Weighted Correlation Coefficient Research Articles

SITW Method: A New Approach to Re-identifying Multi-criteria Weights in Complex Decision Analysis

Multi-Criteria Decision Analysis (MCDA) addresses complex decision-making problems across various fields such as logistics, management, medicine, and sustainability. MCDA tools provide a structured approach to evaluating decisions with multiple conflicting criteria, assisting decision-makers in navigating intricate scenarios. Engaging experts is crucial for identifying multi-criteria models due to the diverse aspects of decision-making problems. Techniques such as pairwise comparisons and criterion weight assignment are commonly used to incorporate expert knowledge into decision models. Criterion weight assignment allows experts to indicate the importance of each criterion; however, issues can arise if model parameters are lost or experts become unavailable. To mitigate these issues, techniques like entropy or standard deviation can determine weights without direct expert input. In this context, the Stochastic Identification of Weights (SITW) method utilizes existing assessment samples to re-identify models and obtain weights that replicate the rankings of a reference model. This study compares information-based methods (Entropy, STD) with the SITW method in re-identifying the TRI medical function as a benchmark. The effectiveness of these methods is evaluated using Spearman's weighted correlation coefficient across various scenarios and alternative numbers. Results indicate that the SITW method provides more significant results than other methods in identifying multi-criteria weights by leveraging previously evaluated alternatives. Future research could explore broader approaches and uncertainty scenarios to ensure comprehensive decision support in complex contexts.

Developing an intuitionistic fuzzy rough new correlation coefficient approach for enhancing robotic vacuum cleaner.

An intuitionistic fuzzy rough model is a powerful tool for dealing with complex uncertainty and imprecision in graph-based models, combining the strengths of intuitionistic fuzzy sets and rough sets. In this research, a correlation coefficient is an established tool for finding the strength of the relationship between two intuitionistic fuzzy rough graphs since correlation coefficients are very capable of processing and interpreting data. Furthermore, an intuitionistic fuzzy rough environment is integrated with attribute decision-making based on correlation coefficients. In order to measure the correlation between two intuitionistic fuzzy rough graphs, this suggests utilising the correlation coefficient concept and weighted correlation coefficient. In order to identify decision-making issues that are supported by intuitionistic fuzzy rough preference relations, the Laplacian energy and new correlation coefficient of intuitionistic fuzzy rough graphs are calculated in this study. We propose a new approach to computing the relative position loads of establishments by adjusting the correlation coefficient between one personality's intuitionistic fuzzy rough preference relation and the other items, as well as the undecided corroboration of the intuitionistic fuzzy rough preference relation. This paper determines the ranking order of all alternatives and the best one by using the correlation coefficient between each option and the ideal choice. In the meantime, the appropriate example improves decision-making for robotic vacuum cleaners by effectively handling uncertain and imprecise data, thereby optimising cleaning performance.

A CMIP6-ensemble-based evaluation of precipitation and temperature projections

Understanding climate change’s effects on dam basins is very important for water resource management because of their important role in providing essential functions such as water storage, irrigation, and energy production. This study aims to investigate the impact of climate change on temperature and precipitation variables in the Altınkaya Dam Basin, which holds significant potential for hydroelectric power generation in Türkiye. These potential impacts were investigated by using ERA5 reanalysis data, six GCMs from the current CMIP6 archive, and two Shared Socioeconomic Pathways (SSP2 − 4.5 and SSP5 − 8.5) scenario data. Four Multi-Model Ensemble (MME) models were developed by using an Artificial Neural Network (ANN) approach (ENS1), simple averaging (ENS2), weighted correlation coefficients (ENS3), and the MARS algorithm (ENS4), and the results were compared to each other. Moreover, quantile delta mapping (QDM) bias correction was used. The 35-year period (1980–2014) was chosen as the reference period, and further evaluations were conducted by dividing it into three future periods (near (2025–2054), mid-far (2055–2084), and far (2085–2100)). Considering the results achieved from the MMEs, variations are expected in the monthly, seasonal, and annual assessments. Projections until the year 2100 indicate that under optimistic and pessimistic scenarios, temperature increases could reach up to 3.11 °C and 5.64 °C, respectively, while precipitation could decrease by as much as 19% and 43%, respectively. These results suggest that the potential changes in temperature and precipitation within the dam basin could significantly impact critical elements such as future water flow and energy production.

