Two-phase TOPSIS of uncertain multi-attribute group decision-making

To solve the uncertain multi-attribute group decision-making of unknown attribute weights,three optimal models are built to decide the corresponding ideal solution weights,standard deviation weights and mean deviation weights.The comprehensive attribute weights are gotten through the product of the above three kinds of weights.And each decision maker's weighted decision matrices are also received by using the integrated attribute weights.The closeness degrees are also gotten by use of technique for order preference by similarity to ideal solution(TOPSIS) through dealing with the weighted decision matrices.At the same time the group decision matrix and weighted group decision matrix are gotten by using each decision-maker's closeness degree to every project.Then the vertical TOPSIS method is used to calculate the closeness degree of each project.So these projects can be ranked according to their values of the closeness degree.The process of the method is also given step by step.Finally,a numerical example demonstrates the feasibility and effectiveness of the approach.

Cotangent Similarity Measure of Consistent Neutrosophic Sets and Application to Multiple Attribute Decision-Making Problems in Neutrosophic Multi-Valued Setting

A neutrosophic multi-valued set(NMVS)is a crucial representation for true,false,and indeterminate multivalued information.Then,a consistent single-valued neutrosophic set(CSVNS)can effectively reflect the mean and consistency degree of true,false,and indeterminate multi-valued sequences and solve the operational issues between different multi-valued sequence lengths in NMVS.However,there has been no research on consistent single-valued neutrosophic similarity measures in the existing literature.This paper proposes cotangent similarity measures and weighted cotangent similarity measures between CSVNSs based on cotangent function in the neutrosophic multi-valued setting.The cosine similarity measures showthe cosine of the angle between two vectors projected into amultidimensional space,rather than their distance.The cotangent similaritymeasures in this study can alleviate several shortcomings of cosine similarity measures in vector space to a certain extent.Then,a decisionmaking approach is presented in viewof the established cotangent similarity measures in the case of NMVSs.Finally,the developed decision-making approach is applied to selection problems of potential cars.The proposed approach has obtained two different results,which have the same sort sequence as the compared literature.The decision results prove its validity and effectiveness.Meantime,it also provides a new manner for neutrosophic multi-valued decision-making issues.

Multi-attribute decision-making approach dealing with uncertain linguistic assessment information

An approach is presented to deal with a multi-attribute decision-making problem in which the attribute weights are unknown and the attribute values take the form of uncertain linguistic variables. First, a linguistic assessment standard is set up to deal with the uncertain linguistic attributes, and the operation laws of uncertain linguistic variables and the uncertain linguistic weighting average(ULWA)operator are introduced. Then a ranking formula of uncertain linguistic variables based on expectation-variance is proposed. As for the case without weight information, a goal program based on a warp function is constructed to determine the attribute weights, and the ULWA operator is utilized to aggregate the assessment information of uncertain linguistic variables, and the corresponding alternatives are ranked by a formula based on expectation-variance. Finally, a numerical example is given, and the results demonstrate that it is much easier and faster for the ranking method based on expectation-variance when compared to the existing methods.

Application of Interval Multi-attribute Decision-Making Method to Aeroengine Performance Ranking

In view of the uncertainty of the monitored performance parameters of aeroengines, the fluctuating scope of the monitored information during a period is taken as interval numbers, and the interval multi-attribute decision-making method is employed to predict the performance of aeroengine. The synthetic weights of interval numbers are obtained by calculating deviation degree and possibility degree. As an example of application, 5 performance parameters monitored on 10 CF6 aeroengines of China Eastern Airlines Co., Ltd are adopted as decision attributes to verify the algorithm. The obtained synthetic ranking result shows the effectiveness and rationality of the proposed method in reflecting the performance status of aeroengins.

