Internet of Things (IoT) adoption barriers for the circular economy using Pythagorean fuzzy SWARA-CoCoSo decision-making approach in the manufacturing sector

To find the correspondence between every number of attributes, the Bonferroni mean (BM) operator is most widely used and proven to be a flexible approach. To express uncertain information, the frame of the interval-valued T-spherical fuzzy set (IVTSFS) is a recent development in fuzzy settings which discusses four aspects of uncertain information using closed sub-intervals of 0, 1 and hence reduces the information loss greatly. In this research study, we introduced the principle of BM operators with IVTSFS to develop the principle of the inter-valued T-spherical fuzzy (IVTSF) BM (IVTSFBM) operator, the IVTSF-weighted BM (IVTSFWBM) operator, the IVTSF geometric BM (IVTSFGBM) operator, and the IVTSF-weighted geometric BM (IVTSFWGBM) operator. To see the significance of the proposed BM operators, we applied these BM operators to evaluate the performance of solar cells that play an important role in the field of energy storage. To do so, we developed a multi-attribute group decision-making (MAGDM) procedure based on IVTSF information and applied it to the problem of solar cells to evaluate their performance under uncertainty, where four aspects of opinion about solar cells were taken into consideration. We studied the results obtained using BM operators with some previous operators to see the significance of the proposed IVTSF BM operators.

/pdf/performance-evaluation-of-solar-energy-cells-using-the-29fxc91u.pdf

Performance Evaluation of Solar Energy Cells Using the Interval-Valued T-Spherical Fuzzy Bonferroni Mean Operators

Sustainable site selection for electric vehicle charging station (EVCS) is a significant process in the promotion of electric vehicle system development. The assessment and selection of suitable EVCS site is a very critical decision, involving complexity due to the presence of several associated criteria. Furthermore, uncertainty is an inevitable component of the information in the decision‐making procedure and its significance in the selection process is relatively high and needs to be cautiously measured. Single‐valued neutrosophic set (SVNS) is one of the valuable and flexible tools for handling such type of uncertain information arising in multi‐criteria decision‐making (MCDM) applications. Thus, the objective of this study is to introduce novel single‐valued neutrosophic information‐based additive ratio assessment (ARAS) approach for evaluating and prioritizing the sustainable EVCS sites. In this method, novel single‐valued subjective and objective weighted integrated approach (SVN‐SOWIA) is developed to compute the criteria by aggregating the objective weights resulted from a similarity measure‐based procedure and the subjective weights given by the experts. For this purpose, an innovative similarity measure is proposed for SVNSs. To display the performance of the present methodology, a computational study of EVCS sites evaluation is conferred under single‐valued neutrosophic environment. Comparative and sensitivity analyses are further performed to verify the strength of the developed approach. The outcome illustrates EVCS site EvUrjaa—Electric Vehicle Charging Station is the most optimal EVCS site in Indore region, India. Also, the environmental (0.324) and social (0.273) criteria are more important than technological (0.236) and economical (0.167) criteria in assessing the EVCS sites. The sensitivity analysis outcomes signify the EVCS option EvUrjaa—Electric Vehicle Charging Station always acquires its highest ranking in spite of how sub‐criteria weights fluctuate. The outcome of this study indicates that the developed approach can suggest more realistic performance under uncertain environment and therefore, provides a wide range of applications.

Single-valued neutrosophic similarity measure-based additive ratio assessment framework for optimal site selection of electric vehicle charging station

In real world, a typical decision-making problem in the medical field can be seen as an uncertain hesitant fuzzy multiattribute decision-making problem when existing experiences of decision-makers are insufficient. A three-way decision model is an effective tool to deal with uncertain decision-making problems, which can realize the classification of objects. However, different psychological behaviors of decision-makers are likely to affect decision-making results in actual decision-making processes. In order to address this challenge, we first propose a regret-theory-based three-way decision model in hesitant fuzzy environments, which mainly combines regret theory with hesitant fuzzy sets to calculate the perceived utility value of objects. Second, since one of the core ingredients for regret theory is the regret–rejoice function, we put forward a new regret–rejoice function that preserves the original indecisive uncertainty in hesitant fuzzy environments. In addition, according to the inherent relationship of the used data, this article provides a three-way classification method based on the preference ranking organization method for enrichment evaluations I method. Finally, the practicability, rationality, and superiority of the proposed method are shown via the case analysis and comparative analysis in the medical field.

