Tangent and Cotangent Similarity Measures of Pentapartitioned Neutrosophic Pythagorean Set in Virtual Education During Covid Pandemic

In this paper, a new tangent and cotangent similarity measures between two Pentapartitioned Neutrosophic Pythagorean [PNP] sets with truth membership, falsity membership, ignorance and contradiction membership as dependent Neutrosophic component is proposed and its properties are investigated. The unknown membership alone will be considered as independent Neutrosophic components. Also, the weighted similarity measures are also studied with a decision making problem © 2023,Neutrosophic Sets and Systems. All Rights Reserved.

Construction of Hyperbolic Fuzzy Set and its Applications in Diverse COVID-19 Associated Problems

This paper's core objective is to introduce a novel notion called hyperbolic fuzzy set (HFS) where, the grades follow the stipulation that the product of optimistic and pessimistic degree must be less than or equal to one (1), rather than their sum not exceeding one (1) as in case of IFSs. The concept of HFS originates from a hyperbola, which provides extreme flexibility to the decision makers in the representation of vague and imprecise information. It is observed that IFSs, Pythagorean fuzzy sets (PFSs), and q-rung orthopair fuzzy sets (Q-ROFSs) often failed to express the uncertain information properly under some specific situations, while HFS tends to overcome such limitations by being applicable under those perplexed situations too. In this paper, we first define some basic operational laws and few desirable properties of HFSs. Second, we define a novel score function, accuracy function, and also establish some of their properties. Third, a novel similarity and distance measure is proposed for HFSs that are capable of distinguishing between different physical objects or alternatives based on the grounds of "similitude degree” and "farness coefficient”, respectively. Later, the advantages of all of these newly defined measures have been showcased by performing a meticulous comparative analysis. Finally, these measures have been successfully applied in various COVID-19 associated problems such as medical decision-making, antivirus face-mask selection, efficient sanitizer selections, and effective medicine selection for COVID-19. The final results obtained with our newly defined measures comply with several other existing methods that we considered and the decision strategy adopted is simple, logical, and efficient. The significant findings of this study are certain to aid the healthcare department and other frontline workers to take necessary measures to reduce the intensity of the coronavirus transmission, so that we can hopefully progress toward the end of this ruthless pandemic.

Combined Application of Various Techniques for Personalized Job Recommendation

The Personalized Job Recommender System is a subset of the custom recommendation system that provides a solution to the problem of information overload and is widely applied in numerous domains to solve a plethora of problems, such as unemployment and employment churn that we have seen emerging at higher rates in the COVID era. Furthermore, different jobs require divergent skill sets from their candidates to get hired. In this paper, we analyze the similarity techniques for Job Recommendation Systems based on the research done in the field of Job Recommendations. In our implementation, we have used three similarity measures: Tanimoto, Cosine (Orchini), and City Block similarity metrics. These techniques have been tested on a new Job Recommendation Systems Dataset taken from Kaggle. We have also analyzed the performance of similar techniques involving other distance measures, such as Euclidean distance. The performance of these similarity score-based techniques for generating the highest score-based recommendations is assessed using different evaluation metrics such as Accuracy, Precision, Recall, and F1-score respectively. © 2023 IEEE.

Multicriteria decision making approach using an efficient novel similarity measure for generalized trapezoidal fuzzy numbers.

Multicriteria Decision Making (MCDM) has a huge role to play while ruling out one suitable alternative among a pool of alternatives governed by predefined multiple criteria. Some of the factors like imprecision, lack of information/data, etc., which are present in traditional MCDM processes have showcased their lack of efficiency and hence eventually it has paved the ways for the development of Fuzzy multicriteria decision making (FMCDM). In FMCDM processes, the decision makers can model most of the real-life phenomena by fuzzy information-based preferences. The availability of a wide literature on similarity measure (SM) emphasizes the vital role of SM of generalized fuzzy numbers (GFNs) to conduct accurate and precise decision making in FMCDM problems. Despite having few advantages, most of the existing approaches possessed a certain degree of counter intuitiveness and discrepancies. Thus, we have attempted to propose a novel SM for generalized trapezoidal fuzzy numbers (GTrFNs) which could deliberately overcome the impediments associated with the earlier existing approaches. Moreover, a meticulous comparative study with the existing approaches is also presented. This paper provides us with an improved method to obtain the similarity values between GTrFNs and the proposed SM consists of calculating the prominent features of fuzzy numbers such as expected value and variance. We use fourteen different sets of GTrFNs, to compare the fruition of the present approach with the existing SM approaches. Furthermore, to show the utility and applicability of our proposed measure, we illustrate few practical scenarios such as the launching of an electronic gadget by a company, a problem of medical diagnosis and finally, a proper anti-virus mask selection in light of the recent COVID-19 pandemic. The obtained results with our proposed SM, for the mentioned FMCDM problems, are analytically correct and they depict the efficiency and novelty of the present article.

