Fuzzy Score Research Articles

An Emotionally Intuitive Fuzzy TODIM Methodology for Decision Making Based on Online Reviews: Insights from Movie Rankings

With the burgeoning growth of the internet, online evaluation systems have become increasingly pivotal in shaping consumer decision making. In this context, this study introduces an intuitionistic fuzzy TODIM (an acronym in Portuguese for interactive and multicriteria decision making) methodology to rank products based on online reviews. Our approach aims to enhance user decision making efficiency and address the prevalent issue of information overload. Initially, we devised a product attribute emotion quantification framework within the confines of the intuitionistic fuzzy paradigm. This allows for the transformation of online reviews into exact functional outputs via our advanced intuitionistic fuzzy scoring mechanism and its associated precise function. Following this, we take into account the inherent correlation among product attributes, leading to the development of an attribute-associated intuitionistic fuzzy model. This model further ascertains the dominance degree of alternative products. Moreover, by integrating the risk aversion factor, we can derive a hierarchical structure for alternative products, aiding in the prioritization process. Finally, this paper validates the proposed method using movie sequencing as a case study. The results show that the proposed method, which takes into account the emotional tendencies of different attributes in a movie and the different preferences of viewers in the attribute weighting and movie selection process, is more reasonable than methods proposed in previous studies.

Diagnosis of Alzheimer’s disease via Intuitionistic fuzzy least squares twin SVM

Neurodegenerative disorders like Alzheimer’s disease (AD) are irreversible and show atrophies in the area of the cerebral cortex of brain. AD leads to loss of memory and other cognitive impairments. The AD subjects are evaluated based on magnetic resonance imaging scans. The data may have the problem of class imbalance, noise and outliers which is a great challenge for classification. Support vector machines and twin support vector machine-based classifiers may not effectively deal with these problems as both these models assume that all the samples are equally important for the separating hyperplane. To overcome these issues, we propose intuitionistic fuzzy least square twin support vector machine for class imbalance problems (IFLSTSVM) and class specific-IFLSTSVM (CS-IFLSTSVM). To minimize the effects of class imbalance, the samples are appropriately weighted to minimize their effect on the optimal hyperplane. Moreover, we use intuitionistic fuzzy scores to overcome the issues of noise and outliers. Intuitionistic fuzzy score values generate appropriate weights by considering both the distance of the samples from the class centroid as well as the heterogeneity of the samples. The proposed models IFLSTSVM and CS-IFLSTSVM are efficient as they need to solve a system of linear equations. In Alzheimer’s disease diagnosis, the proposed IFLSTSVM and CS-IFLSTSVM models showed better performance in MCI_vs_AD and CN_vs_MCI cases, respectively. Moreover, the proposed models showed better performance in the diagnosis of breast cancer classification. The statistical analysis carried out over KEEL and UCI data leads to the superiority of the proposed models. The source code of the proposed model is available at https://github.com/mtanveer1/Diagnosis-of-Alzheimer-s-disease-via-Intuitionistic-fuzzy-least-squares-twin-SVM.

Improving Inter-Helix Contact Prediction With Local 2D Topological Information.

Inter-helix contact prediction is to identify residue contact across different helices in α-helical integral membrane proteins. Despite the progress made by various computational methods, contact prediction remains as a challenging task, and there is no method to our knowledge that directly tap into the contact map in an alignment free manner. We build 2D contact models from an independent dataset to capture the topological patterns in the neighborhood of a residue pair depending it is a contact or not, and apply the models to the state-of-art method's predictions to extract the features reflecting 2D inter-helix contact patterns. A secondary classifier is trained on such features. Realizing that the achievable improvement is intrinsically hinged on the quality of original predictions, we devise a mechanism to deal with the issue by introducing, 1) partial discretization of original prediction scores to more effectively leverage useful information 2) fuzzy score to assess the quality of the original prediction to help with selecting the residue pairs where improvement is more achievable. The cross-validation results show that the prediction from our method outperforms other methods including the state-of-the-art method (DeepHelicon) by a notable degree even without using the refinement selection scheme. By applying the refinement selection scheme, our method outperforms the state-of-the-art method significantly in these selected sequences.

