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The purpose of this study is to construct a gray relational model based on information diffusion to avoid rank reversal when the available decision information is insufficient, or the decision objects vary.

Considering that the sample dependence of the ideal sequence selection in gray relational decision-making is based on case sampling, which causes the phenomenon of rank reversal, this study designs an ideal point diffusion method based on the development trend and distribution skewness of the sample information. In this method, a gray relational model for sample classification is constructed using a virtual-ideal sequence. Subsequently, an optimization model is established to obtain the criteria weights and classification radius values that minimize the deviation between the comprehensive relational degree of the classification object and the critical value.

The rank-reversal problem in gray relational models could drive decision-makers away from using this method. The results of this study demonstrate that the proposed gray relational model based on information diffusion and virtual-ideal sequencing can effectively avoid rank reversal. The method is applied to classify 31 brownfield redevelopment projects based on available interval gray information. The case analysis verifies the rationality and feasibility of the model.

This study proposes a robust method for ideal point choice when the decision information is limited or dynamic. This method can reduce the influence of ideal sequence changes in gray relational models on decision-making results considerably better than other approaches.

The purpose of the paper is to improve the rating prediction accuracy in recommender systems (RSs) by metric learning (ML) method. The similarity metric of user and item is calculated with gray relational analysis.

First, the potential features of users and items are captured by exploiting ML, such that the rating prediction can be performed. In metric space, the user and item positions can be learned by training their embedding vectors. Second, instead of the traditional distance measurements, the gray relational analysis is employed in the evaluation of the position similarity between user and item, because the latter can reduce the impact of data sparsity and further explore the rating data correlation. On the basis of the above improvements, a new rating prediction algorithm is proposed. Experiments are implemented to validate the effectiveness of the algorithm.

The novel algorithm is evaluated by the extensive experiments on two real-world datasets. Experimental results demonstrate that the proposed model achieves remarkable performance on the rating prediction task.

The rating prediction algorithm is adopted to predict the users' preference, and then, it provides personalized recommendations for users. In fact, this method can expand to the field of classification and provide potentials for this domain.

The algorithm can uncover the finer grained preference by ML. Furthermore, the similarity can be measured using gray relational analysis, which can mitigate the limitation of data sparsity.

The following research seeks to focus on optimizing the fused deposition modeling (FDM) process of RP systems.

Early stages of this study are based on the Taguchi method in establishing rapid prototyping building factors and their various levels. The ultimate tensile strength, dimension accuracy and surface roughness (SR), are analyzed. Through analysis of variance (ANOVA) and contribution approximation, significant building factors of each quality characteristic and optimal factors level combinations of each best quality characteristic are obtained. The main steps are setting the weight for each quality characteristic of the previous Taguchi method, obtaining the estimated multiple building quality characteristics through integrating the Gray theory, and obtaining a set of optimal building factors. Finally, the result is verified by the Gray theory and the technique for order preference by similarity to ideal solution (TOPSIS) evaluation method.

It is proven that optimal multiple quality characteristic combinations of building factors can be obtained by integrating the Gray theory and the Taguchi method. The result is further verified by the TOPSIS evaluation method, showing that the model can acquire multiple building quality characteristics of rapid prototyping.

The method is only applied to FDM in this paper but a similar approach could be applied to other RP systems.

RP system use is limited by low product strength, bad SR, and the high dimension errors. This research demonstrates how optimizing the FDM process can improve this situation.

The originality of this paper lies in optimizing the rapid prototyping process by integrating the Taguchi method with the Gray relational analysis.

The purpose of this paper is to develop a system to analyse the characteristics of infrared objects.

According to the gray scale of image pixel by the image entropy, gray scale estimating is carries on to construct the neural networks. And then the grey relational analysis and grey clustering methods are applied to filter the possible object. The target is predicted through image segmentation pretreatment based on the forecasting value by grey system and assigned corresponding mark. The forecasting precision is greatly elevated by GM (1, 1) model.

The paper illustrates that, based on the analysis and its experimental results, this system has a good recognition rate for infrared target.

This paper provides a way to grasp the minutial feature of the image. The filtering operation based on pixel level provided auto‐adapted filtering with a new stage.

Applications of grey theory deepened the content of detecting infrared targets and enriched technology of image processing.

This system introduces an effective method for detecting infrared targets.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

Nowadays, growth is the common target of all societies. But rather than growth, it is more important to ensure the sustainability of growth. Worldwide climate changes, damages to natural capital and financial crises necessitate the transition to green growth. The purpose of this paper is to examine the contribution of socio-economic context to green growth, which represents the sum of environmental and resource productivity, natural asset base, the environmental dimension of quality of life and technology.

