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Artykuły w czasopismach na temat „Rough Set Theory (RST)”

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Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Dwiputranto, Teguh Handjojo, Noor Akhmad Setiawan, and Teguh Bharata Adji. "Rough-Set-Theory-Based Classification with Optimized k-Means Discretization." Technologies 10, no. 2 (2022): 51. http://dx.doi.org/10.3390/technologies10020051.

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The discretization of continuous attributes in a dataset is an essential step before the Rough-Set-Theory (RST)-based classification process is applied. There are many methods for discretization, but not many of them have linked the RST instruments from the beginning of the discretization process. The objective of this research is to propose a method to improve the accuracy and reliability of the RST-based classifier model by involving RST instruments at the beginning of the discretization process. In the proposed method, a k-means-based discretization method optimized with a genetic algorithm

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Chiaselotti, G., T. Gentile, and F. Infusino. "Decision systems in rough set theory: A set operatorial perspective." Journal of Algebra and Its Applications 18, no. 01 (2019): 1950004. http://dx.doi.org/10.1142/s021949881950004x.

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In rough set theory (RST), the notion of decision table plays a fundamental role. In this paper, we develop a purely mathematical investigation of this notion to show that several basic aspects of RST can be of interest also for mathematicians who work with algebraic and discrete methods.In this abstract perspective, we call decision system a sextuple [Formula: see text] [Formula: see text], where [Formula: see text], [Formula: see text], [Formula: see text] are non-empty sets whose elements are called, respectively, objects, condition attributes, values, [Formula: see text] is a (possibly emp

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Rana Aamir Raza. "An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network." Lahore Garrison University Research Journal of Computer Science and Information Technology 5, no. 3 (2021): 92–108. http://dx.doi.org/10.54692/lgurjcsit.2021.0503224.

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In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy

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Quafafou, M. "α-RST: a generalization of rough set theory". Information Sciences 124, № 1-4 (2000): 301–16. http://dx.doi.org/10.1016/s0020-0255(99)00075-4.

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Manikandan, R., Rajesh Kumar Maurya, Tariq Rasheed, et al. "Adaptive cloud orchestration resource selection using rough set theory." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): 311–20. http://dx.doi.org/10.47974/jim-1662.

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The recent research is developing in a vast speed to develop the cloud orchestration system. In cloud system the remotely managed servers are storing, finding, removing, replacing and retrieving the various services in an adaptive optimized manner. The lot of services are provided by the vast number of providers in the market with the help of approximation theory by the rough set system (RST). RST finds in helping in getting the efficient cloud resources as a service to the users. The proposed OCRS (Optimized Cost Resource System) approach is being simulated and compared with the existing clou

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Sun, Xinwei, and Kai Zeng. "RST: Rough Set Transformer for Point Cloud Learning." Sensors 23, no. 22 (2023): 9042. http://dx.doi.org/10.3390/s23229042.

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Point cloud data generated by LiDAR sensors play a critical role in 3D sensing systems, with applications encompassing object classification, part segmentation, and point cloud recognition. Leveraging the global learning capacity of dot product attention, transformers have recently exhibited outstanding performance in point cloud learning tasks. Nevertheless, existing transformer models inadequately address the challenges posed by uncertainty features in point clouds, which can introduce errors in the dot product attention mechanism. In response to this, our study introduces a novel global gui

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Setyaningsih, Nevi, Fitriani Fitriani, and Ahmad Faisol. "Sub-exact sequence of rough groups." Al-Jabar : Jurnal Pendidikan Matematika 12, no. 2 (2021): 267–72. http://dx.doi.org/10.24042/ajpm.v12i2.8917.

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Rough Set Theory (RST) is an essential mathematical tool to deal with imprecise, inconsistent, incomplete information and knowledge Rough Some algebra structures, such as groups, rings, and modules, have been presented on rough set theory. The sub-exact sequence is a generalization of the exact sequence. In this paper, we introduce the notion of a sub-exact sequence of groups. Furthermore, we give some properties of the rough group and rough sub-exact sequence of groups.

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S.Surekha, *. "A COMPARATIVE STUDY OF VARIOUS ROUGH SET THEORY ALGORITHMS FOR FEATURE SELECTION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 21–30. https://doi.org/10.5281/zenodo.495147.