Dynamic Time and Location Information in Ciphertext-Policy Attribute-Based Encryption with Multi-Authorization

Due to the mobility of users in an organization,inclusion of dynamic attributes such as time and location becomes the major challenge in Ciphertext-Policy Attribute-Based Encryption(CP-ABE).By considering this challenge;we focus to present dynamic time and location information in CP-ABE with mul-ti-authorization.Atfirst,along with the set of attributes of the users,their corre-sponding location is also embedded.Geohash is used to encode the latitude and longitude of the user’s position.Then,decrypt time period and access time period of users are defined using the new time tree(NTT)structure.The NTT sets the encrypted duration of the encrypted data and the valid access time of the private key on the data user’s private key.Besides,single authorization of attribute authority(AA)is extended as multi authorization for enhancing the effectiveness of key generation.Simulation results depict that the proposed CP-ABE achieves better encryption time,decryption time,security level and memory usage.Namely,encryption time and decryption time of the proposed CP-ABE are reduced to 19%and 16%than that of existing CP-ABE scheme.

Understanding Applications and Best Practices of DEMATEL:A Method for Prioritizing Key Factors in Multi-Criteria Decision-Making

Decision Making Trial and Evaluation Laboratory(DEMATEL)method is a powerful tool for understanding and visualizing the causal relationships among factors in complex decision-making problems.The method uses diagrams and matrixes to map out the causal relationships and interdependencies among factors,allowing decision-makers to identify key drivers and potential solutions to the problem.DEMATEL has a wide range of application areas,including supply chain management,environmental planning,healthcare,finance,and engineering,among others.The DEMATEL method is a valuable tool for decision-makers who need to understand the complex causal relationships among factors in order to make informed decisions.The method provides a structured approach for analyzing and prioritizing factors and for identifying potential solutions to complex problems.This paper describes the main features of this method,its application areas as well as the main process steps in the DEMATEL method.

A Comprehensive Overview of the ELECTRE Method in Multi-Criteria Decision-Making

The ELECTRE(ELimination Et Choix Traduisant la REalite)method has gained widespread recognition as one of the most effective multi-criteria decision-making(MCDM)methods.Its versatility allows it to be applied in a wide range of areas such as engineering,economics,business,environmental management and many others.This paper aims to provide an overview of the ELECTRE method,including its fundamental concepts,applications,advantages,and limitations.At its core,the ELECTRE method is an outranking family of MCDM techniques,which allows for the direct comparison of alternatives based on a set of criteria.The method takes into account the preferences and importance of decision-makers and generates a ranking of the alternatives based on their relative strengths and weaknesses.The ELECTRE method is a powerful tool for decision-making,and its applicability to a wide range of fields demonstrates its versatility and adaptability.By understanding its concepts,applications,merits,and demerits,decision-makers can use the ELECTRE method to make informed and effective decisions in a variety of contexts.

Attribute-Based Access Control for Multi-Authority Systems with Constant Size Ciphertext in Cloud Computing

In most existing CP-ABE schemes, there is only one authority in the system and all the public keys and private keys are issued by this authority, which incurs ciphertext size and computation costs in the encryption and decryption operations that depend at least linearly on the number of attributes involved in the access policy. We propose an efficient multi-authority CP-ABE scheme in which the authorities need not interact to generate public information during the system initialization phase. Our scheme has constant ciphertext length and a constant number of pairing computations. Our scheme can be proven CPA-secure in random oracle model under the decision q-BDHE assumption. When user's attributes revocation occurs, the scheme transfers most re-encryption work to the cloud service provider, reducing the data owner's computational cost on the premise of security. Finally the analysis and simulation result show that the schemes proposed in this thesis ensure the privacy and secure access of sensitive data stored in the cloud server, and be able to cope with the dynamic changes of users' access privileges in large-scale systems. Besides, the multi-authority ABE eliminates the key escrow problem, achieves the length of ciphertext optimization and enhances the effi ciency of the encryption and decryption operations.

Method for uncertain multi-attribute decisionmaking with preference information in the form of interval numbers complementary judgment matrix

The uncertain multi-attribute decision-making problems because of the information about attribute weights being known partly, and the decision maker's preference information on alternatives taking the form of interval numbers complementary to the judgment matrix, are investigated. First, the decision-making information, based on the subjective uncertain complementary preference matrix on alternatives is made uniform by using a translation function, and then an objective programming model is established. The attribute weights are obtained by solving the model, thus the overall values of the alternatives are gained by using the additive weighting method. Second, the alternatives are ranked, by using the continuous ordered weighted averaging (C-OWA) operator. A new approach to the uncertain multi-attribute decision-making problems, with uncertain preference information on alternatives is proposed. It is characterized by simple operations and can be easily implemented on a computer. Finally, a practical example is illustrated to show the feasibility and availability of the developed method.

