Weights Of Attributes Research Articles

A clustering algorithm based on the weighted entropy of conditional attributes for mixed data

SummaryA novel definition for weighted entropy is proposed to improve clustering performance for small and diverse datasets. First, intra‐class and inter‐class weighted entropies for categorical and numeric conditional attributes are respectively developed using the mathematical definition of entropy. Second, the weighted entropy is used to calculate cluster weights for mixed conditional attributes. A unique weighted clustering algorithm that adopts entropy as its primary description term, after integrating the corresponding distance calculation mechanism, is then introduced. Finally, a theoretical analysis and validation experiment were conducted using the UC‐Irvine dataset. Results showed that the proposed algorithm offers high self‐adaptability, as its clustering performance was superior to the existing K‐prototypes, SBAC, and OCIL algorithms.

BRAW: Block-wise Rating the Attribute Weights in MADM

In Multiple Attribute Decision Making (MADM) context, knowing the weights of the criteria (attributes) is a very important question. Various methods can be employed to give an appropriate value to the weights of the criteria. In a branch of the methods, Decision Maker (DM) does a trade-off between the criteria levels in the mind and gives his/her judgments on the rates or priorities of the alternatives. Such methods have a strong axiomatic foundation, but however the amount of work is often cumbersome and time-consuming. In such a way, the purpose of this paper is to present an easy and sound new trade-off method, entitled Block-wise Rating the Attribute Weights (BRAW). Briefly, in this method, the DM considers a system (named block) including two hypothetical alternatives and two criteria, and expresses his/her willingness to choose one alternative between two. Then the DM’s judgments on different blocks are used to infer the criteria weights. The main contribution of this research, in addition to the block-wise judgments, is an innovative idea for involving the indifference threshold concept at the BRAW procedure. In order to illustrate the capabilities and applicability of the proposed method, the BRAW is tested on some numerical examples. Additionally, the method validation is performed by comparing the BRAW method with the other relevant methods. The result shows several advantages of the proposed method in relation to the existing methods. We believe that employing the proposed method helps the DMs of any company in most effective and efficient manner dealing with his real-world MADM problems.

Banzhaf–Choquet-copula-based aggregation operators for managing q-rung orthopair fuzzy information

Information fusion of fuzzy numbers has played a vital role in the decision support systems under the environment of q-rung orthopair fuzzy set (q-ROFS), which is an effective extension of intuitionistic fuzzy set and fuzzy set. The goals of the present work are to build a family of new aggregation operators (AOs) under q-ROF environment and apply them to MADM problems. First, the extended Archimedean copula (EAC) and extended Archimedean co-copula (EACC) are proposed to handle q-ROF information; consequently, the operational law of q-ROFNs is defined based on EAC and EACC. In order to comprehensively consider the relationship between attributes, the q-rung orthopair fuzzy Banzhaf–Choquet-copula AOs ( $$BCCA^q$$ ) and q-rung orthopair fuzzy Banzhaf–Choquet-copula geometric operators ( $$BCCG^q$$ ) are introduced on the basis of the operation of q-rung orthopair fuzzy numbers (q-ROFNs); consequently, some special cases of $$BCCA^q$$ / $$BCCG^q$$ operators are investigated when the generators of copula take different functions which satisfy the condition of the generators of copulas. In addition, to determine the fuzzy measure (FM) of attribute sets objectively, the improved maximum deviation method and Banzhaf function model are built. Finally, the corresponding decision-making approaches are constructed based on the proposed AOs and proposed models. Proposed approaches can effectively address the some decision-making problems (DMPs), in which the weights of attributes are incompletely unknown (completely unknown), and the correlation also exists among all attribute sets.

Chitosan Films Obtained from Brachystola magna (Girard) and Its Evaluation on Quality Attributes in Sausages during Storage.

