Confidence levels under complex q-rung orthopair fuzzy aggregation operators and their applications

The major contribution of this analysis is to analyze the confidence complex q-rung orthopair fuzzy weighted averaging (CCQROFWA) operator, confidence complex q-rung orthopair fuzzy ordered weighted averaging (CCQROFOWA) operator, confidence complex q-rung orthopair fuzzy weighted geometric (CCQROFWG) operator, and confidence complex q-rung orthopair fuzzy ordered weighted geometric (CCQROFOWG) operator and invented their feasible properties and related results. Future more, under the invented operators, we diagnosed the best crystalline solid from the family of crystalline solids with the help of the opinion of different experts in the environment of decision-making strategy. Finally, to demonstrate the feasibility and flexibility of the invented works, we explored the sensitivity analysis and graphically shown of the initiated works.

Ordered Weighted Averaging (OWA), Decision Making Under Uncertainty, and Deep Learning: How Is This All Related?

Among many research areas to which Ron Yager contributed are decision making under uncertainty (in particular, under interval and fuzzy uncertainty) and aggregation – where he proposed, analyzed, and utilized the use of Ordered Weighted Averaging (OWA). The OWA algorithm itself provides only a specific type of data aggregation. However, it turns out that if we allows several OWA stages one after another, we get a scheme with a universal approximation property – moreover, a scheme which is perfectly equivalent to deep neural networks. In this sense, Ron Yager can be viewed as a (grand)father of deep learning. We also show that the existing schemes for decision making under uncertainty are also naturally interpretable in OWA terms.

A novel extension of TOPSIS with interval type-2 trapezoidal neutrosophic numbers using $(\alpha,\beta,\gamma)$-Cuts

Multi-criteria decision-making (MCDM) is concerned with structuring and solving decision problems involving multiple criteria for decision-makers in vague and inadequate environment. The “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) is one of the mainly used tactic to deal with MCDM setbacks. In this article, we put forward an extension of TOPSIS with interval type-2 trapezoidal neutrosophic numbers (IT2TrNNs) using the concept of (α, β, γ)-cut. First, we present a novel approach to compute the distance between two IT2TrNNs using ordered weighted averaging (OWA) operator and (α, β, γ)-cut. Subsequently, we broaden the TOPSIS method in the context of IT2TrNNs and implemented it on a MCDM problem. Lastly, a constructive demonstration and several contrasts with the other prevailing techniques are employed to articulate the practicability of the proposed technique. The presented strategy yields a flexible solution for MCDM problems by considering the attitudes and perspectives of the decision-makers.

Variances and Logarithmic Aggregation Operators: Extended Tools for Decision-Making Processes

Variance, as a measurement of dispersion, is a basic component of decision-making processes. Recent advances in intelligent systems have included the concept of variance in information fusion techniques for decision-making under uncertainty. These dispersion measures broaden the spectrum of decision makers by extending the toolset for the analysis and modeling of problems. This paper introduces some variance logarithmic averaging operators, including the variance generalized ordered weighted averaging (Var-GOWLA) operator and the induced variance generalized ordered weighted averaging (Var-IGOWLA) operator. Moreover, this paper analyzes some properties, families and particular cases of the proposed operators. Finally, an illustrative example of the characteristic design of the operators is proposed using real-world information retrieved from financial markets. The objective of this paper is to analyze the performance of some equities based on the expected payoff and the dispersion of its elements. Results show that the equity payoff results present diverse rankings combined with the proposed operators, and the introduced variance measures aid decision-making by offering new tools for information analysis. These results are particularly interesting when selecting logarithmic averaging operators for decision-making processes. The approach presented in this paper extends the available tools for decision-making under ignorance, uncertainty, and subjective environments.

A Study of OWA Operators Learned in Convolutional Neural Networks

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.