Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

 The analysis framework of quality management, organizational innovation and organizational performance is constructed to study the relationship and interaction mechanism, and 156 manufacturing enterprises in the more developed manufacturing areas of our country are selected as samples for empirical research. The results show that quality management can directly affect technological innovation in organizational innovation, and directly affect management innovation through technological innovation. Organizational innovation is the intermediate variable of quality management on the organizational performance. In manufacturing enterprises, the impact of technological innovation on organizational performance is greater than that of management innovation on organizational performance.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

The paper proposes a new approach to solving bipartite matching problems with linguistic scorings. The bipartite matching problem based on strict linguistic scorings is firstly described. Some basic concepts are also introduced, including bipartite matching, stable matching, and satisfaction degree. In the proposed approach, linguistic scoring preferences are changed to satisfaction degree preferences. For maximizing satisfaction degrees of agents, a bipartite matching model under the conditions of matching constraints and stability constraints is developed. Considering the important degrees of agents in each side, the bipartite matching model can be changed to a single target model by using the linear weighting approach twice. The stable bipartite matching result is determined through model solution. A numerical case is used to state the practicability of the presented approach.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

 In order to determine the explosion value in the confined space, this time the simulation model of the deep learning algorithm is used to study it. The research status of deep learning algorithm is first expounded, and the numerical record of gas explosion in confined space is constructed according to computer technology. In order to ensure the optimization of numerical processing, the deep learning algorithm is used to process the simulation data to ensure the accuracy of the explosion value. In order to further test the numerical accuracy of the numerical model of gas limited space explosion, the comparison of different values in the constrained space is carried out, and the efficiency and accuracy of the deep learning algorithm are tested. The test results show the application of deep learning algorithm. The accuracy of the explosion value is further guaranteed and needs further application.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

 University educational administration management system is one of the core tasks of digital campus. Data mining is a technology that taps potential information from a large amount of data according to a specific algorithm for researchers to analyze. The experimental comparison between the improved algorithm and the unmodified algorithm shows that the improved algorithm has better performance and can improve the convergence speed of the clustering and the accuracy of the clustering results. The improved algorithm is applied to the mining of student achievement evaluation. Finally, according to the comparison of the results of the traditional rating criteria with the dynamic rating evaluation results, the results confirm the rationality and feasibility of the management of college computer network education according to the clustering algorithm. According to the cluster analysis of these two models, it shows that it is meaningful to introduce data mining into the management of college computer network education administration.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

 The BP neural network algorithm is used to construct a stable slope angle prediction model for open-pit mines, which can successfully predict the ultimate slope angle of the mine. At the same time, a multi-tool combination of Surpac, Madis, and Flac3D is used to create a numerical model for stable slope angle in mines. The model is used to test the final slope angle prediction structure for stable mines. Through a comprehensive analysis of the ore deposit model technology constructed by the series of realms, the economic parameters involved in the generation of realm boundaries are known, and specific primaries are pointed out with the error requirements of each parameter analyzed in the realm of series. A concrete solution is put forward to the issue of realm gap. From the point of view of the power and responsibility parameters, the series of realm production methods are analyzed, and the rapid generation methods of the realm of open-pit mine series are pointed out.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

Using Taiwan’s Manpower Utilization Survey from 2012–2014, this paper investigates the impacts that salary variation caused by insufficient information, then evaluates the condition of information holding between the employee and the employer. The results indicate that the ignorance of employee and employer are deeper in the private sector than that in the public sector; the employees and employer ignorance with the white-collar both larger than the blue-collar. Besides, the results demonstrate insignificantly effect in both ignorance estimation in the public sector, which may reflect the labors enter to the public sector mainly through national examination and cause the insignificant estimation.

 The development of Daqing Oilfield in China has entered the middle and late stages of high water cut. At this time, oil-water two-phase flow is ubiquitous, and its flow rate is very difficult to measure accurately. Addressing this issue, the measurement model and simulation model of electromagnetic flow transducer (EFT) with saddle excitation structure is designed in this paper. Then the distribution characteristics of magnetic flux density of different excitation structures are analyzed by finite element simulation. Furthermore, the prediction model between the parameters of different excitation structures and the performance evaluation indexes is established based on RBF neural network. Through normalization and weight assignment on the output of neural network model, the structure optimization factor is constructed. Then the optimum solution of this factor is gotten, and the optimum parameters of EFT’s excitation structure are obtained. In addition, an EFT with the optimum structure is developed and tested in Daqing oilfield, and the experiment results show that the EFT has high precision, especially in the high viscosity wells.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

 Stampede accidents with serious injuries occur from time to time on escalators. Field observation was conducted on four typical passenger behaviors during taking escalators, namely, walking behavior, subgroup behavior, overtaking behavior and waiting behavior. The effect of behavior characteristics on passenger safety was analyzed according to the observation data. Several scenarios were simulated to quantitatively study the impact of passenger behaviors on crowd stampede risk under different situations. The results show that: (1) the presence of subgroup behavior and overtaking behavior increases the crowd stampede risk by increasing the crowd density and the degree of congestion on the connection plane; (2) the walking behavior reduces the crowd density; (3) the “walk left, stand right” rule decreases the evacuation efficiency; (4) waiting behavior of passengers on the connection plane significantly increases the crowd stampede risk. Management measures were proposed to promote the passenger safety and reduce the stampede injury on escalators.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

In this paper, component values of analog active filters are selected based on the manufacturer’s values of E series. The selection is based on optimization algorithms and here one is nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), and another one is the physics-based method called Sine Cosine Algorithm (SCA), are used for active filter design. The capability of optimization of the above algorithms is evaluated by considering the two active filters of a 4th order Butterworth and State variable filter. The performances of each algorithm are analyzed by applying to above two different filter structures, where the component values are determined by making compatible with different E series manufacturer.

A two stage recognition method combined multiple kind of features was proposed to overcome the limitation of single kind of feature in the lung sound recognition. The method combines the improved Welch power spectrum, Mel cepstrum coefficients and the linear prediction cepstral coefficients based on the wavelet decomposition. In the first stage, pneumonia samples and asthma samples are firstly taken as the abnormal category. Then a two-class classifier based on random forests is trained to identify the normal samples and the abnormal samples. In the second stage, a classifier based on random forests is trained to recognize pneumonia and asthma from the samples classified as the abnormal samples in the first stage. To further improve the accuracy, a multi granularity cycle segmentation method of lung sounds was presented, which is based on the short time zero crossing rate. It can better segment lung sounds. Experimental results showed that the proposed method greatly improved the recognition accuracy, especially for improving the accuracy of pneumonia and asthma.

