Vague Data Research Articles

Fuzzy deep visual panic detection

Deep visual data analysis from social network has become an increasingly important area of research. In fact, this form of assessment makes it viable to recognize new information on social users which incorporates emotions. In order to recognize users’ emotions and other latent attributes, most of the existing approaches have used textual data and have obtained accurate results. However, little attention has been paid to visual data that have become increasingly popular in recent years.This work describes how to develop a conceptual representation model for social network analysis and social emotion analysis based on the integration of fuzzy logic and ontological modeling. The primary aim is to create an ontology that can extract new information about a social user’s mood, which can be classified as panic, no-panic, or neutral. Fuzzy logic is necessary to deal with subjective data, as users share imprecise and vague data on their social profiles. Fuzzy logic has been observed as a successful method to capture the expression of emotions due to the fuzzy nature of emotions and the ambiguous definitions of emotion words. The proposed work investigate the role of fuzzy logic in social network analysis. This study simulate a fuzzy deep system integrated with ontology for classifying social visual data (shared images) into panic, no-panic or neutral classes in order to determine the social users’ stress intensity. The Social distancing and the huge amount of shared data in Tunisia were calculated to demonstrate this classification. The experiments performed in this paper aim to create not only a novel annotated visual database named visual panic database, but also a new semantic model for modeling users’ profiles, in social network, based on the combination between ontology and deep learning techniques. In this context, we will work in the future on the combination between a user’s visual and textual data in order to improve the performance of this recognition. The proposed fuzzy system reflected the viral proliferation in stressed users and achieved an accuracy of 87%.

Navigating ambiguity: A novel neutrosophic cubic shapley normalized weighted Bonferroni Mean aggregation operator with application in the investment environment

The Neutrosophic Cubic Shapley Normalized Bonferroni (NC-SNWBM) method represents a cutting-edge approach to decision making theory, combining three distinct mathematical frameworks the neutrosophic cubic sets (NCS), Shapley values, and the Bonferroni aggregation operator. This innovative method addresses the challenges posed by uncertainty, vagueness, and imprecision in decision-making (DM) processes, offering a comprehensive and versatile tool for handling complex and dynamic scenarios. Neutrosophic cubic sets offers a strong platform to handle ambiguous and vague data due to three components Membership Grade (MG), Non-Membership Grade (NMG) and Indeterminancy Grade (IG) in data. By adding Shapley Fuzzy Measures (SFM), which come from cooperative game theory, distribute values among cooperative agents equally and to account for each agent's contributions to all potential coalitions. The Bonferroni aggregation operator—a statistical aggregative tool that regulates the likelihood of many types in error in statistical tests and the interdependence of the input arguments by allowing different values to parameters involved. These values are further improved by normalization in the framework of the NC-SNWBM approach in order to consider the various degrees of impact that agents exert in various circumstances. This operator is smoothly combined with normalized Shapley values and neutrosophic cubic sets in the NC-SNWBM approach to enable the aggregation of data with different levels of imprecision and uncertainty from various sources using NCS. The MG, NMG and IG connected to NCS are important elements of the NC-SNWBM approach. To evaluate each element's contribution to the overall value distribution SFM are used, and the Bonferroni aggregation operator maintains a careful balance between conservatism and significance. Together, these components provide a thorough framework that successfully tackles the problems caused by ambiguity, imprecision, and uncertainty in scenarios involving decision-making. The NC-SNWBM operator is applied to a numerical problem as an application in investment environment and sensitive and comparative analysis are conducted. The recommendation based on sensitive and comparative analysis proposed.

