There have been several research that have explored problem in multi  criteria analysis ways. TOPSIS and WSA, two multicriteria analysis methodologies, indicate how the eight areas of Slovakia were evaluated based on nine major characteristics of quality of life^{ }^{1}. The Gardner and Korth framework are used in our method to determine the aspects of the learners' collaborative learning styles. Using an Artificial Neural Network (ANN) and the Weighted Sum Model (WSM), proposes a system for recommending collaborative activities to learners ^{2}. Generalised TOPSIS, WSM, and WPM, as well as MATLAB coding approaches, are utilised to determine the optimum option to choose best laser for surgery. MCDM approaches are used in the Neutrosophic soft set environment as a case study ^{3}. By using the compression transformation, all PFNs are unified into the unit triangle in the first quadrant, the distance measure of PFNs is proposed according to the traditional distance meaning, and it is proven that the distance measure meets the axiomatic condition of the traditional distance, and the score function formula of PFNs and its ranking criterion are proposed using the minimum element (0,1)^{ }^{4}. This paper provides a unique fuzzy multicriteria decisionmaking system based on an enhanced scoring function of connection numbers and the Choquet integral in a Pythagorean fuzzy environment with interval values^{ }^{5}. A novel normalisation score function for PFN is presented that minimises information loss while accounting for uncertainty. The suggested combined weight framework is based on the MEREC and SWARA weighted extensive approaches, and it is both objective and subjective ^{6}.
The new addon for evaluating and benchmarking COVID19 machine learning algorithms. When we compared the results of FermateanFDOSM, the basic FDOSM, and TOPSIS, discovered that the FermateanFDOSM conclusion is more rational and consistent with expert opinion. Also, we used the validation method for the final result of FermateanFDOSM, and discovered that the result of FermateanFDOSM is more logical, going through a systematic ranking, and in accordance with decision makers' viewpoints^{ }^{7}. Pythagorean fuzzy VIKOR (PFVIKOR) technique for addressing EVCS site selection issues is devised, in which alternative evaluations are supplied as linguistic words characterised by Pythagorean fuzzy values (PFVs). The rating values of alternatives are considered as linguistic concepts conveyed by PFVs during the performance evaluation process ^{8}. The intuitive fuzzy TOPSIS (IFTOPSIS) approach was used to tackle the challenge of appraising socioeconomic phenomena using survey data. This allows the phenomena to be evaluated using aggregated secondary data by translating these data into intuitionistic fuzzy values ^{9}. The notion of the IFSM is presented in the work utilising Hellwig's technique for intuitionistic fuzzy sets. The IFSM allows complicated phenomena to be measured using respondents' opinions. The IFSM requires respondents to evaluate things in terms of the specified criteria using ordinal measurement scales. The findings of the respondents' opinion measurement are afterwards turned into intuitionistic fuzzy sets ^{10}. Based on ordinal data survey data, IFSM as a tool for quantifying complex phenomena. In this scenario, measurement data at the individual responder level are not necessary. The proposed approach may measure complicated phenomena using aggregated ordinal data from public statistics. The suggested method transforms aggregated ordinal data into intuitionistic fuzzy sets^{ }^{11}.
A literature search was carried out on the conversion of survey questionnaire responses into fuzzy, as well as MATLAB code on a fuzzy backdrop. Only a few researchers have investigated the translation of survey answer data into fuzzy data. Specifically, the Pythagorean fuzzy hunger method's translation of survey data. It was planned to do this study in New approach for translating questionnaire data to Pythagorean coupled with MATLAB to cover this research need. To close this gap, it was decided to find a new way to frame the Pythagorean fuzzy number using opinion of survey respondents. To easy the conversion of Pythagorean number, MATLAB code was created. The learning technique was rated in this study utilising Pythagorean fuzzy WSM using the MATLAB application. The outcomes were also compared in this study utilising Intuitionistic fuzzy WSM.
