Weights Of Attributes Research Articles

Assessment of environmental and social sustainability performance of the freight transportation industry: An index-based approach

The freight transportation activities having an impact on the freight logistics process. The environmental and social sustainability-related processes are poorly managed compared to the economic process. The quality of environmental and social sustainability (EnSoS) practices ought to be enhanced by measuring current sustainability practices, and at the same time, obstacles to sustainability have to be identified. Some of the previous work try to develop a sustainability framework, but less attention is paid to develop the EnSoS assessment framework for the freight transport industry. To bridge this research gap, this study develops an EnSoS framework based on an integrated multi-criteria decision-making (MCDM) method, i.e., fuzzy best-worst method (FBWM) and fuzzy logic approach. The proposed framework evaluates the current sustainability performance with an index and highlights the obstacles to the sustainable freight transportation system. The proposed framework presents two sustainability enablers, nine dimensions, and sixty-three sustainability attributes, including ten new attributes. The FBWM method is used to compute the fuzzy importance weight of attributes, whereas the fuzzy logic is used to assess the performance of each attribute. The proposed framework is validated with the case of the Indian freight industry. The fuzzy transportation EnSoS index (FTEnSoSI) is computed as (3.511, 6.366, 8.418), and close to ‘very environmental and social sustainable (VESS)’ label. The fuzzy performance importance index (FPII) is computed to assess the performance of each attribute. Based on FPII value, thirty attributes are identified as obstacles to transport sustainability. The result analysis presented proposed policy measures for improving EnSoS obstacles.

Autonomic computing based trustworthiness attributes weight estimation using Electre-Tri method

With the advancement in autonomic computing technology, quality assurance for autonomic software is another big challenge to be faced by IT industries. Autonomic software is quite different in working compared to non-autonomic software. So, quality estimation parameters will also be different. Quality assurance method for autonomic software has not been standardized yet but quality parameters based on autonomic features have been suggested by researchers. Based on the functioning of autonomic software and autonomic capabilities, Autonomicity and Trustworthiness are considered to be the major quality estimation metrics. In this paper, trustworthiness estimation is performed using six attributes. To contribute in this area further, attributes are ranked based on the weights estimated using Electre-Tri multi-criteria outranking method. The attributes weights can be used further to estimate trustworthiness for any autonomic software. With this, the quality estimation task can also be processed for autonomic software.

Interval type-2 fuzzy multi-attribute decision-making approaches for evaluating the service quality of Chinese commercial banks

In today’s world, with increased competition, the service quality of Chinese commercial banks is recognized as a major factor that is responsible for enhancing competitiveness. Therefore, it is necessary to evaluate and analyse the service quality of Chinese commercial banks to realize their stable development. The service quality evaluation could be recognized as a multi-attribute decision-making (MADM) problem with multiple assessment attributes, both being of a qualitative and quantitative nature. Owing to the growing complexity and high uncertainty of the financial environment, the assessments of attributes cannot always possibly express using a real and/or type-1 fuzzy number. Additionally, a heterogeneous relationship often exists among the attributes under many real decision cases. In this study, we create two MADM approaches to handle decision-making problems with interval type-2 fuzzy numbers (IT2FNs) and offer their application to service quality evaluations of commercial banks problems. Specifically, we first define some operations on IT2FNs based on Archimedean T-norms (ATs) and develop a bi-directional projection measure of IT2FNs. Next, by combining the generalized Banzhaf index, the Choquet integral and IT2FNs, we propose the interval type-2 fuzzy Archimedean Choquet (IT2FAC) operator, the Banzhaf IT2FAC (BIT2FAC) operator and the 2-additive BIT2FAC (2ABIT2FAC) operator. Then, we establish two optimal models for deriving the weights of attributes based on a bi-directional projection measure of IT2FNs and Banzhaf function. Finally, we create two novel MADM methods under interval type-2 fuzzy contexts, where an illustrative case concerning the service quality evaluation of Chinese commercial banks is used to explain the created MADM approaches.