Hybrid fuzzy optimistic and pessimistic data envelopment analysis models for evaluating decision-making units in the presence of vague data

Hybrid fuzzy optimistic and pessimistic data envelopment analysis models for evaluating decision-making units in the presence of vague data

A Comprehensive Evaluation of Rough Sets Clustering in Uncertainty Driven Contexts

This paper presents a comprehensive evaluation of the Agent BAsed Rough sets Clustering (ABARC) algorithm, an approach using rough sets theory for clustering in environments characterized by uncertainty. Several experiments utilizing standard datasets are performed in order to compare ABARC against a range of supervised and unsupervised learning algorithms. This comparison considers various internal and external performance measures to evaluate the quality of clustering. The results highlight the ABARC algorithm’s capability to effectively manage vague data and outliers, showcasing its advantage in handling uncertainty in data. Furthermore, they also emphasize the importance of choosing appropriate performance metrics, especially when evaluating clustering algorithms in scenarios with unclear or inconsistent data. Keywords: rough sets, clustering, metrics.

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.

Estimating neutrosophic finite median employing robust measures of the auxiliary variable

Our study explores neutrosophic statistics, an extension of classical and fuzzy statistics, to address the challenges of data uncertainty. By leveraging accurate measurements of an auxiliary variable, we can derive precise estimates for the unknown population median. The estimators introduced in this research are particularly useful for analysing unclear, vague data or within the neutrosophic realm. Unlike traditional methods that yield single-valued outcomes, our estimators produce ranges, suggesting where the population parameter is likely to be. We present the suggested generalised estimator's bias and mean square error within a first-order approximation framework. The practicality and efficiency of these proposed neutrosophic estimators are demonstrated through real-world data applications and the simulated data set.

A Type-2 Fuzzy u-Control Chart Considering Probability-Based Average Run Length

Fuzzy sets are an emerging trend in shaping the development of control charts for statistical process control. The sets are germane to vague data that comes from incomplete or inaccurate measurements. Nevertheless, fuzzy sets are inadequate in some areas of industry since their membership functions are crisp numbers. The fuzzy sets are not fully able to compute higher levels of uncertainty, which might degrade the performance of the analysis. Therefore, type-2 fuzzy sets are proposed to be merged with control charts since these sets are hypothesized to be more capable of detecting a defect in process control. This paper aims to develop interval type-2 fuzzy u (IT2Fu) charts as a new approach to detecting defects. In addition, this paper presents a comparative analysis of performances between traditional u-control charts, type-1 fuzzy u-control charts, and type-2 fuzzy u-control charts. 23 samples of lubricant data with 48 subgroups were examined to identify the defects. The output showed that all of the control charts produced almost similar results except for data 14, which is “out of control” in IT2Fu-control charts but “in control” in traditional u-control charts and “rather in control” in type-1 fuzzy u-control charts. Furthermore, the performances of the charts were compared using a probability-based average run length (ARL), where probability type 1 error is computed. It was found that the ARL value of the IT2Fu-control chart showed the lowest value among the three types of charts. The analysis indicated that the IT2Fu-control chart outperformed the traditional u-control chart and the type-1 fuzzy u-control chart. The results obtained seem to support the idea that IT2Fu-control charts are more sensitive compared to type 1 fuzzy u-control charts and traditional u-control charts, so that IT2Fu-control charts are able to adequately support incomplete and vague data on process control.

A Systematic Overview of Fuzzy Extended Entity Relationship (Fuzzy EER) Model Using Practical Examples

Most real-world data are vague and imprecise, so traditional relational databases cannot represent, integrate, and manage them effectively. In this research, we gave an overview of the application of fuzzy logic to the conceptual modeling facet of database design, which is an enhancement of the Extended Entity Relationship (EER) model. This is done to give a clearer picture of how imprecise and vague data are represented and managed, especially in multiple-criteria decision-making database systems. To achieve this, an overview of fuzzy attributes, fuzzy values in the attributes, notation for fuzzy representations, fuzzy degree in each value of an attribute, fuzzy degree in a set of values of different attributes, fuzzy degree with its own meaning and fuzzy degree to the model was carried out with practical examples.