According to Zadeh ^{12}, Let {
where 0 ≤
In this case,
As suggested by Atanassov^{ }^{13}, IFS has various levels of membership and nonmembership. A is a collection of intuitionistic fuzzy sets, and set X in the given universe follows the following pattern:
where 0 ≤
In this case,
According to Xu ^{14}, IF properties states as follows,
According to Liu ^{15}, Let
According to Zeng ^{16}, If
According to Feng ^{17}, If
According to Yager ^{18}, In the given universe Set X follows the following pattern: P is a Pythagorean fuzzy set collection,
In this case,
Consider that A and B are distinct PFS is what is stated in PérezDomínguez ^{19},
According to Zhang ^{20}, Suppose
Where w = (
The Pythagorean fuzzy weighted sum average value of the ith questionnaire is denoted by
According to Wu ^{21}, If
By leveraging survey data, the proposed study will develop a brandnew technique for calculating the Pythagorean fuzzy number. The PF WSM approach was used to assess the learning strategy. New computations for Pythagorean fuzzy numbers and PF WSM are defined in the MATLAB code.
Based on the results, a questionnaire was created to gauge students' favourite learning strategies. The optimal learning technique was built with 10 questions. 6 to12 standard Students in the Krishnagiri district were asked to respond to questionnaires. Two categories of decision makers provided a combined 132 replies. The responder choose any option from the ordinal O = {
The survey data are represented into PFSs using innovative methods in this study, which is coded in MATLAB. The aforesaid approach was verified by PFWSM and contrasted with IFWSM. The actions this paper took are as follows:
This section proposes a novel approach for generating Pythagorean Fuzzy numbers using survey data. Let Q = {
This stage describes new way PFN configuration derived from survey data responses. The opinions of questionnaire respondents
where
In this case,
Utilising the PFs value
It meets condition (II).
Data respondent converted into PFDM. R has m questions and n decision makers. D  Decision makers and Q  Questions. It determined as follows,
A crisp response was converted to Pythagorean fuzzy and the Pythagorean fuzzy decision matrix was created. It shows in the
We believed that the weights of decision makers were identical since the survey items had the same priority in the evaluation. As previously stated by Maggino and Ruviglioni ^{22}, identical weights are employed in many applications. In this research,
According to (16), For example,



























1 
Work sheet 
43 
23 
49 
17 
0.80716 
0.59033 
0.86164 
0.50752 
0.8369 
0.54736 
0.7004 
6 
2 
Project 
45 
21 
44 
22 
0.82572 
0.56408 
0.8165 
0.57735 
0.82118 
0.57067 
0.67433 
7 
3 
Mind map 
41 
25 
47 
19 
0.78817 
0.61546 
0.84387 
0.53654 
0.8184 
0.57465 
0.66978 
8 
4 
Learning by teaching 
53 
13 
56 
10 
0.89612 
0.44381 
0.92113 
0.38925 
0.90953 
0.41564 
0.82725 
2 
5 
Online 
29 
37 
38 
28 
0.66287 
0.74874 
0.75879 
0.65134 
0.71576 
0.69834 
0.51232 
10 
6 
Activity 
48 
18 
60 
6 
0.8528 
0.52223 
0.95346 
0.30151 
0.9179 
0.39681 
0.84254 
1 
7 
Oral 
39 
27 
45 
21 
0.76871 
0.6396 
0.82572 
0.56408 
0.79951 
0.60065 
0.63922 
9 
8 
Video 
47 
19 
55 
11 
0.84387 
0.53654 
0.91287 
0.40825 
0.88372 
0.46802 
0.78096 
3 
9 
Game 
44 
22 
54 
12 
0.8165 
0.57735 
0.90453 
0.4264 
0.86823 
0.49617 
0.75382 
4 
10 
Homeresource 
46 
20 
47 
19 
0.83485 
0.55048 
0.84387 
0.53654 
0.83943 
0.54347 
0.70464 
5 
The new MATLAB code was written from scratch to represent survey data as Pythagorean fuzzy numbers and to verify the conditions of the Pythagorean fuzzy number as well as to compute the PFWSM score value for the 9th Learning method. The outputs are shown below,
Output......