Fuzzy similarity degree is a measurement of the similarity between fuzzy sets through local information, it plays an important role in the design of fuzzy system and controller. This article first proposes a new computational formula for membership functions of a consequent fuzzy set based on fuzzy similarity degree, and an analytic representation of the Mamdani fuzzy system is obtained through the Gauss fuzzification, product inference engine and center average defuzzification. Next, a specific Mamdani fuzzy system constructed by Gauss fuzzifier or singleton fuzzification be expressed through a given fuzzy similarity degree in practice. Finally, the output algorithm of the proposed fuzzy system is given by the space positioning method. The result shows that the Mamdani fuzzy system constructed by fuzzy similarity degree and Gauss fuzzification is superior to that based on singleton fuzzification in terms of approximation capability.

Graph theory is a fundamental and energetic tool for designing and modeling a graph/network. There are certain topological indices based on degree, distance and eccentricity, etc. The topological indices essentially relate certain physio-concoction properties and bio-activity to the corresponding synthetic and atomic structure.

In this paper, our aim is to figure out degree-based topological indices mainly atom-bond connectivity (ABC), geometric-arithmetic (GA), ABC₄ and GA₅ indices for cellular neural network (CNN) and give closed results of these indices for cellular neural network. Moreover, we also compute general Randi c ´ index R _α of CNN for α = { 1 , - 1 , 1 2 , - 1 2 } only and give analytical closed form results. A 3D graph analysis for comparison of indices is also given.

As the extension of intuitionistic fuzzy numbers (IFNs), linguistic intuitionistic fuzzy numbers (LIFNs) are proposed. LIFNs are expressed by linguistic variables and take the membership degree (MD) and non-membership degree (NMD) into consideration. The MD and NMD of LIFNs can easier describe complex and fuzzy information in multiple attribute decision-making (MADM) problems. The TODIM method based on prospect theory can reflect the psychological factors of the decision makers (DMs). However, existing TODIM methods neither handle the decision-making problems under linguistic intuitionistic environment nor consider interrelations among multiple attributes. Based on these problems, in this paper, combining the fuzzy measure with the TODIM method, an extended Choquet-TODIM method is proposed to process the MADM problems. The extended Choquet-TODIM method considers the interrelationship of multiple attributes and the bounded rationality of DMs. Firstly, the relative theories of LIFNs, the classical TODIM method and λ-fuzzy measures are briefly reviewed. Secondly, the TODIM method is extended to linguistic intuitionistic fuzzy environment and C-TODIM method of LIFNs is proposed. Then λ-fuzzy measure is extended to the classical TODIM method and a model of determining fuzzy measures is proposed. The proposed method gives the new solution to calculate the fuzzy measures and address the MADM problems with interrelationship among different attributes. Lastly, two numerical examples are used to compare with two existing methods and explain the effectiveness and superiority of this method.

There are mainly two classes of approaches in the studies of Formal Concept Analysis (FCA), i.e. the constructive and axiomatic approaches. In axiomatic approach, operators are interpreted by using operations in mathematical systems instead of operations in a formal context. Seeking for minimal axioms to characterize the concept generation operators is an important issue in the research of the axiomatic approach. In this paper, axiomatic characterizations of set-theoretic operators are investigated. We construct an adjoint generalized (dual) concept systems in which the pair of classical concept generation operators are represented by one set-theoretic operator, and the other operator can be obtained from the former. Compared with the previous methods, the proposed generalized (dual) concept systems have fewer axioms and is easy to verify. Some properties of adjoint generalized (dual) concept systems are examined.

In today’s society, many decision-making problems cannot be merely solved based on quantitative data. Even though some issues are able to be addressed by quantitative data, researchers may face the difficulty of obtaining accurate and sufficient numbers. Some qualitative evaluations given by experts or decision makers are usually linguistic expressions. Therefore, fuzzy linguistic research has been broadly studied to address the above issues. This research has also been attracting increasing attention from researchers and decision makers in the world. It is believed that analyzing the status quo and emerging trends in this research area is of great necessity, especially for the beginners who are interested in fuzzy linguistic research. To do so, this paper provides the mapping knowledge domain of fuzzy linguistic research based on 648 papers on Web of Science from 1975 to 2018 by using CiteSpace which is an effective tool for scientometric studies. The visualization analyses of cited reference clusters, collaborations networks, author co-citation networks, burst detection and time zone view are presented in this study to show the research streams and the papers that made significant theoretical contributions. Also, the active counties, institutions, journals and authors in this research area are analyzed in detail. Besides, the specific hot spots and emerging trends can be known. There are two contributions in this study. Firstly, we give a comprehensive investigation about the status quo and emerging trends of fuzzy linguistic research in the recent 43 years. Secondly, we make the development of fuzzy linguistic research easier and direct to learn for beginners.

In multi-attribute group decision-making (MAGDM) problems, there exist some multi-polarity for the attributes and criteria. Sometimes in real life situations, we deal with the both membership and non-membership grades for the attributes in the presence of multi-polarity. For this purpose, we change verbally stated information into mathematical language with the help of uncertain linguistic variables to deal with the ambiguities and uncertainties. In that case, we construct some extensions from the existing hybrid structures of fuzzy set to handle these types of problems. That’s why from the prevailing concepts of cubic set and m-polar fuzzy set, we innovate the concept of cubic m-polar fuzzy set (CMPFS). We investigate its numerous operations with the help of examples. With the enthusiasm of CMPFS, we establish certain aggregation operators based on cubic m-polar fuzzy numbers (CMPFNs) namely Cubic m-polar fuzzy weighted averaging (CMPFWA), Cubic m-polar fuzzy ordered weighted averaging (CMPFOWA) and Cubic m-polar fuzzy hybrid averaging (CMPFHA) operators corresponding to R-order and P-order, simultaneously. Using the score function and accuracy function a relation is proposed, through which we can compare the CMPFNs. This manuscript presents a novel approach for treating ambiguities based on the application of land selection using linguistic variables in CMPF decision theory. An algorithm based on MAGDM is intended for a given agricultural project, which will produce results according to the proposed operators one by one. Furthermore, a comparative analysis is listed to demonstrate the difference, advantages, validity, simplicity, flexibility and superiority to the proposed operators.

Software Release Planning (SRP) is to find, for a software, a subset of the requirements with the highest value while respecting the budget. The value of a requirement however may, to various degrees, depend on selecting or ignoring other requirements. However, existing SRP models ignore either Value-Related Dependencies altogether or the strengths of those dependencies even if they consider them. This paper presents an Integer Programming model for software release planning, which considers the variances of strengths of positive and negative value-related dependencies among software requirements. To capture the imprecision associated with strengths of value-related dependencies we have made use of algebraic structure of fuzzy graphs. We have further, contributed a scalable technique for automated identification of value-related dependencies based on user preferences for software requirements. The validity of the work is verified through simulations.

