Subjective Expert Judgment Research Articles

Analysis of NBA player salary based on multiple linear regression model

Abstract. This study explores the use of machine learning techniques to predict NBA player salaries. Traditional salary evaluation methods often rely on subjective expert judgment, whereas data-driven approaches can provide more objective and accurate predictions. With the advancement of data analysis techniques and machine learning algorithms, it is now feasible to predict player salaries based on performance data. This study employs advanced statistical and machine learning techniques to analyze detailed player performance data, including points scored, rebounds, assists, steals, and blocks, to establish a data- and algorithm-based salary prediction model. This model can assist team management in making more scientific decisions during contract negotiations and player acquisitions, thereby avoiding the overvaluation or undervaluation of players and achieving a more balanced and fair salary distribution. Accurate salary predictions help teams allocate their limited salary cap more effectively, optimizing budget management and enhancing overall team competitiveness. This study not only demonstrates the practical value of data analysis and machine learning methods in the sports field but also promotes the further development of data science in sports management. Additionally, the results of the prediction model can provide valuable references for fans and media, enhancing their understanding of player salaries and team management strategies. This transparency enriches the overall fan experience and media coverage of the sport, facilitating more informed discussions and debates about player value and team decisions.

Evaluating Mobile Wallet Adoption Barriers Using Fuzzy Mathematical Model

A tremendous amount of research has been done on the factors influencing mobile wallet adoption as mobile wallet technology has seen rapid growth. Using expert opinion and Fuzzy PROMETHEE approach, this study investigates the key barriers to mobile wallet adoption. Mobile wallet adoption is constrained by Technological, security and infrastructural barriers, making adoption more challenging when user acceptance is skewed in emerging markets. In this study, we use the F-PROMETHEE to rank these barriers based on expert opinions. A panel of fintech and digital payments experts assessed the key adoption obstacles. Included in the PROMETHEE method were methods for handling variability or uncertainty through fuzzy logic and through subjective expert judgments. The results suggest that the major barriers to the adoption of mobile wallets were identified as risk and usage constraints. Moreover, value barriers are a leading factor. This study found that the risk and value barriers are the two principal risks that must be overcome to raise the client accepted rate of m-wallet services. A step forward in the assessment of such obstacles is the innovative use of a fuzzy mathematical model, which provides a more complex and adaptable approach than traditional methods. This study has learnt a few lessons that can help policy makers and industry players understand how to overcome the main barriers to mobile wallet adoption.

The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS

The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (Ri) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.•The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.•The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.•The method is easiest and suitable for short term operation research.

Dynamic risk early warning system for tunnel construction based on two-dimensional cloud model

Due to the uncertainty of geological conditions and construction status during tunnel construction, the risk of tunnel construction will vary with updates to environmental information. The current risk management practice for tunnel construction projects in China is static and relies on the subjective judgment of experts and practitioners, so it is necessary to propose a risk assessment methodology that takes into account dynamically updated risk information and uncertainty. This paper proposes a dynamic risk warning system for tunnel construction that integrates knowledge-driven consequence datasets and data-driven likelihood datasets of risk events, using a probability algorithm based on a two-dimensional cloud model for risk decision-making. The algorithm quantifies the risk probabilities by randomly simulating the risk discrimination results under the influence of uncertainty multiple times and statistically analyzing the distribution pattern of a large number of simulation results within the constructed quantitative risk matrix. Decisions on risk level and warning state are made based on risk probabilities. Moreover, this study conducts extensive simulation tests to determine the optimal number of simulated cloud droplets and develops a software toolbox to facilitate rapid feedback on tunnel construction risk. The proposed algorithm is applied to the construction of Guiyang Metro Line 3. The results show that the standard deviation of the simulation results is less than 0.025 and the algorithm has good stability. The two-dimensional cloud model offers a superior reflection of risk uncertainty compared with its one-dimensional counterpart, making it more appropriate for assessing the risks associated with tunnel construction—a complex scenario characterized by intricate index relationships and significant uncertainty.