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Machine Learning techniques can be used to improve the performance of intelligent software systems. The performance of any Machine Learning algorithm mainly depends on the quality and relevance of the training data. But, in real world the data is noisy, uncertain and often characterized by a number of features. Existence of uncertainties and the presence of irrelevant features in the high dimensional datasets often degrade the performance of the machine learning algorithms in all aspects. In this paper, the concepts of Rough Set Theory(RST) are applied to remove inconsistencies in data and var

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Alawneh, Tahani Nawaf, and Mehmet Ali Tut. "Using Rough Set Theory to Find Minimal Log with Rule Generation." Symmetry 13, no. 10 (2021): 1906. http://dx.doi.org/10.3390/sym13101906.

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Data pre-processing is a major difficulty in the knowledge discovery process, especially feature selection on a large amount of data. In literature, various approaches have been suggested to overcome this difficulty. Unlike most approaches, Rough Set Theory (RST) can discover data de-pendency and reduce the attributes without the need for further information. In RST, the discernibility matrix is the mathematical foundation for computing such reducts. Although it proved its efficiency in feature selection, unfortunately it is computationally expensive on high dimensional data. Algorithm complex

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Subrata Kumar Nayak, Et al. "Detection of HIV by using Rough Set and Homotopy Analysis Method." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1460–70. http://dx.doi.org/10.17762/ijritcc.v11i10.8696.

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The significant objective of this research is to recognize how to calculate the classification process using rough set theory (RST) for the Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV & AIDS) symptoms dataset. RST has a multi-dimensional concept with multiple approaches. In this paper, our main objective is to find the symptoms of (HIV & AIDS) using basic RST and Homotopy Analysis Method (HAM) to validate our claim using statistical techniques. We prefer RST & HAM over other soft computing techniques and Mathematical Modelling as both RST and

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Sharma, Haresh Kumar, and Kriti Kumari. "Tourist Arrivals Demand Forecasting Using Rough Set-Based Time Series Models." Decision Making Advances 3, no. 1 (2025): 216–27. https://doi.org/10.31181/dma31202567.

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This paper uses different univariate and multivariate autoregression and soft computing models for forecasting qualitative time series data. Rough set theory (RST) is one of the most convenient soft computing methods to investigate the imprecision and ambiguity of an information table by using qualitative dependent and independent variables. Moreover, applications based on rough set theory are used for the classification problem. The rough set is quite different from the other statistical and machine learning data analysis approaches based on mathematical equations. The empirical analysis indi

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Xiang, Qian, Zhi Jun Lv, Jian Guo Yang, and Xiang Gang Yin. "Mining Rule of Quality Control for Spinning Process with Rough Set Theory." Applied Mechanics and Materials 80-81 (July 2011): 1021–26. http://dx.doi.org/10.4028/www.scientific.net/amm.80-81.1021.

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Due to absence of an integral mathematical model, quality control in spinning process has been hard problem for a long time. Rough sets theory (RST) is a methodology that effectively deals with the problems with inexact, uncertain or vague knowledge in a complex information system. Considering a mass of data from spinning process and inspection, as well as the variety of knowledge and experience from domain experts, an RST-based intelligent control model for spinning process is presented in this paper. In order to analyze the yarn strength when the characteristics of fibers are given, a rule e

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S, Sathish, and Deepa S. "Analysis of Route Discovery in Mobile ad-hoc Network Routing using Information Systems." International Journal of Computer Science and Engineering Communications 1, no. 1 (2013): 6–12. https://doi.org/10.5281/zenodo.821734.

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Mobile Ad hoc NET works (MANETs) are group of wireless mobile nodes that can dynamically form a network without any infrastructures wherein mobile nodes are highly co-operative. In MANETs, the movement of nodes is uncertain may cause links to break and low battery life of mobile nodes may cause nodes to fail. The connectivity of the entire network is uncertain and may not be known to any particular node. The work presented in this studies routing in cache-based ad hoc routing protocols, based on the Information Systems of Rough Set Theory (RST). The Rough Set Theory is a new mathematical tool

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Sailaja, N. Venkata, L. Padma Sree, and N. Mangathayaru. "New Rough Set-Aided Mechanism for Text Categorisation." Journal of Information & Knowledge Management 17, no. 02 (2018): 1850022. http://dx.doi.org/10.1142/s0219649218500223.

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With the advent of computers and the information age, statistical and analytical problems have grown in terms of both size and complexity. Challenges in core domains of data storage, organisation and searching have evolved to the new research field called data mining. Text classification using various machine learning (ML) mechanisms encounters the difficulty of the high dimensionality of attributes vector. Therefore, a feature selection technique is very much required to discard irrelevant as well as noisy attributes from the feature set vector so that the ML algorithms can work efficiently.

