Rough Set Rules Research Articles

Rough set method-cloud internet of things: a two-degree verification scheme for security in cloud-internet of things

The quick development of innovations and increasing use of the internet of things (IoT) in human life brings numerous challenges. It is because of the absence of adequate capacity resources and tremendous volumes of IoT information. This can be resolved by a cloud-based architecture. Consequently, a progression of challenging security and privacy concerns has emerged in the cloud based IoT context. In this paper, a novel approach to providing security in cloud based IoT environments is proposed. This approach mainly depends on the working of rough set rules for guaranteeing security during data sharing (rough set method-cloud IoT (RSM-CIoTD)). The proposed RSM-CIoTD conspire guarantees secure communication between the user and cloud service provider (CSP) in a cloud based IoT. To manage unauthorized users, an RSM-CIoTD scheme utilizes a registered authority which plays out a two-degree confirmation between the network substances. The security and privacy appraisal techniques utilize minimum and maximum trust benefits of past communication. The experiments show that our proposed system can productively and safely store the cloud service while outperforming other security methods.

A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting.

In recent years, due to the ubiquitous presence of WiFi access points in buildings, the WiFi fingerprinting method has become one of the most promising approaches for indoor positioning applications. However, the performance of this method is vulnerable to changes in indoor environments. To tackle this challenge, in this paper, we propose a novel WiFi fingerprinting method that uses the valued tolerance rough set theory–based classification method. In the offline phase, the conventional received signal strength (RSS) fingerprinting database is converted into a decision table. Then a new fingerprinting database with decision rules is constructed based on the decision table, which includes the credibility degrees and the support object set values for all decision rules. In the online phase, various classification levels are applied to find out the best match between the RSS values in the decision rules database and the measured RSS values at the unknown position. The experimental results compared the performance of the proposed method with those of the nearest-neighbor-based and the random statistical methods in two different test cases. The results show that the proposed method greatly outperforms the others in both cases, where it achieves high accuracy with 98.05% of right position classification, which is approximately 50.49% more accurate than the others. The mean positioning errors at wrong estimated positions for the two test cases are 1.71 m and 1.99 m, using the proposed method.

Supervised Machine Learning in Electrofacies Classification: A Rough Set Theory Approach

Electrofacies were initially introduced for defining a set of recorded log responses in order to characterize a bed and permitted it to be distinguished from the other rock units as an improvement to the traditional use of well logs. Grouping a formation into electrofacies can be used in lithology prediction, reservoir characterization and discrimination. Usually Multivariate statistical analyses, such as principal component analysis ‘PCA’ and cluster analysis are used for this purpose. In this study Extra Tree Classifier (ETC) based feature selection method is used to select the important attributes and three distinctive electrofacies were extracted from the dendrogram plot using the selected attributes. Finally, we proposed a rough set theory (RST) based white box classification approach to extract the pattern of the electrofacies in the form of decision rules which will allow the geosciences researchers to correlate the electrofacieses with the lithofacies from the extracted rough set (RS) rules.

Research on Intelligent Scale Pedestrian Navigation Map Based on Rough Set Rules

With the popularity of smart phones, pedestrian navigation maps based on smart phones are widely used, but the readability of pedestrian navigation map is not satisfactory. In this paper, an intelligent scale pedestrian navigation map model based rules is proposed. The realization process of intelligent scale navigation map includes the measurement of the spatial distribution of road network in adjacent space and peripheral space, the construction of map scale decision table based on rough set, the extraction of rules and the matching of rules. Finally, the experimental results prove that the intelligent scale navigation map based on rules is feasible, and the effect is better than that of static scale navigation map.

Intelligent initial map scale generation based on rough-set rules

A proper initial map scale can help improve map legibility. However, the existing initial scale designs for electronic maps cannot make active adjustments according to the differences in the surrounding geographic information distributions, during map panning or navigation. This causes many redundant zooming operations, which reduce the reading efficiency. To solve this problem, we propose a method based on the rough set, which chooses an initial map scale according to the spatial distribution of the road network. First, the spatial distribution of the road network is evaluated using the neighborhood relation model, with Delaunay triangulations. Next, the data of the road network’s spatial distributions and the corresponding map scale data from user operations are collected at different locations. Then, the relationship rules are extracted based on rough set. Finally, an intelligent initial map scale service is developed according to the rules, and its feasibility and effectiveness are tested using an experimental system. The test results show that the intelligent initial map method can adjust the map scale adaptively and dynamically according to distribution of the road network. Consequently, the map legibility is improved significantly because of the reduction in the number of zooming operations.

Breast Tissue Classification via Interval Type 2 Fuzzy Logic Based Rough Set

BIRADS is a Breast Imaging, Reporting and Data System. A tool to standardize mammogram reports and minimizes ambiguity during mammogram image evaluation. Classification of BIRADS is one of the most challenging tasks to radiologist. An apt treatment can be administered to the patient by the oncologist upon acquiring sufficient information at BIRADS stage. This study aspired to build a model, which classifies BIRADS using mammograms images and reports. Through the implementation of type-2 fuzzy logic as classifier, an automatically generated rules will be applied to the model. Comparison of accuracy, specificity and sensitivity of the modal will be performed vis-í -vis rules given by the experts. The study encompasses a number of steps beginning with collection of the data from Radiology Department of National University of Malaysia Medical Center. The data was initially processed to remove noise and gaps. Then, an algorithm developed by selecting type-2 fuzzy logic using Mamdani model. Three types of membership functions were employed in the study. Among the rules that used by the model were obtained from experts as well as generated automatically by the system using rough set theory. Finally, the model was tested and trained to get the best result. The study shows that triangular membership function based on rough set rules obtains 89% whereas expert driven rules gains about 78% of accuracy rates. The sensitivity using expert rules is 98.24% whereas rough set rules obtained 93.94%. Specificity for using expert rules and rough set rules are 73.33%, 84.34% consecutively. Conclusion: Based on statistical analysis, the model which employed rules generated automatically by rough set theory fared better in comparison to the model using rules given by the experts.