Supplier selection in green supply chain management using correlation-based TOPSIS in a q-rung orthopair fuzzy soft environment

Fuzzy hybrid models are efficient mathematical tools for managing unclear and vague data in real-world scenarios. This research explores the q-rung orthopair fuzzy soft set (q-ROFSS), which presents incomplete and ambiguous details in decision-making problems. The main intention of this study is to describe and evaluate the characteristics of the correlation coefficient (CC) and weighted correlation coefficient (WCC) for q-ROFSS. Also, the technique for order preference should be enhanced by similarity to the ideal solution (TOPSIS) with extended measures in q-ROFSS settings. Furthermore, we integrated mathematical formulations of correlation obstructions to confirm the consistency of the planned technique. It helps handle difficulties involving multi-attribute group decision-making (MAGDM). Moreover, a numerical illustration is presented to clarify how the advocated decision-making methodology can be implemented in evaluating suppliers in green supply chain management (GSCM). As a result, each alternative is assessed using multiple criteria, such as quality and reliability, capacity and scalability, compliance and certifications, and sustainability practices. The technique proposed in this study retains the selected research's specific structure more effectively than current techniques. A comparative analysis further substantiates the feasibility and effectiveness of the proposed approach over other decision-making techniques.

Multiattribute group decision-making based on weighted correlation coefficient of linguistic q-rung orthopair fuzzy sets and TOPSIS method

Multiattribute group decision-making based on weighted correlation coefficient of linguistic q-rung orthopair fuzzy sets and TOPSIS method

Cost Monitoring and Management Algorithms in the Construction Process of Building Projects

Construction project cost estimation is an essential activity in construction-based fields; it offers a valuable source for the project feasibility studies and the design plan comparisons, and the accuracy can directly influence project investment decisions. A successful implementation of project cost forecasts makes it easier to control and manage project costs. Therefore, this paper presents the Ladybug Reptile Search Algorithm_ Deep Maxout Network (LRSA_DMN) (LRSA_DMN) to estimate construction costs. Additionally, LRSA is included to correct DMN weights. Furthermore, Bootstrap scheme is used to augment data and use this data to input the DMN for cost estimation. In addition, features are selected using a Weighted Correlation Coefficient to choose the best features for further processing. The test results show that LRSA_DMN performs better regarding MSE, RMSE, and Accuracy by 0.092, 0.371, and 0.907, respectively

A fair bed allocation during COVID-19 pandemic using TOPSIS technique based on correlation coefficient for interval-valued pythagorean fuzzy hypersoft set

The relationship between two variables is an essential factor in statistics, and the accuracy of the results depends on the data collected. However, the data collected for statistical analysis can be unclear and difficult to interpret. One way to predict how one variable will change about another is by using the correlation coefficient (CC), but this method is not commonly used in interval-valued Pythagorean fuzzy hypersoft set (IVPFHSS). The IVPFHSS is a more advanced and generalized form of the Pythagorean fuzzy hypersoft set (PFHSS), which allows for more precise and accurate analysis. In this research, we introduce the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFHSS and their essential properties. To demonstrate the applicability of these measures, we use the COVID-19 pandemic as an example and establish a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The technique is used to study the problem of optimizing the allocation of hospital beds during the pandemic. This study provides insights into the importance of utilizing correlation measures for decision-making in uncertain and complex situations like the COVID-19 pandemic. It is a robust multi-attribute decision-making (MADM) methodology with significant importance. Subsequently, it is planned to increase a dynamic bed allocation algorithm based on biogeography to accomplish the superlative decision-making system. Moreover, numerical investigations deliberate the best decision structures and deliver sensitivity analyses. The efficiency of our encouraged algorithm is more consistent than prevalent models, and it can effectively control and determine the optimal configurations for the study.