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Cheng, Linhai, Yu Zhang, Yingying He, and Yuejin Lv. "Rough set models of interval rough number information system." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 1655–66. http://dx.doi.org/10.3233/jifs-191096.

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Classical rough set theory (RST) is based on equivalence relations, and does not have an effective mechanism when the attribute value of the objects is uncertain information. However, the information in actual problems is often uncertain, and an accurate or too vague description of the information can no longer fully meet the actual needs. Interval rough number (IRN) can reflect a certain degree of certainty in the uncertainty of the data when describing the uncertainty of the data, and can enable decision makers to make decisions more in line with actual needs according to their risk preferen

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Mac Parthaláin, N., and Q. Shen. "On rough sets, their recent extensions and applications." Knowledge Engineering Review 25, no. 4 (2010): 365–95. http://dx.doi.org/10.1017/s0269888910000263.

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AbstractRough set theory (RST) has enjoyed an enormous amount of attention in recent years and has been applied to many real-world problems including data mining, pattern recognition, and intelligent control. Much research has recently been carried out in respect of both the development of the underlying theory and the application to new problem domains. This paper attempts to summarize the advances in RST, its extensions, and their applications. It also identifies important areas which require further investigation. Typical example application domains are examined which demonstrate the succes

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T, Ashika, and Hannah Grace G. "Enhancing Classification Performance through Rough Set Theory Feature Selection: A Comparative Study across Multiple Datasets." European Journal of Pure and Applied Mathematics 18, no. 2 (2025): 5934. https://doi.org/10.29020/nybg.ejpam.v18i2.5934.

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In Machine Learning (ML), handling high-dimensional data with redundant or irrelevant features presents significant challenges. Effective feature selection is essential for enhancing model performance, reducing computational complexity, and improving interpretability. Rough Set Theory (RST) provides a powerful mathematical framework for managing uncertainty, making it a valuable tool for feature selection. This study applies RST-based feature selection to five diverse datasets, aiming to eliminate insignificant attributes. We evaluate the performance of various ML models, including Logistic Re

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Li, Lan Yun, Zhuan Zhao Yang, and Zhi He. "Research on Intelligent Fault Diagnosis Method Based on Rough Set Theory and Fuzzy Petri Nets." Applied Mechanics and Materials 26-28 (June 2010): 77–82. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.77.

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Rough sets theory (RST) and Fuzzy Petri nets (FPN) have been widely used in fault diagnosis. However, RST has the weakness of over-rigidity decision, and FPN has the dimensional disaster problem. In order to solve these shortcomings, according to complementary strategy, a new fault diagnosis method based on integration of RST and FPN was presented. Firstly, RST was applied to remove redundant fault features and simply fault information, so that the minimal diagnostic rules can be obtained and the fault was roughly diagnosed. Secondly, the optimal FPN structure was built and the fault diagnosis

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Vashist, Renu, and M. L. Garg. "Comparing and Contrasting Rough Set with Logistic Regression for a Dataset." International Journal of Rough Sets and Data Analysis 1, no. 1 (2014): 81–98. http://dx.doi.org/10.4018/ijrsda.2014010106.

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Rough Set Theory (RST) is relatively new and powerful mathematical tool to deal with imperfect data (i.e. data with uncertainty and vagueness) which is primarily used for classification and decision making problems. On the other hand, Logistic regression (Logit) is mainly used in Social Sciences when dependent variable takes limited and categorical data value ranges. However, both RST and Logit regression are powerful predictable models that are used in wide range of applications such as medicine, military, banking, financial markets etc. RST uses approximations and implications as two formal

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Piotrowska-Woroniak, Joanna, Krzysztof Nęcka, Tomasz Szul, and Stanisław Lis. "Comparative Analysis of Methods for Predicting Brine Temperature in Vertical Ground Heat Exchanger—A Case Study." Energies 17, no. 6 (2024): 1465. http://dx.doi.org/10.3390/en17061465.

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This research was carried out to compare selected forecasting methods, such as the following: Artificial Neural Networks (ANNs), Classification and Regression Trees (CARTs), Chi-squared Automatic Interaction Detector (CHAID), Fuzzy Logic Toolbox (FUZZY), Multivariant Adaptive Regression Splines (MARSs), Regression Trees (RTs), Rough Set Theory (RST), and Support Regression Trees (SRTs), in the context of determining the temperature of brine from vertical ground heat exchangers used by a heat pump heating system. The subject of the analysis was a public building located in Poland, in a temperat

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Chelly Dagdia, Zaineb, and Christine Zarges. "A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique." Fundamenta Informaticae 182, no. 2 (2021): 111–79. http://dx.doi.org/10.3233/fi-2021-2069.