基于关联规则挖掘的车载网络入侵检测技术研究

随着汽车信息化，目前很多汽车都通过网络模块与外网连接。由于汽车跟外网相连，那么就给黑客提供了通过网络远程攻击汽车的途径。本文介绍了粗糙集和关联规则的相关背景，接着用粗糙集技术改进传统的Apriori算法应用到车载网络入侵检测方面，最后通过试验验证了对车载网络的入侵检测。 With the development of automobile information, many cars are connected with the external network through the network module. As the car is connected to the extranet, hackers are offered a long way to attack the car via the internet. This paper introduces the background of rough set and association rules, and then uses rough set technology to improve the traditional Apriori algorithm to be applied to the vehicle network intrusion detection, and finally through the test proves the vehicle network intrusion detection.

Medical Reasoning with Rough-Set Influence Diagrams.

There are several advantages to evaluating a problem using influence diagram operations. The analyst can use a representation that is natural to the decision maker, since the algorithm executes all of the inference and analysis automatically. The influence diagram solution procedure can also result in significant gains in efficiency. Conditional independence is clearly exhibited in the diagram, so the size of intermediate calculations can be reduced, resulting in considerable reductions in both processing time and memory requirements. However, when imprecise knowledge from data sets is involved in the systems, how to reason from approximate information becomes a main issue in evaluating influence diagrams effectively. This study develops an alternative knowledge model, rough-set influence diagrams (RSID), which combine rough-set decision rules and graphical structures of influence diagrams in medical settings. The proposed RSID provides a comprehensive schema for knowledge representation and decision support.

A rough set-based association rule approach implemented on exploring beverages product spectrum

When items are classified according to whether they have more or less of a characteristic, the scale used is referred to as an ordinal scale. The main characteristic of the ordinal scale is that the categories have a logical or ordered relationship to each other. Thus, the ordinal scale data processing is very common in marketing, satisfaction and attitudinal research. This study proposes a new data mining method, using a rough set-based association rule, to analyze ordinal scale data, which has the ability to handle uncertainty in the data classification/sorting process. The induction of rough-set rules is presented as method of dealing with data uncertainty, while creating predictive if--then rules that generalize data values, for the beverage market in Taiwan. Empirical evaluation reveals that the proposed Rough Set Associational Rule (RSAR), combined with rough set theory, is superior to existing methods of data classification and can more effectively address the problems associated with ordinal scale data, for exploration of a beverage product spectrum.

Local and Global Measurements of MGRS Rules

Abstract Since the multigranulation rough sets (MGRS) can be considered as the compositions of multi–independent Pawlak's rough sets, the multigranulation rough set rules (MGRS rules) are then the compositions of decision rules, which are supported by multi–independent Pawlak's rough sets. To measure MGRS rules, both the local and global views are employed in this paper. In local view, the support, certainty and coverage factors are proposed to measure MGRS rules, which are support by an object; while in global view, these three factors are proposed to measure MGRS rules, which are coexisting in a decision system. The necessary conditions for these factors to achieve maximal and minimal values are also addressed. Some numerical examples are employed to substantiate the conceptual arguments.

Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump

Rule learning based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. Fault detection and diagnosis of various mechanical components of centrifugal pump is essential to increase the productivity and reduce the breakdowns. This paper presents the use of rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a centrifugal pump. A fuzzy inference system (FIS) is built using rough set rules and tested using test data. The effect of different types of membership functions on the FIS performance is also presented. Finally, the performance of this classifier is compared to that of a fuzzy-antminer classifier and to multi-layer perceptron (MLP) based classifiers.

Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump

Mono-block centrifugal pumps are widely used in a variety of applications. In many applications the role of mono-block centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approach is gaining momentum. Particularly, artificial neural networks, fuzzy logic have been employed for continuous monitoring and fault diagnosis. This paper presents the use of decision tree and rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a mono-block centrifugal pump. A fuzzy classifier is built using decision tree and rough set rules and tested using test data. The results obtained using decision tree rules and those obtained using rough set rules are compared. Finally, the accuracy of a principle component analysis based decision tree-fuzzy system is also evaluated. The study reveals that overall classification accuracy obtained by the decision tree-fuzzy hybrid system is to some extent better than the rough set-fuzzy hybrid system.

PROMOTION DECISIONS:A ROUGH SET APPROACH

Promotions are an important aspect of the careers in managerial accounting. Review of the literature on promotions reveals that the decision‐making process is open to bias due to characteristics of ratees and raters, as well as possible political influences. Such flawed decision making may be due in part to the inherent ambiguity in rating performance of managers. Rough set rules are developed from an decision‐making example in which performance data is used in the decision to promote managers. Implications of incorporating these rules into expert systems used for promotions decisions are presented.