Feature extraction for blind image steganalysis produces much features or high dimensional data, which bring about time consuming and even a low detection percentage. As being one of the most important phases of preprocessing, feature selection can reduce these extracted features, and improve the performance of steganalysis. Firstly, we introduce the Neighborhood Rough Sets (NRS) to the field of blind image steganalysis. Then, some concepts of feature significance and feature reduct are presented based on NRS. Furthermore, we propose a Feature Selection approach by NRS for blind image steganalysis (FSNRS). The FSNRS has the ability to delete redundant features, meanwhile maintaining the classification accuracy of a steganalysis system. The FSNRS is a filter feature selection technique for blind image steganalysis, which filtrates extracted features depending on a positive region preserving in NRS. The compact feature subset with a shortest feature dimension for blind image steganalysis is selected. Moreover, some experiments for blind steganalysis using SVM and KNN classifiers on selected feature subset are carried out. The experimental results show that our proposed approach can obtain compact features for blind image steganalysis and the performances of classifiers on those selected features are improved. Since the FSNRS is used with an adjustable neighborhood parameter, as a result, the classification performance of selected features is better than that of original whole features in most cases.

The concept of interval-valued Pythagorean fuzzy (IVPF) sets is capable of handling imprecise and ambiguous information and managing complex uncertainty in real-world applications. The focus of our this paper is to introduce some generalized operators, such as the generalized interval-valued Pythagorean fuzzy Einstein weighted averaging (abbreviated as GIVPFEWA) operator, the generalized interval-valued Pythagorean fuzzy Einstein ordered weighted averaging (abbreviated as GIVPFEOWA) operator, and the generalized interval-valued Pythagorean fuzzy Einstein hybrid averaging (abbreviated as GIVPFEHA) operator along with their some general properties, such as idempotency, commutativity, monotonicity and boundedness. Furthermore, the method for multiple attribute group decision making problems based on these operators was developed, and the operational processes were illustrated in detail. The main advantage of using the proposed methods and operators is that these operators and methods give a more complete view of the problem to the decision makers. These methods provide more general, more accurate and precise results as compared to the existing methods. Therefore these methods play a vital role in real world problems. Finally the proposed operators have been applied to decision-making problems to show the validity, practicality and effectiveness of the new approach. A systematic comparison between the existing work and the proposed work also has been given.

Theoretical concepts of graphs are highly utilized by computer science applications. Especially in research areas of computer science such as data mining, image segmentation, clustering, image capturing and networking. The intuitionistic fuzzy graphs are more flexible and compatible than fuzzy graphs due to the fact that they have many applications in networks. The main purpose of this paper is to introduce some connectivity concepts in the intuitionistic fuzzy graphs. Analogous to fuzzy cutvertices and fuzzy bridges in fuzzy graphs, intuitionistic fuzzy cutvertices and intuitionistic fuzzy bridges are introduced and characterized. We also proposed the concept of gain and loss for paths and pairs of vertices. Finally, we give an application of intuitionistic fuzzy digraphs.

In this paper, we mainly focus on the relationship between (L, M)-fuzzy closure systems and (L, M)-fuzzy convex structures as well as the relationship between (L, M)-fuzzy closure systems and (L, M)-fuzzy cotopologies. Firstly, we show that there is an adjunction between the category LMFC of (L, M)-fuzzy convex spaces and the category LMFS of (L, M)-fuzzy closure spaces, and there is also an adjunction between the category LMFT of (L, M)-fuzzy cotopological spaces and LMFS. In particular, the categories LMFC and LMFT are both coreflective subcategories of LMFS. Secondly, we prove that there is an adjunction between the category ELFC of extensional L-fuzzy convex spaces and the category LFC of L-fuzzy convex spaces, and there is also an adjunction between the category ELFT of extensional L-fuzzy cotopological spaces and the category LFT of (L, M)-fuzzy cotopological spaces. Specially, ELFC is a coreflective subcategory of LFC and ELFT is a coreflective subcategory of LFT.

The fuzzy control uses qualitative knowledge with linguistics descriptions about the operation of any process. Furthermore, Fuzzy Controller (FC) has several parameters which can be adjusted to change the performance. If this adjust is done through a “trial and error” procedure, then it will be very time-consuming and difficult to reach a good performance. Frequently, the tuning of the Membership Functions (MFs) have the most influence to improve the performance of a FC. Therefore, in this study we tune the MFs of a FC using optimization by Direct Search (DS) method, specifically the pattern search. In this sense, our goal is to improve the performance of the system to satisfy certain conditions and propose optimization method to tune the MFs. The methodology of the optimization is simulation-based. The Objective Function (OF) is the squared error between the set point and the output of the system. The evaluation of the OF and the implementation of the fuzzy control system were performed by simulations. An explanation of the algorithmic method based on the pattern search algorithm is shown. The method proposed in this paper is illustrated with examples for the non-linear systems. The results are compared and discussed with other controllers.

A q-rung orthopair fuzzy model is a powerful framework for illustrating uncertainty and vagueness. This model is more practical, flexible and appropriate as compared to fuzzy, intuitionistic fuzzy and Pythagorean fuzzy models. Directed hypergraphs are utilized to execute higher-order relationships in communications and social webbing. In this research study, we design a new framework for manipulating q-rung orthopair fuzzy data by means of combinative theory of q-rung orthopair fuzzy sets and directed hypergraphs. We introduce certain new concepts, including q-rung orthopair fuzzy directed hypergraphs, dual directed hypergraphs, line graphs and coloring of q-rung orthopair fuzzy directed hypergraphs. Further, we implement some interesting concepts of q-rung orthopair fuzzy directed hypergraphs to real life problems.

The standard backpropagation algorithm has already proven its effectiveness in most of the potential problems, but the major limitation is entrapment of local minima and slow convergence rate. To address these issues, a modified backpropagation algorithm has been proposed by adding a third term called inertia, the physical component used to accelerate the network towards the convergence without getting stuck into local minima. The Chebyshev polynomial form is a convenient method for expanding a function in a linear independent term. Inertia has been expanded using Chebyshev polynomial which is used as a third term in weight updation. The performance of the proposed algorithm outperforms the standard backpropagation algorithm (SBP) and the backpropagation algorithm with momentum (SBPM). The proposed algorithm was tested with the standard benchmark problems such as XOR problem, parity checking problem and dataset from UCI machine learning repository such as iris flower classification, wheat classification, breast cancer detection and wine classification. Experimental results show that the addition of the third parameter called inertia in the backpropagation algorithm gave better performance and faster convergence rate compared to the SBP and SBPM.