An Integrated T-Spherical Fuzzy Einstein Interaction Aggregator Group Decision-Making Approach: A Case Study of Concrete 3D Printing Robot Application in Vietnam

This study introduces the integrated T-spherical fuzzy Einstein interaction aggregator group decision-making approach, a novel framework designed to enhance multi-criteria decision-making (MCDM). Implementing the case study of concrete 3D printing technology in Vietnam, this approach integrates T-spherical fuzzy sets with Einstein aggregation operators to handle the complexities of uncertain and subjective expert judgments effectively. The methodology provides a robust mechanism for evaluating and prioritizing the barriers and strategies associated with the implementation of concrete 3D printing. Findings from this study underline the significance of technological advancements and strategic financial incentives, with R&D strategy emerging as the top priority. This research contributes to both theoretical advancements in decision-making frameworks and offers practical insights for industries looking to integrate emerging technologies. Moreover, it demonstrates the application of advanced fuzzy set theories in real-world settings, providing a valuable tool for decision-makers facing similar technological adoption challenges.

A novel multi-attribute three-way decision model with three-parameter interval grey number decision-theoretic rough sets

PurposeTo overcome the limitations of traditional multi-attribute decision making (MADM) methods, which only provide deterministic rankings of decision objects, this paper proposes a novel multi-attribute 3WD model. This model presents three-parameter interval grey number decision-theoretic rough sets (TPIGNDTRSs), aiming to offer a reasoned interpretation of loss functions in grey environments and ensure objective assessment of conditional probabilities.Design/methodology/approachFirstly, the traditional equivalence relation is replaced with the probabilistic dominance relation (PDR), categorizing decision objects into two state sets in DTRS for more objective conditional probabilities. Secondly, as the three-parameter interval grey number (TPIGN) introduces the most probable value on the basis of the traditional two-parameter interval grey number, it provides a more comprehensive method for describing grey information. Consequently, integrating TPIGN into DTRS refines the interpretations of loss functions in grey environments. Finally, by utilizing two main sorting techniques, relative kernel and degree of accuracy ranking and possibility ranking, two types of 3WD rules with TPIGNDTRSs, are constructed.FindingsThis study has successfully developed and validated a new multi-attribute 3WD model. The model was tested in two distinct domains: evaluating innovation efficiency in high-tech enterprises and recommending movies in a practical case. The findings reveal that the model can effectively integrate relevant information of high-tech enterprises, provide the government with enterprise-level assessments, and gather consumer preferences to recommend the most suitable movies.Research limitations/implicationsThis study treats the loss function as grey information in the 3WD model but overlooks the grey nature of evaluation values, limiting its applicability. Additionally, the model’s reliance on subjective expert judgments and historical data to establish the loss function may affect its objectivity. The implications of this research are that the novel model overcomes traditional MADM limitations, enhancing decision-making quality and efficiency in complex and grey scenarios. The model’s successful application in evaluating high-tech enterprises and recommending movies illustrates its dual value in both theory and practice.Originality/valueInitially, the model proposed in this study is of significant importance for the development of the 3WD field, as it successfully addresses the challenges of uncertain loss functions and unknown conditional probabilities in grey information environments. Moreover, by integrating the 3WD model with MADM problems, it has broken through the bottlenecks of traditional MADM methods, offering new perspectives and strategies for solving MADM issues. Therefore, this research not only advances theoretical research but also provides powerful tools for practical applications.

Model for Technology Risk Assessment in Commercial Banks

As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not effectively reduce the frequency of technology-related incidents. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based on machine learning. It first identifies risk factors in bank IT, preprocesses the sample data, and uses different regression prediction models to train the processed data to build an intelligent assessment model. The experimental results indicated that the Genetic Algorithm–Backpropagation Neural Network model achieved the best performance. Based on assessment indicators, indicator weight values, and risk levels, commercial banks can develop targeted prevention and control measures by applying limited resources to the most critical corrective actions, thereby effectively reducing the frequency of technology-related incidents.