The paper uses grey relational analysis together with the entropy method to examine the weight of 22 green growth indicators. The green growth indicators based on the compilation of the data from 36 Organization for Economic Co-operation and Development countries in 2015.

The results point out carbon dioxide emissions and environment-related technology are the most essential indicators in achieving green growth across the world.

It provides an objective evaluation of the green growth indicators that creates awareness-raising in green growth, enables the measurement of global developments and the determines opportunities and risks.

The purpose of this paper is to establish a more reasonable evaluation system and model for the development level of rural tourism, and provides a method for quantifying the development level of regional rural tourism.

This paper provides a method for evaluating the development level of rural tourism, constructs an evaluation index system according to the connotation of rural tourism, then calculates the index weight by entropy method, and makes a comprehensive evaluation by grey relational analysis. Taking the development of rural tourism in 11 cities in Jiangxi Province as the research object, the ranking results of 11 cities were obtained by using grey relational analysis.

The overall development level of rural tourism in Jiangxi Province is positive, but the spatial distribution is uneven, showing the characteristics of “low-level aggregation and high-level dispersion”. The barrier model diagnoses that the degree of financial input is the biggest constraint to the development level of rural tourism in Jiangxi Province.

This study puts forward an evaluation model based on entropy weight and grey relational analysis, which is an important supplement to the grey relational analysis method system and has a positive role in promoting the quantitative evaluation of regional rural tourism level.

The purpose of this paper is to establish a new evaluation system to assess the degree of integration between industry and the internet. And use the gray correlation matrix method to evaluate the “internet + industry” integration degree of China’s provinces.

This paper establishes a new evaluation system to assess the degree of integration between industry and the internet using the matrix gray relational analysis method.

The main indexes and its rankings of the provinces’ integration degree and the rankings of the provinces’ integration degree are obtained.

The ranking of the degree of integration of various provinces in the country has certain guiding significance in promoting the development of “internet +” and “industry 4.0.”

Establishing a new model for the quantitative assessment of the degree of fusion, this method has a positive impact on the quantitative assessment of “internet + industrial” integration.

The purpose of this paper is to synthesize the review of the existing literature attached to the grey economic system theory and applications and aims to offer a comprehensive picture of the contribution brought by the researchers to this particular field. Also, the paper underlines the main research areas within the grey economic theory and applications and serves as an informative summary kit for future research works and research directions.

For appreciating the scientific progress made since the grey systems theory has been initiated to the present, with an accent on the literature dedicated to the economic field, a bibliometrics analysis has been conducted. The Perish or Publish software was used for extracting the needed data from Google Scholar for the entire period since the appearance of grey systems to now-a-days. In addition, an ISI Web of Science (WoS) search has been performed for extracting the grey economic papers. As the main focus is on the economic subject area of the grey systems, only the papers related to this field have been selected.

The total number of grey economic paper from both Google Scholar and ISI WoS database, the number of authors, some citation metrics, H-index, authors’ provenience country, papers’ language, etc., have been presented and analysed. Also, a list with the most cited papers in the grey economic relational analysis, grey economic prediction models and grey economic incidence is putted forward.

Through the bibliometric analysis on grey economic papers written over time, a qualitative analysis was performed on this field in order to underline the main research direction, to analyse what has been done in this field and to determine which can be the next research directions that can emerge from here.

The paper succeeds in enlarging the view regarding the usage of grey systems theory in the economic field, offering a suitable analysis on the considered areas. Even though bibliometrics analysis have been conducted on the grey systems theory field, a grey economic bibliometric analysis has not been done yet, to the authors’ knowledge. Therefore, a synthesized of the existing literature attached to the grey economic system theory and applications is presented in order to offer a more comprehensive picture of the contribution brought by the researchers to this particular field.

Introduction: Decision making is always based on several factors which may affect the possible outcomes, especially in financial markets. Instead of having many criteria which may be required for decision making, “Multiple Criteria Decision Making” (MCDM) models might be used as a tool to reduce all criteria into a single one.

Purpose: The aim of this study is to measure the financial performance of commercial banks listed on Borsa Istanbul (BIST) by the MCDM.

Method: To this end, data from 15 different financial ratios from 11 commercial banks were used between the periods of 2002 and 2018. Both TOPSIS and gray relational analysis (GRA) models were used, which are commonly used in the literature for detecting the financial performance of listed banks in BIST based on their consolidated financial statements.

Results: According to the TOPSIS method, while the best bank is QNB Finansbank, HALKB, a public bank, was determined as the best bank using the GRA method. There is no significant correlation between financial performance indicators and market returns obtained by either method, with exceptions. There is no generally significant correlation detected between financial ratios and market returns. Accordingly, it is concluded that the bank stock prices in the study are shaped by the influence of external factors and expectations. The study results include information that can be used for different purposes among bank managers, academics and financial investors.