Comparison of Results from Two Multi-Criteria Decision-Making Methods for Solar Photovoltaic Plant Site Location: Case Study Rio De Janeiro

Photovoltaic (PV) energy has become a low-cost, renewable, and environmentally friendly alternative to meet increasing energy demand. Nevertheless, there is still a lack of projects in this field in Brazil. Therefore, this study compares the results of two studies on the optimal site selection of PV in the Brazilian state of Rio de Janeiro. These studies used different methodologies to reach the conclusions and the resulting map. First, the final map of both studies was divided into a grid, and then the results of each cell were weighted for PV site selection. To compare the results using the maps, an intersection of the 10% of the grid cells with the best results from each study was formed. The results showed an 83% similarity between the different Multi-Criteria Decision-Making (MCDM) methods. The other part of the comparison focused on the following rank similarity coefficients: Spearman Correlation Coefficient, WS Coefficient, Spearman Weighted Correlation Coefficient, and Blest Correlation Coefficient. All these coefficients had values greater than 0.9, indicating a high degree of correlation between the results of the studies. Therefore, the two studies have a high degree of similarity and a high potential for installing photovoltaic solar power plants in Rio de Janeiro, especially in its intersection zones.

Prognostic impact of MRI-derived feature tracking myocardial strain in patients with non-ischaemic dilated cardiomyopathy: a systematic review and meta-analysis

To evaluate the clinical utility of feature tracking (FT)-derived myocardial strain in patients with non-ischaemic dilated cardiomyopathy (NIDCM). Electronic database searches of PubMed, Web of Science Core Collection, Cochrane advanced search, and EMBASE were performed. Studies on NIDCM were divided into categories according to left ventricular ejection fraction (LVEF; <30%, 30-40%, >40%), and correlations between strains and prevalence of late gadolinium enhancement (LGE) were evaluated by weighted correlation coefficients. Global longitudinal strain (GLS) hazard ratios were also integrated for prediction of future adverse events. The present meta-analysis analysed data from 5,767 patients with NIDCM from 30 eligible studies. GLS and global circumferential strain significantly differed across the three LVEF categories (all p<0.05); however, global radial strain did not. Only GLS showed a strong correlation with the prevalence of LGE (Spearman's correlation coefficient=0.61). The pooled HR of GLS for predicting adverse events was 1.15 (95% confidence interval [CI]: 1.07-1.23, p<0.001). In this meta-analysis, FT-derived GLS was strongly correlated with myocardial fibrosis and was an important predictor of future adverse events. These results suggest that FT-derived GLS may be useful in the pathological evaluation and risk stratification of NIDCM.

Research on Vegetable Pricing and Replenishment Strategy Based on TOPSIS Method and Simulated Annealing Algorithm

By mining the relationship between categories and individual products, this paper puts forward enterprise replenishment and pricing strategies under different conditions. First of all, from the 49 kinds of vegetables sold, the TOPSIS method based on entropy weight is used to select the top 20 important items by taking the market demand and excess income as the first-level index, and taking the historical sales rate, average daily sales volume, variance of sales volume, vegetable loss rate and unit profit as the second-level index. Then, based on the concept of complementary and alternative goods, through the Spearman correlation coefficient, and using the weighted correlation coefficient method, select the top 9 items, such as ginger, garlic, millet pepper combination and so on. Using these selected items, taking the profit maximization of fresh merchants as the objective function and the demand of each category as constraints, a 0-1 integer nonlinear programming model is established. Then, the simulated annealing algorithm is used to simulate the planning for 1000000 times, and the pricing strategy and supply plan of the enterprise on July 1st are obtained, and the maximum profit on that day is 2098.17 yuan.

An application of fuzzy bipolar weighted correlation coefficient in decision-making problem.