Integration of Level 3 probabilistic risk assessment for nuclear power plants with transportation simulation considering earthquake hazards

This research improves the realism of Level 3 probabilistic risk assessment (PRA) for nuclear power plants (NPPs) to avoid subjective expert judgement by modeling the evacuation behavior of the residents and making two advancements. First, evacuation speed taken from a transportation simulation code was used as an input to the Level 3 PRA code, i.e., the MELCOR Accident Consequence Code System (MACCS). The transportation simulation code uses the Multiagent Transport Simulation (MATSim) evacuation extension software. MATSim is interfaced with MACCS, by generating input-output communication through evacuation routes and speed distributions. Integrating the agent-based transportation simulation with the Level 3 PRA code enables the explicit incorporation of spatiotemporal evacuation processes into NPP offsite risk assessment. Second, this research is the first to explicitly incorporate the probability of seismic damage to roadway bridges into Level 3 PRA of NPPs. The likelihood of seismic bridge damage, given an earthquake, is estimated using the HAZUS program developed by the Federal Emergency Management Agency (FEMA). The road closure due to seismic damage, estimated by HAZUS, is used as input to the agent-based transportation simulation, where evacuation routes and speed distributions are calculated considering the impact of seismic damage. These advancements can improve the realism of Level 3 PRA for NPPs by enhancing the evacuation analysis resolution and reducing the reliance on subjective judgment to characterize evacuation routes, speed, and seismic impact on Level 3 PRA. Integrating the transportation simulation and the natural hazard risk assessment with the Level 3 PRA code can also provide more in-depth causal insights for offsite risk management, such as recommending optimal evacuation routes in an emergency response plan. These contributions are demonstrated using the Sequoyah NPP case study adopted from the state-of-the-art reactor consequence analyses (SOARCA) conducted by the U.S. Nuclear Regulatory Commission (NRC).

Space-dependent turbulence model aggregation using machine learning

Computational models of fluid flows based on the Reynolds-Averaged Navier–Stokes (RANS) equations supplemented with a turbulence model are the gold standard in engineering applications. A plethora of turbulence models and related variants exist, none of which are fully reliable outside the range of flow configurations for which they were calibrated. Thus, the choice of a suitable turbulence closure largely relies on subjective expert judgement and engineering know-how. In this article, we propose a data-driven methodology for combining the solutions of a set of competing turbulence models. The individual model predictions are linearly combined for providing an ensemble solution accompanied by estimates of predictive uncertainty due to the turbulence model choice. First, for a set of training flow configurations we assign high weights to component models in the regions where they perform best, and vice versa, based on a measure of distance between high-fidelity data and individual model predictions. The model weights are then mapped into a space of features, representative of the local flow physics, and regressed using a Random Forests (RF) algorithm. Finally, the RF regressor is used to infer spatial distributions of the model weights for unseen configurations. Predictions of new cases are constructed as a convex linear combination of the underlying models solutions, while the between-model variance provides information about regions of high model uncertainty. The method is demonstrated for a class of flows through the compressor cascade NACA65 V103 at Re≃3×105. The results show that the aggregated solution outperforms the accuracy of individual models for the quantity used to inform the RF regressor, and performs well for other quantities well-correlated with the previous one. The estimated uncertainty intervals are generally consistent with the target high-fidelity data. The present approach then represents a viable methodology for a more objective selection and combination of alternative turbulence models in configurations of interest for engineering practice.

