Abstract
Aiming at the inadequacy of the group decision-making method with the current attribute value as interval language information, an interval binary semantic decision-making method is proposed, which considers the decision maker's psychological behavior. The scope of this research is that this paper is based on localized amplification method. The localized amplification method used in this research may amplify physiological movement after removing unwanted noise, allowing the movement trend to be seen with the naked eye, improving the CNN network's mental identification accuracy. These two algorithms analyze the input picture from various perspectives, allowing the CNN network to extract more information and enhance identification accuracy. A new distance formula with interval binary semantics closer to decision-makers thinking habits is defined; time degree is introduced. An optimization model is established to solve the time series weights by considering the comprehensive consistency of expert evaluation. Based on prospect theory, a prospect deviation value is constructed and minimized weight optimization model, using the interactive multiple attribute decision community making (TODIM) method based on the new distance measure to calculate the total overall dominance of the schemes to rank the schemes. Taking the selection and evaluation of supply chain collaboration partners as an example, the effectiveness and rationality of the proposed method are verified.
Highlights
To sum up, based on existing research, this paper proposes an interval binary semantic dynamic group decision-making that considers the decision maker’s psychological behavior for the multiattribute group decision-making problem in which the attribute value is interval language, and the attribute weight and time series weight are entirely unknown method
Considering the thinking habits of human beings, it is believed that the density of evaluation information in the interval is more similar to the normal distribution, and based on this, a new interval binary semantic distance formula is proposed
The convolution neural network model network is designed using the idea of VGG16 to realize emotion recognition. e local amplification technology applied in this paper can amplify the psychological-expression movement after reducing the noise interference so that the movement trend can be seen by the naked eye, which can increase the accuracy of the Convolution Neural Networks (CNN) network for psychological-expression recognition. ese two methods process the input image from different angles so that the CNN network can better extract features and improve the accuracy of recognition
Summary
With the increasing complexity of decision-making problems in various fields, individual decision-making has long been unable to meet the requirements of scientific decisionmaking, and group decision-making has attracted more and more attention and attention from experts and scholars [1, 2]. In the actual decision-making process, due Computational Intelligence and Neuroscience to the ambiguity of decision-making information and the limitations of decision-makers cognition, decision-makers are often more willing to give evaluation information in the form of interval language to reduce decision-making pressure In response to such problems, [11] showed the definition of interval binary semantics and several set counters and used the interval binary semantic possibility formula to sort the solutions; reference [12], based on the maximum dispersion [13], defined a new interval binary semantic Bonferroni average operator and its corresponding weighting method; reference [14] combined interval binary semantics and VIKOR method, and proposed an outsourcing supplier selection method; reference [15] proposed a subway door failure risk assessment method based on interval binary semantics and failure mode. To sum up, based on existing research, this paper proposes an interval binary semantic dynamic group decision-making that considers the decision maker’s psychological behavior for the multiattribute group decision-making problem in which the attribute value is interval language, and the attribute weight and time series weight are entirely unknown method. Face psychological expression recognition involves image processing and analysis, computer vision, artificial intelligence, psychology, biology, and other directions
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