High molecular weight chitosan (≈322 kDa) was obtained from chitin isolated from Brachystola magna (Girard) to produced biodegradable films. Their physicochemical, mechanical and water vapor permeability (WVP) properties were compared against commercial chitosan films with different molecular weights. Brachystola magna chitosan films (CFBM) exhibited similar physicochemical and mechanical characteristics to those of commercial chitosans. The CFBM films presented lower WVP values (10.01 × 10−11 g/m s Pa) than commercial chitosans films (from 16.06 × 10−11 to 64.30 × 10−11 g/m s Pa). Frankfurt-type sausages were covered with chitosan films and stored in refrigerated conditions (4 °C). Their quality attributes (color, weight loss, pH, moisture, texture and lipid oxidation) were evaluated at 0, 5, 10, 15 and 20 days. Sausages covered with CFMB films presented the lowest weight loss (from 1.24% to 2.38%). A higher increase in hardness (from 22.32 N to 30.63 N) was observed in sausages covered with CFMB films. Compared with other films and the control (uncovered sausages), CFMB films delay pH reduction. Moreover, this film presents the lower lipid oxidation level (0.10 malonaldehyde mg/sample kg). Thus, chitosan of B. magna could be a good alternative as packaging material for meat products with high-fat content.

Comparison Between the Facial Flow Lines of Androids and Humans.

The behavior of an android robot face is difficult to predict because of the complicated interactions between many and various attributes (size, weight, and shape) of system components. Therefore, the system behavior should be analyzed after these components are assembled to improve their performance. In this study, the three-dimensional displacement distributions for the facial surfaces of two android robots were measured for the analysis. The faces of three adult males were also analyzed for comparison. The visualized displacement distributions indicated that the androids lacked two main deformation features observed in the human upper face: curved flow lines and surface undulation, where the upstream areas of the flow lines elevate. These features potentially characterize the human-likeness. These findings suggest that innovative composite motion mechanisms to control both the flow lines and surface undulations are required to develop advanced androids capable of exhibiting more realistic facial expressions. Our comparative approach between androids and humans will improve androids’ impressions in future real-life application scenes, e.g., receptionists in hotels and banks, and clerks in shops.

Pythagorean fuzzy weighted discrimination‐based approximation approach to the assessment of sustainable bioenergy technologies for agricultural residues

The inappropriate dumping of agricultural residues (ARs) can result in environmental pollution and the waste of valuable energy resources. The process of converting ARs to energy has been considered an important step for regional energy, agricultural development, and environmental sustainability and recently, many sustainable bioenergy technologies (BETs) have been developed for ARs. Since the assessment of ARs-to-energy conversion technologies contains several alternatives concerning multiple criteria with imprecise information, it is deliberated as an uncertain multicriteria decision-making (MCDM) problem. The Pythagorean fuzzy set (PFS) is an important and effective way to tackle the uncertainty present in real-life decision-making problems. To select a suitable conversion technology from a set of options and upgrade the ARs-to-energy industries, the present study develops a combined approach to PFSs based on weighted discrimination-based approximation (WDBA). This method extends the classical WDBA approach using an improved score function and discrimination measure within the PFS context, to evaluate MCDM problems with partial information on the criteria weights. To estimate the weights of the unknown attributes, a score function-based linear programming model is developed. A new ranking method is extended to grade the options using the proposed discrimination measure within the PFS environment. Further, a case study assessing ARs-to-energy conversion technologies is conducted to illustrate the practicality and feasibility of this method. A comparative analysis shows that the approach developed is more effective and proficient in facilitating decision experts' selection of desirable BETs for ARs.

Multi-attribute decision making approach for green efficiency of water utilization in Taiyuan

The problem of water shortage and pollution exists in Taiyuan. Under the advocacy of the concept of green development, it is necessary to carry out the research on the green efficiency of water utilization (WUGE) from the three-dimensional perspective of “economy environment society”. In this study, the evaluation index system of WUGE was optimized, which consist of 17 attributes. Interval analytic hierarchy process is used to determine the weight of attributes, and multi-attribute decision making approach is used to evaluate the WUGE from 2009 to 2018. The evaluation results show that the comprehensive evaluation value of WUGE in 2018 is the highest.

