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Belief and plausibility functions based on evidence theory (ET) have been widely used in managing uncertainty. Various generalizations of ET to fuzzy sets (FSs) have been reported in the literature, but no generalization of ET to q-rung orthopair fuzzy sets (q-ROFSs) has been made yet. Therefore, this paper proposes a novel, simple, and intuitive approach to distance and similarity measures for q-ROFSs based on belief and plausibility functions within the framework of ET. This research addresses a significant research gap by introducing a comprehensive framework for handling uncertainty in q-ROFSs using ET. Furthermore, it acknowledges the limitations inherent in the current state of research, notably the absence of generalizations of ET to q-ROFSs and the challenges in extending belief and plausibility measures to certain aggregation operators and other generalizations including Hesitant fuzzy sets, Bipolar fuzzy sets, Fuzzy soft sets etc. Our contribution lies in the proposal of a novel approach to distance and similarity measures for q-ROFSs under ET, utilizing Orthopairian belief and plausibility intervals (OBPIs). We establish new similarity measures within the generalized ET framework and demonstrate the reasonability of our method through useful numerical examples. Additionally, we construct Orthopairian belief and plausibility GRA (OBP-GRA) for managing daily life complex issues, particularly in multicriteria decision-making scenarios. Numerical simulations and results confirm the usability and practical applicability of our proposed method in the framework of ET. [ABSTRACT FROM AUTHOR]

In the medical profession, recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality. The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans. Automatic (computerized) illness detection in medical imaging has found you the emergent region in several medical diagnostic applications. Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio. The brain tumor is one of the most common causes of death. Researchers have already proposed various models for the classification and detection of tumors, each with its strengths and weaknesses, but there is still a need to improve the classification process with improved efficiency. However, in this study, we give an in-depth analysis of six distinct machine learning (ML) algorithms, including Random Forest (RF), Naïve Bayes (NB), Neural Networks (NN), CN2 Rule Induction (CN2), Support Vector Machine (SVM), and Decision Tree (Tree), to address this gap in improving accuracy. On the Kaggle dataset, these strategies are tested using classification accuracy, the area under the Receiver Operating Characteristic (ROC) curve, precision, recall, and F1 Score (F1). The training and testing process is strengthened by using a 10-fold crossvalidation technique. The results show that SVM outperforms other algorithms, with 95.3% accuracy. [ABSTRACT FROM AUTHOR]

The objective of this study was to find out the mediating role of Psychological Capital (PC) between the relationships of work engagement (W.E) and job performance (J.P) in teachers of public universities of Khyber Pakhtunkhwa, Sindh, Panjab and Balochestan. Data were collected from four hundred and fifteen (N=415) faculty members including Lecturers (N=172), Assistant Professor (N=114), Associate Professors (N=95) and Professors (N=34). Structure Equation Modeling (SEM) was used to investigate the interrelationship among PC, W.E and J.P. Barron and Kenny's (1986) rules for testing the mediating role of a variable between the relationship of independent and dependent variable were used in this study. The results of correlation showed that W.E, PC and J.P were significantly interrelated. The 3-factor model confirmed that PC partially mediated the relationship between W.E and J.P. [ABSTRACT FROM AUTHOR]

Fuzzy sets (FSs) with belief and plausibility measures in Dempster-Shafer theory (DST) are recognized as different methodology to model imperfect, uncertain, and vague information more accurately than probability. Various generalizations of DST to FSs are suggested in the numerous literatures but the generalization of DST to Pythagorean fuzzy sets (PFSs) has not yet been considered so far. In this paper, we first suggest an intuitive and simple way to develop a generalization of DST to PFSs with the characterization of belief function in terms of membership function and plausibility function in terms of 1-nonmemberhip function respectively. We give the interpretation of belief and plausibility on PFSs and then construct belief-plausibility intervals (BPIs) of PFSs. On the basis of suggested BPIs, we use Hausdorff distance to describe the distance between two BPIs and then construct several similarity measures in the generalized context of DST to PFSs. By utilizing the method of VIekriterijumsko KOmpromisno Rangiranje (VIKOR), the suggested belief and plausibility measures on PFSs in the framework of DST enable us to develop a belief-plausibility VIKOR (BP-VIKOR) to manage multicriteria decision-making (MCDM) problems related to daily life settings. Numerical analysis with examples are given to show the suggested method is reasonable, and suitable in the environment of PFSs in the context of generalization of DST. [ABSTRACT FROM AUTHOR]