Classification of Renewable Energy Trends by Utilizing the Novel Entropy Measures under the Environment of q-rung Orthopair Fuzzy Soft Sets

Energy generated from naturally replenishing sources such as the sun and wind, is said to be renewable energy (RE). The production of electricity water heating and cooling can all be done with renewable energy. Some recent trends in renewable energy are (1) Advanced photovoltaic (2) Distributed energy storage systems (3) Hydro Power (4) Bioenergy. The utilization of fuzzy set theory is very important for the classification of renewable energy trends. q-rung orthopair fuzzy soft set (q-ROFSS) is a parameterization tool and a more efficient apparatus to resolve unclear and vague data in general. As entropy is a quantitated measure of disordered or randomness in a system it can handle the uncertainty of data in fuzzy set theory for a disordered system. So, in this article, based on q-ROFSS, we have initiated some novel entropy measures under the environment of q-ROFSS to relate more complex data and to give more room to decision-makers for handling many decision-making problems. So, the entropy of q-ROFSS is the measure of ambiguity related to q-ROFSS. To show the authenticity of the established work further, we have established an algorithm for the introduced work along with a descriptive example to substantiate the reliability of the initiated work. We have also initiated a comparative study of the introduced notion with some existing theories to demonstrate the usefulness and consistency of the given idea.

Cubic Pythagorean Hesitant Fuzzy Linear Spaces and Its Relevance in Multi Criteria Decision Making

Pythagorean fuzzy sets and interval valued Pythagorean fuzzy sets have an important role in decision making techniques. Pythagorean hesitant fuzzy sets are time and again used in dealing with uncertain and vague data. The motive of this paper is to introduce the notion cubic Pythagorean hesitant fuzzy linear spaces. We also present the notion of P(R)-intersection, P(R)-union of cubic Pythagorean hesitant fuzzy linear spaces with examples. Secondly, a series of operators like cubic Pythagorean hesitant fuzzy weighted averaging aggregation operators, cubic Pythagorean hesitant fuzzy order weighted averaging aggregation operators and cubic Pythagorean hesitant fuzzy hybrid order weighted averaging aggregation operators are developed. Then, these aggregation operators are further extended to cubic Pythagorean hesitant fuzzy prioritized weighted averaging aggregation operators by assigning priorities to the criteria. A real life MCDM problem has been illustrated and the effectiveness of the results are compared with those solved using cubic picture hesitant fuzzy prioritized weighted averaging aggregation operators.

Life settlement pricing with fuzzy parameters

Existing literature asserts that the growth of life settlement (LS) markets, where they exist, is hampered by limited policyholder participation and suggests that to foster this growth appropriate pricing of LS transactions is crucial. The pricing of LSs relies on quantifying two key variables: the insured's mortality multiplier and the internal rate of return (IRR). However, the available information on these parameters is often scarce and vague. To address this issue, this article proposes a novel framework that models these variables using triangular fuzzy numbers (TFNs). This modelling approach aligns with how mortality multiplier and IRR data are typically provided in insurance markets and has the advantage of offering a natural interpretation for practitioners. When both the mortality multiplier and the IRR are represented as TFNs, the resulting LS price becomes a FN that no longer retains the triangular shape. Therefore, the paper introduces three alternative triangular approximations to simplify computations and enhance interpretation of the price. Additionally, six criteria are proposed to evaluate the effectiveness of each approximation method. These criteria go beyond the typical approach of assessing the approximation quality to the FN itself. They also consider the usability and comprehensibility for financial analysts with no prior knowledge of FNs. In summary, the framework presented in this paper represents a significant advancement in LS pricing. By incorporating TFNs, offering several triangular approximations and proposing goodness criteria of them, it addresses the challenges posed by limited and vague data, while also considering the practical needs of industry practitioners.

