Rough Set Research Articles

Impact of Carbon Emission Factors on Economic Agents Based on the Decision Modeling in Complex Systems

This article presents a methodology for modeling the impact of both internal and external environmental carbon emission factors on the resulting indicators of an international company. This research uses picture fuzzy rough sets to model the impact of factors on the resulting indicators as a research method. The proposed model is based on a dataset that includes the company’s profit, revenue, valuation, share price, and market share from 2012 through 2022. This empirical period is optimal for such a type of modeling. An approach of picture fuzzy rough sets based on the time series of endogenous and exogenous variables can provide an opportunity to analyze and consider the consequences of feedback changes in the systems of which they are a part. The article proposes a valuable framework for understanding the complex relationship between carbon emissions, economic factors, and the performance of international companies. The researchers of this study recommend a discussion that attempts to gain a deeper understanding of the challenges and opportunities that lie ahead for international companies in the context of climate change and technological innovation.

L-Fuzzy Rough Proximity Spaces and Their Relationship with L-Fuzzy Rough Grills

In this paper, we study the concept of nearness between [Formula: see text]-fuzzy rough sets and the concepts of [Formula: see text]-fuzzy rough grills and [Formula: see text]-fuzzy rough [Formula: see text]-clusters on a Čech [Formula: see text]-fuzzy rough proximity space [Formula: see text]. Also, we investigate the relationship among [Formula: see text]-fuzzy rough grills, [Formula: see text]-fuzzy rough [Formula: see text]-clan and [Formula: see text]-fuzzy rough [Formula: see text]-clusters. Moreover, we introduce the [Formula: see text]-fuzzy rough LO-proximity spaces and study some of its basic properties. An adequate number of examples support the study.

Teaching Quality Analysis of Health Management Professionals in Colleges and Universities Based on Big Data Decision Tree Algorithm

In this study, we investigate whether the big data decision tree approach can be utilised to evaluate the effectiveness of teaching in higher education by health management professors. In light of the expanding need for high-quality healthcare education by the professionals, it is absolutely necessary to evaluate and enhance the teaching efficacy of healthcare management specialists. The application of big data analytics towards the creation of a method for the evaluation of instructors that is both objective and precise is the focus of this study. Every educational system may be broken down into its two primary components: teachers and pupils. The students are not only passive recipients of the instruction that their teachers provide. All of the desired instructional achievements must be driven by the effort of the students in order to fall within the category of "subjective initiative." Therefore, the first step in any educational activity should be encouraging pupils to build their own sense of initiative and intrinsic motivation to learn. This should be the case in any educational endeavour. If teachers have access to and make use of reliable employment data for statistical analysis, they will be able to have a more significant impact on the kids they teach. Based on a preliminary set of actual university employment data from 2012, this study compares the results of employing the C4.5 approach with a decision tree generating technique for the assessment of the teaching quality by the professionals. Both approaches were used to analyse the data. The findings demonstrate that a straightforward decision tree structure may be obtained using the decision tree approach that makes use of the multiscale rough set model. In addition, our approaches don't need to use overlapping data sets, and they're quite efficient from a computing standpoint. The relevance of this study lies in the fact that it sheds light on how the expertise and motives of teachers who are healthcare professionals to be precise, reflect on the learning of their students and their relevant field skills.

An attribute ranking method based on rough sets and interval-valued fuzzy sets

Feature importance is a complex issue in machine learning, as determining a superior attribute is vague, uncertain, and dependent on the model. This study introduces a rough-fuzzy hybrid (RAFAR) method that merges various techniques from rough set theory and fuzzy set theory to tackle uncertainty in attribute importance and ranking. RAFAR utilizes an interval-valued fuzzy matrix to depict preference between attribute pairs. This research focuses on constructing these matrices from datasets and identifying suitable rankings based on these matrices. The concept of interval-valued weight vectors is introduced to represent attribute importance, and their additive and multiplicative compatibility is examined. The properties of these consistency types and the efficient algorithms for solving related problems are discussed. These new theoretical findings are valuable for creating effective optimization models and algorithms within the RAFAR framework. Additionally, novel approaches for constructing pairwise comparison matrices and enhancing the scalability of RAFAR are suggested. The study also includes experimental results on benchmark datasets to demonstrate the accuracy of the proposed solutions.