Decoding the fashion trend of sports shoes with empowered computer vision

To adapt to the rapidly changing market and capricious trends, fashion brands need to understand trends and market conditions precisely and fast to produce marketable products. The traditional fashion trend analysis has relied heavily on the subjective judgement of experts, inevitably leading to biased decisions and is time-consuming. The development of computer vision and machine learning provides an objective and systematic approach to processing images and analysis of fashion products. However, most studies focus on clothing trends analysis, few are on shoe trends analysis. Hence, this study aimed to decode and analyse the fashion trend of sports shoes with empowered computer vision technology. In this paper, a dataset containing e-commerce images of sports shoes with precise annotations was established; then Mask-RCNN was utilized to classify and extract the shoe from the background image; a modified version of the K-means clustering algorithm was employed to detect the shoe colour. The results indicated that fashionable sports shoes and casual sports shoes were the most prevalent two styles. Besides neutral tones, yellow, red ochre and reddish orange were popular in casual sports shoes, fashion sports shoes and basketball shoes respectively and Atlantic Blue in board shoes and trainers. This study demonstrated the promising potential of computer vision and machine learning as a new method to analyse footwear fashion trends efficiently and economically.

Software systems supporting remote education – Fuzzy assessment using a multi-criteria group decision-making method

During the COVID-19 pandemic, the demand for systems supporting remote education increased significantly, and after the pandemic this form of education remained in place to supplement the stationary education. Among the tools that comprehensively support remote learning, there are two most popular systems, i.e. Google Workspace for Education and Microsoft Office 365, available in several different variants, differing in the functionalities offered. The aim of the article is to analyse the functionality, as well as to compare and evaluate this software. Since the assessment of e-learning systems is based on the subjective judgements of experts, and the assessment criteria are qualitative in nature, a new MCGDM (Multi-Criteria Group Decision Making) method called NEAT F-PROMETHEE GDSS (New Easy Approach To Fuzzy PROMETHEE – Group Decision Support System) was developed for the purposes of the study. This method captures the uncertain and imprecise qualitative assessments expressed by many experts and aggregates them into an overall quantitative assessment. As a result of the assessment, it was found that all variants of Google Workspace for Education and Microsoft Office 365 meet the basic requirements for tools for conducting remote learning. In addition, the use of the NEAT F-PROMETHEE GDSS method made it possible to create a ranking of individual software variants. Based on the group evaluation, it was determined that the best system in terms of the relationship between the offered capabilities and the cost of use is Google Workspace for Education Plus, with other variants of Google Workspace for Education taking subsequent positions in the ranking. In turn, Microsoft Office 365 solutions received a worse group rating, largely due to the higher cost of use and slightly less ability to control student work.

Assessing handwriting task difficulty levels through kinematic features: a deep-learning approach.

Introduction: Handwriting is a complex task that requires coordination of motor, sensory, cognitive, memory, and linguistic skills to master. The extent these processes are involved depends on the complexity of the handwriting task. Evaluating the difficulty of a handwriting task is a challenging problem since it relies on subjective judgment of experts. Methods: In this paper, we propose a machine learning approach for evaluating the difficulty level of handwriting tasks. We propose two convolutional neural network (CNN) models for single- and multilabel classification where single-label classification is based on the mean of expert evaluation while the multilabel classification predicts the distribution of experts' assessment. The models are trained with a dataset containing 117 spatio-temporal features from the stylus and hand kinematics, which are recorded for all letters of the Arabic alphabet. Results: While single- and multilabel classification models achieve decent accuracy (96% and 88% respectively) using all features, the hand kinematics features do not significantly influence the performance of the models. Discussion: The proposed models are capable of extracting meaningful features from the handwriting samples and predicting their difficulty levels accurately. The proposed approach has the potential to be used to personalize handwriting learning tools and provide automatic evaluation of the quality of handwriting.

Methodology for Environmental Risk Analysis Based on Intuitionistic Fuzzy Values

Ecological risks are characterized by a high degree of uncertainty about the chances of unfavorable event outcomes and the losses associated with those outcomes. Subjective expert judgment is widely used when baseline data are insufficient. This introduces additional uncertainties in the results of risk analyses. In order to successfully model the existing uncertainties, this paper presents a methodology for ecological risk analysis that is based on input evaluations in the form of intuitionistic fuzzy values (IFVs). The advantage of this approach is the ability to model a wide range of uncertainties in ecological risk analysis tasks.

