Unification of imprecise data - translation of fuzzy to multi-valued knowledge over Y-axis

Inference systems are a well-defined technology derived from knowledge-based systems. Their main purpose is to model and manage knowledge as well as expert reasoning to insure a relevant decision making while getting close to human induction. Although handled knowledge are usually imperfect, they may be treated using a non classical logic as fuzzy logic or symbolic multi-valued logic. Nonetheless, it is required sometimes to consider both fuzzy and symbolic multi-valued knowledge within the same knowledge-based system. For that, we propose in this paper an approach that is able to standardize fuzzy and symbolic multi-valued knowledge. We intend to convert fuzzy knowledge into symbolic type by projecting them over the Y-axis of their membership functions. Consequently, it becomes feasible working under a symbolic multi-valued context. Our approach provides to the expert more flexibility in modeling their knowledge regardless of their type. A numerical study is provided to illustrate the potential application of the proposed methodology.

Measuring efficiency of a recycling production system with imprecise data

<p style='text-indent:20px;'>Resources scarcity and environmental degradation have made sustainable resource utilization and environmental protection worldwide. A circular economy system considers economic production activities as closed-loop feedback cycles in which resources are used sustainably and cyclically. Improving the eco-efficiency of the circular economy system has both theoretical value and practical meaning. In this work, the efficiency measurement model of the circular economy system with imprecise data based on network data envelopment analysis is proposed. The two-level mathematical programming approach is employed for measuring the system and process efficiencies. The lower and upper bounds of the efficiencies scores are calculated by transformed conventional one-level linear programs so that the existing solution methods can be applied. The proposed method is applied to assess the circular economy system of EU countries. Our results show that most countries have large difference among fuzzy efficiencies between the production efficiency and recycling efficiency stages, which reveals the source that causes the low efficiency of the circular economy system.</p>

Big Data Cleaning Model of Multi-Source Heterogeneous Power Grid Based On Machine Learning Classification Algorithm

Aiming at the low cleaning rate of the traditional multi-source heterogeneous power grid big data cleaning model, a multi-source heterogeneous power grid big data cleaning model based on machine learning classification algorithm is designed. By capturing high-quality multi-source heterogeneous power grid big data, weight labeling of data source importance measurement, data attributes and tuples, and constructing Tan network based on the idea of machine learning classification algorithm, the data probability value is finally used to complete the classification and cleaning of inaccurate data. Experiments show that the model based on machine learning classification algorithm can effectively improve the imprecise data cleaning rate compared with the traditional model to solve multi-source heterogeneous imprecise data cleaning.

A View on Pythagorean Fuzzy Contra 𝓖∗ Continuous Function

The new dimension of non-standard fuzzy sets called Pythagorean fuzzy sets which can handle the inaccurate data very strongly has been established in recent days. Even though intuitionistic fuzzy sets were generously used in decision making to handle the imprecise data the novelty and the voluminous of Pythagorean fuzzy environment gives motivation to use it in decision making process. The Pythagorean fuzzy topological spaces are the novel generalization of fuzzy topological spaces. Herein the concept of Pythagorean fuzzy contra 𝒢∗ continuous functions are explored. Interrelations have been studied elaborately for the defined functions using various examples.

Fixed Point Results in Orthogonal Neutrosophic Metric Spaces

Neutrosophy deals with neutrosophic logic, probability, and sets. Actually, the neutrosophic set is a generalization of the classical set, fuzzy set, and intuitionistic fuzzy set. A neutrosophic set is a mathematical notion serving issues containing inconsistent, indeterminate, and imprecise data. The notion of intuitionistic fuzzy metric space is useful in modelling some phenomena, where it is necessary to study the relationship between two probability functions. In this study, the concept of an orthogonal neutrosophic metric space is initiated. It is a generalization of the neutrosophic metric space. Some fixed point results are investigated in this setting. For the validity of the obtained results, some nontrivial examples are given.

3D Reconstruction using convolution smooth method

3D imagery is an image with depth data. The use of depth information in 3D images still has many drawbacks, especially in the image results. Raw data on the 3D camera even does not look smooth, and there is too much noise. Noise in the 3D image is in the form of imprecise data, which results in a rough image. This research will use the convolution smooth methods to improve the 3D image. Will smooth noise in the 3D image, so the resulting image will be better. This smoothing system is called the blurring effect. This research has been tested on flat objects and objects with a circle contour. The test results on the flat surface obtained a distance of 1.3177, the test in the object with a flat surface obtained a distance of 0.4937, and the test in circle contour obtained a distance of 0.3986. This research found that the 3D image will be better after applying the convolution smooth method.

Analyzing the efficiency bank branches via novel weighted stochastic imprecise data envelopment analysis

As of 21 th century, the terms of efficiency and productivity have become notions which dwells on both business and academic world more frequently compared to past. It is known that it is hard to increase the efficiency and productivity of both production and service systems. In this study, the efficiency analysis of the branches of a bank was conducted. Furthermore, a Weighted Stochastic Imprecise Data Envelopment Analysis (WSIDEA), which is a new approach developed based on Data Envelopment Analysis (DEA), was proposed. Efficiency levels and results of decision-making units were examined according to the proposed new method. Additionally, six different DEA model results are obtained. The results of the six different DEA model and the proposed "WSIDEA" model were compared in terms of efficiency level of decision-making units, and the differences between them were examined. Sensitivity of the inefficient units were also examined. On the other hand, unrealistic efficiency levels created by traditional methods for branches were also analyzed. Apart from all these sensitivity analyses, the sensitivity of the data set used in the analysis is scrutinized.