Abstract
Some new concepts of grill nano topological structures throughout this paper are presented and studied. We generalize the famous closure operators that can be induced by binary operations. ...Some basic investigations for proposed structures are discussed. In addition, the connection between rough sets and grill set theory can be illustrated via some numerous examples. Finally, a comparison between proposed approaches and other studies is established.
Abstract
This paper presents the concepts of prepaths, paths, and cycles in α-topological spaces and studies them in orderable spaces. Also, many relationships are proved with their equivalences ...using some properties in topological spaces like compactness and locally connectedness.
This paper presents a brief review on major accidents and conducts bibliometric analysis of risk assessment methods for excavation system in recent year. The summarization of potential risks during ...excavation provides an important index for establishing an early warning system. The applications of fuzzy set theory and machine learning methods in risk assessment during excavation are presented. A case study of excavation in Guangzhou metro station is used to demonstrate the application of a machine learning method for risk evaluation. The large amount of data collected by 3S techniques (RS, GIS and GPS) and sensors increases accuracy of risk assessment levels in excavation. These procedures, integrated into building information modelling (BIM) management platform, can manipulate dynamic safety risk monitoring, control, and management. Finally, the processing and analysis of big data obtained from 3S techniques and sensors provide promising perspectives for establishing integrated technology system for excavation.
Display omitted
•Potential risks occurred during excavation construction are summarized.•The methods for risk assessment of excavation are presented.•Characteristics of excavation risk assessment: (i) subjective to objective; (2) qualification to quantification.•Random forest model for evaluating risk for excavation system is illustrated.•Perspective using advanced technologies for excavation management is proposed.
Many real data increase dynamically in size. This phenomenon occurs in several fields including economics, population studies, and medical research. As an effective and efficient mechanism to deal ...with such data, incremental technique has been proposed in the literature and attracted much attention, which stimulates the result in this paper. When a group of objects are added to a decision table, we first introduce incremental mechanisms for three representative information entropies and then develop a group incremental rough feature selection algorithm based on information entropy. When multiple objects are added to a decision table, the algorithm aims to find the new feature subset in a much shorter time. Experiments have been carried out on eight UCI data sets and the experimental results show that the algorithm is effective and efficient.
Abstract
The study of
metric dimension
of graph G has widely given some results and contribution of graph research of interest, including the domination set theory. The dominating set theory has been ...quickly growing and there are a lot of natural extension of this study, such as vertex domination, edge domination, total domination, power domination as well as the strong domination. In this study, we initiate to combine the two above concepts, namely metric dimension and strong domination set. Thus we have a resolving strong domination set. We have obtained the resolving strong domination number, denoted by γ
rst
(G), of some graphs.
For the past two decades, most of the people from developing countries are suffering from heart disease. Diagnosing these diseases at earlier stages helps patients reduce the risk of death and also ...in reducing the cost of treatment. The objective of adaptive genetic algorithm with fuzzy logic (AGAFL) model is to predict heart disease which will help medical practitioners in diagnosing heart disease at early stages. The model consists of the rough sets based heart disease feature selection module and the fuzzy rule based classification module. The generated rules from fuzzy classifiers are optimized by applying the adaptive genetic algorithm. First, important features which effect heart disease are selected by rough set theory. The second step predicts the heart disease using the hybrid AGAFL classifier. The experimentation is performed on the publicly available UCI heart disease datasets. Thorough experimental analysis shows that our approach has outperformed current existing methods.
•A fault-tolerant gait is designed for quadruped robots with one locked leg.•The gait takes full advantage of the mobility analyzed using the GF set theory.•The static stability margin keeps larger ...than zero throughout the gait.•The leg distributions and mechanisms do not affect the feasibility of the gait.•The gait is omnidirectional and can adapt to rough terrains.
