Analysis of medical diagnosis based on variation co-efficient similarity measures under picture hesitant fuzzy sets and their application

<abstract> <p>One of the most dominant and feasible technique is called the PHF setting is exist in the circumstances of fuzzy set theory for handling intricate and vague data in genuine life scenario. The perception of PHF setting is massive universal is compared to these assumptions, who must cope with two or three sorts of data in the shape of singleton element. Under the consideration of the PHF setting, we utilized some SM in the region of the PHF setting are to diagnose the PHFDSM, PHFWDSM, PHFJSM, PHFWJSM, PHFCSM, PHFWCSM, PHFHVSM, PHFWHVSM and demonstrated their flexible parts. Likewise, a lot of examples are exposed under the invented measures based on PHF data in the environment of medical diagnosis to demonstrate the stability and elasticity of the explored works. Finally, the sensitive analysis of the presented works is also implemented and illuminated their graphical structures.</p> </abstract>

q -Rung Orthopair Fuzzy Matroids with Application to Human Trafficking

The theory of q -rung orthopair fuzzy sets ( q -ROFSs) is emerging for the provision of more comprehensive and useful information in comparison to their counterparts like intuitionistic and Pythagorean fuzzy sets, especially when responding to the models of vague data with membership and non-membership grades of elements. In this study, a significant generalized model q -ROFS is used to introduce the concept of q -rung orthopair fuzzy vector spaces ( q -ROFVSs) and illustrated by an example. We further elaborate the q -rung orthopair fuzzy linearly independent vectors. The study also involves the results regarding q -rung orthopair fuzzy basis and dimensions of q -ROFVSs. The main focus of this study is to define the concepts of q -rung orthopair fuzzy matroids ( q -ROFMs) and apply them to explore the characteristics of their basis, dimensions, and rank function. Ultimately, to show the significance of our proposed work, we combine these ideas and offer an application. We provide an algorithm to solve the numerical problems related to human flow between particular regions to ensure the increased government response action against frequently used path (heavy path) for the countries involved via directed q -rung orthopair fuzzy graph ( q -ROFG). At last, a comparative study of the proposed work with the existing theory of Pythagorean fuzzy matroids is also presented.

Vague data analysis using neutrosophic Jarque–Bera test

In decision-making problems, the researchers’ application of parametric tests is the first choice due to their wide applicability, reliability, and validity. The common parametric tests require the validation of the normality assumption even for large sample sizes in some cases. Jarque-Bera test is among one of the methods available in the literature used to serve the purpose. One of the Jarque-Bera test restrictions is the computational limitations available only for the data in exact form. The operational procedure of the test is helpless for the interval-valued data. The interval-valued data generally occurs in situations under fuzzy logic or indeterminate state of the outcome variable and is often called neutrosophic form. The present research modifies the existing statistic of the Jarque-Bera test for the interval-valued data. The modified design and operational procedure of the newly proposed Jarque-Bera test will be useful to assess the normality of a data set under the neutrosophic environment. The proposed neutrosophic Jarque-Bera test is applied and compared with its existing form with the help of a numerical example of real gold mines data generated under the fuzzy environment. The study’s findings suggested that the proposed test is effective, informative, and suitable to be applied in indeterminacy compared to the existing Jarque–Bera test.

Penerapan Algoritma Fuzzy Tahani Untuk Rekomendasi Penerima Beasiswa Peningkatan Prestasi Akademik

Relational database systems that exist until now are only able to handle data that is definite (crisp), deterministic and precise. In fact, in real conditions, vague data is often needed for the decision-making process. For decision making involving fuzzy variables based on crisp data in the database, you can use a query on the database system with the concept of fuzzification on the data. In every educational institution, especially universities, there are several types of scholarships given to students. To get a scholarship, students must meet all the requirements that have been set. This study discusses the application of the Fuzzy Tahani algorithm for the recommendation of Academic Achievement Improvement (PPA) scholarship recipients at Bumigora University, Mataram. Data for PPA scholarship recipients was used in 2014 with details of the number of registrants 64 people and recipients (quota) of 15 people. Every year the number of applicants for this scholarship is increasing, while the processing and selection process is still done semi-manually so that the expected results are less than optimal, especially in terms of transparency and distribution. There are several variables that must be calculated by PPA scholarship recipients, namely the value of the Grade Point Average (GPA), Parents' Income, Number of Dependent Parents and Number of Diplomas. From the results of trials conducted in this study, it can be seen that the system's accuracy level reaches a value of 73.3%. This value is obtained by comparing the results of the semi-manual selection of PPA scholarship recipients with the results of the PPA scholarship selection using a system that uses the Fuzzy Tahani Algorithm.

Domination of Fuzzy Incidence Graphs with the Algorithm and Application for the Selection of a Medical Lab

Fuzzy graphs (FGs), broadly known as fuzzy incidence graphs (FIGs), have been recognized as being an effective tool to tackle real-world problems in which vague data and information are essential. Dominating sets (DSs) have multiple applications in diverse areas of life. In wireless networking, DSs are being used to find efficient routes with ad hoc mobile networks. In this paper, we extend the concept of domination of FGs to the FIGs and show some of their important properties. We propose the idea of order, size, and domination in FIGs. Two types of domination, namely, strong fuzzy incidence domination and weak fuzzy incidence domination, for FIGs are discussed. A relationship between strong fuzzy incidence domination and weak fuzzy incidence domination for complete fuzzy incidence graphs (CFIGs) is also introduced. An algorithm to find a fuzzy incidence dominating set (FIDS) and a fuzzy incidence domination number (FIDN) is discussed. Finally, an application of fuzzy incidence domination (FID) is provided to choose the best medical lab among different labs for the conduction of tests for the coronavirus.

Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

This contribution proposes a new model for sentiment analysis, which combines the convolutional neural network (CNN), C4.5 decision tree algorithm, and Fuzzy Rule-Based System (FRBS). Our suggested method consists of six parts. Firstly we have applied several pre-processing techniques. Secondly, we have used the fastText method for vectoring the analysed tweets. Thirdly, we have implemented the CNN for extracting and selecting the pertinent features from the tweets. Fourthly, we have fuzzified the CNN output using the Gaussian Fuzzification (GF) method for coping with vague data. Then we have applied fuzziness C4.5 for creating the fuzziness rules. Finally, we have used the General Fuzziness Reasoning (GFR) approach for classifying the new tweets. In summary, our method integrates the advantages of CNN and C4.5 techniques and overcomes the shortcomings of ambiguous data in the tweets using FRBS, which is consists of three-phase: fuzzification phase using GF, inference mechanism using fuzziness C4.5, and defuzzification phase using GFR. Also, to give our approach the ability to deal with the massive data, we have implemented it on the Hadoop framework of five computers. The experiential findings confirmed that our model operates excellently compared to other chosen models form the literature.

Innovative q-rung orthopair fuzzy prioritized aggregation operators based on priority degrees with application to sustainable energy planning: A case study of Gwadar

<abstract><p>Clean energy potential can be used on a large scale in order to achieve cost competitiveness and market effectiveness. This paper offers sufficient information to choose renewable technology for improving the living conditions of the local community while meeting energy requirements by employing the notion of q-rung orthopair fuzzy numbers (q-ROFNs). In real-world situations, a q-ROFN is exceptionally useful for representing ambiguous/vague data. A multi-criteria decision-making (MCDM) is proposed in which the parameters have a prioritization relationship and the idea of a priority degree is employed. The aggregation operators (AOs) are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, some prioritized operators with q-ROFNs are proposed named as "q-rung orthopair fuzzy prioritized averaging (q-ROFPA_{<italic>d</italic>}) operator with priority degrees and q-rung orthopair fuzzy prioritized geometric (q-ROFPG_{<italic>d</italic>}) operator with priority degrees". The results of the proposed approach are compared with several other related studies. The comparative analysis results indicate that the proposed approach is valid and accurate which provides feasible results. The characteristics of the existing method are often compared to other current methods, emphasizing the superiority of the presented work over currently used operators. Additionally, the effect of priority degrees is analyzed for information fusion and feasible ranking of objects.</p></abstract>

A Study on Domination in Vague Incidence Graph and Its Application in Medical Sciences

Fuzzy graphs (FGs), broadly known as fuzzy incidence graphs (FIGs), have been acknowledged as being an applicable and well-organized tool to epitomize and solve many multifarious real-world problems in which vague data and information are essential. Owing to unpredictable and unspecified information being an integral component in real-life problems that are often uncertain, it is highly challenging for an expert to illustrate those problems through a fuzzy graph. Therefore, resolving the uncertainty accompanying the unpredictable and unspecified information of any real-world problem can be done by applying a vague incidence graph (VIG), based on which the FGs may not engender satisfactory results. Similarly, VIGs are outstandingly practical tools for analyzing different computer science domains such as networking, clustering, and also other issues such as medical sciences, and traffic planning. Dominating sets (DSs) enjoy practical interest in several areas. In wireless networking, DSs are being used to find efficient routes with ad-hoc mobile networks. They have also been employed in document summarization, and in secure systems designs for electrical grids; consequently, in this paper, we extend the concept of the FIG to the VIG, and show some of its important properties. In particular, we discuss the well-known problems of vague incidence dominating set, valid degree, isolated vertex, vague incidence irredundant set and their cardinalities related to the dominating, etc. Finally, a DS application for VIG to properly manage the COVID-19 testing facility is introduced.

Real Time Data Capture: A Response to Unsustainable Dumping of Disposable Diapers and Sanitary Pads in Gweru City, Zimbabwe

The sustainable management of used disposable diapers and sanitary pads (Absorbent Hygienic Products) is undoubtedly a topical issue in municipalities across the developing world. This article is based on a bigger study which investigated how the city of Gweru can best manage this waste using a model that adjusts itself in response to real time data. Purposive and cluster sampling were employed to select the wards and the respondents respectively. Data was gathered using both qualitative and quantitative techniques. Particularly, semi-structured questionnaires were augmented by observations and in-depth interviews of key informants. The study found out that the municipality in Gweru was fighting a problem they had not yet measured; hence they were acting on vague data. Results of this study are likely to stimulate further research on how real time data on waste can be gathered. The results are also likely to shape the future of waste management in cities across the developing world. The main contribution of this study to the existing body of literature is its recommendation on the usage of real time data to drive waste management.

Data Mining in Cancer Diagnosis and Prediction: Review about Latest Ten Years

Data Mining [DM] has exceptional and prodigious potential for examining and analyzing the vague data of the medical domain. Where these data are used in clinical prognosis and diagnosis. Nevertheless, the unprocessed medical data are widely scattered, diverse in nature, and voluminous. These data should be accumulated in a sorted out structure. DM innovation and creativity give a customer a situated way to deal with new fashioned and hidden patterns in the data. The advantages of using DM in medical approach are unbounded and it has abundant applications, the most important: it leads to better medical treatment with a lower cost. Consequently, DM algorithms have the main usage in cancer detection and treatment through providing a learning rich environment which can help to improve the quality of clinical decisions. Multi researches are published about the using of DM in different destinations in the medical field. This paper provides an elaborated study about utilization of DM in cancer prediction and classifying, in addition to the main features and challenges in these researches are introduced in this paper for helping apprentice and youthful scientists and showing for them the key principle issues that are still exist around there.