A Binary Entropy Measure to Assess Nonrigid Registration Algorithms

In the real multi-criteria group decision making (MCGDM) problems, there will be an interactive relationship among different decision makers (DMs). To identify the overall influence, we define the Shapley value as the DM’s weight. Entropy is a measure which makes it better than similarity measures to recognize a group decision making problem. Since we propose a relative entropy to measure the difference between two systems, which improves the accuracy of the distance measure.In this paper, a MCGDM approach named as TODIM is presented under q-rung orthopair fuzzy information.The proposed TODIM approach is developed for correlative MCGDM problems, in which the weights of the DMs are calculated in terms of Shapley values and the dominance matrices are evaluated based on relative entropy measure with q-rung orthopair fuzzy information.Furthermore, the efficacy of the proposed Gq-ROFWA operator and the novel TODIM is demonstrated through a selection problem of modern enterprises risk investment. A comparative analysis with existing methods is presented to validate the efficiency of the approach.

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Edge Permutation Entropy: An Improved Entropy Measure for Time-Series Analysis

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon.2019.8927449 ◽

2019 ◽

Cited By ~ 1

Author(s):

Zhiqiang Huo ◽

Yu Zhang ◽

Lei Shu ◽

Xiaowen Liao

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Permutation Entropy ◽

Entropy Measure ◽

Series Analysis

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Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device

Sensors ◽

10.3390/s21155135 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5135

Author(s):

Ngoc-Dau Mai ◽

Boon-Giin Lee ◽

Wan-Young Chung

Keyword(s):

Machine Learning ◽

Emotion Recognition ◽

Temporal Lobe ◽

Affective Computing ◽

Electrode Placement ◽

Entropy Measure ◽

Support Vector ◽

Emotion Classification ◽

Eeg Signals ◽

Computing Method

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our proposed models using three popular classifiers: a support vector machine (SVM), multi-layer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) for emotion classification; both subject-dependent and subject-independent strategies were used. Our experiment results showed that the highest average accuracies achieved in the subject-dependent and subject-independent cases were 85.81% and 78.52%, respectively; these accuracies were achieved using a combination of the sample entropy measure and 1D-CNN. Moreover, our study investigates the T8 position (above the right ear) in the temporal lobe as the most critical channel among the proposed measurement positions for emotion classification through electrode selection. Our results prove the feasibility and efficiency of our proposed EEG-based affective computing method for emotion recognition in real-world applications.

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Extended TOPSIS method based on the entropy measure and probabilistic hesitant fuzzy information and their application in decision support system

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202700 ◽

2021 ◽

pp. 1-12

Author(s):

Muhammad Naeem ◽

Muhammad Ali Khan ◽

Saleem Abdullah ◽

Muhammad Qiyas ◽

Saifullah Khan

Keyword(s):

Decision Making ◽

Fuzzy Set ◽

Distance Measure ◽

Group Decision ◽

Hesitant Fuzzy Set ◽

Entropy Measure ◽

Fuzzy Information ◽

Score Functions ◽

Fuzzy Environment ◽

Basic Operating

Probabilistic hesitant fuzzy Set (PHFs) is the most powerful and comprehensive idea to support more complexity than developed fuzzy set (FS) frameworks. In this paper, it can explain a novel, improved TOPSIS-based method for multi-criteria group decision-making (MCGDM) problem through the Probabilistic hesitant fuzzy environment, in which the weights of both experts and criteria are completely unknown. Firstly, we discuss the concept of PHFs, score functions and the basic operating laws of PHFs. In fact, to compute the unknown weight information, the generalized distance measure for PHFs was defined based on the Probabilistic hesitant fuzzy entropy measure. Second, MCGDM will be presented with the PHF information-based decision-making process.

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A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

Mathematics ◽

10.3390/math9070700 ◽

2021 ◽

Vol 9 (7) ◽

pp. 700

Author(s):

Belén Pérez-Sánchez ◽

Martín González ◽

Carmen Perea ◽

Jose J. López-Espín

Keyword(s):

Experimental Study ◽

Computational Study ◽

Estimation Method ◽

Information Criteria ◽

Simultaneous Equations ◽

Estimation Methods ◽

Entropy Measure ◽

Economic Science ◽

Akaike Information Criteria ◽

Simultaneous Equations Models

Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimating methods is an important line of research. In fact, if we want to apply the SEM to medical problems with the main goal being to obtain the best approximation between the parameters of model and their estimations. This paper shows a computational study between different methods for estimating simultaneous equations models as well as a new method which allows the estimation of those parameters based on the optimization of the Bayesian Method of Moments and minimizing the Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter criteria to compare the estimation methods studied. The comparison between those methods is performed through an experimental study using randomly generated models. The experimental study compares the estimations obtained by the different methods as well as the efficiency when comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the proposed estimation method offered better approximations and the entropy measured results more efficiently than the rest.

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