Diagnosing and finding the disease in medical sciences is a complex procedure. The basic steps involved in finding starts with signs, symptoms, and test. This study is based on the diagnosis of a skin disorder. The identification of a disease has been made on the basis of symptoms that sometimes show bipolarity. To address this bipolarity, the bipolar fuzzy sets are used as bipolar fuzzy sets cover the positive as well as negative aspects of a specific symptom. It is combined with the idea of soft sets, which gives more precise results. We have proposed a new technique in which a correlation coefficient is used to measure bipolar fuzzy soft set, which has been applied for diagnosis. The BFSSs deal most effectively with dual and fuzzy information. The correlation coefficient and the weighted correlation coefficient of BFSSs are suggested in this research. Based on said techniques, the decision-making method is suggested under a bipolar fuzzy environment to resolve ambiguous and unclear information. The implementation and effectiveness of the proposed and existing strategy has been checked by numerical computation.

Joint data-driven and physics-driven prestack amplitude-variation-with-angle elastic parameter inversion

Elastic parameters such as P-wave and S-wave velocity and density are of great significance for subsurface quantitative interpretation and reservoir prediction. Current prestack amplitude-variation-with-angle (AVA) inversion methods have been widely used in the industry to obtain subsurface elastic parameters. Conventional AVA inversion methods are theoretically based on a linearized amplitude-variation-with-offset model that formulates the relationship between prestack seismic reflection coefficients and the subsurface model of elastic parameters, which is called physical model-driven inversion. However, due to the linearized physical model, it is difficult to obtain high-accuracy and high-resolution inversion results using model-driven inversion when the incident angles are greater than 30°. In recent years, several neural network-based prestack AVA inversion methods, known as data-driven inversion, have been developed to address this issue. However, these methods typically require a large amount of labeled data for training networks, and there is no physical mechanism in the network training process. To solve this problem, a joint data-driven and physics-driven inversion of prestack AVA elastic parameters is proposed. Under the framework of semisupervised learning, a 2D convolutional neural network and a recurrent neural network are used to establish the mapping between several adjacent prestack AVA gathers and 1D elastic parameters in the time domain. The full Zoeppritz equation suitable for large incidence angles is used as a physical constraint for training the neural network. The loss functions of the network are constructed by combing well-log data and prestack AVA seismic data. This approach enables us to use fewer labeled data for training the network and increases the physical interpretability of the training process. To further improve the inversion results, the inverse distance weighted correlation coefficient of seismic data is applied to the loss function of seismic data and well-log data. Synthetic and field data examples demonstrate that the joint data-driven and physics-driven prestack AVA elastic parameter inversion improves accuracy and resolution compared with conventional model-driven inversion.

Optimal cloud service provider selection: An MADM framework on correlation-based TOPSIS with interval-valued q-rung orthopair fuzzy soft set

The interaction between two factors significantly measures the results' exactness. But, acquiring information for statistical computation is frequent, and the data obtained could be challenging to interpret. To predict how a particular factor will fluctuate regarding another as well, the correlation coefficient (CC) is usually employed. But this approach is rarely utilized for interval-valued q-rung orthopair fuzzy soft set (IVq-ROFSS). The situation in which IVq-ROFSS grows as it is modern, along with a broad depiction of the q-rung orthopair fuzzy soft set (q-ROFSS), engaging for a more reflective and precise assessment. The current study explores the CC and weighted correlation coefficient (WCC) for IVq-ROFSS and their fundamental characteristics. This research is designed to improve the prioritization technique for order preference by similarity to the ideal solution (TOPSIS) with expanded measures. Also, to check the linearity of the intended approach, we integrated mathematical formulations of correlation constrictions. This study demonstrates that the suggested methodology is a robust multi-attribute decision-making (MADM) tool for intricate information set interpretation and prioritizing. We presented a numerical illustration demonstrating the actual application of our recommended decision-making strategy for choosing Cloud Service Providers (CSPs) in cloud service management. The approach developed in this research is superior to conventional models in maintaining the precise structure of the determined studies. Thus, the algorithm produces more reliable and consistent decisions. The influence of our studies grows within the scope of this research, as the originated algorithm can enhance the ability to analyze realities and make informed decisions in light of the information provided. Therefore, this study can significantly impact data analysis and decision-making by revealing the importance of the proposed TOPSIS approach and the vitality of perpetual development in methods for making decisions to get more reliable and precise outcomes.

