forward neural networks have been frequently used in forecasting problems, recently. In this study, we propose a naive method to improve the forecasting ability of feed-forward neural networks with a single hidden ...
Type-1 recurrent intuitionistic fuzzy functions for forecasting
(Pergamon-Elsevier Science Ltd, 2020)
In this study, a novel forecasting method that employs intuitionistic fuzzy c-means clustering and a grey wolf optimizer in recurrent type-1 fuzzy functions (R-T1FFs) is introduced. R-T1FFs, which adapt to the moving average ...
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 ...
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 ...
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 ...