ABSTRACT
In this paper, some statistical properties of the Choquet integral are discussed. As an interesting application of Choquet integral and fuzzy measures, we introduce a new class of exponential-like distributions related to monotone set functions, called Choquet exponential distributions, by combining the properties of Choquet integral with the exponential distribution. We show some famous statistical distributions such as gamma, logistic, exponential, Rayleigh and other distributions are a special class of Choquet distributions. Then, we show that this new proposed Choquet exponential distribution is better on daily gold price data analysis. Also, a real dataset of the daily number of new infected people to coronavirus in the USA in the period of 2020/02/29 to 2020/10/19 is analyzed. The method presented in this article opens a new horizon for future research.
ABSTRACT
Reliable prediction of natural gas consumption helps make the right decisions ensuring sustainable economic growth. This problem is addressed here by introducing a hybrid mathematical model defined as the Choquet integral-based model. Model selection is based on decision support model to consider the model performance more comprehensively. Different from the previous literature, we focus on the interaction between models when combine models. This paper adds grey accumulation generating operator to Holt-Winters model to capture more information in time series, and the grey wolf optimizer obtains the associated parameters. The proposed model can deal with seasonal (short-term) variability using season auto-regression moving average computation. Besides, it uses the long short term memory neural network to deal with long-term variability. The effectiveness of the developed model is validated on natural gas consumption due to the COVID-19 pandemic in the USA. For this, the model is customized using the publicly available datasets relevant to the USA energy sector. The model shows better robustness and outperforms other similar models since it consider the interaction between models. This means that it ensures reliable perdition, taking the highly uncertain factor (e.g., the COVID-19) into account. © 2023 Elsevier Ltd
ABSTRACT
The COVID-19 outbreak has resulted in a global pandemic and led to more than a million deaths to date. COVID-19 early detection is essential for its mitigation by controlling its spread from infected patients in communities through quarantine. Although vaccination has started, it will take time to reach everyone, especially in developing nations, and computer scientists are striving to come up with competent methods using image analysis. In this work, a classifier ensemble technique is proposed, utilizing Choquet fuzzy integral, wherein convolutional neural network (CNN) based models are used as base classifiers. It classifies chest X-ray images from patients with common Pneumonia, confirmed COVID-19, and healthy lungs. Since there are few samples of COVID-19 cases for training on a standard CNN model from scratch, we use the transfer learning scheme to train the base classifiers, which are InceptionV3, DenseNet121, and VGG19. We utilize the pre-trained CNN models to extract features and classify the chest X-ray images using two dense layers and one softmax layer. After that, we combine the prediction scores of the data from individual models using Choquet fuzzy integral to get the final predicted labels, which is more accurate than the prediction by the individual models. To determine the fuzzy-membership values of each classifier for the application of Choquet fuzzy integral, we use the validation accuracy of each classifier. The proposed method is evaluated on chest X-ray images in publicly available repositories (IEEE and Kaggle datasets). It provides 99.00%, 99.00%, 99.00%, and 99.02% average recall, precision, F-score, and accuracy, respectively. We have also evaluated the performance of the proposed model on an inter-dataset experimental setup, where chest X-ray images from another dataset (CMSC-678-ML-Project GitHub dataset) are fed to our trained model and we have achieved 99.05% test accuracy on this dataset. The results are better than commonly used classifier ensemble methods as well as many state-of-the-art methods.
Subject(s)
COVID-19 , Deep Learning , Neural Networks, Computer , COVID-19/diagnosis , Humans , PandemicsABSTRACT
This paper develops a new method for interactive multi-criteria group decision-making (MCGDM) with probabilistic linguistic information and applies to the emergency assistance area selection of COVID-19 for Wuhan. First, a new possibility degree for PLTSs is defined and a new possibility degree algorithm is devised to rank a series of probabilistic linguistic term sets (PLTSs). Second, some new operational laws of PLTSs based on the Archimedean copulas and co-copulas are defined. A generalized probabilistic linguistic Choquet (GPLC) operator and a generalized probabilistic linguistic hybrid Choquet (GPLHC) operator are developed and their desirable properties are discussed in details. Third, a tri-objective nonlinear programming model is constructed to determine the weights of DMs. This model is transformed into a linear programming model to solve. The fuzzy measures of criterion subsets are derived objectively by establishing a goal programming model. Fourth, using the probabilistic linguistic Gumbel weighted average (PLGWA) operator, the collective normalized decision matrix is obtained by aggregating all individual normalized decision matrices. The overall evaluation values of alternatives are derived by the probabilistic linguistic Gumbel hybrid Choquet (PLGHC) operator. The ranking order of alternatives is generated. Finally, an emergency assistance example is illustrated to validate the proposed method of this paper.