Identification of Critical Locations for Improvement of Air Quality Developing a Prioritized Clean Air Assessment Tool (PCAT)

Fourteen Indian cities, including urban and rural locations, were chosen for the present study across India, with unhealthy air quality based on National Air Quality Index (NAQI > 100). However, it was found that NAQI values over the locations are driven by the undifferentiated mass concentration of particulate matter (PM, both PM10 and PM2.5) than other criteria pollutants. The PM2.5 and PM10 concentration during the winter violated the National Ambient Air Quality Standards (NAAQS) of India by two to five times at six urban locations, with the mean daily PM2.5 concentration averaged over the month; the the largest being at Patna (353 µg m−3) during the winter and lowest at Bengaluru (27 µg m−3) during the summer. The analysis of chemical species, in general, indicated NO2 (SO2, CO) as having a 25% to 70% (16% to 50%, 16% to 85%) increase in concentration from the summer to winter, which is adequately reflected in higher fuzzy scores during the winter. Thus, to provide a realistic approach to air quality management, the present study focuses on identifying priority-based locations requiring immediate mitigation measures by developing a Prioritized Clean Air Assessment Tool (PCAT). The tool utilizes a fuzzy-based algorithm to incorporate the cumulative effect of all six criteria pollutants, taking into consideration the severity of their expected health implications. Using PCAT, Delhi and Varanasi cities are identified for prioritized mitigation considering the NAAQS of India, unlike all cities (except Bengaluru) during the winter and nine out of fourteen cities during the summer, considering the NAQI. Using more stringent WHO guideline values in PCAT, six cities out of fourteen were identified requiring immediate mitigation during the winter and summer months; locations such as Solapur and Patna are identified to need season-specific mitigation measures during the summer and winter, respectively. The tool is simplistic, user-friendly, and quickly evaluates multiple locations simultaneously to provide priority sites.

T2-fuzzy multi-fused facial image fusion (T2FMFImgF): An efficient face recognition

This paper presents a novel decision-making method for face recognition where the features were extracted from the original image fused with its corresponding true and partial diagonal images. To extract features, we adopted the generalized two-dimensional FLD (G2DFLD) feature extraction technique. The feature vectors from a test image are given as input to neural network-based classifier. It is trained with the feature vectors of original image and diagonally fused images and thereby the merit weights with respect to different classes were generated. To address the factors that affect the face recognition accuracy and uncertainty related to raw biometric data, a fuzzy score for each of the classes is generated by treating a type-2 fuzzy set. This type-2 fuzzy set is formed by the feature vectors of both the diagonally fused training samples and the test image of the respective classes. A concluding score for each of the classes under consideration is computed by fusing complemented merit weight with the complemented fuzzy score. These class-wise concluding scores are considered in the face recognition process. In this study, the well-known face databases (AT&T, UMIST and CMU-PIE) are used to evaluate the performance of the proposed method. The experimental results illustrate the fact that the proposed method has exhibited superior classification precision as compared with other state-of-art methods. Our T2FMFImgF method achieves highest face recognition accuracies of 99.41%, 98.36% and 89.80% in case of AT&T, UMIST and CMU-PIE (with expression), respectively while for CMU-PIE (with Light) the highest recognition accuracy is 97.957%. In addition to it, the presented method is quite successful in fusing and classifying textural information from the original and partial diagonal images by integrating them with type-2 fuzzy set-based treatment.

Multi-view intuitionistic fuzzy support vector machines with insensitive pinball loss for classification of noisy data

Multi-view support vector machines (MvSVMs) have been widely used to solve multi-view classification problems. However, the conventional MvSVMs often overlook the presence of noise and outliers that commonly exist in the original data. In this paper, we propose two novel multi-view intuitionistic fuzzy support vector machines with insensitive pinball loss that can not only handle the general multi-view classification problems but also be robust to noisy data. In the proposed convex optimization models, the pinball loss is incorporated into the multi-view learning, which enables the maximization of the quantization distance. Moreover, to utilize multi-view information more effectively, the intuitionistic fuzzy score is introduced to assign a weight to each multi-view sample. The intuitionistic fuzzy score combines the membership and non-membership functions, which provides an efficient mechanism to assign weights to the multi-view samples. Further, we provide a discussion of the proposed models with the state-of-the-art technologies. Experiments are conducted on a series of datasets, and the results show that the proposed convex models outperform several state-of-the-art models. The code is available at 1.

An efficient spherical fuzzy MEREC-CoCoSo approach based on novel score function and aggregation operators for group decision making.

The major objective of the current investigation is to build an integrated multiple criteria group decision-making (MCGDM) methodology based on combined compromise solution (CoCoSo) andspherical fuzzy set for determining the optimal solar power station. To begin with, an innovative spherical fuzzy score function is brought forward to strengthen the efficiency of the comparison for spherical fuzzy number (SFN). Secondly, several newly operational laws for SFN are defined and some novel aggregation operation based on them are propounded. The corresponding excellent properties of the novel operators are also explored at length. Further, the spherical fuzzy method on the removal effects of criteria (MEREC) technique is presented by the proposed score function to work out the importance of the criteria. Lastly, an MCGDM approach is propounded based on improved spherical fuzzy CoCoSo to obtain the ranking of the solar power station locations. The feasibility and practicability of the proposed SF-MEREC-CoCoSo method are investigated through the comparison study with the extant methods. The sensibility analysis is also executed to discuss the robustness and stability of the propounded methodology.