In this paper we consider the category M-FQMod of M-fuzzy (left) Q-modules over a given quantale Q. We introduce and investigate the notion of M-fuzzy Q-submodule of a left Q-module. Also, the notions of M-fuzzy module nuclei and conuclei on a left Q-module are defined, and the elementary properties of both are studied. The relationships between such notions and M-fuzzy Q-submodule are discussed. Finally, the relationship between the category of M-fuzzy Q-modules and the category of (L, M)-quasi-fuzzy topological spaces is discussed.

In this paper, we have used a novel initialization strategy to improve Whale optimization algorithm (WOA), which is named as The Improved Whale Optimization Algorithm (IWOA). To evaluate the capability of the algorithm in terms of efficiency and performance, we have implemented it to solve thermal economic multi-objective optimization problems of Plate Fin Heat Exchanger (PFHE). We have investigated the design problem with a single-objective as well as multi-objectives. In single-objective we have minimized the total cost and maximized the effectiveness of PFHE. In multi-objective, we have combined the total cost and effectiveness, with the help of design weights and a penalty parameter. The sensitivity of IWOA is checked towards the change in population sizes and the target prey numbers. The algorithm was stable in calculating the best values but was variative in number of functions evaluations. The performance of IWOA is compared with Genetic Algorithm (GA), Elitist-Jaya Algorithm (EJA), and modified-TLBO (Teaching Learning Based Optimization). Which show that IWOA has significantly improved the results. The suggested algorithm has less parameters to be set by designers. It converges to the required results quickly and is easy to implement. Similarly, all the experiments suggested that IWOA is applicable to design problems with complex objectives and highly non-linear constraints.

Dual hesitant fuzzy linguistic set consists of linguistic terms, membership hesitancy degrees and non-membership hesitancy degrees, which is widely applied to describe the quantitative and qualitative information in the decision-making problem. In this article, some new power aggregation operators of dual hesitant fuzzy linguistic set on the Archimedean t-conorms and t-norms functions are introduced. Then, the properties of those new operators are studied and the relationships between novel operators and existing ones are discussed. Furthermore, an approach to resolve group decision making problem is described. Finally, an example is used to illustrate the developed approach.

A neutrosophic cubic set (NCS) can depict single-valued and interval neutrosophic information simultaneously in real life. Then, the NCS concept cannot describe neutrosophic cubic information regarding the assessment problems of two-dimensional universal sets (TDUSs), while a Q-neutrosophic set (Q-NS) can depict neutrosophic information in TDUSs but not describe neutrosophic cubic information in TDUSs. Motivated by the Q-NS and NCS concepts, we need to extend the Q-NS concept to Q-NCS for indicating neutrosophic cubic information in TDUSs. Therefore, this study first proposes a Q-NCS concept, which indicates its truth, falsity, and indeterminacy values independently in TDUSs, and then the basic operations of Q-neutrosophic cubic elements (Q-NCEs) and some weighted aggregation operators of Q-NCEs, such as a Q-NCE weighted arithmetic averaging (Q-NCEWAA) operator and a Q-NCE weighted geometric averaging (Q-NCEWGA) operator. Next, Q-neutrosophic cubic multi-attribute decision-making (MADM) methods regarding the proposed Q-NCEWAA and Q-NCEWGA operators are proposed under TDUSs and Q-NCS setting. Eventually, an illustrative example shows the applicability of the proposed MADM methods in TDUSs and Q-NCS setting.

 In this paper, the notion of soft set-valued mappings and E-soft fixed points are introduced. These ideas are used to establish some analogues of classical fixed point theorems in the literature. Some Examples are given to support the hypotheses of the theorems. A few graphical and tabular illustrations are also provided so as to visualize the novel concepts pictorially. Moreover, as an application of one of the presented results, an existence condition for a solution of nonlinear discrete-type delay differential equation is provided.

A multitude of rapid prototyping (RP) systems and technologies have come up since the introduction of additive process. Owing to the enlarging number of these systems with distinctive efficacy, the problem of selecting an appropriate system for a particular requirement is a cumbersome task. Henceforth, this work comes up with a strategy based on multi-attribute decision making to select a most suitable RP system. The presence of subjectivity in decision making as well as the existence of imprecision from various sources emphasize the methods which must consider uncertainty and vagueness. A decision advisor based on uncertainty theories, including fuzzy analytical hierarchy process (FAHP) and grey relational analysis (GRA) has been introduced. It provides a comprehensive database comprising thirty nine commercially available RP systems. The evaluation attributes consisting of machine cost, accuracy, layer thickness, machine speed, material cost, net build size volume, machine weight, surface roughness, and material strength were utilized to characterize the different machines. The FAHP based on trapezoidal fuzzy number was implemented to determine the priority weights of various attributes, while the GRA was employed to realize the best RP system and technology. The authors believe that this system has the potential to transform into a fully developed RP selection system.

Accelerated adventures in computer Science and technology has made digital technology, a need of the day. Academicians, researchers, and technologists are anxious to share their secret data through the communication channel along with its security. The security of data, transmission rate, and error correction capability are the fundamental questions against the data transmission through any algebraic code dependent communication channel. Though, data security is always questioned due to the synchronized encoding and decoding algorithms. In this paper, a novel approach is developed to ensure the data security issues occurred due to synchronized encoding-decoding of a BCH code and for data transmission, a computational technique is designed by which data can be encoded and transmitted by using Field-Linear BCH code or a Ring-Linear BCH code of the same dimension, designed distance and code length. Although the Ring-Linear BCH code is preferable for encoding, on the other hand decoding of data is adept by Field-Linear BCH code. Accordingly, a computational technique of Barlekamp Massey Algorithm is utilized for the purpose. This scheme provides a quick code selection of the desired level of transmission rate and error correction capability during the communication. Thus, it also addresses the dimension issue of primitive BCH code. In addition, for the data security perspective we utilize a BCH code in round key addition and mixed column matrix steps in AES algorithm and then put on this modified AES algorithm to image encryption. The image encryption quality permits to incorporate this alteration in AES.

In this paper, by considering the concept of hesitancy fuzzy magic labeling of a graph, we show that whether any simple graph is hesitancy fuzzy magic labelizing. For this we prove that, any finite path graph, cyclic graph, star graph and by using them, any complete graph and so any connected graph has hesitancy fuzzy magic labelizing. Finally, we give some applications for hesitancy fuzzy magic labeling graphs in plumbing system and traffic flow.