Surface runoff associated with climate change and land use and land cover in southeast region of Brazil

Changes in climate and in land use/cover can compromises water resources availability and quality, conditioning the planning and management of these essential resources. The objective of this research was to understand the environmental dynamics of the studied region considering this context and to develop instruments for the best management of water resources, based on Potential Runoff Charts generated from natural and anthropic environment attributes consideration in the years 2001 and 2017. The study area was the Rio Claro Watershed (RCW), which covers 251.91 km² and is located in the northwest region of São Paulo State, Brazil. Analytic Hierarchy Process (AHP) was used to define weights of the environmental attributes: steepness; total annual rainfall; land use/cover; soil; landforms; and aquifer units. The charts present the surface runoff in five classes (Very Low, Low, Medium, High, and Very High). The results showed significant changes in the period, due to climate changes, and, secondly, due to land use/cover changes. While in 2001 the Low and Medium Potential classes (59.3%) predominated, in 2017 the Medium and Very Low Potential classes (56.5% total) were the most common. High and Very High Potential classes showed small variations in the two years. Results derivate especially from lower rainfall in 2017 compared to 2001, showing that climate changes does not always mean an increase in rainfall on a regional scale and that water resources management instruments should consider such situations. The proposed measures aim to favor infiltration, reduce erosive processes, and favoring aquifers recharge.

Systemic stress test model for shared portfolio networks

We propose a dynamic model for systemic risk using a bipartite network of banks and assets in which the weight of links and node attributes vary over time. Using market data and bank asset holdings, we are able to estimate a single parameter as an indicator of the stability of the financial system. We apply the model to the European sovereign debt crisis and observe that the results closely match real-world events (e.g., the high risk of Greek sovereign bonds and the distress of Greek banks). Our model could become complementary to existing stress tests, incorporating the contribution of interconnectivity of the banks to systemic risk in time-dependent networks. Additionally, we propose an institutional systemic importance ranking, BankRank, for the financial institutions analyzed in this study to assess the contribution of individual banks to the overall systemic risk.

Patient-centered benefit-risk analysis of transcatheter aortic valve replacement.

Background: Aortic stenosis (AS) treatments include surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement(TAVR). Choosing between SAVR and TAVR requires patients to trade-off benefits and risks. The objective of this research was to determine which TAVR and SAVR outcomes patients consider important, collect quantitative data about how patients weigh benefits and risks, and evaluate patients' preferences for SAVR or TAVR. Methods: Patients were recruited from advocacy organization databases. Patients self-reported as being diagnosed with AS, and as either having received AS treatment or as experiencing AS-related physical activity limitations. An online adapted swing weighting (ASW) method - a pairwise comparison of attributes - was used to elicit attribute trade-offs from 219 patients. Survey data were used to estimate patients' weights for AS treatment attributes, which were incorporated into a quantitative benefit-risk analysis (BRA) to evaluate patients' preferences for TAVR and SAVR. Results: On average, patients put greater value on attributes that favored TAVR than SAVR. Patients' valuation of the lower mortality rate, reduced procedural invasiveness, and quicker time to return to normal quality of life associated with TAVR, offset their valuation of the time over which SAVR has been proven to work. There was substantial heterogeneity in patients' preferences. This was partly explained by age, with differences in preference observed between patients <60 years to those ≥60 years. A Monte Carlo Simulation found that 79.5% of patients prefer TAVR. Conclusions: Most AS patients are willing to tolerate sizable increases in clinical risk in exchange for the benefits of TAVR, resulting in a large proportion of patients preferring TAVR to SAVR. Further work should be undertaken to characterize the heterogeneity in preferences for AS treatment attributes. Shared decision-making tools based on attributes important to patients can support patients' selection of the procedure that best meets their needs.