Human error probability prediction for cargo sampling process on chemical tanker ship under extended SLIM Evidential Reasoning approach

Cargo sampling, which indicates the condition of the cargo on the ship, is one of the importantchemical tanker shipboard operations where human performance is prominent. Any negligence during thecargo sampling process can result in loss of human life, environmental disasters and financial losses.Therefore, evaluating human performance in the cargo sampling process on chemical tanker ships is vitalto avoid these. This paper aims to evaluate the contribution of human errors to the cargo sampling process.Hence, the Success Probability Index Method (SLIM) is conducted, incorporating Evidential Reasoning(ER) approach. While SLIM systematically predicts human error probabilities (HEP) consideringperformance shaping factors (PSFs), ER deals with the uncertain and subjective judgments of experts inthe step of rating and weighting PSFs. Based on the presented ER-SLIM model, HEP can be estimated byaggregating the belief degree of the experts and human performance for the cargo sampling process can beevaluated. The outputs of the paper provide a practical contribution to chemical tanker ship owners, healthsafety environment and quality (HSEQ) managers, maritime safety professionals and, chemical tankerofficers in order to minimize the probability of human error in the cargo sampling process, as well as thetheoretical background.

Prediction system of rolling contact fatigue on crossing nose based on support vector regression

It is essential to assess the rolling contact fatigue (RCF) of turnouts and maintain them in advance. It saves a lot of money while protecting the safety of railway operations. In Germany, the damage on rails, especially crossing noses, mainly depends on the subjective judgment of experts. There are no objective and comprehensive evaluation criteria. This paper presents the application of image processing and supervised machine learning algorithms to crossing nose fatigue judgment. The fatigue characteristics of the crossing nose rolling contact surface along the life cycle of the crossing nose are analyzed. The study used crack information from magnetic particle inspection (MPI) images of crossing nose surfaces. It uses basic image processing methods to collect physical information about features of fatigue cracks in images. Existing feature selection methods are used to exclude irrelevant features and retain valuable features. And we select the best feature selection method through the regression results. Statistically significant crack features and combinations that depict the surface fatigue state are found. In this paper, by comparing several usually machine learning regression algorithms, it is found that the supervised learning of support vector machine regression (SVR) has achieved the best results in the regression fitting of the crack feature data in this paper. The regression results form a simple system to evaluate the life cycle of crossing nose. The system finds the location of cracks that can create dangerous defects in the crossing nose surface. The research result consists of the early prediction of rail contact fatigue.

Transportation Mode Selection for Organ Transplant Networks by a New Multi-Criteria Group Decision Model Under Interval-Valued Intuitionistic Fuzzy Uncertainty

This study introduces a new multi-criteria group decision-making model in organ transplant transportation networks under uncertain situations. A new combined weighting approach is presented to obtain expert weights with various kinds of opinions by integrating similarity measure and subjective judgments of experts. Also, the CRITIC approach is given to obtain transportation criteria weights. Finally, a novel integrated ranking approach is proposed to calculate the rank of each alternative based on ideal point solution and relative preference relation (RPR) methods. This study regards an interval-valued intuitionistic fuzzy set to cope with the vagueness of uncertain conditions in a real case study.

Optimizing Resource Allocation Based on Predictive Process Monitoring

Recent breakthroughs in predictive business process monitoring equip process analysts with predictions on running process instances, supporting the elicitation of proactive measures to mitigate risks that can be caused by the process instance. However, contrary to active research on providing various predictions and improving the accuracy of prediction models, the practical application of such predictions has been left to the subjective judgment of domain experts. In this work, we explore the exploitation of the insights from predictive information for the actual process improvement in practice. Concretely, we focus on improving resource allocation in business processes where the goal is to allocate appropriate resources to tasks at the proper time. Based on design science methodology, we develop a two-phase method to improve resource allocation by leveraging predictions. Based on the method, we instantiate an algorithm to optimize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">total-weighted completion time</i> and evaluate its effectiveness and efficiency. From an academic standpoint, our work demonstrates the combination of predictions using machine learning and optimizations based on scheduling. From a practical standpoint, our work provides a general approach to optimize resource allocations for different objectives using predictions.