Multi-Criteria Group Decision-Making for Selection of Green Suppliers under Bipolar Fuzzy PROMETHEE Process

The preference ranking organization method for enrichment of evaluations (PROMETHEE) method considers a significant outranking class of multi-criteria decision analysis (MCDA), as it is easy to deal with its simple computations. In the PROMETHEE, different preference functions are used according to the type and nature of attributes or criteria that demonstrate the clearness and reliability of this method. This study provides a new version of the PROMETHEE method using bipolar fuzzy information, named the bipolar fuzzy PROMETHEE method. Bipolar fuzzy sets or numbers constitute an asymmetrical relationship between two judgmental factors of human reasoning. Vague and imprecise knowledge is characterized by bipolar fuzzy linguistic terms which are further represented in the form of trapezoidal bipolar fuzzy numbers. The trapezoidal bipolar fuzzy numbers are used by analysts to assign the preferences of alternatives on the basis of criteria. Further, a ranking function of bipolar fuzzy numbers is considered to access the crisp real preferences of alternatives. The entropy weighting information is employed to calculate the weights of attributes by considering the condition of normality. A numerical example such as the selection of green suppliers by using the bipolar fuzzy PROMETHEE is performed on the basis of the usual criterion preference function in order to explain the procedure of the proposed method. Comparable results are derived by using the combination of linear and level preference functions. The results obtained by using different types of preference functions are the same, representing the authenticity of the proposed bipolar fuzzy PROMETHEE method.

Correlation-Based Weight Adjusted Naive Bayes

Naive Bayes (NB) is an extremely simple and remarkably effective approach to classification learning, but its conditional independence assumption rarely holds true in real-world applications. Attribute weighting is known as a flexible model via assigning each attribute a different weight discriminatively to improve NB. Attribute weighting approaches can fall into two broad categories: filters and wrappers. Wrappers receive a bigger boost in terms of classification accuracy compared with filters, but the time complexity of wrappers is much higher than filters. In order to improve the time complexity of a wrapper, a filter can be used to optimize the initial weight of all attributes as a preprocessing step. So a hybrid attribute weighting approach is proposed in this paper, and the improved model is called correlation-based weight adjusted naive Bayes (CWANB). In CWANB, the correlation-based attribute weighting filter is used to initialize the attribute weights, and then each weight is optimized by the attribute weight adjustment wrapper where the objective function is designed based on dynamic adjustment of attribute weights. Extensive experimental results show that CWANB outperforms NB and some other existing state-of-the-art attribute weighting approaches in terms of the classification accuracy. Meanwhile, compared with the existing wrapper, the CWANB approach reduces the time complexity dramatically.

A Regularized Attribute Weighting Framework for Naive Bayes

The Bayesian classification framework has been widely used in many fields, but the covariance matrix is usually difficult to estimate reliably. To alleviate the problem, many naive Bayes (NB) approaches with good performance have been developed. However, the assumption of conditional independence between attributes in NB rarely holds in reality. Various attribute-weighting schemes have been developed to address this problem. Among them, class-specific attribute weighted naive Bayes (CAWNB) has recently achieved good performance by using classification feedback to optimize the attribute weights of each class. However, the derived model may be over-fitted to the training dataset, especially when the dataset is insufficient to train a model with good generalization performance. This paper proposes a regularization technique to improve the generalization capability of CAWNB, which could well balance the trade-off between discrimination power and generalization capability. More specifically, by introducing the regularization term, the proposed method, namely regularized naive Bayes (RNB), could well capture the data characteristics when the dataset is large, and exhibit good generalization performance when the dataset is small. RNB is compared with the state-of-the-art naive Bayes methods. Experiments on 33 machine-learning benchmark datasets demonstrate that RNB outperforms the compared methods significantly.

A novel multiple attribute decision making method based on grey relational projection and its application for e-commerce risk assessment

With respect to multiple attribute decision making (MADM) problems in which the attribute values take the form of hesitant fuzzy elements, the traditional grey relational projection (GRP) method is extended to solve multiple attribute decision making problems under hesitant fuzzy environment. Since the weights of attributes are incompletely known, a nonlinear optimisation model is established, i.e., a reasonable weight vector of attributes should make all alternatives have the largest grey relational projections on positive ideal solution and the smallest grey relational projections on negative ideal solution. Then, based on the hesitant fuzzy decision matrix provided by decision makers, all feasible alternatives are ranked according to the descending order of relative grey relational projections and the most desirable alternative(s) should have the largest grey relational projection on positive ideal solution and the smallest grey relational projection on negative ideal solution. Finally, a numerical example of e-commerce risk assessment is given to illustrate the application of the proposed method.

An Extended VIKOR Method for Multiple Attribute Decision Making with Linguistic D Numbers Based on Fuzzy Entropy

The linguistic [Formula: see text] numbers (LDNs) can express the fuzzy evaluation information more easily and precisely by combining the advantages of linguistic terms (LTs) and [Formula: see text] numbers (DNs). Existing researches on the fuzzy entropy of LDNs are rare, and most of the definitions of fuzzy entropy for LDNs are unreasonable. In view of this research gap, this paper improves the definition of fuzzy entropy of LDNs, which simultaneously considers the effects of confidence degrees and LTs on the value of fuzzy entropy in LDNs. Then, the weights of attributes can be calculated by the improved fuzzy entropy. Further, a new combination rule for LDNs is also presented in this paper. Based on these studies, we extend the traditional Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to the LDNs and develop the LD-VIKOR method. The proposed LD-VIKOR method is convenient to handle MADM problems with unknown attributes weights under the environment of LDNs. Finally, we verify the validity and reliability of the proposed method by an illustrative example, and analyze the advantages of the proposed method by comparing it with other existing MADM methods.