Multicriteria group decision making via generalized trapezoidal intuitionistic fuzzy number-based novel similarity measure and its application to diverse COVID-19 scenarios.

Havoc, brutality, economic breakdown, and vulnerability are the terms that can be rightly associated with COVID-19, for the kind of impact it is having on the whole world for the last two years. COVID-19 came as a nightmare and it is still not over yet, changing its form factor with each mutation. Moreover, each unpredictable mutation causes more severeness. In the present article, we outline a decision support algorithm using Generalized Trapezoidal Intuitionistic Fuzzy Numbers (GTrIFNs) to deal with various facets of COVID-19 problems. Intuitionistic fuzzy sets (IFSs) and their continuous counterparts, viz., the intuitionistic fuzzy numbers (IFNs), have the flexibility and effectiveness to handle the uncertainty and fuzziness associated with real-world problems. Although a meticulous amount of research works can be found in the literature, a wide majority of them are based mainly on normalized IFNs rather than the more generalized approach, and most of them had several limitations. Therefore, we have made a sincere attempt to devise a novel Similarity Measure (SM) which considers the evaluation of two prominent features of GTrIFNs, which are their expected values and variances. Then, to establish the superiority of our approach we present a comparative analysis of our method with several other established similarity methods considering ten different profiles of GTrIFNs. The proposed SM is then validated for feasibility and applicability, by elaborating a Fuzzy Multicriteria Group Decision Making (FMCGDM) algorithm and it is supportedby a suitable illustrative example. Finally, the proposed SM approach is applied to tackle some significant concerns due to COVID-19. For instance, problems like the selection of best medicine for COVID-19 infected patients; proper healthcare waste disposal technique; and topmost government intervention measures to prevent the COVID-19 spread, are some of the burning issues which are handled with our newly proposed SM approach.

Clustering-Based Recommendation System for Preliminary Disease Detection

The catastrophic outbreak COVID-19 has brought threat to the society and also placed severe stress on the healthcare systems worldwide. Different segments of society are contributing to their best effort to curb the spread of COVID-19. As a part of this contribution, in this research, a clustering-based recommender system is proposed for early detection of COVID-19 based on the symptoms of an individual. For this, the suspected patient's symptoms are compared with the patient who has already contracted COVID-19 by computing similarity between symptoms. Based on this, the suspected person is classified into either of the three risk categories: high, medium, and low. This is not a confirmed test but only a mechanism to alert the suspected patient. The accuracy of the algorithm is more than 85%.

"eRReBIS” Business Intelligence based Intelligent Recommender System for e-Recruitment Process

Due to the continuous and growing spread of the corona virus worldwide, it is important, especially in the business era, to develop accurate data driven decision-aided system to support business decision-makers in processing, managing large amounts of information in the recruitment process. In this context, e-Recruitment Recommender systems emerged as a decision support systems and aims to help stakeholders in finding items that match their preferences. However, existing solutions do not afford the recruiter to manage the whole process from different points of view. Thus, the main goal of this paper is to build an accurate and generic data driven system based on Business intelligence architecture. The strengths of our proposal lie in the fact that it allows decision makers to (1) consider multiple and heterogeneous data sources, access and manage data in order to generate strategic reports and recommendations at all times (2) combine many similarity's measure in the recommendation process (3) apply prescriptive analysis and machine learning algorithms to offer adapted and efficient recommendations. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Analyzing the Sustainability of Industry Affected in COVID-19 Pandemic Scenario Using Cosine Similarity Measure under SVPNS and PNN Model

COVID-19 outbreak is a reminder of the fact that the pandemics have happened in the past and will also occur in the future. The COVID-19 not only has affected the economy;but also it has affected the livelihood, which leads to the changes in businesses. This study aims to identify the most significant indicator (or parameter) that impacts the sustainability of industries. The study should also develop a real-time monitoring system for the sustainability of industries affected by COVID 19. In this work, the Polynomial Neural Network (PNN) and cosine similarity measure under SVPNS (Single-Valued Pentapartitioned Neutrosophic Set) environment have found their use in analyzing the sustainability of the industry. © 2022, American Scientific Publishing Group (ASPG). All rights reserved.

Performance of exponential similarity measures in supply of commodities in containment zones during COVID-19 pandemic under Pythagorean fuzzy sets.

Following the breakout of the novel coronavirus disease 2019 (COVID-19), the government of India was forced to prohibit all forms of human movement. It became important to establish and maintain a supply of commodities in hotspots and containment zones in different parts of the country. This study critically proposes new exponential similarity measures to understand the requirement and distribution of commodities to these zones during the rapid spread of novel coronavirus (COVID-19) across the globe. The primary goal is to utilize the important aspect of similarity measures based on exponential function under Pythagorean fuzzy sets, proposed by Yager. The article aims at finding the most required commodity in the affected areas and ensures its distribution in hotspots and containment zones. The projected path of grocery delivery to different residences in containment zones is determined by estimating the similarity measure between each residence and the various necessary goods. Numerical computations have been carried out to validate our proposed measures. Moreover, a comparison of the result for the proposed measures has been carried out to prove the efficacy.