 A precise recruitment can improve the efficiency of human resource management and enhance the core competitiveness of enterprises. However, the information asymmetry in recruitment leads enterprises to make recruitment decisions in fuzzy evaluation environments, thus reducing the accuracy of recruitment. This paper applies a robust approach to the recruitment optimization problem in an interval-valued fuzzy evaluation environment in which the actual abilities of applicants are randomly distributed within given intervals. The objective of this paper is to establish a robust recruitment scheme with the minimal maximum regret for recruitment revenue. Both exact and heuristic algorithms are proposed to solve the problem, which is proven to be NP hard. Computational experiments are conducted to evaluate the performance of the proposed algorithms. In addition, the paper reveals the key factors that affect the ability of an enterprise to implement accurate employment schemes. Corresponding suggestions on enterprise recruitment management are also proposed.

Today, many companies are faced with the huge network traffics mainly consisting of the various type of network attacks due to the increased usage of the botnet, fuzzier, shellcode or network related vulnerabilities. These types of attacks are having a negative impact on the organization because they block the day-to-day operations. By using the classification models, the attacks could be identified and separated earlier. The Distributed Denial of Service Attacks (DDoS) primarily focus on preventing or reducing the availability of a service to innocent users. In this research, we focused primarily on the classification of network traffics based on the deep learning methods and technologies for network flow models. In order to increase the classification performance of a model that is based on the deep neural networks has been used. The model used in this research for the classification of network traffics evaluated and the related metrics showing the classification performance have been depicted in the figures and tables. As the results indicate, the proposed model can perform well enough for detecting DDoS attacks through deep learning technologies.

Cloud computing is used for processing resources that are conveyed as an administration over a network and a prototype to enable beneficial on-interest network access to a general loch of configurable reckoning resources which are rapidly provisioned and discharged. While adopting cloud computing, major challenges like resource provisioning, resource allocation and security are arising. Only prevailing resource provisioning algorithm are depending upon single tier application utilizing meta-heuristic methodology. Here, we presented a multi-tier application for provisioning dynamic resources utilizing meta-heuristic methodology like Ant Colony Optimization algorithm (ACO), Simulated Annealing (SA) algorithm and hybrid algorithm which fuses ACO and SA and also an improved cost based scheduling is used to schedule jobs within the cloud with reduced cost. Implementation outcomes displays the efficiency of provisioning resources using ACO-SA algorithm in multitier application of hybrid cloud is greater than other resource provisioning algorithms in cloud computing.

In this paper, a novel method for fault detection based on an adaptive interval regression model characterized by the upper regression model (URM) and lower regression model (LRM) has been proposed. Applying the proposed method, a confidence band for the measured data, derived in the normal operating conditions of a system, is constructed.The method combines the superiorities of model sparse representation and computational efficiency of linear programming support vector regression (LP-SVR) with some ideas from L₁-norm on approximation errors. First, the upper and lower L₁-norms with respect to upper bound approximation error are considered, and the both norms subject to respective constraints are integrated into LP-SVR to form new upper and lower optimization problems, respectively. Following that, optimization problem corresponding to URM and LRM are solved by linear programming and interval regression model is thus constructed to judge whether the fault occurs or not. The proposed method returns an interval output as opposed to a point output. Finally, the efficacy of this method is demonstrated by applying it on the benchmark Tennessee Eastman problem, and has been compared with conventional techniques such as principal component analysis (PCA), dynamic-PCA (DPCA) and One-Class Support Vector Machine(1-class SVM). It is shown that the proposed method is superior to those approaches in terms of performance measure of detection latency.

 Deep learning is a field of Artificial Intelligence that has recently drawn a lot of attention with the desire to build up a quick, automatic and accurate system for image identification and classification. Deep learning serves as a fundamental part of modern computer vision solutions. However, as the architectures become deep and powerful new challenges in the process of training emerge. This includes the computational cost associated with training deep and large networks. In this work, the focus is on pruning and evaluation of state-of-the-art deep convolutional neural network for image-based plant disease and plants species classification. Pruning filters allow the reduction of parameters by removing unimportant filters and its feature maps. In this paper, the performance of pruned networks is evaluated across three datasets. It is observed that pruned DenseNet with Self-Normalization Neural Network (SNN) approach learns 2x faster compared to the initial DenseNet architecture. Additionally, pruning filters allow the reduction of the number of parameters and FLOPs by approximately 14% and 25% respectively. The aim is to create a fast and efficient model for the purpose of identification of plant diseases. Fast methods are desired for early identifications of diseases before damages occur. The proposed method achieves a satisfactory accuracy performance on PlantVillage, LeafSnap and Swedish-leaf dataset using held-out dataset. Our best pruned model gives an accuracy of 99.24%, 86.64%, and 97.5% on PlantVillage, LeafSnap, and Swedish-leaf datasets respectively.

The increase of depth is essential for the success of Deep Neural Networks while also leads to the difficulty of training. In light of this, the authors propose a novel multi-layer LSTM model called Highway-DC via introducing Highway Networks (Highway) to Densely Connected Bi-LSTM (DC-Bi-LSTM) which representation of each layer concatenates the output of itself and all preceding layers. Highway is applied to control the volume of input or output of each layer in DC-Bi-LSTM to the next. However, results reveal that Highway-DC shows no improvement over DC-Bi-LSTM, thus an extended version of Highway named Highway II is proposed via eliminating the multiplicative connections between transform gate and the output in Highway thus preserve the learning of each layer. And the Highway II-based model is named Highway II-DC. Evaluated on 7 benchmark datasets of text classification with compare to DC-Bi-LSTM and other state-of-the-art approaches, results indicate that Highway II-DC shows promising performance for achieving state-of-the-art on 3 datasets and surpassing DC-Bi-LSTM on 6 datasets with faster speed to converge. Besides, it can still enjoy the gain of increased layers with depth up to 30, while DC-Bi-LSTM gets saturated early at a depth of 15.

Recently, the Adaptive-Network-Based Fuzzy Inference System (ANFIS) is applied in many areas of knowledge, and there are multiple optimization algorithms for its learning. This work shows the design of a novel optimization algorithm for an ANFIS system that learns and classifies the behavior of brain signals between normal and abnormal. For this goal, different types of optimization algorithms for the learning of an ANFIS system are evaluated, such as the backpropagation, the mini-lots, and the Adam algorithm (adaptive moment estimation). As a result, utilizing the ANFIS with Adam and mini-lots provides the most accurate, fastest, and with least computational costs results.