Extension of correlation coefficient based TOPSIS technique for interval-valued Pythagorean fuzzy soft set: A case study in extract, transform, and load techniques.

Correlation is an essential statistical concept for analyzing two dissimilar variables' relationships. Although the correlation coefficient is a well-known indicator, it has not been applied to interval-valued Pythagorean fuzzy soft sets (IVPFSS) data. IVPFSS is a generalized form of interval-valued intuitionistic fuzzy soft sets and a refined extension of Pythagorean fuzzy soft sets. In this study, we propose the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFSS and examine their necessary properties. Based on the proposed correlation measures, we develop a prioritization technique for order preference by similarity to the ideal solution (TOPSIS). We use the Extract, Transform, and Load (ETL) software selection as an example to demonstrate the application of these measures and construct a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The method investigates the challenge of optimizing ETL software selection for business intelligence (BI). This study offers to illuminate the significance of using correlation measures to make decisions in uncertain and complex settings. The multi-attribute decision-making (MADM) approach is a powerful instrument with many applications. This expansion is predicted to conclude in a more reliable decision-making structure. Using a sensitivity analysis, we contributed empirical studies to determine the most significant decision processes. The proposed algorithm's productivity is more consistent than prevalent models in controlling the adequate conformations of the anticipated study. Therefore, this research is expected to contribute significantly to statistics and decision-making.

Dual hesitant fuzzy Correlation coefficient-based decision-making algorithm and its applications to Engineering Cost Management problems

Engineering management plays a significant part in project construction, and it is a process involved with making decision under uncertainty. When decision makers are hesitant to assess investment projects, fuzzy set theory offers an effective tool for dealing with uncertainty. In this paper, a decision-making method is put forward for handling engineering management problems, where dual hesitant fuzzy set is adopted to model the uncertainty. Firstly, we raise an improved dual hesitant fuzzy correlation coefficient and its weighted form. Compared with the current dual hesitant fuzzy correlation coefficients, the presented one has more desirable properties. It not only can manifest the positive or negative correlation between dual hesitant fuzzy sets, but also can guarantee the dual hesitant fuzzy correlation coefficient is 1 (−1) if and only if the two dual hesitant fuzzy sets are identical (complementary). Secondly, a mathematical programming model, which integrates within-attribute and between-attribute variation information, is designed to derive the attribute weights. More importantly, it can fully utilize the decision information. Finally, the weighted correlation coefficients are calculated, and an illustrative example concerning engineering management is offered to demonstrate the rationality of the proposed technique.

A new correlation-based measure on Fermatean fuzzy applied on multi-criteria decision making for electric vehicle selection