Improving the entropy weighting method performance by using alternative normalisation procedures

There is no doubt that the Shannon entropy's is the most popular method to obtain attributes weights in multi-criteria decision making (MCDM) area. Despite its usefulness, entropy has some notable limitations. Historically, since Shannon and Weaver introduced entropy concept in 1947, and Zeleny in which applied it for attribute weights in 1976, up to present times, a significant drawback with this approach is that a very important attribute may have little weight in analysis. Because all the alternatives are close in terms of that, attribute. So, in this paper to resolve this limit a new normalized method is considered. The finding of this study reveals that some methods (i.e., Peldschus, Juttler-Korth, Max and Stopp) are better than others (total ratios, vector, Weitendorf's, Voogd ratio, logarithmic and product method).

N-Intuitionistic Polygonal Fuzzy Aggregation Operators and Their Application to Multi-Attribute Decision Making

The n-intuitionistic polygonal fuzzy set (n-IPFS), combined by the intuitionistic fuzzy and polygonal fuzzy sets, is an extended form of the triangular intuitionistic fuzzy set (TIFS) and the trapezoidal intuitionistic fuzzy set (TrIFS). The aim of this paper is to develop some new aggregation operators for n-IPFSs and apply them to multi-attribute group decision making (MAGDM) problems. First, the operational properties and the score function of n-IPFSs are defined. Then, three kinds of n-intuitionistic polygonal fuzzy aggregation operators are investigated including n-intuitionistic polygonal fuzzy weighted averaging (n-IPFWA) operator, n-intuitionistic polygonal fuzzy ordered weighted averaging (n-IPFOWA) operator and n-intuitionistic polygonal fuzzy hybrid aggregation (n-IPFHA) operator. Finally, we propose an improved technique for order preference by similarity to an ideal solution (TOPSIS) approach with n-IPFSs and unknown attributes weights. The attributes weights are obtained by combining the entropy weights and the subjective weights, and the entropy weights are calculated based on the score function of n-IPFS. The spatial closeness reflected by the Hamming distance and the grey relationship with the positive/negative solution are both considered in getting the relative closeness degree to rank the alternatives. The example analysis of a location selection is given to verify the practicality and the effectiveness of the proposed approach in this paper.

A comparative analysis between LINMAP, paired comparison method and naturalistic ranking in different data display contexts

This article presents a comparative analysis between two widely used decision-making methods, LINMAP and paired comparison method (PCM), using three different judging contexts. Decision makers ranked alternatives (for LINMAP) and criteria (for PCM) for contexts involving quantitative data only, qualitative data only, and a mix between the two. Attribute weights were calculated and final rankings of alternatives were deducted and compared to a naturalistic ranking of alternatives by the decision makers. LINMAP was found to be the closest match to a naturalistic decision-making. It was also found that incorporating qualitative data or a mixture between qualitative and quantitative data in multi-attribute decision-making problems was more consistent with the naturalistic ranking of alternatives.

Trusted Cloud Service Selection Algorithm Based on Lightweight Intuitionistic Fuzzy Numbers

Trusted cloud service selection algorithms aim to select the cloud service with the highest satisfaction from many cloud services with the same or similar functions. However, many existing selection algorithms adopt the inaccurate QoS measurement method and subjective weights of QoS attributes, which make it difficult for cloud users to select the trusted cloud service. Therefore, this paper proposes a trusted cloud service selection algorithm based on lightweight intuitionistic fuzzy numbers. Firstly, based on the consideration of precise and imprecise QoS metrics, three kinds of QoS attributes are transformed into lightweight intuitionistic fuzzy numbers in a uniform way, and corresponding weight coefficients are expressed with the calculated standard deviation. Then, the trusted cloud service selection algorithm based on improved TOPSIS is proposed to guide cloud users to choose the cloud service with the highest satisfaction. The accuracy of the proposed algorithm was validated by two cases and the efficiencies of the proposed algorithm and the traditional cloud service ranking algorithm based on triangular fuzzy numbers were compared.