Pedestrian Attributes Recognition in Surveillance Scenarios with Hierarchical Multi-Task CNN Models

Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.

Novel combinatorial algorithm for the problems of fuzzy grey multi-attribute group decision making

To study the fuzzy and grey information in the problems of multi-attribute group decision making, the basic concepts of both fuzzy grey numbers and grey interval numbers are given firstly, then a new model of fuzzy grey multi-attribute group decision making based on the theories of fuzzy mathematics and grey system is presented. Furthermore, the grey interval relative degree and deviation degree is defined, and both the optimistic algorithm of the grey interval relational degree and the algorithm of deviation degree minimization for solving this new model are also given. Finally, a decision making example to demonstrate the feasibility and rationality of this new method is given, and the results by using these two algorithms are uniform.

A decision support system for satellite layout integrating multi-objective optimization and multi-attribute decision making

A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the C.G. offset, the cross moments of inertia and the space debris impact risk), we develop a threedimensional layout optimization model. Unlike most of the previous works just focusing on mass characteristics of the system, a space debris impact risk index is developed. Secondly, we develop an efficient optimization framework for the integration of computer-aided design (CAD) software as well as the optimization algorithm to obtain the Pareto front of the layout optimization problem. Thirdly, after obtaining the candidate solutions, we present a multi-attribute decision making approach, which integrates the smart Pareto filter and the correlation coefficient and standard deviation (CCSD) method to select the best tradeoff solutions on the optimal Pareto fronts. Finally, the framework and the decision making approach are applied to a case study of a satellite platform.

Face Detection Detection, Alignment Alignment, Quality Assessment and Attribute Analysis with Multi-Task Hybrid Convolutional Neural Networks

This paper proposes a universal framework,termed as Multi-Task Hybrid Convolutional Neural Network(MHCNN),for joint face detection,facial landmark detection,facial quality,and facial attribute analysis.MHCNN consists of a high-accuracy single stage detector(SSD)and an efficient tiny convolutional neural network(T-CNN)for joint face detection refinement,alignment and attribute analysis.Though the SSD face detectors achieve promising results,we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes.By multi-task training,our T-CNN aims to provide five facial landmarks,facial quality scores,and facial attributes like wearing sunglasses and wearing masks.Since there is no public facial quality data and facial attribute data as we need,we contribute two datasets,namely FaceQ and FaceA,which are collected from the Internet.Experiments show that our MHCNN achieves face detection performance comparable to the state of the art in face detection data set and benchmark(FDDB),and gets reasonable results on AFLW,FaceQ and FaceA.

Multi-attribute decision making method for air target threat evaluation based on intuitionistic fuzzy sets

The function of the air target threat evaluation (TE) is the foundation for weapons allocation and senor resources management within the surface air defense. The multi-attribute evaluation methodology is utilized to address the issue of the TE in which the tactic features of the detected target are treated as evaluation attributes. Meanwhile, the intuitionistic fuzzy set (IFS) is employed to deal with information uncertainty in the TE process. Furthermore, on the basis of the entropy weight and inclusion-comparison probability, a hybrid TE method is developed. In order to accommodate the demands of naturalistic decision making, the proposed method allows air defense commanders to express their intuitive opinions besides incorporating into the threat features of the detected target. An illustrative example is provided to indicate the feasibility and advantage of the proposed method.

Approach to stochastic multi-attribute decision problems using rough sets theory

Multi-attribute decision problems where the performances of the alternatives are random variables are considered. The suggested approach grades the probabilities of preference of one alternative over another with respect to the same attribute. Based on the graded probabilistic dominance relation, the pairwise comparison information table is defined. The global preferences of the decision maker can be seen as a rough binary relation. The present paper proposes to approximate this preference relation by means of the graded probabilistic dominance relation with respect to the subsets of attributes. At last, the method is illustrated by an example.