Tomato fruit rot caused by fungi is the most common postharvest problem. The fungi are responsible for spoilage of produce between harvest and consumption. This study analyzed the distribution of tomato fruit rot using descriptive as well as geostatistical techniques and diversity of associated fungal species. Descriptive statistics indicated that the rot incidence and severity ranged from 10.22-44.17% and 0.7 - 7.10 mm. Geostatistical techniques were used to predict the spatial dependency class. Results revealed that rot incidence data had a nugget/sill ratio of 0.487 inferring moderate spatial dependence, whereas severity inferring weak spatial dependence with nugget/sill ratio of 0.221. Furthermore, geostatistical analysis was also applied to examine spatial variability within-field using semivariogram and kriged maps. The maps of current study showed spatial distribution of rot incidence and severity in the study area. These digital maps will be helpful to develop pre-and post-harvest management strategies against tomato fruit rot. A total of nine fungal species were isolated from infected tomato fruit namely; Geotrichum candidum, Alternaria alternata, Aspergillus flavus, Aspergillus niger, Bipolaris, Fusarium oxysporum, Rhizopus stolonifer, Culvularia lunata and Penicillum digitatum. Aspergillus flavus was dominating species (17.02%) followed by Fusarium oxysporum (14.89%) and Alternaria alternata (12.76%), while Culvularia lunata has the least occurrence (6.38%). This study provides information about post-harvest rotting fungi of tomato to the growers, extension workers and researchers in the district Gilgit. [ABSTRACT FROM AUTHOR]

The study is designed to identify the determinants of profitability in the insurance industry of Pakistan. The panel data set of 41 insurance companies (life, non-life, and takaful insurance) from 2007-2017 was employed. Hausman's specification and Breusch-Pagan Lagrange multiplier (LM) tests have selected the most appropriate test i.e. fixed effect and pooled OLS model for this study. The results of pooled OLS and fixed effect model reveal that insurance specific and macroeconomic factor like leverage, business risk, and inflation rate are negative but significantly affect the profitability of the insurance sector. However, size and GDP rate has a positive and statistically significant influence on profitability. Liquidity and growth are insignificant determinants of the study. This is the first study that covers the entire insurance industry, which is also composed of the takaful industry, along with firm-specific and country-specific attributes and applies the most suitable models for the study. Thus, this study is very important for top-echelon and policy-makers of the insurance sector of Pakistan regarding the profitability of the firm and wealth of the shareholders. [ABSTRACT FROM AUTHOR]

This study was an attempt to verify and test a commonly held assumption that low level of employee work engagement may leads to deviant behavior, absenteeism and high turnover intentions. For this purpose, in the current research work 246 individuals from banking sectors of Pakistan were selected through stratified sampling technique. The responses of the respondents were measure through a structured questionnaire having a five point Likert scale. The reliability of the instrument was checked and found satisfactory. All the regression assumptions were tested and found that the data fulfill all the regression assumptions like data normality, autocorrelation, multicollinearity, heteroscedasticity and homoscedasticity. To test the study hypotheses; regression and mediation were applied. A negative significant relationship was found between employee work engagement and behavioral outcomes (p= .00, B= -.406). A negative significant relationship was also found between employee work engagement and turnover intention (p= .00, B= -.130). Similarly, the study found a significant negative association between work engagement and deviant behavior (p= .00, B= -.167). The results suggest that team work effectiveness moderates the relationship between work engagement and behavioral outcomes. Implication for theory and future research direction was also highlighted. [ABSTRACT FROM AUTHOR]

The choice of similarity measure (SM) plays an important role in distinguishing between objects. Similarity measure of Pythagorean fuzzy sets (PFSs) is very useful and effective in discriminating between different Pythagorean fuzzy sets. Therefore, in this paper, we suggest a new similarity measure for PFSs based on converting the PFSs into their lower, upper and middle fuzzy sets (FSs) to calculate their degree of similarity. We construct an axiomatic definition for a new SM of PFSs. Furthermore, we put forward a new way to express the similarity measure of PFSs to show its competency, reliability and applicability. For establishing reasonability and usefulness of the proposed methods, we present several practical examples related to pattern recognition and multicriteria decision making problems. Finally, we construct an algorithm for Portuguese of interactive and multiple attributes decision making (TODIM) method based on the proposed similarity measures, for handling complex multicriteria decision making problems related to day to day life. Our final results show that the suggested method is reasonable, reliable and useful in managing different complex decision making problems in the context of Pythagorean fuzzy sets as the domain. [ABSTRACT FROM AUTHOR]