In this paper, a new optimization methodology to assess the designs of the various renewable generation systems of electrical energy is used. This methodology utilizes Whale Optimization Algorithm (WOA) to minimize the cost of the electrical energy generated. The methodology permits to examine and to combine different sources of energy as to touch base at an optimal configuration of the hybrid system. This system is capable of providing energy to the predefined site in an achievable way as indicated by certain specialized and financial criteria. The system incorporates wind generation, photovoltaic generation and batteries for energy storage. The recreation results have been acquired with the help of MATLAB programming. Moreover, the outcomes of the proposed methodology have been compared with Particle Swarm Optimization (PSO) Algorithm for validation. The recreation results demonstrated the predominance of the proposed methodology.

First-order recurrent neural networks can be trained to recognize strings of a regular language. Finite state automata can be extracted from these neural networks. Normally, a search process in the output domain of the neurons is necessary for carrying out this extraction procedure. On the other hand, studies about fuzzy rules extraction from feedforward multilayered neural networks can be considered to define new techniques that transform first-order recurrent neural networks into finite state automata. With these new techniques, a fuzzy description of the action of each neuron can be obtained. From these descriptions, the transition function of the automaton can be directly found and, in this way, the search process is not necessary. A technique with this approach is presented in this paper. Besides, the used method to extract fuzzy rules from a neuron has the advantage that the inputs of the fuzzy system coincide with the inputs of the neuron. Thus, the fuzzy system is more intuitive. Once the transition function is obtained, the automaton structure can be found with the analysis of the transitions for every state and input from the initial state. Finally, several examples are presented to illustrate the method.

 A Pythagorean fuzzy soft set is a parameterized family of Pythagorean fuzzy sets and a generalization of intuitionistic fuzzy soft sets. In this paper, the notions of entropy and distance measures are defined for the Pythagorean fuzzy soft sets (PFSSs). Since, the already existing techniques for finding entropy and distance measures are not working for PFSSs, it is necessary to introduce these techniques in the contest of PFSSs. This work proposes a characterization of the Pythagorean fuzzy soft entropy. Also, the expressions for the standard distance measures like Hamming distance and Euclidean distance are obtained. Further, the applications of PFSSs in decision making problem and pattern recognition problem are discussed. Finally, comparative studies with other existing equations are also carried out.

This paper proposes an optimal control of Induction Motor (IM) drives using a new optimization technique. The optimization technique is the joined execution of both the Improved Moth flame Optimization (IMFO) algorithm and Radial Basis Function Neural Network (RBFNN). The main objective of the proposed strategy is to enhance the control performance of the IM while reducing the Total Harmonic Distortion (THD), eliminating the oscillation period of the stator current, torque, and speed. Here, the IMFO technique is optimized the gain parameters of the PI controller based on the IM speed variation and generates the reference quadrature axis current. By using the RBFNN, the reference three-phase current for accurate control pulses of the voltage source inverter (VSI) is predicted. The RBFNN is trained by the input motor actual quadrature axis current and the reference quadrature axis current with the corresponding target reference three-phase current. Furthermore, the proposed method control signals are connected with random pulse width modulation (RPWM) scheme and appropriate pulses are generated and applied to the inverter. With the proposed strategy, the control pulses of VSI are optimized and the proposed system offers a reliable solution. The proposed methodology is implemented in MATLAB/Simulink working platform. The performance of the IM drive is assessed by utilizing the comparative analysis with the existing techniques. The result obtained using the proposed optimization strategy showed that; it can provide the optimal control of IM drive. Also, the proposed strategy is effective in minimize the acoustic noise, torque ripple, eliminate the oscillation period with less computation, and reduces the complexity of the algorithm.

Uncertain delay differential equations are a type of differential equations driven by a Liu process. So far, it has been proved that uncertain delay differential equation has a unique solution in the finite domain, in which its coefficients satisfy the global Lipschitz continuity. This paper continues to focus on the existence and uniqueness of the solutions of uncertain delay differential equations on the infinite domain. Meanwhile, a new existence and uniqueness theorem for uncertain delay differential equations under one-sided local Lipschitz condition and linear growth condition is deduced.

The intersection and union of non-normal fuzzy sets using general aggregation functions are studied in this paper. The conventional intersection and union of fuzzy sets are based on the membership functions using the max and min functions, or t-norm and s-norm in general in which the fuzzy sets may assume to be normal in order to follow the boundary conditions that include 0 and 1 in the unit interval [0, 1]. When the fuzzy sets are taken to be non-normal, the α-level sets may be empty for some α ∈ [0, 1]. In this paper, we shall consider the aggregation functions, instead of t-norm and s-norm, satisfying the concepts of compatibility that are based on the α-level sets of intersection and union. In this case, the emptiness of α-level sets should be avoided, which is the main issue of this paper.

Social network (SN) provides a new perspective for large-scale multi-attribute group decision making (LMAGDM), and the scale and complexity of group compositions have received considerable attention. In recent study, the SN is constructed artificially and subjectively by using the number of communication or by giving the trust value directly. This paper constructs a directed and weighted SN by integrating collaboration network and reference network of decision makers (DMs) objectively. The spin-glass of community detection method is used to identify the subgroups and the weight of DMs in subgroups and then obtain the weight of subgroup pair. The uncertain linguistic weighted average operator is used to represent each subgroup’s assessment. The closeness between two subgroups is defined to measure consensus level. A targeted local feedback mechanism with three identification rules and a recommendation rule is designed to guide the consensus reaching process (CRP) more precisely and effectively. An illustrative example proves the feasibility and validity of the proposed consensus method, and the comparative analysis highlights the advantages and characteristics of this model.

In this paper, we give a new characterization of M-fuzzifying span mappings defined by M-fuzzifying rank functions. Then we research the relations between M-fuzzifying span mappings and other M-fuzzifying mappings on M-fuzzifying matroid, and characterize the M-fuzzifying coindependent, nonspan and hyperplane mappings. Based on these characterizations, we put forward one class of special fuzzifying matroids.

This paper proposes a new approach to uncertainty multiple attribute group decision problem with linguistic preference relation based on multigranulation probabilistic rough set and the Multimoora method. According to the classical Pawlak rough set and the neighborhood rough set, we present a multigranulation probabilistic fuzzy rough set based on neighborhood relation with linguistic preference information. We investigate the rough approximation of a crisp decision-making object and a fuzzy decision-making object under the framework of multigranulation rough set theory with linguistic preference inforamtion, respectively. That is, a multigranulation probabilistic rough set model and a multigranulation probabilistic fuzzy rough set model based on δ-neighborhood relation are established, respectively. Meanwhile, the proposed multigranulation probabilistic fuzzy rough set model is compared with the existing model and illustrate the superiority of the new model established. Furthermore, by combining the multigranulation probabilistic fuzzy rough set and the Multimoora method, a new method for multiple attribute group decision making with linguistic preference information is proposed. The decision-making procedure and the algorithm of the proposed method are also given. Finally, the effectiveness and validity of the proposed method is verified by investigating a multiple attribute group decision problem with the selection of suppliers in the context of e-commerce.