Using the Fuzzy Best Worst Method for Evaluating Strategic Planning Models

During the last few decades, various strategic planning models have been suggested in the literature. It is difficult for a company to decide which of these models is most useful to adopt, as each of them shows different strengths and weaknesses. We consider this problem a multicriteria decision problem and investigate the evaluation of six strategic planning models in the context of smaller and medium-sized manufacturing companies in Iran. We consider a methodology that supports the analysis of the input from several decision-makers based on multiple criteria and assume vagueness in the input data elicited from them. For the purpose considered, the fuzzy best worst method (FBWM) appears appropriate. Based on a literature review, six evaluation criteria for strategic management models are considered: formality, clarity, measurability, objectivity, coverage, and consistency. These criteria are evaluated based on the input provided by thirteen managers using linguistic variables. FBWM is used to provide criteria weights that are used to determine fuzzy scores for the six considered strategic planning models. Finally, a defuzzification of the scores indicates the model by Wright is best suited for the application purpose. A consistency analysis included in FBWM shows that the input provided by the managers is sufficiently consistent.

Fuzzy Delphi Method (FDM) to Identify Elements, Constructs, and Items for Designing And Developing Contents for Pre-Braille Skills Module

On the basis of the discrepancy model, a need analysis questionnaire was distributed to 92 randomly selected special education teachers in Malaysia to determine if they required a specialised module for early braille learning lessons. The results of the survey indicate that teachers strongly agree that they need a guided module with detailed instructions and video demonstrations in teaching early braille lessons. Fuzzy Delphi Method (FDM) was chosen to determine the elements, constructs, and items required to design and develop the contents for Pre-Braille Skills Module. The Content Assessment of the Pre-Braille Skills Module questionnaire is used to collect data from seven authorised subject matter experts. The chosen elements, constructs, and items must satisfy three criteria of triangular fuzzy numbers: threshold value (d) ≤ 0.2, percentage of experts in agreement > 75%, and fuzzy score with a minimum value of 0.5. The data analysis was conducted using an Excel template, called FDM

Evaluation of risks impeding sustainable mining using Fermatean fuzzy score function based SWARA method

Sustainability in the mining and raw materials sector is a key target in the EU Green deal agenda. The aim of this work is to determine the degree of importance of risks that may impede sustainable mining, considering UN Sustainable Development Goals (SDGs) indicators and EU initiatives, taking as a case study the mining sector in Greece. A total of 49 risks for sustainable mining, under six categories, were identified by means of expert consultation and review of the literature. The identification and prioritization of potential risks can provide a pathway towards sustainable mining operations. The risks factors weighting is enhanced using a new Fermatean fuzzy score function with Stepwise Weight Assessment Ratio Analysis (SWARA). The proposed model is a powerful tool to handle the uncertainties and inaccuracies in the information regarding the weights of the risks. The main research findings indicate that the most important risks for sustainable mining in Greece are irresponsible mining, the lack of license to operate, and poor environmental monitoring, which are directly connected to the aim and scope of SDG12: responsible consumption and production. In addition, according to the results the category with the highest risk for sustainable mining is the one of “Risk to Environment”. A complete list of risks and risk categories, and their ranking is presented and discussed creating a priority of actions in the framework of European and international initiatives to set a road map to sustainable mining. This work provides a benchmark for future studies, with the aim of providing a tool for evaluating and ranking global risk factors that may affect sustainable mining development.

Development of framework integrating ergonomics in Lean’s Muda, Muri, and Mura concepts

Lean and ergonomics are two disciplines that shared common goals to improve manufacturing production systems. However, there are limited studies that specifically link ergonomics with Lean’s 3 M concept of Muda (waste), Muri (overburden), and Mura (inconsistency). The study employed semi-structured interview with 10 experienced practitioners on past lean projects to establish potential linkage between ergonomics and Lean’s 3 M. A conceptual framework integrating ergonomics components into the Lean’s 3 M components was then generated through group discussion, and validated using Fuzzy Delphi Method analysis. The interviews revealed four themes linking ergonomics to Lean’s 3 M: (1) Human energy waste, (2) Uneven workload distribution, (3) Overburden of worker’s capacity, and (4) Worker performance affects work performance. A conceptual framework integrating ergonomics components into the Lean’s 3 M components was then proposed, which redefines several components of Lean’s 3 M from ergonomics perspective. Fuzzy Delphi Method analysis on 12 practitioners showed overall agreement with the content, structure, and applicability of the proposed framework (threshold (d) value ≤0.2, expert consensus ≥75%, and fuzzy score ≥0.5). The findings may provide platform for lean and ergonomics practitioners to communicate and bridge the gaps between the two disciplines.