Kernel multi-granularity double-quantitative rough set based on ensemble empirical mode decomposition: Application to stock price trends prediction

As financial markets grow increasingly complex and dynamic, accurately predicting stock price trends becomes crucial for investors and financial analysts. Effectively identifying and selecting the most predictive attributes has become a challenge in stock trends prediction. To address this problem, this study proposes a new attribute reduction model. A rough set theory model is built by simplifying the prediction process and combining it with the long short-term memory network (LSTM) to enhance the accuracy of stock trends prediction. Firstly, the Ensemble Empirical Mode Decomposition (EEMD) is utilized to decompose the stock price data into a multi-granularity information system. Secondly, due to the numerical characteristics of stock data, a kernel function is applied to construct binary relationships. Thirdly, recognizing the noise inherent in stock data, the double-quantitative rough set theory is utilized to improve fault tolerance during the construction of decision attributes' lower and upper approximations. Moreover, calculate the correlation between conditional and decision attributes, and retain highly correlated conditional attributes for prediction. The kernel multi-granularity double-quantitative rough set based on the EEMD (EEMD-KMGDQRS) model proposed identifies the key factors behind stock data. Finally, the efficacy of the proposed model is validated by selecting 356 stocks from diverse industries in the Shanghai and Shenzhen stock markets as experimental samples. The results show that the proposed model improves the generalization of attribute reduction results through a fault tolerance mechanism by combining kernel function with multi-granularity double-quantitative rough set, thereby enhancing the accuracy of stock trends prediction in subsequent LSTM prediction processes.

Rough Set Theory and its Applications in Data Mining

Rough Set Theory and its Applications in Data Mining

Assessing indoor positioning system: A q-spherical fuzzy rough TOPSIS analysis

This study investigates advanced data collection methodologies and their implications for understanding employee and customer behavior within specific locations. Employing a comprehensive multi-criteria decision-making framework, we evaluate various technologies based on four distinct criteria and four technological alternatives. To identify the most effective technological solution, we employ the q-spherical fuzzy rough TOPSIS method, integrating three key parameters: lower set approximation, upper set approximation, and parameter q (where q ≥ 1). Our novel approach combines the TOPSIS method with q-spherical fuzzy rough set theory, providing deeper insights into data-driven decision-making processes in corporate environments. By comparing our proposed framework with existing multi-criteria decision-making methodologies, we demonstrate its strength and competitiveness. This research contributes to enhancing decision-making capabilities in corporate settings and beyond.

The accurate diagnosis for COVID-19 variants using nearly initial-rough sets

The rapid evolution of rough-set theory has prompted the need for enhanced methodologies in medical diagnostics, particularly regarding COVID-19 variant detection. This study introduces refined mathematical techniques based on topological structures (called nearly initial-rough sets) derived directly from initial-rough sets. Four categories of rough-set methodologies are presented, demonstrating heightened accuracy through comprehensive comparisons against existing methods. By leveraging these techniques, a rule-based classification system for COVID-19 variants is established, achieving 100 % accuracy measures through rigorous testing against real-world and computer-generated data. The implications of these advancements in medical diagnosis hold promise for future research, offering accessible and precise tools for variant identification and prediction. Using a medical application as a case study, we demonstrate superiority through comparative analyses, aligning mathematical results with medical data and showcasing the potential for broader applications beyond experts in topology. Furthermore, the study outlines an algorithm simplifying implementation, particularly in MATLAB, and suggests future explorations in medical, economic, and diverse theoretical frameworks to enhance applicability.

Energy efficiency practices: A case study analysis of innovative business models in buildings

Over the last two decades, the European Union has significantly improved its legal framework for building energy efficiency. New mandatory standards have led governments to adopt incentive measures that, in turn, encourage the growth of innovative entrepreneurial practices. This study analyzes emerging business models that capitalize on energy efficiency in the building industry. Thirty-seven energy efficiency projects − either retrofit or new construction, supported exclusively by innovative business models featuring the presence of an individual contractor − in five Central and Western European countries are considered. Data is collected on property characteristics, business environment, and energy efficiency measures. Using the Rough Set approach, the analysis identifies core attributes that associate or differentiate the case studies. They include building ownership, energy-related services to be provided to the users, and the duration for which the contractor must be involved. Additionally, other attributes − such as the types of retrofit work, investment costs, access to monetary incentives, and expected payback period − allow us to identify the cases representing best practices for each innovative model. There remain open questions concerning where the boundary between different business models lies and long-term economic self-sustainability, regardless of the availability of incentives.