 The concept of an (∈ , ∈ ∨ (k^*, q _k ))-fuzzy semiprime subset in an ordered semigroup is introduced, and investigate the properties of (∈ , ∈ ∨ (k^*, q _k ))-fuzzy generalized bi-ideals by concerning the (∈ , ∈ ∨ (k^*, q _k ))-fuzzy semiprime subsets. Moreover, some properties of (∈ , ∈ ∨ (k^*, q _k ))-fuzzy semiprime subsets are defined in terms of its (k^*, k)-lower part. Finally, completely regular ordered semigroups are characterized in terms of its (∈ , ∈ ∨ (k^*, q _k ))-fuzzy semiprime generalized bi-ideals and their (k^*, k)-lower parts.

In this paper, an iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle. Two iteration procedures are used in the method, which are the i-iteration and the j-iteration.The i-iteration aims to obtain iterative trajectory-tracking control laws and the j-iteration aims to obtain iterative value functions in the i-iteration. The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. The policy iteration algorithm is the interacting policy and value iteration algorithms. The neural networks are used to realize the proposed algorithm and the convergence and optimality properties of the proposed algorithm are analysed. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.

In this paper, we have achieved fixed point results for pair of fuzzy mappings satisfying Ciric type contraction on a sequence contained in an open ball in ordered left (right) K-sequentially complete dislocated quasi metric space. An example and an application are presented to demonstrate the novelty of the results. Our results generalize and extend some recent results in literature.

Z-numbers contain both of the cognitive information and the reliability of information. Since there are many types of cognitive information, by considering the reliability of these information, decision making with hybrid Z-information would be a practical issue, which has merely been considered. The aim of this paper is to introduce a multi-attribute decision making (MADM) with hybrid Z-information based on ranking aggregation method. By using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, single rankings of alternatives under attributes are firstly produced. Then, final ranking(s) of alternatives with the lowest disagreement among single rankings could be derived according to ranking aggregation. As a result, a MADM with hybrid Z-information is transformed to be a ranking aggregation problem. The advantage of the developed method is that no algebra structures of hybrid Z-information is needed, so complex calculating process is avoided. In this paper, the mathematical structure of the case that MADM with hybrid Z-numbers is clear. Besides, the combination of ranking aggregation method and studies on Z-numbers could simplify the processing of hybrid Z-numbers. Finally, an example of supplier selection in green supply chain environment is introduced to illustrate the feasibility and validity of the developed model.

In this study, we propose a vision-based mouse controller capable of controlling objects from a distant location via hand gestures. The proposed hybrid model constitutes hand detection, prediction of hand states and direction and finally, with the aid of deep learning algorithm, we systematically control hand gestures to reposition objects on computer screen. This hybrid system is explicitly designed to control mouse on computer screen during formal presentation. Random movement of hand from up to down and right to left move the mouse pointer and sends signal to the system utilizing states of the hand. Here, close hand places the mouse button on active mode while open hand releases the button. The proposed hybrid model is made up of two modules: Single Shot Multi Box Detection (SSD) structure utilized to detect hand while Convolutional Neural Network (CNN) is utilized for prediction. For comparative purposes, we performed similar experiment where SSD is used for hand detection while Radial Basis Function Network (RBFN) is used for hand states prediction. In the comparative results of hand states prediction, SSD+CNN greatly outperformed SSD+RBFN. The proposed hybrid model is vision-based hence, it does not require additional hardware to perform its task. Overall performance of the framework depicts that the system is accurate and robust.

The stability of a colluvial slope, which is different from a rock or soil slope, is determined by the properties of both the bedrock and the colluvium. Coupled with artificial excavation and environmental effects, the stability factors of such slopes are complicated. To rapidly and effectively evaluate the risk of a colluvial cutting slope, a risk evaluation system for this type of slope is established herein. First, an evaluation index system is established, and reasonable risk evaluation indices are selected. Second, the fuzzy analytic hierarchy process (FAHP) is applied, a fuzzy pairwise comparison matrix, that must satisfy a consistency test, is constructed, and the weight of each index is determined. Third, the risk evaluation grades are divided into 4 risk grades, and the risk evaluation criteria for each basic index are determined. Finally, the three-level fuzzy comprehensive evaluation (FCE) method is applied, the membership function for each index is constructed, the membership degree is calculated, and the risk grade of the colluvial cutting slope is determined. This risk evaluation system is used to evaluate the risks of 148 colluvial cutting slopes along the Xiaomengyang-Mohan highway in Yunnan, China. The results show that there are 24 slopes of low risk (grade I), 85 of medium risk (grade II), 22 of high risk (grade III), and 17 of very high risk (grade IV). The evaluation results obtained are in good agreement with the actual slope instability states: failure occurred in 15 out of 85 slopes of risk grade II, 13 out of 22 slopes of risk grade III, and 16 out of 17 slopes of risk grade IV. This application demonstrates that the proposed risk evaluation system for colluvial cutting slopes is universal, and stable and that the calculation results are objective.

In this paper, the edge version of the geodesic number of a fuzzy graph is introduced and the properties satisfied are identified. A comparison between the vertex and edge version of the geodesic number of fuzzy graphs is obtained. The edge geodesic number of fuzzy trees, complete fuzzy graphs, complete bipartite fuzzy graphs and of fuzzy cycles are identified. A necessary and sufficient condition for the existence of an edge geodesic cover in a fuzzy graph is obtained. An application of edge geodesic sets in transportation systems in optimizing the number of traffic inspectors patrolling an urban road network is demonstrated. The fuzziness in the problem helps to identify routes receiving less priority among passengers, elimination of which minimizes the loss suffered by various transport corporations due to lack of collection.

 In this paper a new centroid based ranking grade for generalized intuitionistic fuzzy numbers is proposed. The centroid point of membership function and non membership function of generalized intuitionistic fuzzy numbers in term of its parametric form is used for grading. The parametric representation of generalized intuitionistic fuzzy numbers involves left fuzziness index, right fuzziness index and modal value of membership and non membership functions. To reveal the performance of the proposed ranking grade, a comparison study has been made over the existing methods. Furthermore the proposed ranking method has been used for estimating the minimum total elapsed time to a flow shop scheduling problem involving generalized intuitionistic fuzzy number. An improved result for flow shop scheduling problem has been attained using the proposed ranking grade and has been illustrated through an example.