Semantic Positioning Model Incorporating BERT/RoBERTa and Fuzzy Theory Achieves More Nuanced Japanese Adverb Clustering

Japanese adverbs are difficult to classify, with little progress made since the 1930s. Now in the age of large language models, linguists need a framework for lexical grouping that incorporates quantitative, evidence-based relationships rather than purely theoretical categorization. We herein address this need for the case of Japanese adverbs by developing a semantic positioning approach that incorporates large language model embeddings with fuzzy set theory to achieve empirical Japanese adverb groupings. To perform semantic positioning, we (i) obtained multi-dimensional embeddings for a list of Japanese adverbs using a BERT or RoBERTa model pre-trained on Japanese text, (ii) reduced the dimensionality of each embedding by principle component analysis (PCA), (iii) mapped the relative position of each adverb in a 3D plot using K-means clustering with an initial cluster count of n=3, (iv) performed silhouette analysis to determine the optimal cluster count, (v) performed PCA and K-means clustering on the adverb embeddings again to generate 2D semantic position plots, then finally (vi) generated a centroid distance matrix. Fuzzy set theory informs our workflow at the embedding step, where the meanings of words are treated as quantifiable vague data. Our results suggest that Japanese adverbs optimally cluster into n=4 rather than n=3 groups following silhouette analysis. We also observe a lack of consistency between adverb semantic positions and conventional classification. Ultimately, 3D/2D semantic position plots and centroid distance matrices were simple to generate and did not require special hardware. Our novel approach offers advantages over conventional adverb classification, including an intuitive visualization of semantic relationships in the form of semantic position plots, as well as a quantitative clustering “fingerprint” for Japanese adverbs that express vague language data as a centroid distance matrix.

Prediction of Heatwave Using Advanced Soft Computing Technique

At present, there is no suitable instrument available to simulate modeling the thermal performance of various areas of our states due to its complicated meteorological behavior. To accurately predict a heatwave, we studied the research gaps and current ongoing research on the prediction of heatwaves. For the accurate prediction of a heatwave, we considered two soft computing concepts, (a) Rough Set Theory (RST) and (b) Support Vector Machine (SVM). All the ongoing research on the prediction of heatwaves is based on future predictions with an error margin. All the available techniques use a particular pattern of heatwave data, and these methods do not apply to vague data. This paper used an innovative RST and SVM technique, which can be applied to vague and imprecise datasets to produce the best outcomes. RST is helpful in finding the most significant attributes that will be alarming in the future. This analysis identifies the heat wave as the most prominent characteristic among various meteorological data. SVM is responsible for the future prediction of heat waves, which includes various parameters. By further classification of heatwaves, we found that a lack of greenery will increase the heatwave in the future. Although the survey was conducted based on a sampling distribution, we expect this result to represent the population as we collected our sample in a heterogeneous environment. These outcomes are validated using a statistical method.

Assessment the Health Sustainability using Neutrosophic MCDM Methodology: Case Study COVID-19

As a result of the severe difficulties presented by the COVID-19 pandemic, a holistic response is required, one that takes into account both the urgent needs of patients and the long-term viability of healthcare institutions. This study aims to give a complete knowledge of the tactics and techniques necessary to maintain the continuing well-being of people and communities by examining the idea of health sustainability in the context of the COVID-19 pandemic. This study conducts a literature review to investigate the many facets of health sustainability, such as emergency preparedness, mental health care, health workforce support, health education and communication, research and innovation, international cooperation, and resilience in the face of pandemics. This study's results call attention to the necessity for universal healthcare access, mental health services, the upkeep of critical services, and international coordination as part of the COVID-19 response strategy. Societies may construct robust healthcare systems that can deal with the short- and long-term effects of the pandemic if they use a comprehensive strategy that takes into account social, economic, and environmental aspects. So, we used the concept of multi-criteria decision-making (MCDM) to deal with various criteria of health sustainability. The AHP MCDM method is used to deal with various criteria and give the weights of these criteria. The AHP used a comparison between various criteria, so we used the neutrosophic environment to deal with the vague data in the comparison process. The proposed framework is applied in the application of COVID-19.