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In the context of big data, granular computing has recently been implemented by some mathematical tools, especially Rough Set Theory (RST). As a key topic of rough set theory, feature selection has been investigated to adapt the related granular concepts of RST to deal with large amounts of data, leading to the development of the distributed RST version. However, despite of its scalability, the distributed RST version faces a key challenge tied to the partitioning of the feature search space in the distributed environment while guaranteeing data dependency. Therefore, in this manuscript, we pr

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Saravana Kumar, R., and G. Tholkappia Arasu. "Rough Set Theory and Fuzzy Logic Based Warehousing of Heterogeneous Clinical Databases." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 03 (2017): 385–408. http://dx.doi.org/10.1142/s0218488517500167.

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Large amounts of data about the patients with their medical conditions are presented in the Medical databases. Analyzing all these databases is one of the difficult tasks in the medical environment. In order to warehouse all these databases and to analyze the patient’s condition, we need an efficient data mining technique. In this paper, an efficient data mining technique for warehousing clinical databases using Rough Set Theory (RST) and Fuzzy Logic is proposed. Our proposed methodology contains two phases – (i) Clustering and (ii) Classification. In the first phase, Rough Set Theory is used

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Naouali, Sami, and Oussama El Othmani. "Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models." Applied Sciences 15, no. 9 (2025): 5148. https://doi.org/10.3390/app15095148.

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This study introduces a novel framework leveraging Rough Set Theory (RST)-based feature selection—MLReduct, MLSpecialReduct, and MLFuzzyRoughSet—to enhance machine learning performance on uncertain data. Applied to a private cardiovascular dataset, our MLSpecialReduct algorithm achieves a peak Random Forest accuracy of 0.99 (versus 0.85 without feature selection), while MLFuzzyRoughSet improves accuracy to 0.83, surpassing our MLVarianceThreshold (0.72–0.77), an adaptation of the traditional VarianceThreshold method. We integrate these RST techniques with preprocessing (discretization, normali

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CAO, YU, GUANGYU WAN, and FUQIANG WANG. "PREDICTING FINANCIAL DISTRESS OF CHINESE LISTED COMPANIES USING ROUGH SET THEORY AND SUPPORT VECTOR MACHINE." Asia-Pacific Journal of Operational Research 28, no. 01 (2011): 95–109. http://dx.doi.org/10.1142/s0217595911003077.

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Effectively predicting corporate financial distress is an important and challenging issue for companies. The research aims at predicting financial distress using the integrated model of rough set theory (RST) and support vector machine (SVM), in order to find a better early warning method and enhance the prediction accuracy. After several comparative experiments with the dataset of Chinese listed companies, rough set theory is proved to be an effective approach for reducing redundant information. Our results indicate that the SVM performs better than the BPNN when they are used for corporate f

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Rana, Hemant, and Manohar Lal. "A Comparative Study Based on Rough Set and Classification Via Clustering Approaches to Handle Incomplete Data to Predict Learning Styles." International Journal of Decision Support System Technology 9, no. 2 (2017): 1–20. http://dx.doi.org/10.4018/ijdsst.2017040101.

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Handling of missing attribute values are a big challenge for data analysis. For handling this type of problems, there are some well known approaches, including Rough Set Theory (RST) and classification via clustering. In the work reported here, RSES (Rough Set Exploration System) one of the tools based on RST approach, and WEKA (Waikato Environment for Knowledge Analysis), a data mining tool—based on classification via clustering—are used for predicting learning styles from given data, which possibly has missing values. The results of the experiments using the tools show that the problem of mi

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Su, Chung-Ho, Ken T. K. Chen, and Kuo-Kuang Fan. "Rough Set Theory Based Fuzzy TOPSIS on Serious Game Design Evaluation Framework." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/407395.