A new Vegetation Integrity metric for trading losses and gains in terrestrial biodiversity value

The Vegetation Integrity metric has been designed within a clear management and decision-making context: to quantify losses and predict gains in terrestrial biodiversity value at development and offset sites. The metric incorporates a number of key developments that differentiate it from others. Firstly, the intent is explicitly defined and therefore falsifiable: scores have a positive relationship with the species richness and/or abundance of native plants and animals at sites within the same vegetation type. This relationship is posited because the metric is constructed from a practical set of habitat attributes representative of biodiversity composition, structure and function with known relationships with species occurrence. Secondly, the quantitative features of the metric improve both its practical and theoretical properties. These features include: normalisation of attribute scores by reference to best-on-offer benchmarks that account for inherent differences in attribute abundance among vegetation types; dynamic weighting of attributes to accommodate differences in relative abundance of attributes within vegetation types; attribute scoring using a non-linear continuous function to better represent metric intent and avoid exacerbation of measurement error; and aggregation of composition, structure and function scores using the geometric mean to reduce substitution effects. Thirdly, the prediction of gain in biodiversity value at offset sites is explicit with: attribute-specific probabilistic estimates of reaching best-on-offer benchmark status over 20 years with the management of threats and pressures (assisted natural regeneration); modification of these estimates based on expected site resilience, competition from invasive non-native plants and connectivity to the surrounding landscape; and where appropriate, additional risk-adjusted estimates of gain arising from the reintroduction of missing plant species or other habitat attributes (active restoration). In the absence of active restoration, the design of the Vegetation Integrity metric favours biodiversity offset sites with moderate Vegetation Integrity scores. Early evaluation of sites assessed under the Biodiversity Conservation Act 2016 suggest that offset sites are dominated by areas with moderate scores which is consistent with the intent of improving biodiversity value rather than averting future losses. Although developed within the Australian context, the new Vegetation Integrity metric has general appeal to robust, transparent and practical quantification of terrestrial biodiversity value elsewhere.

Nanofluid selection used for coolant in heat exchanger by multiple attribute decision-making method

Nanofluids are suspensions of nanosized nanoparticles in the base fluid which shows superior performance in heat transfer applications. As different nanoparticles have different characteristics, thus the selection of a proper nanoparticle for a given application becomes a very crucial task. Thus, the selection of nanoparticle has to be performed in such a manner that it will give a maximum thermal performance that to at least cost. This study involves the multiple attribute decision-making (MADM) method to select optimal metal oxide nanoparticles. In the present method, to determine the weights of different attributes considered, the analytical hierarchy method (AHP) is used and then to obtain the final ranking, a technique for order preference by similarity to ideal solution (TOPSIS) is used. In the present study, the problem regarding the selection of suitable nanoparticle for heat exchanger applications is discussed. The most studies nanomaterials i.e., CuO, Al2O3, TiO2, SiO2, ZnO, Fe2O3 are considered with different attributes. It is found that copper oxide (CuO) and aluminum oxide (Al2O3) is the first-best and second-best alternatives, respectively. Empirical results obtained prove that this is a feasible approach to solving the nanoparticle selection problem.

Residual Effect of Weed Management Practices Executed in Preceding Maize on Succeeding Greengram

Background: Maize followed by summer pulses is the emerging cropping system in many places in India. Herbicides are the efficient tools for checking weed infestation and their usage is increasing throughout the globe due to increased labour cost and quick weed control. Some herbicides do not degrade quickly and may remain in the soil for weeks, months or years after application and may inhibit the growth of succeeding crops. Methods: Field experiment was conducted in a randomized block design (RBD) during rabi and summer seasons of 2017-18 and 2018-19 to know the residual effect of different herbicides applied in maize on succeeding greengram. Result: The present study revealed that lower density and dry weight of total weeds and higher yield attributes and yield was recorded with the practice of adopting two hand weedings at 15 and 30 DAS, which was however, at par with atrazine 1.0 kg ha-1 as PE fb one HW at 30 DAS, atrazine 1.0 kg ha-1 as PE fb topramezone 30 g ha-1 as PoE and atrazine 1.0 kg ha-1 as PE fb tembotrione 120 g ha-1 as PoE, without any significant disparity among them. Herbicides applied in maize did not alter statistically the enzyme activity and microbial count in the soil at harvest of succeeding greeengram.

