New doctors ranking system based on VIKOR method

Nowadays, we can use different websites that help us make decisions about various aspects of our lives. However, privacy protection prevents websites from providing personalised guidelines to users. We propose a novel doctor‐ranking system (DRS) based on multi‐criteria group decision‐making (MCGDM) method to address the problems of privacy protection. The following aspects differentiate our proposed DRS model from previous works: (a) textual information reviews are used to identify user preferences and complementary criteria, (b) criteria weights are determined by term frequency inverse document frequency (TF‐IDF) instead of Delphi method or expert opinion, (c) intuitionistic fuzzy sets (IFSs) are used to replace sentiment analysis to express subjective user criteria, and (d) VIsekriterijumsko KOmpromisno Rangiranjie (VIKOR) method for MCGDM with IFSs is used to solve the doctor‐ranking problem. We apply our proposed model to datasets from Haodf.com to compare the performance of our method with that of sentiment analysis and technique for order performance by similarity to ideal solution (TOPSIS) methods. The experimental results show that our method provides accurate ranking and increases the reliability of DRS.     

Human uncertainty in explicit user feedback and its impact on the comparative evaluations of accurate prediction and personalisation

ABSTRACT

In this article, we report on the lack of reliability of explicit user feedback and its interpretation in the light of system evaluation. It is known, that given feedback strongly depends on the situational context. But also when many contextual factors are held constant, user feedback still proves to be unreliable. This impacts the evaluation of predictive algorithms since it is not clear whether a deviation between a user response and its corresponding prediction can be seen as a flaw by the system or just as usual ‘human uncertainty’. As a result, the perspective on the evaluation of adaptive systems basically changes. The main goal of this article is to demonstrate that simply increasing the amount of explicit feedback is not the key to sustainable system design innovation, as long as that information is not appropriately evaluated. To this end, we will exploit a novel probabilistic approach of processing user feedback and identify biasing effects on accuracy metrics, error probabilities for system rankings as well as natural limitations of evaluation. Finally, we will discuss possible solution strategies and give advice for handling explicit user feedback that is associated with uncertainty.     

Assessing robustness of factor ranking for supersaturated designs

Supersaturated designs can potentially be a beneficial tool for efficiently exploring a large number of factors with a moderately sized design. However, because more factors are being considered than there are runs, the stability of the identified factors depends heavily on effect sparsity and the lack of highly influential observations. A helpful tool for the analysis of supersaturated designs is least absolute shrinkage and selection operation (LASSO), which is useful when the effects of many explanatory variables are sparse in a high‐dimensional dataset. To understand the impact of individual observations on the selected factors, the LASSO influence plot was created. This paper describes an application of this plot and its variants that can be used to identify influential points, increase understanding of the impact of individual observations on model parameters, and the robustness of results in analyses with supersaturated designs. These graphical methods can serve as a complement to other regression diagnostics techniques in the LASSO regression setting.     

Preliminary identification of potential markets for off-grid electrification: tool development and a case study of Ghana

With more than one billion people lacking access to electricity in the world, ensuring universal access to electricity by 2030 remains a major challenge which cannot be left to the government initiatives alone. Access to local information and identification of potential areas for investment can be a challenge for investors. This paper provides a tool for preliminary assessment of potential markets for off-grid electrification in developing countries and applies this to Ghana to demonstrate its applicability. A multi-criteria approach is used to rank the districts according to the overall potential and the best markets and least favourable areas for investment are identified. The tool offers flexibility to include new inputs to the analysis and the factor weights can be adjusted as appropriate. The case study shows that the tool can effectively identify potential areas from a list of candidates and offers support to analysts.     

Prioritization of chemical hazards in spices and herbs for European monitoring programs

Monitoring programs are preferably risk-based, which allows focusing on the most relevant human health risks. In this study, a risk matrix was used to identify those chemical hazards that have the highest human health risk for the following spices and herbs: paprika/chilli powder, black pepper, nutmeg, basil, thyme, and parsley. Both the probability of occurrence and the severity of the hazard were assessed for 36 chemical compounds and classified into low, medium, high, and severe. Probability of occurrence was evaluated based on available monitoring data and RASFF notifications as well as possibilities for economic adulteration. Severity was assessed based on available toxicological reference values and classification of carcinogenicity. The results demonstrated that the mycotoxins aflatoxins and ochratoxin A, the pesticides chlorpyrifos and triazophos, and the dye Sudan I posed the highest human health risk for spices and herbs. These compounds should, therefore, have an increased monitoring frequency in these products.     

Comparison of discriminability of common food acceptance scales for the elderly

This study was aimed to compare four commonly used methods for evaluating consumer acceptance (the 5-point and the 9-point hedonic scales) and preference (the ranking scale and the best-worst scaling) in terms of their ability in discriminating samples and perceived ease of use by the elderly. Elderly (n = 100) and young adult (n = 100) consumers participated in this study. Each consumer evaluated five commercial orange juice samples in duplicate for hedonic rating and preference rank separately and then evaluated ten sets of three samples for the best-worst scale, following the balanced incomplete block design. Nonparametric tests were employed as normality of data was violated. A significant sample effect was observed in all four methods (P < .001). Discriminating power of the best-worst scaling was more pronounced in the elderly than in the young adults; however, the best-worst scaling was easy to use for the young adults than for the elderly.     