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This study presents a hybrid methodology for solving the serious game design evaluation in which evaluation criteria are based on meaningful learning, ARCS motivation, cognitive load, and flow theory (MACF) by rough set theory (RST) and experts’ selection. The purpose of this study tends to develop an evaluation model with RST based fuzzy Delphi-AHP-TOPSIS for MACF characteristics. Fuzzy Delphi method is utilized for selecting the evaluation criteria, Fuzzy AHP is used for analyzing the criteria structure and determining the evaluation weight of criteria, and Fuzzy TOPSIS is applied to determi

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Kumar, S. Udhaya, Ahmad Taher Azar, H. Hannah Inbarani, O. Joseph Liyaskar, and Khaled Mohamad Almustafa. "Weighted Rough Set Theory for Fetal Heart Rate Classification." International Journal of Sociotechnology and Knowledge Development 11, no. 4 (2019): 1–19. http://dx.doi.org/10.4018/ijskd.2019100101.

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A novel weighted rough set-based classification approach is introduced for the evaluation of fetal nature acquired from a CardioTocoGram (CTG) signal. The classification is essential to anticipate newborn's well-being, particularly for the life-threatening cases. CTG monitoring comprises of electronic fetal heart rate (FHR), fetal activities and the uterine contraction (UC) signals. These signals are extensively used as a part of the pregnancy and give extremely significant data on fetal health. The obtained data from these recordings can be utilized to anticipate the condition of the newborn

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Hua, Mingfeng, Taihua Xu, Xibei Yang, Jianjun Chen, and Jie Yang. "A novel approach for calculating single-source shortest paths of weighted digraphs based on rough sets theory." Mathematical Biosciences and Engineering 21, no. 2 (2024): 2626–45. http://dx.doi.org/10.3934/mbe.2024116.

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<abstract><p>Calculating single-source shortest paths (SSSPs) rapidly and precisely from weighted digraphs is a crucial problem in graph theory. As a mathematical model of processing uncertain tasks, rough sets theory (RST) has been proven to possess the ability of investigating graph theory problems. Recently, some efficient RST approaches for discovering different subgraphs (e.g. strongly connected components) have been presented. This work was devoted to discovering SSSPs of weighted digraphs by aid of RST. First, SSSPs problem was probed by RST, which aimed at supporting the fu

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Hao, L. N., Wen Lin Chen, X. F. Zhang, and Wan Shan Wang. "Rough Set Data Analysis System and Its Applications in Machinery Fault Diagnosis." Materials Science Forum 471-472 (December 2004): 850–54. http://dx.doi.org/10.4028/www.scientific.net/msf.471-472.850.

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The characteristics of fault diagnosis are as follows. First, features extraction is the key of improving diagnosis efficiency and correct rate. Secondly, fault diagnosis method based on rule reasoning has a wide application, but rule acquisition is one of the bottlenecks. Thirdly, rule modification is a key question of solving the real-time rule acquisition in the dynamic environments, and a primary question of knowledge base modification of expert system, etc. In this paper, Rough Set Theory (RST) was used to solve the key problems of machinery fault diagnosis, and a Rough Set Data Analysis

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R. Venmani. "Rough Set Analysis for Categorizing Motorcycles based on 100 Cubic Centimeters (CC) Engine Displacements." Communications on Applied Nonlinear Analysis 31, no. 2s (2024): 32–41. http://dx.doi.org/10.52783/cana.v31.591.

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Rough Set Theory (RST) is a mathematical approach used for dealing with uncertainty and vagueness in decision-making and data analysis. It provides a framework for classifying objects into different equivalence classes based on their attributes or characteristics. In RST, the concept of different bikes can be analyzed based on their attributes or characteristics. Each bike can be represented as an object with a set of attributes such as engine displacement, weight, top speed, fuel efficiency and price. Another class may consist of bikes with lower engine displacement, lighter weight, and bette

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Setiawan, Noor Akhmad. "Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory." International Journal of Rough Sets and Data Analysis 1, no. 1 (2014): 65–80. http://dx.doi.org/10.4018/ijrsda.2014010105.

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The objective of this research is to develop an evidence based fuzzy decision support system for the diagnosis of coronary artery disease. The development of decision support system is implemented based on three processing stages: rule generation, rule selection and rule fuzzification. Rough Set Theory (RST) is used to generate the classification rules from training data set. The training data are obtained from University California Irvine (UCI) data repository. Rule selection is conducted by transforming the rules into a decision table based on unseen data set. Furthermore, RST attributes red

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Sant'Anna, Annibal Parracho. "Rough sets analysis with antisymmetric and intransitive attributes: classification of brazilian soccer clubs." Pesquisa Operacional 28, no. 2 (2008): 217–30. http://dx.doi.org/10.1590/s0101-74382008000200003.

