A generalized form of the distance-induced OWA operators–Demonstrating its use for evaluation indicator system in China
Using multi-criteria decision making (MCDM) technique to rank alternatives is a well-known
area of study in which aggregation operators, such as ordered weighted averaging (OWA) …
area of study in which aggregation operators, such as ordered weighted averaging (OWA) …
A Generalized Family of Constant Orness Ordered Weighted Averaging Operators
V Srivastava, AK Singh, A Kishor… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In this article we propose a novel Beta Binomial OWA operator (BBN-OWAO) based on the
Beta Binomial distribution. The proposed bi-parametric operator can generate an unlimited …
Beta Binomial distribution. The proposed bi-parametric operator can generate an unlimited …
OWA-based multi-criteria decision making based on fuzzy methods
One of the most important challenges in Multi-Attribute Decision Making (MADM) problems
is how can the optimal weights of the criteria can be determined properly by the decision …
is how can the optimal weights of the criteria can be determined properly by the decision …
[PDF][PDF] An approach to optimize transportation problemswith neutrosophic numbers based on a newranking function
A transportation problem (TP) is built on the framework of supply-demand and cost
parameters which are uncertain in nature. Neutrosophic numbers are capable of handling …
parameters which are uncertain in nature. Neutrosophic numbers are capable of handling …
Adaptive Real-Time Multi-Loss Function Optimization Using Dynamic Memory Fusion Framework: A Case Study on Breast Cancer Segmentation
Deep learning has proven to be a highly effective tool for a wide range of applications,
significantly when leveraging the power of multi-loss functions to optimize performance on …
significantly when leveraging the power of multi-loss functions to optimize performance on …
Loss2Net: Loss Meta-Learning for Regression with A-priori Unknown Metrics
There exist many practical applications where regression tasks must cope with a generally
overseen problem: the output variable to be computed, which is often a decision variable …
overseen problem: the output variable to be computed, which is often a decision variable …