Evaluation of Online Teaching Quality: An Extended Linguistic MAGDM Framework Based on Risk Preferences and Unknown Weight Information

Evaluation of online teaching quality has become an important issue because many universities are turning to online classes due to the Corona Virus Disease 2019 (COVID-19) pandemic. In this paper, online teaching quality evaluation is considered as a linguistic multi-attribute group decision-making (MAGDM) problem. Generally, the evaluation sematic information can be symmetrically or asymmetrically distributed in linguistic term sets. Thus, an extended linguistic MAGDM framework is proposed for evaluating online teaching quality. As the main contribution, the proposed method takes into account the risk preferences of assessment experts (AEs) and unknown weight information of attributes and sub-attributes. To be specific, the Delphi method is employed to establish a multi-level evaluation indicator system (EIS) of online teaching quality. Then, by introducing the group generalized linguistic term set (GLTS) with two risk preference parameters, a two-stage optimization model is developed to calculate the weights of attributes and sub-attributes objectively. Subsequently, the linguistic MAGDM framework was divided into two stages. The first stage maximizes the group comprehensive rating values of teachers on different attributes to obtain partial ranking results for teachers on each attribute. The latter stage maximizes the group comprehensive rating values of teachers to evaluate the overall quality. Finally, a case study is provided to illustrate how to apply the framework to evaluate online teaching quality.

A novel multi-attribute decision-making method based on fuzzy rough sets

The purpose of this paper is to propose a novel decision-making method based on fuzzy rough sets (FRSs) to deal with the uncertainty and imprecision existed in various multi-attribute decision-making (MADM) problems. In view of the effectiveness of fuzzy neighborhood operators in handling uncertain numerical data and the deficiencies of existing fuzzy neighborhood operators, we first define a reflexive fuzzy neighborhood operator in fuzzy information systems. Then, two types of FRS models are presented and their relationships are discussed. Whereafter, we use the tight FRS model to transform uncertain data into intuitionistic fuzzy data. A new MADM method is established under the intuitionistic fuzzy environment by using the idea of the PROMETHEE II and EDAS methods. Meanwhile, the intuitionistic fuzzy weights (IFWs) of attributes and the global intuitionistic fuzzy thresholds are introduced. Furthermore, a real-world example from the UCI database is utilized to expound the feasibility of the proposed method. At last, the validity and stability of the proposed method are demonstrated by comparative analysis and experimental analysis.

Innovative mutual information‐based weighting scheme in stateless opportunistic networks

Recently, opportunistic networks (OppNets) are considered as one of the most attractive developments of mobile ad hoc networks that have emerged, thanks to the development of intelligent devices. Owing to the harsh and dynamic topology of these networks, attaining high delivery ratio is a challenging issue. Hence, it is imperative to select which node's attribute must be adjusted to achieve a higher performance in such unpredictable networks. A mutual information-based weighting scheme (MIWS) that exploits the entropy concept to assess the impact of the nodes' attributes on the network performance was proposed. The weighting procedure aims to figure out the correlative relations between different attributes and delivery ratio performance of the network. The high weight of certain attributes implies a correspondingly high impact in achieving efficient data forwarding. The proposed scheme is proofed conceptually and simulated using the Opportunistic Network Environment simulator. In contrast to previous studies conducted in the context of weight resolution, the proposed approach allows us to address this issue in real-time stateless non-social OppNets. Regardless of the deployed routing protocol, experiments show that adjusting nodes' attributes based on the proposed MIWS can improve the performance up to encouraging delivery ratios.

Understanding the importance of Artistic Aspect of Camera Image Quality Tuning and Quantifying the Artistic Attributes by using Scientific Principles