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This work aims to develop alternative classifications for teams in a Championship. Data from the 2005 Brazilian National Soccer Championship are analyzed. Rough Sets Theory (RST) is employed in this analysis. By evaluating the quality of the approximation in terms of probabilities of concordance and discordance between the classification by the set of decision attributes and by the set of condition attributes of a randomly chosen pair of objects as discernible or indiscernible, the modification of RST employed allows to consider antisymmetric and intransitive relations. The balance between the

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Fiedukowicz, Anna. "The use of rough rules in the selection of topographic objects for generalizing geographical information." Polish Cartographical Review 52, no. 1 (2020): 1–15. http://dx.doi.org/10.2478/pcr-2020-0001.

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AbstractSelection is a key element of the cartographic generalisation process, often being its first stage. On the other hand it is a component of other generalisation operators, such as simplification. One of the approaches used in generalization is the condition-action approach. The author uses a condition-action approach based on three types of rough logics (Rough Set Theory (RST), Dominance-Based Rough Set Theory (DRST) and Fuzzy-Rough Set Theory (FRST)), checking the possibility of their use in the process of selecting topographic objects (buildings, roads, rivers) and comparing the obtai

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M. Shaaban, Shaaban, Anas M. El-Sherif, Ahmed A. Hammam, Husham M. Attaalfadeel, and Yehya I. Mesalam. "Petroleum Well Site Selection Using MCRAT Integrated with Rough Set Theory." European Journal of Pure and Applied Mathematics 18, no. 2 (2025): 6049. https://doi.org/10.29020/nybg.ejpam.v18i2.6049.

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Shale Oil (SO) has emerged as an attractive additional supply of conventional crude oil throughout the world in recent years. Shale oil quality evaluation includes a variety of geochemical parameters. In this research, we propose a novel Integrated shale oil evaluation approach. This method firstly determines the relative weights of parameters using rough set theory. Finally, Multiple Criteria Ranking by Alternative Trace (MCRAT) technique is utilized to calculate the rank of Shale oil wells. Twenty-seven samples of shale oil were collected from various distinct well sites. Twelve different ge

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Tiwari, Ashish, and Ritu Garg. "Adaptive Ontology-Based IoT Resource Provisioning in Computing Systems." International Journal on Semantic Web and Information Systems 18, no. 1 (2022): 1–18. http://dx.doi.org/10.4018/ijswis.306260.

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The eagle expresses of cloud computing plays a pivotal role in the development of technology. The aim is to solve in such a way that it will provide an optimized solution. The key role of allocating these efficient resources and making the algorithms for its time and cost optimization. The approach of the research is based on the rough set theory RST. RST is a great method for making a large difference in qualitative analysis situations. It's a technique to find knowledge discovery and handle the problems such as inductive reasoning, automatic classification, pattern recognition, learning algo

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R. Venmani. "Analyzing Electrical Bikes Risk Factors Using Rough Set Theory and the Hybrid Logistic Regression Model." Advances in Nonlinear Variational Inequalities 28, no. 2 (2024): 153–58. http://dx.doi.org/10.52783/anvi.v28.1908.

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The increasing popularity of electric bikes (e-bikes) has brought to light various risk factors associated with their use, necessitating a thorough analysis to enhance safety and reliability. This research paper aims to identify and evaluate the risk factors of e-bikes by employing Rough Set Theory (RST) and a Hybrid Logistic Regression Model. This research underscores the importance of comprehensive risk analysis for e-bikes and demonstrates the effectiveness of combining Rough Set Theory with logistic regression for predictive modeling. The findings of this study reveal that rider behavior,

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Wang, Lei, Tian Rui Li, and Jun Ye. "A Matrix Method for Calculation of the Approximations under the Asymmetric Similarity Relation Based Rough Sets." Advanced Materials Research 187 (February 2011): 251–56. http://dx.doi.org/10.4028/www.scientific.net/amr.187.251.

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The essence of the rough set theory (RST) is to deal with the inconsistent problems by two definable subsets which are called the lower and upper approximations respectively. Asymmetric Similarity relation based Rough Sets (ASRS) model is one kind of extensions of the classical rough set model in incomplete information systems. In this paper, we propose a new matrix view of ASRS model and give the matrix representation of the lower and upper approximations of a concept under ASRS model. According to this matrix view, a new method is obtained for calculation of the lower and upper approximation

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Sharma, Haresh Kumar, Aarti Singh, Dixy Yadav, and Samarjit Kar. "Criteria selection and decision making of hotels using Dominance Based Rough Set Theory." Operational Research in Engineering Sciences: Theory and Applications 5, no. 1 (2022): 41–55. http://dx.doi.org/10.31181/oresta190222061s.