Identity Attributes Metric Modelling based on Mathematical Distance Metrics Models

Internet has brought a lot of security challenges on the interaction, activities, and transactions that occur online. These include pervasion of privacy of individuals, organizations, and other online actors. Relationships in real life get affected by online mischievous actors with intent to misrepresent or ruin the characters of innocent people, leading to damaged relationships. Proliferation of cybercrime has threatened the value and benefits of internet. Identity theft by fraudsters with intent to steal assets in real space or online has escalated. This study has developed a metrics model based on distance metrics in order to quantify the credential identity attributes used in online services and activities. This is to help address the digital identity challenges, bring confidence to online activities and ownership of assets. The application forms and identity tokens used in the various sectors to identify online users were used as the sources of the identity attributes in this paper. The corpus toolkits were used to mine and extract the identity attributes from the various forms of identity tokens. Term weighting schemes were used to compute the term weight of the identity attributes. Other methods used included Shannon Entropy and the Term Frequency-Inverse Document Frequency scheme (TF*IDF). Standardization of data using data normalization method has been applied. The results show that using the Cosine Similarity Measure, we can identify the identity attributes in any given identity token used to identify individuals and entities. This will help to attach the legitimate ownership to the digital identity attributes. The developed model can be used to uniquely identify an online identity claimant and help address the security challenge in identity management systems. The proposed model can also identify the key identity attributes that could be used to identify an entity in real or cyber spaces.

Marker-assisted selection in C. oleifera hybrid population

Abstract Marker-assisted selection (MAS) is implemented to improve Camellia oleifera yield and fruit attributes for meeting China’s increased demand for new varieties development. We conducted three-generational (G0, G1, and G2) hybridization (diallel mating) and selection experiment and used Sequence-Related Amplified Polymorphism (SRAP). SRAP markers to investigate their utility in a MAS framework. The utilized SRAP markers were instrumental in hybrid authenticity and the identification of matroclinal or patroclinal inheritance presence, thus guiding mating pair selection and direction (their role as male or females). Across the studied 3 generations, estimates of genetic diversity parameters showed steady increase with percentage increase of ((G0 to G1 and G1 to G2) 9.25 and 9.05: observed number of alleles; 3.12 and 7.80: means effective number of alleles; 12.35 and 22.34: Nei‘s gene diversity; and 14.21 and 21.77: Shannon‘s index), indicating lack of diversity reduction associated with selection. Estimates of genetic distance and their correlation with heterosis were useful in guiding selection of mating pairs for achieving the desired yield and fruit attributes (fruit diameter, height, weight, and index, peel thickness, number of seeds per fruit, seed weight per fruit, and seed rate). Most yield and fruit attributes exhibited high broad-sense heritability with increasing trend over generation intervals, indicating the increased potential of hybrid breeding for this species.

Attribute rank-based weighted decision tree

Decision tree is a renowned classifier reformed by innumerable tree generation techniques to construct efficient trees. In this paper, a novel framework for constructing decision tree classifiers using ranked attributes and weights is presented. The tree is fabricated using proposed framework hierarchically from root to leaf using ranks and weights of attributes assigned to branches based on their contribution towards classification accuracy. The weights are perpetually updated until the tree furnishes maximum classification accuracy. This framework is experimented with cuckoo search, firefly search, wolf search and proposed limited lazy wolf search for ranking attributes and the experimental results illustrates the framework achieving excellent performance than the antecedent. For demonstrating the stability of proposed framework in all problems, numerous experiments are carried out using prominent benchmark datasets and a proposed crime propensity prediction dataset. The crime propensity prediction accuracy accomplished by fine tuning the proposed algorithm was 99.3617% directing crime free society.

Generalized Ordered Weighted Simplified Neutrosophic Cosine Similarity Measure for Multiple Attribute Group Decision Making

The paper proposes a generalized ordered weighted simplified neutrosophic cosine similarity (GOWSNCS) measure by combining the cosine similarity measure of simplified neutrosophic sets (SNSs) with the generalized ordered weighted averaging (GOWA) operator and investigates its properties and special cases. Then, the author develops a simplified neutrosophic group decision-making method based on the GOWSNCS measure to handle multiple attribute group decision-making problems with simplified neutrosophic information. The prominent characteristics of the GOWSNCS measure are that it not only is a generalization of the cosine similarity measure but also considers the associated weights for attributes and decision makers in the aggregation of the cosine similarity measures of SNSs to alleviate the influence of unduly large or small similarities in the process of information aggregation. Finally, an illustrative example of investment alternatives is provided to demonstrate the application and effectiveness of the developed approach.