Purpose:Since the pollution from the transportation sector has become intolerable in urban areas, many authorities are beginning to ban vehicles powered by fossil fuels. In this scenario, electric vehicle (EV) mobility is the only viable option for the transportation sector. Despite the fact that EVs offer a number of advantages over fuel-powered vehicles, it can be challenging for a purchaser to select a model due to its technical features and their lack of knowledge about EVs. This article aims to assist purchasers of EV by constructing an MCDM problem with multiple features as criteria and EV models as alternatives. Design/methodology/approach:The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method ranks alternatives to a multi-criteria decision-making (MCDM) problem based on the distance between the positive and negative ideal solutions. Even though the TOPSIS methodology has several fuzzy enhancements, it is difficult to select an appropriate distance measure in an uncertain context. To replace the distance measure, we introduced an improved correlation coefficient measure in fermatean fuzzy contexts by considering the hesitancy function of fermatean fuzzy sets (FFS). First, we establish the proposed correlation coefficient by defining its main characteristics, including the weighted correlation coefficient, type I and type II closeness measures, and the weighted index coefficient. Then, the TOPSIS method is extended using the proposed correlation measure in a fermatean fuzzy environment. There is an algorithm that presents the proposed fermatean fuzzy TOPSIS (FF-TOPSIS) method. The proposed approach is used to determine the ranking order of the EVs. To assist the decision-maker, the sensitivity of the proposed technique is investigated by varying its parameters. Finally, the consistency of the proposed FF-TOPSIS is evaluated by comparing it to existing crisp as well as fuzzy MCDM methods. Findings:By implementing the FF-TOPSIS approach, it has been determined that the fifth EV model is the most favorable option, while the first model is deemed the least desirable choice. The primary factors to evaluate an EV are its range and its price, rather than battery life, storage, and charging time. Originality:This article presents a novel correlation coefficient for FFSs, which incorporates the hesitancy function into the calculation for the first time. The correlation-based FF-TOPSIS method is a novel extension of the TOPSIS technique in fermatean fuzzy systems.

Smart IoT Application in Soil Moisture and Heat Level Prediction Using Sine Cosine-Horse Herd Optimized Deep Learning

Agriculture assisted by Internet of Things (IoT) is termed as smart agriculture, which offers an increase in precision farming. Soil monitoring with IoT technology helps in the increase of agriculture by growing the yield through measuring accurate soil content information, like temperature, nutrition content, humidity, potential of hydrogen (PH), moisture and so on. In this research, the soil moisture and heat level is measured through an optimized deep learning technique namely, Sine Cosine Horse Herd optimization-based Deep Recurrent Neural Network (SCHHO-based Deep RNN). Here, the moisture and heat level is predicted using Deep RNN in which its weights are trained using SCHHO. In order to progress the effectiveness of prediction, the feature selection is done prior to prediction for choosing the appropriate features using weighted correlation coefficient. In addition, the gathered soil information is transmitted to the IoT nodes using SCHHO routing algorithm by considering fitness measures. Besides, the experimental outcome proves that the SCHHO-based Deep RNN algorithm provides better performance with the accuracy and precision of 0.918 and 0.908, respectively.

Weighted correlation coefficient measure for intuitionistic fuzzy set based on cosine entropy measure

Weighted correlation coefficient measure for intuitionistic fuzzy set based on cosine entropy measure

Data analysis for panoramic X-ray selection: Fermatean fuzzy type correlation coefficients approach

An interval-valued Fermatean hesitant fuzzy set (IVFHFS) not only can be regarded as the union of some interval-valued Fermatean fuzzy sets (IVFFSs) but also represent the Fermatean hesitant fuzzy elements (FHFEs) in the form of interval values. So IVFHFSs are extensions of FHFSs and IVFFSs, which are powerful tools to represent more complicated, uncertain, and vague information. This paper focuses on the four kinds of correlation coefficients for FHFSs and extends them to the correlation coefficients and the weighted correlation coefficients for IVFHFSs. In the processing, we develop the least common multiple expansion (LCME) methods to solve the problem that the cardinalities of Fermatean hesitant fuzzy elements (FHFEs) (or interval-valued Fermatean hesitant fuzzy elements (IVPHFEs)) are different. In addition, we propose score functions and accuracy functions of FFEs (or IVFFEs) to rank all the FFEs (or IVFFEs) in an FHFE (or an IVFHFE). Especially, score functions and accuracy functions of IVFFEs are both presented as interval numbers. Then use the comparison method of interval numbers to compare two revised IVFHFEs in order to keep the original fuzzy information as far as possible. What is more, we define the local correlations and local informational energies which can depict the similarity between two IVFHFEs more meticulously and completely. At last, the numerical examples show the feasibility and applicability of the proposed methods in multiple criteria decision-making (MCDM) and clustering analysis.

An improved collaborative filtering model based on time weighted correlation coefficient and inter-cluster separation

An improved collaborative filtering model based on time weighted correlation coefficient and inter-cluster separation