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Accommodation is one of the necessities of tourists and travel agencies' significant responsibilities. With the growing competition and profit-making various tour organising companies have started providing attractive accommodation options to the travellers to win their choices. Present research performs a case study on accommodation providing hotels through designing a strategy to enhance their profit earrings by welcoming more and more tourists. The methodology comprises rough set theory (RST) using the Dominance Based rough set theory (DRST) on the collected data of selected variables such

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D'amato, Maurizio. "A COMPARISON BETWEEN MRA AND ROUGH SET THEORY FOR MASS APPRAISAL. A CASE IN BARI." International Journal of Strategic Property Management 8, no. 4 (2004): 205–17. http://dx.doi.org/10.3846/1648715x.2004.9637518.

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Rough Set Theory is a property valuation methodology recently applied to property market data (d'Amato, 2002). This methodology may be applied in property market where few market data are available or where econometric analysis may be difficult or unreliable. This methodology was introduced by a polish mathematician (Pawlak, 1982). The model permit to estimate a property without defining an econometric model, although do not give any estimation of marginal or hedonic prices. I : ,he first version of RST was necessary to organize the data in classes before the valuation .The relationship betwee

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Prathviraj, N., and Deshpande L. "Rough set based QoS enabled multipath source routing in MANET." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1915–23. https://doi.org/10.11591/ijece.v10i2.pp1915-1923.

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The single constrained Quality of Service (QoS) routing in Mobile Ad-hoc NETwork (MANET) is disastrous in consideration of MANET characteristics, inference, collision and link failure as it maintains a single path. The QoS enabled routing yields better packet delivery and maintains consistency among nodes in the network by incorporating multi-constrained and multipath routing. The Dynamic Source Routing (DSR) is best suited source routing algorithm to maintain multipath information at the source node, but performance degrades with larger number of mobile nodes. Multilayer mechanism should be i

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N., Prathviraj, and Santosh L. Deshpande. "Rough set based QoS enabled multipath source routing in MANET." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1915. http://dx.doi.org/10.11591/ijece.v10i2.pp1915-1923.

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The single constrained Quality of Service (QoS) routing in Mobile Ad-hoc NETwork (MANET) is disastrous in consideration of MANET characteristics, inference, collision and link failure as it maintains a single path. The QoS enabled routing yields better packet delivery and maintains consistency among nodes in the network by incorporating multi-constrained and multipath routing. The Dynamic Source Routing (DSR) is best suited source routing algorithm to maintain multipath information at the source node, but performance degrades with larger number of mobile nodes. Multi-layer mechanism should be

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Su, Yuebin, and Jin Guo. "A Novel Strategy for Minimum Attribute Reduction Based on Rough Set Theory and Fish Swarm Algorithm." Computational Intelligence and Neuroscience 2017 (August 15, 2017): 1–7. http://dx.doi.org/10.1155/2017/6573623.

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For data mining, reducing the unnecessary redundant attributes which was known as attribute reduction (AR), in particular, reducts with minimal cardinality, is an important preprocessing step. In the paper, by a coding method of combination subset of attributes set, a novel search strategy for minimal attribute reduction based on rough set theory (RST) and fish swarm algorithm (FSA) is proposed. The method identifies the core attributes by discernibility matrix firstly and all the subsets of noncore attribute sets with the same cardinality were encoded into integers as the individuals of FSA.

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Hossain, Touhid Mohammad, Junzo Watada, Izzatdin A. Aziz, and Maman Hermana. "Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach." Applied Sciences 10, no. 17 (2020): 5940. http://dx.doi.org/10.3390/app10175940.

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Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous records of several physical properties of drilled rocks that can be related to different lithologies by experienced log analysts. This work is time consuming and likely to be imperfect because human analysis is subjective. Thus, any automated classification approach with high promptness and accuracy is very welcome by log analysts. One of the cruc

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Zhou, Yong Quan, and Xu Tan. "Mould Risk Assessment Based on RST-BN Approach." Advanced Materials Research 601 (December 2012): 611–17. http://dx.doi.org/10.4028/www.scientific.net/amr.601.611.