Some modified picture fuzzy average aggregation operators with priority roles of stakeholders in implementation of education 4.0

Picture fuzzy sets (PFSs), the generalization of intuitionistic fuzzy sets (IFSs), are more capable of dealing with vague data in real-life problems. Models based on PFSs may be suitable particularly in those circumstances where human perceptions become challenging as well as various kinds of reasoning, like yes, no, abstention, or denial. The aggregation operators (AOs) are essential components in information aggregation as they have the ability to aggregate a group of fuzzy numbers into a single fuzzy number of the same kind. A lot of aggregation operations for PFSs have been developed. Nevertheless, the existing aggregation operators for picture fuzzy information are inaccurate as they fail to aggregate a group of picture fuzzy numbers into a single picture fuzzy number (PFN). To cover the drawbacks of existing AOs, we developed some modified picture fuzzy aggregation operators (PFAOs) named as picture fuzzy modified weighted averaging (PFMWA), picture fuzzy modified ordered weighted averaging (PFMOWA) and picture fuzzy modified hybrid averaging (PFMHA) aggregation operator along with their distinctive features. These operators are essential in developing new multi-criteria decision-making (MCDM) techniques. This paper defines a number of stakeholder roles (or tactics), with an objective of overcoming the challenges to executing Education 4.0 (EDUC4) that have recently been highlighted in the literature. A MCDM problem provides the basis for the evaluation of the responsibilities of the stakeholders with respect to these constraints. Several management concerns are provided as stepping stones for the development of EDUC4 implementation. The purpose of this study is to identify the qualities that influence the degree of optimism for the adoption and implementation of the EDUC4 in Pakistan’s education system while taking government policies into account.

Analysis of Traditional and Fuzzy Quality Control Charts to Improve Short-Run Production in the Manufacturing Industry

Quality control charts are limited to controlling one characteristic of a production process, and it needs a large amount of data to determine control limits to control the process. Another limitation of the traditional control chart is that it doesn’t deal with the vague data environment. The fuzzy control charts work with the uncertainty that exists in the data. Also, the fuzzy control charts investigate the random variations found between the samples. In modern industries, productivity is often of different designs and a small volume that depends on the market need for demand (short-run production) implemented in the same type of machines to the production units. In such cases, it is difficult to determine the control limits for the operations carried out on the same machines. This work aims to compare the traditional control charts and the fuzzy control charts for short-run production. In the traditional case, the data collected were processed using the (Minitab 21) software. It was found that the fuzzy control charts were more flexible and accurate in determining the control limits of the machine under study. The traditional deviation from nominal control charts showed false alarm of observation (15) as out-of-control, while the fuzzy (DNOM) showed that these observations were under control. Also, the standard deviation of the process was dropped from (σ =0.209041) to (σ =0.204401) after using the fuzzy control chart.

Energy Efficiency and Material Cost Savings by Evolution of Solar Panels Used in Photovoltaic Systems under Neutrosophic Model


 Traditional applications of solar panels have been limited to smaller-scale energy production, such as that required by single houses or apartment complexes. Researchers from all around the globe have been working together to develop creative, efficient goods, increase the energy efficiency of solar panels, and build new, ground-breaking practices using photovoltaic system design. Solar photovoltaic (PV) system planning demands a strategic decision-making approach to socioeconomic growth in many nations due to the rising understanding of the financial, social, and ecological aspects. The primary goal of this study is to provide a novel, adaptable method of Multi-Criteria Decision Making (MCDM) for government decision-makers (DMs) to use in evaluating solar PV panel manufacturers using various variables. This paper used the single-valued neutrosophic set to deal with the vague data. The neutrosophic set is used with the Characteristic Objects Method (COMET). The COMET method is an MCDM method. It is used to compute the rank of alternatives. The application of the proposed method is introduced with eleven criteria and ten solar panels in PV.