The camera tuning process contains multiple stages under the image signal processing (ISP) pipeline through which the RAW image gets processed and displayed. The objective image quality is well defined and important when tuning the color imaging pipeline in the Camera. The ISP pipeline is tuned by optimizing objective criteria, resulting image and video may not be aesthetically appealing to end-users and may not be sufficient to provide the best visual experience. There are certain key artistic factors affecting the overall user experience in the process of ISP pipeline tuning. However, currently there is no industry standard for artistic image quality (IQ) quantification and is mostly based on expert based subjective evaluation. In this paper, we emphasize the importance of artistic attributes and to quantify them with two studies. First study focusses on importance of artistic attribute by two alternate forced choice pairwise comparison user study on artistic vs objective tuning. Based on this study, artistic tuning is statistically significantly better than objective tuning with 95% confidence interval based on fisher least significant difference test. Second study develop a method to quantify holistic IQ. The novel mathematical equation has been formulated to calculate the weight of different IQ attributes which are impacting on overall IQ as a first step. Rank based user study was performed on expert and nonexpert users to find their preference on different attributes with the use of novel mathematical equation. In this equation, user preferences were converted into weights of artistic attributes in the holistic IQ. The study shows that color saturation and memory color attributes have higher impact for both expert and non-expert user with weightage of 14.04% and 13.62% in holistic IQ, respectively. This study validates the importance of the artistic approach and our first step to quantify these artistic attributes in a scientific way. It also exhibits the need of a novel image quality assessment criteria to tune and validate the final visual experience of the consumer camera. Especially, in the era of Artificial Intelligence (AI), quantifying artistic attributes is far more important than ever.

Work Resumption After Epidemic Using Three-Way Decisions

After the epidemic, all enterprises are faced with the difficult decision about whether the enterprise should resume work and production immediately, which is related to the safety and development of enterprises. The three-way decisions offer a novel study perspective to solve this problem. Firstly, we describe some relevant attributes of the enterprise with double hierarchy hesitant fuzzy linguistic term sets, and construct double hierarchy hesitant fuzzy linguistic information systems for each enterprise. Secondly, we get the weights of attributes with the entropy weight method, and take the weighted aggregation of attributes information as the conditional probability that the enterprise is in a safe state. Next, we classify each enterprise according to its size. Considering the influence of different sizes of enterprises, we put forward the corresponding loss function matrix. Then we get the decision results about work resumption based on the principle of minimizing the loss, which demonstrates the practicability and effectiveness of our method. Finally, we compare the method proposed by us with the other method and discuss the advantages and limitations of our proposed method.

Venture capital project selection based on interval number grey target decision model

In this study, aiming at the multi-attribute decision-making problem with incomplete and uncertain attribute weight information and attribute value of interval numbers, a grey target decision-making model of interval numbers based on positive and negative clouts is proposed. Firstly, in this model, the linear transformation operator of interval number is used to normalize the original decision information, and the positive and negative clouts of interval number are designed. Secondly, after the space projection distance between each scheme and the positive and negative clouts is considered comprehensively, the off-target distance is taken as the basis of vector analysis in space to obtain a new comprehensive off-target distance. The existing interval number grey target decision-making model ignores the important influence of interval distribution and the correlation between the attributes in scheme evaluation, and there are some fuzzy errors when setting the weight of attributes. In order to solve the above problems, this paper combined with the uncertainty analysis of the attribute weights, a goal programming is constructed for the objective function based on the comprehensive off-target distance minimization to solve the attribute weight vector, and finally determine the order of the scheme. Finally, the feasibility and effectiveness of the proposed grey target decision model are verified by an example of venture capital projects. Compared with traditional models, the improved model fully considers the characteristics of interval data and the correlation between the attributes.

Implicit and hybrid methods for attribute weighting in multi-attribute decision-making: a review study

Attribute weighting is a task of paramount relevance in multi-attribute decision-making (MADM). Over the years, different approaches have been developed to face this problem. Despite the effort of the community, there is a lack of consensus on which method is the most suitable one for a given problem instance. This paper is the second part of a two-part survey on attribute weighting methods in MADM scenarios. The first part introduced a categorization in five classes while focusing on explicit weighting methods. The current paper addresses implicit and hybrid approaches. A total of 20 methods are analyzed in order to identify their strengths and limitations. Toward the end, we discuss possible alternatives to address the detected drawbacks, thus paving the road for further research directions. The implicit weighting with additional information category resulted in the most coherent approach to give effective solutions. Consequently, we encourage the development of future methods with additional preference information.