Pythagorean fuzzy number from survey data frequency for DM1
Pm91v91 = 0.8165 0.5774
Condition1: m91+v91>1 Satisfies
Condition2: ((m91)^2+(v91)^2)=1 Satisfies
Pythagorean fuzzy number from survey data frequency for DM2
Pm92v92 = 0.9045 0.4264
Condition1: m92+v92>1 Satisfies
Condition2: ((m92)^2+(v92)^2)=1 Satisfies
PF Weighted Sum Average
r9 = 0.8682 0.4962
Pythagorean fuzzy number scoring functions
s9 = 0.7538
Activitybased learning came in first place for preferred learning technique, as shown by
The videobased learning was given to third place by the students. Because now a days students are the interested to see videos. Game based learning got fourth place. Always students are interested to play. It is also a key to success learning approach among the students. Home based resources on students' learning approach came in fifth. However, parents should actively encourage their children to use these tools at home. Worksheet, project, mind map, oral presentation, and online learning also received respectable rankings from sixth to tenth. This finding indicates that students are not motivated to pursue online or oral learning. Every learning technique was developed with consideration for a child's whole development.
By assembling 10 learning approaches and categorising them into two groups, such as Preferred learning approaches and Unpreferred learning approaches, the suggested research offers an overview. The top five learning approaches have been determined as the recommended standards by the students' replies. It was shown in
Use the formulas 16, 7, and 8 to calculate the PF WSM, IF WSM (S) and IF WSM (ES) score values. The score value is contrasted with PFWSM, IFWSM(S), and IFWSM(ES) in the graph. For each learning strategy, the PFWSM and IFWSM (ES) score values are almost the same visually. The IFWSM (S) score value, however, rarely changes. It illustrates that the rank identified in PFWSM will vary in all comparisons to the rank of score, except for two learning approaches. The PFWSM is listed in the same order in every other learning strategy. The
PFWSM Rank of Score is as
IFWSM (S) Rank of score is
IFWSM (ES) Rank of score is
This study explains how to turn survey data into Pythagorean fuzzy using a novel approach and new MATLAB code. In this study, we presented the Pythagorean fuzzy WSM (PFWSM) method for analysing learning approaches using survey data. For starters, the suggested method does not require raw data and takes ambiguity in respondents' opinions into account. This permits the phenomena to be evaluated using aggregated secondary data by translating these data into Pythagorean fuzzy values. The degree of participation in the Pythagorean fuzzy value is equal to the square root of the proportion of positive views of the questionnaire in connection to the decision maker. The degree of nonmembership to Pythagorean fuzzy value is the square root of the proportion of respondents who had an adverse view of the decision maker on the questionnaire.
Second, the suggested method for transforming aggregate secondary data into Pythagorean fuzzy values does not contradict the assumptions about the measurement level of ordinal scales, as well as the acceptable relations and transformations of their values. Following data transformation, the PFWSM technique evaluates the complicated phenomena using arithmetic operations, comparisons, and transformations that are permitted for Pythagorean fuzzy values. Typically, researchers are unable to specify the ideal values of the criterion. This is because, among other things, to the fact that adding another object to the study sample may affect the coordinates of the Pythagorean fuzzy good and unfavourable objects, hence changing their ranking position. The parameters of Pythagorean fuzzy values are used in the article's technique of calculating coordinates. This method has the added benefit of allowing the findings to be compared. Comparing the PFWSM technique against the standard IFWSM (S) and IFWSM (ES) methods allowed the study to highlight the proposed approach's strengths and drawbacks.
The next research challenge will be to propose a change to the PFWSM approach that will allow for the consideration of the distribution of ratings into distinct categories on the positive and negative side. Future study will also concentrate on applying the proposed technique to various challenges. The usage of MATLAB code in future problemsolving will be advantageous. Transform the replies into numerous fuzzy types in the future, such as Neutrosophic Pythagorean. Using multiple ranking systems, use the survey replies to address a variety of challenges in the future.