Mathematical modeling and AI based decision making for COVID-19 suspects backed by novel distance and similarity measures on plithogenic hypersoft sets.

It goes without saying that coronavirus (COVID-19) is an infectious disease and many countries are coping with its different variants. Owing to the limited medical facilities, vaccine and medical experts, need of the hour is to intelligently tackle its spread by making artificial intelligence (AI) based smart decisions for COVID-19 suspects who develop different symptoms and they are kept under observation and monitored to see the severity of the symptoms. The target of this study is to analyze COVID-19 suspects data and detect whether a suspect is a COVID-19 patient or not, and if yes, then to what extent, so that a suitable decision can be made. The decision can be categorized such that an infected person can be isolated or quarantined at home or at a facilitation center or the person can be sent to the hospital for the treatment. This target is achieved by designing a mathematical model of COVID-19 suspects in the form of a multi-criteria decision making (MCDM) model and a novel AI based technique is devised and implemented with the help of newly developed plithogenic distance and similarity measures in fuzzy environment. All findings are depicted graphically for a clear understanding and to provide an insight of the necessity and effectiveness of the proposed method. The concept and results of the proposed technique make it suitable for implementation in machine learning, deep learning, pattern recognition etc.

An Intelligent Understanding of the Post-COVID-19 Uncertainty: Provided Guidelines and Strategies for Resilient Supply Chain Networks

COVID-19 outbreak has damaged the global supply chains, it has affected both goods and service provider supply chains unprecedentedly. Post COVID-19 era is full of uncertainty based on many changes that have happened. Some new parameters are introduced because of the outbreak and bring out new circumstances. These new challenges consequently will increase the ambiguity around the supply chain networks. This study is designed to investigate and evaluate the ambiguity of supply chain networks in the post-COVID-19 era, to strengthen and increase the resilience of SCN systems. The challenges are clustered into different patterns and for each pattern, many strategy approaches are introduced in the literature part. But not only those are not useful without understanding challenges specifically for each SCN but also, it is not possible to apply all of those strategy solutions. This study aims to first understand the challenges and effects of each disruption pattern specifically for each SCN and then select in a more detailed way the most appropriate strategy. To catch the goal of evaluating the resilience of supply chain networks, some significant challenges are identified based on the literature part. An algorithm consists of three stages, first define the uncertainty, second pattern recognition of disruption patterns, and third strategy selection to increase SCN resilience is proposed based IVq-ROFSs Hamacher Aggregation operators and Dice similarity measures. An illustrative example of the SCN resilience problem is evaluated by the proposed algorithm under the Interval Valued q-Rung Ortho Pair Fuzzy structure to show the applicability and reliability of the proposed method. Finally, this paper provides guidelines and strategies for increasing the resilience of supply chain networks in the post-COVID-19 outbreak. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach.

Image segmentation has attracted a lot of attention due to its potential biomedical applications. Based on these, in the current research, an attempt has been made to explore object enhancement and segmentation for CT images of lungs infected with COVID-19. By implementing Pythagorean fuzzy entropy, the considered images were enhanced. Further, by constructing Pythagorean fuzzy measures and utilizing the thresholding technique, the required values of thresholds for the segmentation of the proposed scheme are assessed. The object extraction ability of the five segmentation algorithms including current sophisticated, and proposed schemes are evaluated by applying the quality measurement factors. Ultimately, the proposed scheme has the best effect on object separation as well as the quality measurement values.

Construction of hyperbolic fuzzy set and its applications in diverse COVID-19 associated problems

This paper’s core objective is to introduce a novel notion called hyperbolic fuzzy set (HFS) where, the grades follow the stipulation that the product of optimistic and pessimistic degree must be less than or equal to one (1), rather than their sum not exceeding one (1) as in case of IFSs. The concept of HFS originates from a hyperbola, which provides extreme flexibility to the decision makers in the representation of vague and imprecise information. It is observed that IFSs, Pythagorean fuzzy sets (PFSs), and q-rung orthopair fuzzy sets (Q-ROFSs) often failed to express the uncertain information properly under some specific situations, while HFS tends to overcome such limitations by being applicable under those perplexed situations too. In this paper, we first define some basic operational laws and few desirable properties of HFSs. Second, we define a novel score function, accuracy function, and also establish some of their properties. Third, a novel similarity and distance measure is proposed for HFSs that are capable of distinguishing between different physical objects or alternatives based on the grounds of “similitude degree” and “farness coefficient”, respectively. Later, the advantages of all of these newly defined measures have been showcased by performing a meticulous comparative analysis. Finally, these measures have been successfully applied in various COVID-19 associated problems such as medical decision-making, antivirus face-mask selection, efficient sanitizer selections, and effective medicine selection for COVID-19. The final results obtained with our newly defined measures comply with several other existing methods that we considered and the decision strategy adopted is simple, logical, and efficient. The significant findings of this study are certain to aid the healthcare department and other frontline workers to take necessary measures to reduce the intensity of the coronavirus transmission, so that we can hopefully progress toward the end of this ruthless pandemic. [ FROM AUTHOR] Copyright of New Mathematics & Natural Computation is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