Modeling the Quantitative Assessment of the Condition of Bridge Components Made of Reinforced Concrete Using ANN

Bridges in Ukraine are one of the most important components of the infrastructure, requiring attention from government agencies and constant funding. The object of the study was the methodology for quantifying the condition of bridge components. The Artificial Neural Network-based (ANN) tool was developed to quantify the technical condition of bridge components. The literature analysis showed that in most cases the datasets were obtained during the inspection of bridges to solve the problems of assessing the current technical condition. The lack of such a database prompted the creation of a dataset on the basis of the Classification Tables of the Operating Conditions of the Bridge Components (CT). Based on CTs, five datasets were formed to assess the condition of the bridge components: bridge span, bridge deck, pier caps beam, piers and abutments, approaches. The next step of this study was creating, training, validating and testing ANN models. The network with ADAM loss function and softmax activation showed the best results. The optimal values of MAPE and R2 were achieved at the 100th epoch with 64 neurons in the hidden layer and were equal to 0.1% and 0.99998, respectively. The practical application of the ANN models was carried out on the most common type of bridge in Ukraine, namely, a road beam bridge of small length, made of precast concrete. The novelty of this study consists of the development of a tool based on the use of ANN model, and the proposal to modify the methodology for quantifying the condition of bridge components. This will allow minimizing the uncertainties associated with the subjective judgments of experts, as well as increasing the accuracy of the assessment.

Risk assessment of complex footbridge based on Dempster–Shafer evidence theory using Fuzzy matter–element method

Owing to the landscapes of cities and the requirements of subchannels, the construction of large-span footbridges is complex and extremely risky. These risks may lead to the collapse of a footbridge during construction, resulting in casualties and other catastrophic consequences, delays in projects and significant property losses, and potential safety hazards during follow-up maintenance. At present, China’s urban footbridge construction has not been included in the overall urban construction category, and its risk assessment is static and temporary, mainly relying on the subjective judgment of experts and construction personnel, and the different cases are difficult to learn from each other. To address this issue, a new risk assessment method and a novel system quality management framework are proposed herein. In this system, real-time engineering quality data under 4M1E (Man, Method, Material, Machine, Environment) framework is used to replace traditional risk factors. An improved Dempster–Shafer theory is adopted to effectively combine heterogeneous data from the 4M1E framework and calculate the dynamic total risk index. Finally, the proposed method is verified. The research results show that the new system can predict the risk level of each stage of bridge construction objectively and effectively by using real-time monitoring data, and has high robustness in different project testing.

A hybrid multi-criteria decision-making framework for offshore wind turbine selection: A case study in China

The proper selection of offshore wind turbines is crucial for the long-term development of offshore wind farms. It also contributes to achieving the long-term sustainability goals of offshore wind power. To select the optimal offshore wind turbine with massive difficulties lying in diversity of evaluation criteria, uncertainties in the decision environment and different risk preferences of decision makers, a hybrid multi-criteria decision-making (MCDM) framework is proposed. This framework integrates the interval 2-tuple linguistic (I2TL) model, power weighted average (PWA) operator, stepwise weighted assessment ratio analysis II (SWARA II), method based on the removal effects of criteria (MEREC), cumulative prospect theory (CPT) and combined compromise solution (CoCoSo) method. First, a more realistic and holistic evaluation criteria system is developed for wind turbines based on a two-phase procedure. Then, the I2TL variables are employed to transform the subjective judgments of experts into quantitative evaluation information, and the individual evaluations are fused by the similarity measure-based PWA operator. After that, a new integrated weighing method combined with SWARA II and MEREC is adopted to acquire the criterion weights. Next, the CoCoSo method based on CPT is applied to rank alternatives. An actual case study in China is conducted to demonstrate the applicability of the proposed framework. Finally, the robustness and reliability of the framework are validated through sensitivity and comparison analysis. The hybrid MCDM framework can provide a beneficial reference for decision makers to analyze and select the optimal wind turbine.