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Mould industry is crucial to all manufacturing fields, but there has been very little research work of mould project risk assessment in the world so far, it doesn’t match with mould’s important role in all industries. Mould project risks often became issues, made over 8% rework rate after mould delivery and 20% process rework rate in average. This paper summarized the disadvantage of FTA-BN approach to quantitative risk assessment of mould project, which is widely used in other industry fields, and proposed RST-BN approach instead, The attribute selection and fault diagnosis was developed base

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Fityah, Farhatul, and Pramudya Rakhmadyansyah Sofyan. "Impact of Cover Parameter Value on Rule Generation in Rough Set Classification." MALCOM: Indonesian Journal of Machine Learning and Computer Science 5, no. 2 (2025): 578–86. https://doi.org/10.57152/malcom.v5i2.1831.

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Machine learning plays a crucial role in healthcare classification, with Rough Set Theory (RST) offering effective tools for managing data uncertainty. Within RST, the RSES2 tool supports algorithms like LEM2 and Covering, yet the influence of cover parameter values on rule generalization and specificity remains underexplored. This study investigates these effects using the Differentiated Thyroid Cancer dataset. The research investigates the trade-offs between rule generalization and specificity by adjusting cover parameter settings, which dictate the minimum and maximum cases a rule must cove

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Sharma, H. Kumar, K. Kumari, and S. Kar. "Air passengers forecasting for Australian airline based on hybrid rough set approach." Journal of Applied Mathematics, Statistics and Informatics 14, no. 1 (2018): 5–18. http://dx.doi.org/10.2478/jamsi-2018-0001.

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Abstract Accurate and reliable air passenger demand is very important for policy-making and planning by tourism management as well as by airline authorities. Therefore, this article proposed a novel hybrid method based on rough set theory (RST) to construct decision rules for long-term forecasting of air passengers. Level (mean) and trend components are first estimated from the air passengers time series data using DES model in the formulation of the proposed hybrid method. Then the rough set theory is employed to combine the output of DES model and generated decision rules is used to forecast

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Gomolińska, Anna. "Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach." Fundamenta Informaticae 85, no. 1-4 (2008): 139–53. https://doi.org/10.3233/fun-2008-851-411.

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In this article we discuss judgment of satisfiability of formulas of a knowledge representation language as an object classification task. Our viewpoint is that of the rough set theory (RST), and the descriptor language for Pawlak's information systems of a basic kind is taken as the study case. We show how certain analogy-based methods can be employed to judge satisfiability of formulas of that language.

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Akbari, Saeed, Farzad Pour Rahimian, Moslem Sheikhkhoshkar, Saeed Banihashemi, and Mostafa Khanzadi. "Dynamic sustainable success prediction model for infrastructure projects: a rough set based fuzzy inference system." Construction Innovation 20, no. 4 (2020): 545–67. http://dx.doi.org/10.1108/ci-04-2019-0034.

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Purpose Successful implementation of infrastructure projects has been a controversial issue in recent years, particularly in developing countries. This study aims to propose a decision support system (DSS) for the evaluation and prediction of project success while considering sustainability criteria. Design/methodology/approach To predict sustainable success factor, the study first developed its sustainable success factors and sustainable success criteria. These then formed a decision table. A rough set theory (RST) was then implemented for rules generation. The decision table was used as the

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Li, Xiaoqing, Qingquan Jiang, Maxwell K. Hsu, and Qinglan Chen. "Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network." Sustainability 11, no. 17 (2019): 4513. http://dx.doi.org/10.3390/su11174513.

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Software supports continuous economic growth but has risks of uncertainty. In order to improve the risk-assessing accuracy of software project development, this paper proposes an assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST). First, a risk list with 35 risk factors were grouped into six risk categories via the brainstorming method and the original sample data set was constructed according to the initial risk list. Subsequently, an attribute reduction algorithm of the rough set was used to eliminate the redundancy attributes from t

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Piotrowska-Woroniak, Joanna, and Tomasz Szul. "Application of a Model Based on Rough Set Theory (RST) to Estimate the Energy Efficiency of Public Buildings." Energies 15, no. 23 (2022): 8793. http://dx.doi.org/10.3390/en15238793.

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The study was carried out on a group of 85 public buildings, which differed in type of use, construction technology and heating systems. From the collected data, a set of qualitative and quantitative variables characterizing them in terms of heat demand was extracted. In this paper, the authors undertook to test the suitability of a model based on rough set theory (RST), which allows the analysis of imprecise, general and uncertain data. To obtain input data for the RST model in quantitative form, the authors used an alternative approach, which is a method based on the thermal properties of bu

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