Regret Theory-Based Three-Way Decision Model in Hesitant Fuzzy Environments and Its Application to Medical Decision

In real world, a typical decision-making problem in the medical field can be seen as an uncertain hesitant fuzzy multiattribute decision-making problem when existing experiences of decision-makers are insufficient. A three-way decision model is an effective tool to deal with uncertain decision-making problems, which can realize the classification of objects. However, different psychological behaviors of decision-makers are likely to affect decision-making results in actual decision-making processes. In order to address this challenge, we first propose a regret-theory-based three-way decision model in hesitant fuzzy environments, which mainly combines regret theory with hesitant fuzzy sets to calculate the perceived utility value of objects. Second, since one of the core ingredients for regret theory is the regret–rejoice function, we put forward a new regret–rejoice function that preserves the original indecisive uncertainty in hesitant fuzzy environments. In addition, according to the inherent relationship of the used data, this article provides a three-way classification method based on the preference ranking organization method for enrichment evaluations I method. Finally, the practicability, rationality, and superiority of the proposed method are shown via the case analysis and comparative analysis in the medical field. 

Since the future of the society depends upon the role of students and their services construct the prosperous and advanced society, so suitable career selection for the students' is considered to be an important problem to explore. As per psychology, if a student has the required capability, and positive attitudes towards a subject in terms of interest, attitude, memory, knowledge, environment, and career, then the student will achieve more in that subject. To consider this kind of uncertain issues, picture fuzzy set and rough set are found to be appropriate due to their inherent characteristics to deal with incomplete and imprecise information. In this study, picture fuzzy set and rough set-based approaches are proposed to help the student to choose an appropriate subject and consequently provide a good service or contribution to the society particularly in that domain. The main purpose of the article is to analyse student's features in terms of career, memory, interest, knowledge, environment and attitude and then predict the appropriate stream for making the career comfortable so that the student can conveniently explore much in that area. To select students' career, a hybridized distance measure under picture fuzzy environment is proposed where the evaluating information regarding students, subjects and student's features are given in picture fuzzy numbers. In this paper, two types of hybridization approaches are proposed which are the hybridization of Hausdorff and Hamming distance measures and hybridization of Hausdorff and Euclidean distance measures. Next, we apply rough set theory to determine whether a particular subject is appropriate for a student even if there is controversy to select a stream. Lower and higher approximation with boundary region of rough set theory is used to manage inconsistency situations. Finally, two case studies are demonstrated to validate the applicability of the proposed idea.

/pdf/career-selection-of-students-using-hybridized-distance-3tfw3oqeaw.pdf

Career selection of students using hybridized distance measure based on picture fuzzy set and rough set theory

Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accurate diagnosis and appropriate drug therapy to patients. This work aims to categorize electroencephalography signals on different channels' recordings for classifying and predicting epileptic seizures. The collection of the electroencephalography recordings contained in the dataset attributes 179 information and 11,500 instances. Instances are of five categories, where one is the symptoms of epilepsy seizure. We have used traditional, ensemble methods and deep machine learning techniques highlighting their performance for the epilepsy seizure detection task. One dimensional convolutional neural network, ensemble machine learning techniques like bagging, boosting (AdaBoost, gradient boosting, and XG boosting), and stacking is implemented. Traditional machine learning techniques such as decision tree, random forest, extra tree, ridge classifier, logistic regression, K-Nearest Neighbor, Naive Bayes (gaussian), and Kernel Support Vector Machine (polynomial, gaussian) are used for classifying and predicting epilepsy seizure. Before using ensemble and traditional techniques, we have preprocessed the data set using the Karl Pearson coefficient of correlation to eliminate irrelevant attributes. Further accuracy of classification and prediction of the classifiers are manipulated using k-fold cross-validation methods and represent the Receiver Operating Characteristic Area Under the Curve for each classifier. After sorting and comparing algorithms, we have found the convolutional neural network and extra tree bagging classifiers to have better performance than all other ensemble and traditional classifiers.