A Method of Job Scheduling on SMEs' Key Equipments Based on Hybrid Multi-attribute Decision Making

An analysis of the key factors affecting on the single production process job scheduling of the parts waiting for be- ing processed on the key equipments for SMEs (Small Manufacturing Enterprises) is given in this paper,which include interval number,real number and uncertain linguistic value.A kind of hybrid multi-attribute decision making method for the single pro- duction process job scheduling is presented in this paper,that the parts are firstly sorted about each factor,and then the total evalu- ative attributive value of each part is calculated with the method of weighted arithmetic average,and thus the part with the highest total evaluative attributive value is chosen for being processed firstly.The mathematic model corresponding to the method is set up in this paper.An example is studied in this paper,and the results of the example testify the correctness of this model.

Multi-attributed decision making for mining methods based on grey system and interval numbers

In the case of unknown weights, theories of multi-attributed decision making based on interval numbers and grey related analysis were used to optimize mining methods. As the representative of independence for the indicator, the smaller the correlation of indicators is, the greater the weight is. Hence, the weights of interval numbers of indicators were determined by using correlation coefficient. Relative closeness based on positive and negative ideal methods was calculated by introducing distance between interval numbers, which made decision making more rational and comprehensive. A new method of ranking interval numbers based on normal distribution was proposed for the optimization of mining methods, whose basic properties were discussed. Finally, the feasibility and effectiveness of this method were verified by theories and practice.

Extension of TOPSIS for fuzzy multi-attribute decision making problem based on experimental analysis

This paper is concerned with a technique for order performance by similarity to ideal solution(TOPSIS) method for fuzzy multi-attribute decision making,in which the information about attribute weights is partly known and the attribute values take form of triangular fuzzy numbers.Considering the fact that the triangular fuzzy TOPSIS results yielded by different distance measures are different from others,a comparative analysis of triangular fuzzy TOPSIS ranking from each distance measure is illustrated with discussion on standard deviation.By applying the most reasonable distance,the deviation degrees between attribute values are measured.A linear programming model based on the maximal deviation of weighted attribute values is established to obtain the attribute weights.Therefore,alternatives are ranked by using TOPSIS method.Finally,a numerical example is given to show the feasibility and effectiveness of the method.

Multi-attribute decision making model based on optimal membership and relative entropy

To study the problems of multi-attribute decision making in which the attribute values are given in the form of linguistic fuzzy numbers and the information of attribute weights are incomplete,a new multi-attribute decision making model is presented based on the optimal membership and the relative entropy.Firstly,the definitions of the optimal membership and the relative entropy are given.Secondly,for all alternatives,a set of preference weight vectors are obtained by solving a set of linear programming models whose goals are all to maximize the optimal membership.Thirdly,a relative entropy model is established to aggregate the preference weight vectors, thus an optimal weight vector is determined.Based on this optimal weight vector,the algorithm of deviation degree minimization is proposed to rank all the alternatives.Finally,a decision making example is given to demonstrate the feasibility and rationality of this new model.

Power Aggregation Operators of Simplified Neutrosophic Sets and Their Use in Multi-attribute Group Decision Making

The simplified neutrosophic set(SNS) is a useful generalization of the fuzzy set that is designed for some practical situations in which each element has different truth membership function, indeterminacy membership function and falsity membership function. In this paper, we develop a series of power aggregation operators called simplified neutrosophic number power weighted averaging(SNNPWA) operator, simplified neutrosophic number power weighted geometric(SNNPWG) operator, simplified neutrosophic number power ordered weighted averaging(SNNPOWA) operator and simplified neutrosophic number power ordered weighted geometric(SNNPOWG) operator. We present some useful properties of the operators and discuss the relationships among them. Moreover, an approach to multiattribute group decision making(MAGDM) within the framework of SNSs is developed by the above aggregation operators.Finally, a practical application of the developed approach to deal with the problem of investment is given, and the result shows that our approach is reasonable and effective in dealing with uncertain decision making problems.