ISA Evaluation Framework for Security of Internet of Health Things System Using AHP-TOPSIS Methods

Security has become a vital factor for any Internet of things network but it is of paramount importance for Internet of Health Things (IoHT). IoHT also known as Internet of Medical Things (IoMT) is integration of IoT and healthcare environment, where fragile data related to the patients is transmitted from IoT devices to server. During this transmission, if, any eavesdropping or intrusion occurs then it will not only lead to the serious mutilation of entire network but this data will be handled maliciously for wrong doings as well. Therefore, a proper security is indispensable for IoHT based equipments due to exposure to different attacks. Security of IoHT has been the burning issue in last couple of years. In this regard different security models, surveys, frameworks have been presented. In this article, a proposed Identified Security Attributes (ISA) framework is presented to evaluate the security features of IoHT based device in healthcare environment. The proposed framework uses hybrid MCDM methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This framework works in two phase: in first phase the weights of attributes are derived by using AHP method and in second phase security assessment of alternatives is performed based upon security criteria by using TOPSIS method. The outcomes of proposed security assessment framework demonstrate that the reliable and secure alternative among alternatives is selected in IoMT system. This approach can be used as a guideline for future use in IoMT systems or any other IoT based domain. To the best of our knowledge, it is novel approach to address the security assessment of IoT and these MCDM methods have never been used before for assessment and decision making in IoHT system for security.

Interval-valued intuitionistic fuzzy MADM method based on TOPSIS and grey correlation analysis.

In this paper, we propose an interval-valued intuitionistic fuzzy Multi-Attribute Decision Making (MADM) method based on improved TOPSIS and Grey Correlation Analysis (GCA), in which the attribute values are interval-valued intuitionistic fuzzy numbers. So that we can deal with imprecise information in fuzzy and rough form in MADM problems by using interval-valued intuitionistic fuzzy numbers Firstly, the concept of interval intuitionistic fuzzy entropy is introduced to calculate the entropy weight of attributes. And the combined weight is calculated by combining the entropy weight with the subjective weight. Secondly, the reverse order phenomenon in the traditional TOPSIS method is eliminated by constructing absolute Positive Ideal Solution (PIS) and absolute Negative Ideal Solution (NIS) in the form of interval-valued intuitionistic fuzzy numbers. Furthermore, the improved TOPSIS method and grey correlation analysis method are combined to describe the degree of closeness for each alternative from the ideal solution, and then the ranking and selection of each alternative are made accordingly to this degree. Finally, the rationality and effectiveness of our method are verified by an example and its sensitivity analysis. The result shows that our method makes the solution of MADM problems more objective and reasonable.

A Semantic Inference Based Method for Privacy Measurement

In the era of Internet and big data, an increasing number of intelligent applications have been developed. As the result, a lot of user data can be collected and stored by Internet companies as well as by ordinary users through various media platforms such as Facebook, WeChat, etc. that may contain information related to personal privacy. Even though privacy protection has been declared by Internet service providers, after collecting enough amount of seemingly less relevant data, an attacker can still infer user privacy via one means or another, e.g., by running a data mining algorithm. This can undoubtedly bring high risk of privacy disclosure to users under such an attack model. So, accurately measuring the leakage of privacy becomes an urgent issue. Although many privacy measurement and protection methods have been proposed in recent years, they mainly target at structured datasets and are thus inadequate to the measurement of the disclosure of specific privacy information. In addition, most of the methods have failed to consider the internal connections and relationships between privacy information and thus cannot be used to measure the implicit privacy disclosure risk on unstructured data. In this paper, we propose a semantic inference method based on the WordNet ontology to measure privacy disclosure in which we employ an information content (IC) based method to determine the weight of attributes to describe the inference preferences in the process of inferring privacy. Experiment was performed to verify the effectiveness of the IC based inference weight assignment method and to compare the proposed measurement method to some privacy disclosure behavior learned through a data mining algorithm and some existing privacy measurement methods to demonstrate the advantages of the proposed method for measuring privacy disclosure.

Model for selection of hospital constructions with probabilistic linguistic GRP method

Probabilistic linguistic term sets are used to express uncertain decision information in multiple attribute group decision making problems. For probabilistic linguistic multiple attribute group decision making (MAGDM) with weight determined by CRITIC (Criteria Importance Through Intercriteria Correlation) method, the probabilistic linguistic grey relational projection method is proposed in this paper. Firstly, the correlation coefficient among attributes and standard deviation of each attribute are utilized to compute the attributes weights. Then the most ideal alternative is decided by means of counting the grey relational projection (GRP) from probabilistic linguistic positive ideal solution and probabilistic linguistic negative ideal solution. In the end, a numerical example for site selection of hospital constructions is applied to further account for the extended method. The result demonstrates the availability of the proposed method and it can be used in other fields which refers to problems of selection.