Rough Sets provide a mathematical tool to handle decision making under uncertainty. One major domain that can be characterized with inherent ambiguity is natural language texts which often leads to uncertainty in understanding the intent and relative importance of a sentence with respect to its context in the whole text. As a consequence, the process of sentence selection for generation of extractive summary can logically be considered as a process of decision making under uncertainty. In this paper we use rough set based techniques to deal with this uncertainty. This paper’s contribution is two-fold. Firstly, this paper proposes a novel Rough Set based uncertainty measure called span and define special Rough subsets of universe called spanning sets. Span is Rough Set based measure for salience of a subset of universe and spanning set is the subset that maximizes the span. This corresponds to the key elements representing a problem and can be used to solve various real-life applications. Secondly, the concepts are applied to determine extracts of text documents. The idea behind the present work is to determine the most suitable subset(s) of the universe of sentences under consideration. An optimization problem is formulated to generate the extract for the text under consideration using the proposed uncertainty measure of span and is solved using Particle Swarm Optimization. The experimental results on DUC2001, DUC2002 single document data sets and Enron Email datasets establish the effectiveness of the proposed technique. There has been substantial work on Rough Sets though considering a stochastic Rough-subset of the universe and determining its aptness as a representative of the universe is still unexplored. The proposed technique is a novel effort to fill this gap.

Interval fuzzy preference relations (IFPRs) have been widely adopted in describing vagueness and uncertainty in real-life decision problems. Different methods have been applied in aggregating decision makers’ (DMs’) IFPRs. Nevertheless, the objective weights of DMs are often neglected in the group decision literature. Besides, the commonly methods used in aggregating decision makers’ (DMs’) IFPRs may make the final result too average. This paper investigates the plant growth simulation algorithm (PGSA) to aggregate interval fuzzy preference relations (IFPRs) and then derives the objective weights of decision makers (DMs) based on the deviation measure method. Next, the weighted aggregation IFPR is obtained by PGSA and the alternatives are ranked based on the continuous ordered weighted averaging (COWA) operator. The new aggregation method creatively converts the elements of IFPRs into two-dimensional coordinates and the ideal IFPR can be aggregated by PGSA based on the minimum Euclidean distance model. Then the weight of each DM can be derived according to the Euclidean distance between the individual IFPR and ideal IFPR based on the deviation measure method. Finally, a weighted aggregated IFPR can be obtained by PGSA and the ranking of alternatives is obtained by the COWA operator. Numerical examples are given to verify the efficiency and superiority of the method.

Attribute reduction is one of the crucial issues in Formal Concept Analysis. Discernibility matrix plays an important role in attribute reduction, and has been achieved many successful applications in different concept lattice models. Nevertheless, it requires the construction of the concept lattice before the discernibility matrices are computed when applying traditional approaches, which is both time and space consuming. Furthermore, in some discernibility matrices, the comparisons between every two concepts result in a high computation complexity. To address these problems, granular concepts, i.e., the object concepts and the attribute concepts, are considered in this paper, and a simple discernibility matrix named Object-Attribute discernibility matrix is proposed. It averts the construction of the whole concept lattice and the comparisons between every two concepts. Consequently, the time complexity is greatly reduced, and a lot of storage space can also be saved. Theoretical analysis and experimental results show the efficiency of Object-Attribute discernibility matrix.

Fuzzy c-means algorithm (Fcm) frequently applid in machine learning has been proven an effective clustering approach. However, the traditional Fcm cannot distinguish the importance of the different data objects and the discriminative ability of the different features in the clustering process. In this paper, we propose a new kind of Fcm clustering framework: DwfwFcm.Considering the different data weights and feature weights, an adaptive data weights vector and an adaptive feature weights matrix are introduced into the conventional Fcm and a new objective function is constructed. By the proposed objective function, the corresponding scientific updating iterative rules of the membership matrix, the weights of the different feature, the weights of the different data object and the cluster centers can be derived theoretically.Experimental results have demonstrated that the algorithm proposed in this paper can deliver consistently promising results and improve the clustering performance greatly.

Quality function deployment (QFD) is an effective tool for the design and improvement of products/services. The prioritization of customer requirements (CRs), as an essential component of the house of quality, is fundamental and strategic in the whole process of QFD product planning. This study proposes a novel ordinal scale values based group decision-making (GDM) approach first and it is subsequently used to prioritize CRs in QFD product planning. The proposed approach is composed of three stages, that is, constructing the integrated preference vector, defining the extraction sequence and constructing the comprehensive preference vector. The proposed GDM approach is good for utilizing the ordinal scale values provided by respondents due to limited experience and knowledge. An illustrative example is presented to verify the applicability and efficiency of the proposed approach. Further, to demonstrate the superiority of the proposed approach, comparisons are made between the proposed approach and two other similar methods. Practical results demonstrated that the proposed approach can be effective when the importance of customers and the preference evaluations of CRs are given by an ordinal scale.

 The notions of fuzzy upward β-covering, the fuzzy upward β-neighborhood, upward β-neighborhood and fuzzy complement β-neighborhood are introduced and several related properties are studied. Furthermore, multigranulation optimistic/pessimistic fuzzy rough sets based on fuzzy upward β-covering are initiated and their fundamental properties are investigated. We also find the upward β-neighborhood in the fuzzy upward covering approximation space and present the optimistic/pessimistic multigranulation rough sets to further enrich the presented notions. The medicine selection via fuzzy upward β-covering rough sets in medical diagnosis is another main contribution of the present work. It is also explored that which medicine can be prescribed for which particular symtom(s) and which disease.

Cross-efficiency assessment method is a useful tool for assessing the relative performance of decision-making units (DMUs). It is generally assumed that decision makers (DMs) are completely rational in the cross-efficiency model, and DMs’ risk attitude has not been considered important in the evaluation process. When the self-evaluation score of the DMU is optimal, the input and output weights are non-unique, resulting in non-unique cross-efficiency score, which affects the ranking result. The relative importance of DMUs is ignored when aggregating cross-efficiency scores. in view of the above problems, a cross-efficiency method based on prospect theory is proposed to capture the bounded rational psychological behavior of risk DMs. This method considers all multiple optimal solutions and constructs interval cross-efficiency. The credibility of each cross-efficiency score is obtained based on the D-S evidence theory, and the weight of each DMU is obtained by using the Dempster rule. DMUs are ranked by calculating the prospect value. The validity and feasibility of the proposed method and how does the risk preference of DMs characterized by parameters α, β, λ affect the evaluation results are verified by an illustrative example.