The utilization of rough set theory and data reduction based on artificial intelligence in recommendation system

2020 ◽  
Author(s):  
Huizhi Cao
Keyword(s):  
Artificial Intelligence ◽  
Set Theory ◽  
Rough Set ◽  
Data Reduction ◽  
Recommendation System ◽  
Rough Set Theory

scholarly journals IDENTIFYING KEY FACTORS FOR ADOPTING ARTIFICIAL INTELLIGENCE-ENABLED AUDITING TECHNIQUES BY JOINT UTILIZATION OF FUZZY-ROUGH SET THEORY AND MRDM TECHNIQUE

2020 ◽  
Vol 0 (0) ◽  
pp. 1-34
Author(s):  
Kuang-Hua Hu ◽  
Fu-Hsiang Chen ◽  
Ming-Fu Hsu ◽  
Gwo-Hshiung Tzeng
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Audit Quality ◽  
Rough Set Theory ◽  
Decision Makers ◽  
Fuzzy Rough Set ◽  
Rule Based ◽  
Cpa Firms

In today’s big-data era, enterprises are able to generate complex and non-structured information that could cause considerable challenges for CPA firms in data analysis and to issue improper audited reports within the required period. Artificial intelligence (AI)-enabled auditing technology not only facilitates accurate and comprehensive auditing for CPA firms, but is also a major breakthrough in auditing’s new environment. Applications of an AI-enabled auditing technique in external auditing can add to auditing efficiency, increase financial reporting accountability, ensure audit quality, and assist decision-makers in making reliable decisions. Strategies related to the adoption of an AI-enabled auditing technique by CPA firms cover the classical multiple criteria decision-making (MCDM) task (i.e., several perspectives/criteria must be considered). To address this critical task, the present study proposes a fusion multiple rule-based decision making (MRDM) model that integrates rule-based technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) into MCDM techniques that can assist decision makers in selecting the best methods necessary to achieve the aspired goals of audit success. We also consider potential implications for articulating suitable strategies that can improve the adoption of AI-enabled auditing techniques and that target continuous improvement and sustainable development.

Representations of Vague Approximation Operators

2011 ◽  
Vol 282-283 ◽  
pp. 287-290
Author(s):  
Hai Dong Zhang ◽  
Yan Ping He
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Model ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Fuzzy Relation ◽  
Partial Knowledge ◽  
Data Bases ◽  
Approximation Operators ◽  
Rough Approximation

As a suitable mathematical model to handle partial knowledge in data bases, rough set theory is emerging as a powerful theory and has been found its successive applications in the fields of artificial intelligence such as pattern recognition, machine learning, etc. In the paper, a vague relation is first defined, which is the extension of fuzzy relation. Then a new pair of lower and upper generalized rough approximation operators based on the vague relation is first proposed by us. Finally, the representations of vague rough approximation operators are presented.

Use of Rough Set Theory and Neural Networks Methods in Supply Chain Management

2020 ◽  
Vol 22 (1) ◽  
pp. 44-49
Author(s):  
Artem Lopatin ◽  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Science Field ◽  
Chain Management ◽  
Practical Implications

Introduction The decision to align a specific order with a supplier depends on a no of criteria. Generally the buyer’s decision depends on his assessment of the supplier’s ability to meet the criteria of quality, volume, terms of delivery, price and service. But to evaluate these criteria, the company needs to manage information from different sources through whole supply chain. One way to control may comprise artificial intelligence methods. The main purposes of this article are to identify the AI subsectors that are most suitable for SCM programs, and characterize other subsectors in terms of their usefulness for improving SC performance. Synthesize the existing research on the appliance of rough set theory and neural networks methods touching SCM, on their practical implications and technical merits. Summarize research trends in rough set theory and neural networks methods and identify potential utilization of SCM that haven’t yet been studied in Ukrainian science field. Justify future prospects for expanding existing AI literature and unused AI research in Ukrainian science field topics related to SCM. Results The article identifies the sub-sectors of artificial intelligence that are most suitable for supply chain management programs, and describes other sub-sectors in terms of their usefulness for improving the efficiency of supply chain management. Synthesize the existing literature on the appliance of rough set theory and neural networks methods in supply chains, on their practical implications and technical merits. The tendencies of researches of rough set theory and neural networks methods are generalized and potential spheres of their appliance in management of supply chains which haven’t been investigated yet are defined. Conclusions. Despite the long history of AI, the potential of AI as a means of solving complex issues and finding info in the field of SC hasn’t been fully used in the past especially in the Ukrainian scientific literature. In particular, some groups of AI technologies, such as expert systems and GAs, are increasingly used to solve management issues, including inventory management, procurement, location planning, shipment coordination between contractors, and routing / planning issues. Further study of the issue requires consideration of the use of other AI methods in supply chain management, such as fuzzy logic and agent modeling and recognition of their practical aspects.