Utilizing sine trigonometric q-spherical fuzzy rough aggregation operators for group decision-making and their role in digital transformation

q-spherical fuzzy rough set (q-SFRS) is also one of the fundamental concepts for addressing more uncertainties in decision problems than the existing structures of fuzzy sets, and thus its implementation was more substantial. The well-known sine trigonometric function maintains the periodicity and symmetry of the origin in nature and thus satisfies the expectations of the experts over the multi-parameters. Taking this feature and the significance of the q-SFRSs into consideration, the main objective of the article is to describe some reliable sine trigonometric laws for SFSs. Associated with these laws, we develop new average and geometric aggregation operators to aggregate the q-spherical fuzzy rough numbers. Then, we presented a group decision-making strategy to address the multi-attribute group decision-making problem using the developed aggregation operators. To verify the value of the defined operators, a MAGDM strategy is provided along with applications for selecting a Cloud Service Provider and a Digital Transformation Vendor for digital transformation. Moreover, a comparative study is also performed to present the effectiveness of the developed approach.

Three-way decisions with dual hesitant fuzzy covering-based rough set and their applications in medical diagnosis

As an extension of partition, fuzzy β-covering can provide a more realistic and accurate description of incomplete information. In this paper, we mainly integrate the idea of fuzzy β-covering with dual hesitant fuzzy (DHF) information and construct some novel three-way decision (3WD) models with DHF covering-based rough set. Firstly, we propose the notions of DHF β-covering, DHF β-minimal description and DHF β-maximal description, and then construct three types of DHF neighborhood operators. Meanwhile, we introduce DHF conditional probability by using DHF neighborhood operator. Secondly, in terms of DHF conditional probability, DHF covering-based probabilistic rough set, DHF covering-based decision-theoretic rough set and their 3WD models are established. In light of DHF neighborhood operator, we propose two pairs of optimistic and pessimistic DHF decision evaluation functions and build two 3WD models through them. Lastly, a numerical example is employed to elaborate the application of the above models, which is effective and credible to medicine screening for Alzheimer’s disease.

SIMILARITY-BASED ROUGH SET APPROACH IN INCOMPLETE INFORMATION SYSTEM USING POSSIBLE EQUIVALENT VALUE-SET

Data analytics generally helps businesses or entities to make better and efficient decision making. But in the face of growing volume of data or information, it becomes challenging to achieve these goals. One of which is on classification of information with high accuracy. Furthermore, when the information is incomplete, definitely it is more challenging in order to classify the information with high accuracy. Although incomplete information is well discussed using rough set theory for data classification, such as based on tolerance and similarity relations, there are still issues on accuracy to evaluate data classification. The main objective of this paper is to introduce a new similarity approach with semantically justified based on possible equivalent value-set related to incomplete information systems. It is based on a classification of three semantics types of incomplete information i.e., “any value”, “maybe value” and “not applicable value” for modelling similarity. Subsequently, the similarity precision between objects in incomplete information systems is considered. The comparative studies and simulation results between the proposed approach in terms of accuracy on synthetic data, four well-known classification datasets and one real marine dataset are presented. The proposed approach improves the accuracy up to two orders of magnitude and, thus verifying its data classification accuracy.

Label disambiguation-based feature selection for partial label learning via fuzzy dependency and feature discernibility

Partial label learning is a multi-class classification issue in which each training instance is associated with a set of candidate labels. Feature selection is an effective method to improve the performance of the learning model, and at the same time, feature selection being a challenging problem in partial label learning due to the label ambiguity of partial labels. To tackle this challenge, this paper proposes a label disambiguation-based feature selection for partial label learning via fuzzy dependency and feature discernibility. Specifically, considering the high sensitivity of the fuzzy rough sets model to pseudo labels, a instance distribution-based label disambiguation method is presented to reduce the noise from the candidate labels. Based on this, a weighted fuzzy rough sets model is constructed in accordance with the distribution information of labels, and the function of fuzzy dependency is redefined. Then, the evaluation function of feature significance is obtained by fusing fuzzy dependency and feature discernibility for identifying the critical features. Finally, extensive experiments have confirmed the feasibility and effectiveness of the proposed method. Compared to other feature selection algorithms, the proposed method exhibits superior performance and enhances the generalization of partial label learning models. Furthermore, the feasibility of the proposed label disambiguation method is demonstrated through comparison with state-of-the-art label disambiguation methods.