The fault-tolerant gait plays an important role in improving the reliability and prolonging the service life of legged robots. However, few fault-tolerant gaits are available for quadruped robots and the static stability margin in some gaits cannot avoid being zero. This paper designs a novel fault-tolerant gait for quadruped robots with one locked leg using the GF set theory. First, a quadruped robot with serial-parallel leg mechanism and its typical static gait are introduced. Then, the mobility of the robot with one locked leg in different stages is addressed using the GF set theory. The fault-tolerant gait pattern is developed by taking full advantage of the mobility. Further, the performances of the fault-tolerant gaits are analyzed to demonstrate its capability. Finally, simulations are conducted to validate the fault-tolerant gait and its performance. The results show that the fault-tolerant gait has the capability of omnidirectional walking and adapting to rough terrains while maintaining a nonzero static stability margin.
•Automated Valuation Models (AVMs) can be applied both for large and small source databases.•One of the problems of applying AVMs is insufficient source data.•Fuzzy logic give possibility the more ...flexible way to deal with the similarity relation that perfect match to the real estate market analysis.•The developed decision making algorithm for property valuation, based on RST and VTR, allows results with high efficiency/accuracy to be obtained.
Objective monitoring of the real estate value is a requirement to maintain balance, increase security and minimize the risk of a crisis in the financial and economic sector of every country. The valuation of real estate is usually considered from two points of view, i.e. individual valuation and mass appraisal. It is commonly believed that Automated Valuation Models (AVM) should be devoted to mass appraisal, which requires a large size of databases (wider knowledge) and automated procedures. These models, however, have a wider spectrum of application.
The main aim of the study is to elaborate on a decision-making algorithm in the form of an Automated Valuation Model that uses the assumptions of the decision-making theory and data mining technology (Rough Set Theory (RST) and Value Tolerance Relation (VTR) - Fuzzy logic). The algorithm gives the opportunity to obtain the value of real estate where, using “if...then...” rules, we can account for the possibility of a non-deterministic relationship between real estate variables. It is applied to a small dataset of commercial real estate properties in Italy and residential ones in Poland. The proposed solution is universal and may be used in any other domain with imprecise and vague data.
•An ensemble parallel processing bi-objective genetic algorithm based feature selection method is proposed.•Rough set theory and Mutual information gain are used to select informative data removing ...the vague one.•Parallel processing in genetic algorithm reduces time complexity.•The method is compared with the existing state-of-the-art methods using suitable datasets.•Classification accuracy and statistical measures outperforms that of other state-of-the-art methods.
Feature selection problem in data mining is addressed here by proposing a bi-objective genetic algorithm based feature selection method. Boundary region analysis of rough set theory and multivariate mutual information of information theory are used as two objective functions in the proposed work, to select only precise and informative data from the data set. Data set is sampled with replacement strategy and the method is applied to determine non-dominated feature subsets from each sampled data set. Finally, ensemble of such bi-objective genetic algorithm based feature selectors is developed with the help of parallel implementations to produce much generalized feature subset. In fact, individual feature selector outputs are aggregated using a novel dominance based principle to produce final feature subset. Proposed work is validated using repository especially for feature selection datasets as well as on UCI machine learning repository datasets and the experimental results are compared with related state of art feature selection methods to show effectiveness of the proposed ensemble feature selection method.
In multi-label learning, each sample is related to multiple labels simultaneously, and attribute space of samples is with high-dimensionality. Therefore, the key issue for attribute reduction in ...multi-label data is to measure the quality of each attribute with respect to a set of labels. Stimulated by fuzzy rough set theory, which allows different fuzzy relations to measure the similarity between samples under different labels. In this paper, we propose a novel fuzzy rough set model for attribute reduction in multi-label learning. Different from single-label attribute reduction, a bottleneck of fuzzy rough set for multi-label attribute reduction is to find the true different classes’ samples for the target sample, which deeply affects the robustness of fuzzy upper and lower approximations. We first define the score vector of each sample to evaluate the probability of being different class’s sample with respect to the target sample. Then, local sampling is leveraged to construct a robust distance between samples. It can implement the robustness against noisy information when calculating the fuzzy lower and upper approximations under the whole label space. Moreover, multi-label fuzzy rough set model is proposed, and some related properties are discussed. Finally, the significance measure of a candidate attribute is defined, and a greedy forward attribute selection algorithm is designed. Extensive experiments are carried out to verify the effectiveness of the proposed algorithm by comparing it with some state-of-the-art approaches on eight publicly available data sets.
PDF