LM-GA: A Novel IDS with AES and Machine Learning Architecture for Enhanced Cloud Storage Security

Cloud Computing (CC) is a relatively new technology that allows for widespread access and storage on the internet. Despite its low cost and numerous benefits, cloud technology still confronts several obstacles, including data loss, quality concerns, and data security like recurring hacking. The security of data stored in the cloud has become a major worry for both Cloud Service Providers (CSPs) and users. As a result, a powerful Intrusion Detection System (IDS) must be set up to detect and prevent possible cloud threats at an early stage. Intending to develop a novel IDS system, this paper introduces a new optimization concept named Lion Mutated-Genetic Algorithm (LM-GA) with the hybridization of Machine Learning (ML) algorithms such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Initially, the input text data is preprocessed and balanced to avoid redundancy and vague data. The preprocessed data is then subjected to the hybrid Deep Learning (DL) models namely the CNN-LSTM model to get the IDS output. Now, the intruded are discarded and non-intruded data are secured using Advanced Encryption Standard (AES) encryption model. Besides, the optimal key selection is done by the proposed LM-GA model and the cipher text is further secured via the steganography approach. NSL-KDD and UNSW-NB15 are the datasets used to verify the performance of the proposed LM-GA-based IDS in terms of average intrusion detection rate, accuracy, precision, recall, and F-Score.

Analysis and Prioritization of the Factors of the Robotic Industry With the Assistance of EDAS Technique Based on Intuitionistic Fuzzy Rough Yager Aggregation Operators

The focus of the interdisciplinary and scientific discipline of robotics is to design, maintain and use mechanical robotics. There exist many issues faced by the robotic industry but there are some factors that can cover these complexities effectively. Handling vague and imprecise data is a difficult task nowadays. So there is a need to define such kind of effective and valuable tool that can handle complex and vague data more dominantly. The evaluation based on distance from average solution (EDAS) method is a very useful tool that can handle complex data more effectively. The best alternative can be chosen based on distance from the average solution. The EDAS method is relatively simple to use and provide a quick evaluation of alternative based on multiple criteria. Yager t-norm and t-conorm are two fuzzy logic operators proposed by Yager. So based on the importance of Yager t-norm and t-conorm, initially, in this article, we have proposed the basic operative laws for intuitionistic fuzzy rough numbers. Based on these developed operational laws, we have developed some new intuitionistic fuzzy rough aggregation operators called intuitionistic fuzzy rough Yager average (weighted, ordered weighted, hybrid) aggregation operators and intuitionistic fuzzy rough Yager geometric (weighted, ordered weighted, hybrid) aggregation operators. Moreover, we have proposed the EDAS technique based on intuitionistic fuzzy rough Yager aggregation operators and used these notions for the selection of suitable factors that play a vital role in the robotic industry. Also, to show the effective use of these introduced notions, we have proposed an algorithm for the EDAS method based on intuitionistic fuzzy rough Yager aggregation operators along with a descriptive example. To show the superiority of the introduced work we have developed a comparative analysis.

Ranking fuzzy numbers by volume of solid of revolution of membership function about axis of support

It is admissible that fuzzy numbers (FNs) are apt for representing imprecise or vague data in real-world problems. While using FNs in decision-making problems, selecting the best alternative among available alternatives is challenging, and therefore, ranking FNs is essential. We can find different studies in the literature, but to our knowledge, no one attempted to rank FNs using the concept of volume. This paper proposes a new method for ranking generalized fuzzy numbers (GFNs) using the volume of the solid obtained by revolving its membership function (MF) about the x-axis. We calculate the volumes of positive and negative sides along with the centroid of a generalized fuzzy number(GFN) to define the fuzzy number(FN) score. This score represents the defuzzified value of FN, is used to select the best alternative, and overcomes the limitations in some existing methods like ranking FNs having the same centroid, crisp numbers, symmetric fuzzy numbers, and FNs with the same core.