scholarly journals The Use of Artificial Intelligence Methods to Assess the Effectiveness of Lean Maintenance Concept Implementation in Manufacturing Enterprises

2020 ◽  
Vol 10 (21) ◽  
pp. 7922
Author(s):  
Katarzyna Antosz ◽  
Lukasz Pasko ◽  
Arkadiusz Gola
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Trees ◽  
Set Theory ◽  
Rough Set ◽  
Intelligent Systems ◽  
High Efficiency ◽  
Rough Set Theory ◽  
Conservation Strategy ◽  
Artificial Intelligence Methods

The increase in the performance and effectiveness of maintenance processes is a continuous aim of production enterprises. The elimination of unexpected failures, which generate excessive costs and production losses, is emphasized. The elements that influence the efficiency of maintenance are not only the choice of an appropriate conservation strategy but also the use of appropriate methods and tools to support the decision-making process in this area. The research problem, which was considered in the paper, is an insufficient means of assessing the degree of the implementation of lean maintenance. This problem results in not only the possibility of achieving high efficiency of the exploited machines, but, foremost, it influences a decision process and the formulation of maintenance policy of an enterprise. The purpose of this paper is to present the possibility of using intelligent systems to support decision-making processes in the implementation of the lean maintenance concept, which allows the increase in the operational efficiency of the company’s technical infrastructure. In particular, artificial intelligence methods were used to search for relationships between specific activities carried out under the implementation of lean maintenance and the results obtained. Decision trees and rough set theory were used for the analysis. The decision trees were made for the average value of the overall equipment effectiveness (OEE) indicator. The rough set theory was used to assess the degree of utilization of the lean maintenance strategy. Decision rules were generated based on the proposed algorithms, using RSES software, and their correctness was assessed.

scholarly journals Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ying Zhao ◽  
Guocheng Wei
Keyword(s):  
Artificial Intelligence ◽  
Set Theory ◽  
Network Model ◽  
Image Recognition ◽  
Rough Set ◽  
Rough Set Theory ◽  
Image Data ◽  
Good Time ◽  
Svm Model ◽  
Improved Pso

Image recognition is an important field of artificial intelligence. Its basic idea is to use computers to automatically classify different scenes in the acquired images, instead of traditional manual classification tasks. In this paper, through the analysis of rough set theory and artificial intelligence network, as well as the role of the two in image recognition, the rough set theory and artificial intelligence network are organically combined, and a network based on rough set theory and artificial intelligence network is proposed. Using BP artificial intelligence network model, improved BP artificial intelligence network model, and improved PSO-SVM model to identify and classify the extracted characteristic signals and compare the results, all reached 85% correct rate. The PCA and SVM are combined and applied to the MNIST handwritten digit collection for recognition and classification. At the data level, dimensionality reduction is performed on high-dimensional image data to compress the data. This greatly improves the performance of the algorithm, the recognition accuracy rate is as high as 98%, and the running time is shortened by about 90%. The model first preprocesses the original image data and then uses rough set theory to select features, which reduces the input dimension of the artificial intelligence network, improves the learning and recognition speed of the artificial intelligence network, and further improves the accuracy of recognition. The paper applies the model to handwritten digital image recognition, and the experimental results show that the model is effective and feasible. The system has the characteristics of easy deployment and easy maintenance and integration. Experiments show that the system has good time characteristics in the process of multialgorithm parallel image fusion processing.

scholarly journals Data Reduction with Rough Sets

2011 ◽  
pp. 556-560 ◽  
Author(s):  
Richard Jensen
Keyword(s):  
Feature Selection ◽  
Knowledge Discovery ◽  
Set Theory ◽  
Rough Set ◽  
Data Reduction ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Feature Subset ◽  
Data Dependencies ◽  
Additional Information

Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. These techniques fall in to one of the following categories: those that transform the underlying meaning of the data features and those that are semantics-preserving. Feature selection (FS) methods belong to the latter category, where a smaller set of the original features is chosen based on a subset evaluation function. The process aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In knowledge discovery, feature selection methods are particularly desirable as they facilitate the interpretability of the resulting knowledge. For this, rough set theory has been successfully used as a tool that enables the discovery of data dependencies and the reduction of the number of features contained in a dataset using the data alone, while requiring no additional information.