An Interval-Valued Intuitionistic Fuzzy Model Based on Extended VIKOR and MARCOS for Sustainable Supplier Selection in Organ Transplantation Networks for Healthcare Devices

The selection of proper healthcare device suppliers in sustainable organ transplantation networks has become an essential topic of increasing life expectancy. Assessment of sustainable healthcare device suppliers can be regarded as a complex multi-criteria decision-making (MCDM) problem that consists of multiple alternative solutions with sustainable criteria. For this reason, this paper proposes a new integrated MCDM model based on combining an extended vlsekriterijuska optimizacija i komoromisno resenje (E-VIKOR) and measurement alternatives and ranking according to the compromise solution (MARCOS) approaches under interval-valued intuitionistic fuzzy sets (IVIFSs). The aggregating technique of the E-VIKOR method is a strong point of this method compared to the original approach. The IVIFS is taken to cope with the uncertain situation of real-world applications. In this regard, an IVIF-similarity measure is introduced to compute weights of the decision-makers (DMs). The IVIF-Shannon entropy method is utilized to calculate the criteria weights, and a new hybrid proposed model is developed by presenting the IVIF-E-VIKOR method and IVIF-MARCOS, to calculate the ranking of sustainable supplier alternatives in organ transplantation networks to supply the surgery devices. Afterward, an illustrative example is introduced to evaluate the performance of the proposed model, and a comparative analysis is presented to confirm and validate the proposed approach. Moreover, sensitivity analysis for essential parameters of the proposed model is performed to assess their effects on outcomes.

Some Linear Diophantine Fuzzy Similarity Measures and Their Application in Decision Making Problem

The uncertainty in the data information for decision making is a most challenging and critical fear. In order to reduce the uncertainty in the decision making expert information for decision making problem, the Linear Diophantine fuzzy number is taking more critical part in reducing the uncertainty in information. Therefore the primary aim of this paper is to develop some different types of similarity and distance measures for linear Diophantine fuzzy numbers. With the frequent occurrence of emergency events, emergency decision making (EDM) plays a significant role in the emergency situations. It is essential for decision makers to make reliable and reasonable emergency decisions within a short time period since inappropriate decisions may result in enormous economic losses and chaotic social order. Accordingly, to ensure that EDM problems can be solved effectively and quickly, this paper proposes a new EDM method based on the novel distance and similarity measures under Linear Diophantine fuzzy (LDF) information. The similarity measure is one of the beneficial tools to determine the degree of similarity between objects. It has many crucial applications such as decision making, data mining, medical diagnosis, and pattern recognition. In this study, some novel distances and similarity measures of linear Diophantine fuzzy sets are presented. Then, the Jaccard similarity measure, exponential similarity measure, Cosine and Cotangent function based on similarity measures for LDFSs were proposed. The newly defined similarity measures are applied to medical diagnosis problem for COVID-19 virus and the results are discussed. A comparative study for the new similarity measures is established, and some advantages of the proposed work are discussed. Author

Application of Neutrosophic Similarity Measures in Covid-19

The contemporary situation of the world is very pathetic due to the spread of COVID-19. In this article, we have prepared a decision making model on COVID-19 pandemic patients with the help of the neutrosophic similarity measures. The model is to predict the COVID-19 patents for testing positive and testing negative. The decision making is based on the testing result of the COVID-19 cases. We have used the neutrosophic similarity measure theory and the distance function. We have used the C-programming for finding the result of the suspected patients.

Geospatial analytics for COVID-19 active case detection

Ever since the COVID-19 pandemic started in Wuhan, China, much research work has been focusing on the clinical aspect of SARS-CoV-2. Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus. Limited studies have, however, reported on COVID-19 transmission pattern analysis, and using geography features for prediction of potential outbreak sites. Predicting the next most probable outbreak site is crucial, particularly for optimizing the planning of medical personnel and supply resources. To tackle the challenge, this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude, when would the outbreak likely to happen and the duration of the outbreak. The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia. The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19. Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases, next outbreak location, and the time interval between start dates of two similar sites. Such findings provided valuable insights for policymakers to perform Active Case Detection. © 2021 Tech Science Press. All rights reserved.