Emotion Analysis and Expression Algorithm of Dance Action Based on Machine Learning

Emotion analysis and expression algorithms represent a pivotal frontier in the intersection of artificial intelligence and human-computer interaction. These algorithms aim to decode and understand human emotions from various modalities such as text, speech, facial expressions, and physiological signals. This paper introduces the Context-Based Rough Sugeno Fuzzy (CBRSF) model tailored for emotion analysis and expression algorithms in the context of dance actions. With machine learning techniques, the CBRSF model integrates contextual information, rough set theory, and Sugeno fuzzy logic to accurately analyze and express emotions conveyed through dance movements. the power of machine learning techniques, the CBRSF model integrates various components, including contextual information, rough set theory, and Sugeno fuzzy logic, to provide a comprehensive framework for emotion analysis and expression. One of the key strengths of the CBRSF model lies in its ability to incorporate contextual information surrounding dance movements. Emotions conveyed through dance are often influenced by factors such as choreographic context, music, and cultural background. By integrating contextual cues into the analysis process, the CBRSF model can better capture the nuanced emotional nuances embedded within dance performances. The CBRSF model utilizes rough set theory to handle uncertainty and imprecision inherent in emotion analysis. Dance movements can be inherently ambiguous, making it challenging to accurately categorize the associated emotions. Rough set theory provides a principled framework for managing this uncertainty, allowing the CBRSF model to make informed decisions even in situations where data may be incomplete or inconsistent. Through comprehensive experimentation and evaluation, our proposed model achieves an emotion recognition accuracy of 98% across diverse dance action datasets, surpassing existing methods by 10.2%. Moreover, the CBRSF model enables nuanced emotion expression by dynamically adjusting dance movements based on real-time emotional cues.

Evaluation of the barriers to and drivers of the incorporation of unmanned aerial vehicles into dam asset management

ABSTRACT Dam asset management entails the consistent evaluation of the state and usefulness of dams as tangible assets in terms of their expected lifespan, criticality, operational history, maintenance history, and long-term financing plan. This research aims to identify and evaluate the critical drivers and barriers associated with the use of unmanned aerial vehicles in dam asset management. This study uses rough decision-making trial and evaluation laboratory and interpretive structure modelling to analyze the interactions that take place between the barriers to and the drivers of unmanned aerial vehicles incorporation. To overcome the problem of vagueness, it develops rough set theory to determine the driving and dependence power of the drivers and the barriers, respectively. According to the findings, the most significant barrier to the incorporation of unmanned aerial vehicles into dam asset management is a lack of skilled operators, while the most significant driver of their incorporation is their cost-effectiveness. The findings of this study will be highly valuable to practitioners and government agencies working on the implementation of unmanned aerial vehicles in dam asset management, as they will enable them to correctly assess the different aspects of unmanned aerial vehicles incorporation.

A Study on Rough Fuzzy Bipolar Soft Ideals in Semigroups

The theories of rough sets (RSs) and soft sets (SSs) are practical mathematical techniques to accommodate data uncertainty. On the other hand, fuzzy bipolar soft sets (FBSSs) can address uncertainty and bipolarity in various situations. The key objective of this study is to establish the notions of rough fuzzy bipolar soft ideals in semigroup (SG), which is an extension of the idea of rough fuzzy bipolar soft sets in a SG. Also, we have analyzed the roughness in the bipolar fuzzy subsemigroup (BF-SSG) by employing a congruence relation (CR) defined on the SG and investigating several related characteristics. Further, the idea is expanded to the rough fuzzy bipolar soft ideal, rough fuzzy bipolar soft interior ideal, and rough fuzzy bipolar soft bi-ideal in SGs. Moreover, it is observed that CR and complete CR (CCR) are critical in developing rough approximations of fuzzy bipolar soft ideals. Therefore, their related characteristics are studied via CRs and CCRs.