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques.

Feature prediction is necessary in different areas like Death Rate, Thunder Basin Antelope Study, Systolic Blood Pressure Data, General Psychology, child health, examination result, etc For that, we can collect previous information and study the relationship between the different facts Using the neurons concept, we can train our system to predict what must happen according to different situations Multiple regression models can be used to predict the death rate The key information of the article is the mathematical formulation for the forecast linear equation that estimates the multiple regression model calculation The value of dependent variable forecast under the influence of independent variables is explained

Prediction of Death Rate Using Regression Analysis

Allotment of nurses to patients is a critical task in terms of better treatment. Nurses should be appointed according to a patient’s health condition, type of disease & financial condition. Again understaffing of nurses may hamper patient health & condition. Similarly, overstaffing of nurses is a waste of man powers. Adequate staffing of nurses is crucial. We propose a technique using game theory to meet over staffing and under staffing of nurses. Game theory plays a vital role to meet the exact requirement. Nash equilibrium can be used for taking all possible decisions, like appointment of nurses in different categories for smooth treatment of patients. However, final & most suitable decision can be taken using perfect Nash equilibrium. This technique is a perfect technique to implement in case of vital & critical decision points.

/pdf/prediction-of-nurses-allotment-to-patient-in-hospital-1ob9t2r5.pdf

Prediction of Nurses Allotment to Patient in Hospital through Game Theory

Sufficient numbers of staff are required for any hospital for providing better services to patients with satisfactory treatment. In most hospitals, the patient's treatment and care depend on the availability of nurses. Less number of nurses are a reason for average care and treatment to patients, whereas more than the required number of nurses may cause wastage of expenditure and manpower. More importantly, an additional number of nurses may be assigned to some hospitals where a sufficient number of nurses are not available. Moreover, nurses are of different categories such as qualified nurses having experience in providing service to patients, qualified nurses without having experience, and nurses without qualification but have experience. The expenditure also depends on categories of nurses' appointments. To reach an equilibrium point in appointing nurses, we propose a hybridization model using linear regression, fuzzy set theory and game-theoretic approaches. Regression analysis is implemented for prediction based on different fuzzy membership values of independent variables. We implement two concepts of game theory, the Nash equilibrium and the perfect Nash equilibrium. Implementing the Nash equilibrium, different equilibria values are generated and by implementing perfect Nash equilibrium, a subgame is generated to reach one equilibrium value. We have illustrated the proposed approach using a case study in which the linear regression approach is implemented to predict the patients’ arrival rate based on the monetary standard of the patient, communication facilities, patients curing chances, and patient choice towards the hospital, where these four features are quantified using fuzzy membership values. Nash equilibrium decides all possible ways of the nurses' allocations and perfect Nash equilibrium assist to reach an appropriate allocation. Thus, the hybridization of the regression approach, fuzzy membership and game theory approaches finalize the exact allocation of nurses. Finally, a comparative analysis is given to demonstrate the effectiveness of the proposed approach. 

https://link.springer.com/content/pdf/10.1007/s12046-022-01932-0.pdf

Rekha Sahu

Papers

Career selection of students using hybridized distance measure based on picture fuzzy set and rough set theory

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques.

Prediction of Death Rate Using Regression Analysis

Prediction of Nurses Allotment to Patient in Hospital through Game Theory

Nurse allocation in hospital: hybridization of linear regression, fuzzy set and game-theoretic approaches