Study on Mining Association Rules from Stock Time Series Data

2013 ◽  
Vol 303-306 ◽  
pp. 1119-1124
Author(s):  
Xian Tan
Keyword(s):  
Time Series ◽  
Set Theory ◽  
Association Rules ◽  
Rough Set ◽  
Data Reduction ◽  
Stock Price ◽  
Time Series Data ◽  
Rough Set Theory ◽  
Series Data ◽  
Extraction Study

Rough set theory is a kind of ambiguity and imprecision new mathematical tools, using precise mathematical analysis of imprecise system an ideal method. Rough set theory has powerful data reduction capability, this paper rough set theory to model the stock time series data, reduction, rule extraction, study the ups and downs of the relationship between the stock price, the use of advanced data mining techniques to dig out price linkage between stock association rules, has a very important significance.

scholarly journals ACQUISITION AND EXTRACTION OF DYNAMIC KNOWLEDGE USING D-ROUGH SET

2011 ◽  
pp. 13-17
Author(s):  
Brojo Kishore Mishra ◽  
Susanta Kumar Das
Keyword(s):  
Artificial Intelligence ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Knowledge Extraction ◽  
Equivalence Classes ◽  
Theory Of Knowledge ◽  
Intelligence System ◽  
Special Case ◽  
Rich History

Theory of knowledge has a long and rich history. Various aspects of knowledge are widely discussed issues at present, mainly by logicians and artificial intelligence (AI) researchers. It is one of the concepts, used to build intelligence system. Many soft computing tools are available for extraction, acquisition and validation of knowledge. Rough set is one such tool, mainly used for classification and extraction of knowledge. Rough Set Theory was proposed by Pawlak in 1982 as a tool for knowledge Extraction. However, when knowledge extraction is studied, we observed that most of the knowledge is static in nature. For analyzing Knowledge having dynamic in nature, Pawlak’s Rough Set Theory must be reconsidered. Dong Ya Li (et. al.) has already proposed the concept of dynamic Rough Set in 2007. We here, further analyze this concept and try to find out some more properties of it. Dynamic Rough Set (D-rough set) is a common form of Pawlak’s Rough Set as Pawlak’s rough set can be considered as a special case of D-rough set. Drough set is based on concepts, such as elementary transfer coefficient. D-rough set and D-Approximate set can be used for studying and analyzing dynamic knowledge. Further, we study and analyze the properties mentioned by Bussee. Grzymala-Busse has established some properties of approximation of classifications. These results are irreversible by nature. Pawlak has noted that these results of Busse establish that the two concepts, approximation of sets and approximation of families of sets (or classifications) are two different issues and that the equivalence classes of approximate classifications cannot be arbitrary sets. He has further stated that if we have positive example of each category in the approximate classification then we must have also negative examples of each category. In this paper, we have mentioned these aspects of the theorems of Busse and tried to study their properties, when D-rough and D-Approximate set has been incorporated. Lastly, we had provided the physical interpretation of each one of them.

ROUGH SET-BASED DECISION TREE USING A CORE ATTRIBUTE

2008 ◽  
Vol 07 (02) ◽  
pp. 275-290 ◽  
Author(s):  
SANG-WOOK HAN ◽  
JAE-YEARN KIM
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Tree ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Classification Rules ◽  
Decision Tree Algorithm ◽  
Decision Tree Classification ◽  
Id3 Algorithm

Decision trees are widely used in machine learning and artificial intelligence. In this paper, we extend previous research and present a new decision tree classification algorithm that uses a rough set theory to produce classification rules. Our algorithm is based on core attributes and on comparing the values of attributes between objects. Our experiments compared the performance of the Iterative Dichotomiser 3 (ID3) algorithm, C4.5, and the proposed decision tree algorithm to demonstrate its accuracy and ability to simplify rules.

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