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

The United Nations Sustainable Development Goals (SDGs) agenda has stated the importance of green investment. Energy-related green investment involves intricate economic behavior and ecological objectives. Green investment definitely requires agile decisions, e.g., rule-based decisions, to respond to changes outside the country. The identification of significant rules with numerous result features and the assurance of the stability and robustness of the rules in decision-making are crucial for green energy investment. The rough set (RS) methodology works well for processing qualitative data that are difficult to examine with traditional statistical methods in order to induce decision rules. The RS methodology starts with the analysis of the limits of discernibility of a subset of objects belonging to the domain to induce rules. However, traditional RS methods cannot incrementally generate rules with outcome features when new objects are added, which frequently occurs in green energy investment with the inclusion of big data. In this paper, an intelligent RS approach is proposed. This approach effectively identifies the rules that either stay the same or are altered based on four classified cases after a new object is introduced; it is novel because it can deal with a complicated investment environment by imposing multiple outcome features, specifically when it is required to flexibly extract new decision rules via adding new data sets.

ON THE ANALYTICAL STUDY OF THE SERVICE QUALITY OF INDIAN RAILWAYS UNDER SOFT-COMPUTING PARADIGM

Indian Railway Catering and Tourism Corporation (IRCTC) is among the busiest railways reservation systems since the Indian Railways (IR) is the vital and economical mode of transportation in India. Hence, rating of the trains seems to be critical aspect for selecting an appropriate train for travelling. In this study, we have considered 7 vital attributes of 500 popular trains and rate their performance based on 7 important related attributes. For this purpose, we have employed 2 different approaches to analyse of the train attributes, which eventually contribute to the overall performance of the trains. Here, we have developed a rule based rough set decision support system to analyse the criticality of the train attributes while rating the train performance. Furthermore, we have also used 3 Machine Learning (ML) model estimators: Extra Trees Classifier (ETC), Support Vector Machine Classifier (SVMC) and Multinomial Naive Bayes Classifier (MNBC) and perform their comparative analysis with respect to 7 performance metrics while predicting the overall train rating based.

The Development of Digital Competencies for Royal Thai Armed Forces Headquarters Lead to an Intelligent Headquarters

Purpose: The study aims to enhance digital competencies towards establishing an Intelligent Headquarters within the Royal Thai Armed Forces. Theoretical Framework: It leverages third-order confirmatory factor analysis based on the Rough set Delphi theory to evaluate digital competencies. Design/Methodology/Approach: A mixed-method approach is adopted, combining qualitative insights from 21 experts with quantitative data from 600 individuals, using EFA and CFA for analysis. Findings: A validated model highlights six primary and eighteen secondary factors crucial for digital competency, including Digital Literacy, Communication, Management, Analysis, Development Tools, and Performance. Implications: The model provides a strategic framework for officers to enhance digital competencies, essential for transitioning to an Intelligent Headquarters, offering implications for organizational development and digital transformation strategies.

Rought Set: Effective Method for Determining Scholarship Recipients

Every year, higher education institutions receive a KIP Tuition scholarship quota that has been determined by Ristek Dikti through LLDIKTI which is given during the new student admissions process. The process of determining recipients is carried out manually resulting in inaccurate scholarship recipients being selected and the selection results may not be the same based on those who participated in making the decision. This research is motivated by the need for an algorithm for determining prospective scholarship recipients that is appropriate and effective because the recipient selection process often takes a long time because many high school and equivalent students register so that they exceed the quota limit while the quota given is limited. This research aims to use a system for scholarship recipients and provide rules and knowledge, namely rough set Theory and adapted to the Rosetta application, using prospective student data during the selection process for new students who apply for the KIP Kuliah scholarship in the 2020/2021 academic year. The resulting decision is the KIP Opportunity which consists of 4 (four) attributes, including parents' income, housing status, dependents, and parental status. The results of this research using sample data from 12 people produced 6 (six) rules and knowledge of 26 rules. This research is very supportive in identifying the eligibility of KIP Kuliah recipients.