An evolved VIKOR method for multiple-criteria compromise ranking modeling under T-spherical fuzzy uncertainty

The development of T-spherical fuzzy (T-SF) sets in qualitative multiple-criteria choice analysis has been well investigated, but little is explored about how the VIKOR (i.e., VIšekriterijumsko KOmpromisno Rangiranje in Serbian) mechanism is generalized into intricate T-SF environments. This paper has the objective of propounding a creative T-SF VIKOR methodology for compromise ranking modeling in multiple criteria analysis. The exploitation of the T-SF configuration brings about superior information space representation in response to intricate realistic environments. However, because of the increased complexity of decision situations involving T-spherical fuzziness, treatment with the T-SF information arises a pragmatic difficulty in developing methodological approaches to T-SF VIKOR. In light of this concern, this paper unfolds an analytical framework for the T-SF VIKOR method predicated on new notions of an evolved T-SF score function and the Minkowski-type T-SF distance measure along with its special cases of the Manhattan-, Euclidean-, and Chebyshev-type distances. The aspired and despised T-SF characteristics can be identified supported by the evolved T-SF score function of T-SF performance ratings. This paper gives a new delineation of the group utility, individual regret, and joint generalized measures with the aid of the Minkowski-type T-SF distance measure relative to the aspired/despised T-SF characteristics. A synthetic mechanism is built to validate the standards of acceptable advantage and stability and yield the ultimate compromise choices for multiple criteria decision aiding. The practicality of the T-SF VIKOR methodology in down-to-earth decision situations has been well demonstrated through the selection issues of warehouse locations and advertisement strategies. Moreover, the developed approach has shown better application outcomes than the past methods did. The comparative study with parametric analysis has revealed the steadiness and effectiveness of the compromise choice results in T-SF circumstances.     

会泽铅锌矿方铅矿、闪锌矿和黄铁矿的浮游性研究

本文考查了ZnSO4和可溶性淀粉对方铅矿、闪锌矿和黄铁矿浮游性的影响，为会泽铅锌矿铅硫异步等可浮工艺提供了理论依据。试验结果表明，在乙黄药浮选体系中，ZnSO4和可溶性淀粉强烈抑制闪锌矿，而对方铅矿和黄铁矿的浮游性影响不大，且两者的浮游性相近。     

Multi-criteria ranking and receptor modelling of airborne fine particles at three sites in the Pearl River Delta region of China

The multi-criteria decision making methods, Preference Ranking Organization METHods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and the two-way Positive Matrix Factorization (PMF) receptor model were applied to airborne fine particle compositional data collected at three sites in Hong Kong during two monitoring campaigns held from November 2000 to October 2001 and November 2004 to October 2005. PROMETHEE/GAIA indicated that the three sites were worse during the later monitoring campaign, and that the order of the air quality at the sites during each campaign was: rural site > urban site > roadside site. The PMF analysis on the other hand, identified 6 common sources at all of the sites (diesel vehicle, fresh sea salt, secondary sulphate, soil, aged sea salt and oil combustion) which accounted for approximately 68.8 ± 8.7% of the fine particle mass at the sites. In addition, road dust, gasoline vehicle, biomass burning, secondary nitrate, and metal processing were identified at some of the sites. Secondary sulphate was found to be the highest contributor to the fine particle mass at the rural and urban sites with vehicle emission as a high contributor to the roadside site. The PMF results are broadly similar to those obtained in a previous analysis by PCA/APCS. However, the PMF analysis resolved more factors at each site than the PCA/APCS. In addition, the study demonstrated that combined results from multi-criteria decision making analysis and receptor modelling can provide more detailed information that can be used to formulate the scientific basis for mitigating air pollution in the region.     

基于社会信任正则化的排名推荐算法

随着在线商品交易额逐年增大和社交网络不断深入发展,推荐系统已成为解决信息过载的重要工具之一。当评分矩阵数据稀疏性较大时推荐精度就会显著下降,特别是用户冷启动的时候这个问题更加明显。因此,本文提出一种新的基于隐式反馈信息的社会化排序推荐算法。该算法首先利用矩阵分解方法计算不同项目间的用户偏好。其次将用户偏好信息融入Bayesian Personalized Ranking (BPR)算法中。然后挖掘用户之间相似关系以及信任用户直接和间接关系,并量化它们之间的信任关系,从而研究不同项目之间用户偏好差异。最后将以上这些信任关系和BPR算法进行融合,进而构建出社会化排序推荐模型。为了验证所提出的社会化排序推荐算法,在DouBan数据集和FilmTrust数据集上,进行该算法的有效性验证。主要通过Precision、MAP和NGCD这三种排序评估指标分别在全数据集和用户冷启动中验证本文所提算法与SBPR、TBPR、BPRMF和MostPopular等算法之间排序推荐的优劣性。实验结果证明本文所提算法明显优于其他排序推荐算法,并可以获得更好的推荐准确率。可见该算法可以有效改善由于数据稀疏性和用户冷启动所带来推荐效果差的问题。