A fuzzy rule‐based system with decision tree for breast cancer detection

AbstractBreast cancer is possibly the deadliest illness in the world and the risks are gradually increasing. One out of eight women has the chance to be detected with breast cancer in their lifetime. The utmost cause for the higher fatality rates is the prolonged prognosis for the detection of breast cancer. The focus of this study is therefore to develop a better fuzzy expert system for the detection of breast cancer using decision tree analysis for deriving the rule base. For this classification problem, the input features of the dataset are converted into human‐understandable terms‐linguistic variables. The Mamdani Fuzzy Rule‐Based system is deployed as the main inference engine and the centroid method for the defuzzification process to convert the final fuzzy score into class labels‐ benign (not cancerous) or malignant (cancerous). A decision tree algorithm is applied the creating a novel set of 27 fuzzy rules which are fed into FRBS. The investigation is performed on the publicly available Wisconsin Breast Cancer Dataset. The accuracy obtained by the proposed system is about 97%, recall is 99.58% and precision is about 93%. The experiments on this dataset yield higher performance as compared to the state‐of‐the‐art dataset.

Research on a Comfort Evaluation Model for High-Speed Trains Based on Variable Weight Theory

As a result of the continuous improvement in passengers’ requirements for the quality of train operation, the comfort of high-speed train operation has been paid increasing attention. The evaluation of comfort has gradually changed from the narrow sense of a comfort evaluation model containing only vibration to the generalized evaluation of passengers’ overall satisfaction with the ride environment of specific lines. The factors affecting comfort evaluation include physical, physiological, and psychological aspects. To address the problems that the existing comfort evaluation model has a single index and that the weight determination of some indicators is greatly affected by subjectivity, we built a high-speed train comfort evaluation model based on variable weight theory. Combined with the actual working conditions of the Baolan passenger dedicated line, dynamic detection data and noise monitoring data captured by a track inspection car were combined with a passenger ride comfort questionnaire survey. In addition, the initial weight value of each factor was optimized by constructing an equilibrium function to realize the balance between the various factors, so as to realize the comprehensive fuzzy evaluation of high-speed train comfort. The results show that the comprehensive evaluation result of the comfort degree of the high-speed train on the Tongwei to Lanzhou section of the Baolan passenger dedicated line has a grade of II. The fuzzy scores of the evaluations using variable weights and constant weights were analyzed from the perspective of membership degree. The variable weight optimization avoids the one-sidedness and extremeness of the constant weight calculation. The comprehensive evaluation results are closer to the real situation. The research results can provide a reference for the comfort evaluation of high-speed trains with extreme differences in state values and constant weights and help in the acquisition of more realistic evaluation results.

Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.

SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host-pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host-pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host-pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host-pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs.

A modified EDAS model for comparison of mobile wallet service providers in India

The present paper has two-fold purposes. First, the current work provides an integrated theoretical framework to compare popular mobile wallet service providers based on users' views in the Indian context. To this end, we propose a new grey correlation-based Picture Fuzzy-Evaluation based on Distance from Average Solution (GCPF-EDAS) framework for the comparative analysis. We integrate the fundamental framework of the Technology Acceptance Model and Unified theory of acceptance and use of technology vis-à-vis service quality dimensions for criteria selection. For comparative ranking, we conduct our analysis under uncertain environments using picture fuzzy numbers. We find that user-friendliness, a wide variety of use, and familiarity and awareness about the products help reduce the uncertainty factors and obtain positive impressions from the users. It is seen that PhonePe (A3), Google Pay (A2), Amazon Pay (A4) and PayTM (A1) hold top positions. For validation of the result, we first compare the ranking provided by our proposed model with that derived by using picture fuzzy score based extensions of EDAS and another widely used algorithm such as The Technique for Order of Preference by Similarity to Ideal Solution. We observe a significant consistency. We then carry out rank reversal test for GCPF-EDAS model. We notice that our proposed GCPF-EDAS model does not suffers from rank reversal phenomenon. To examine the stability in the result for further validation, we carry out the sensitivity analysis by varying the differentiating coefficient and exchanging the criteria weights. We find that our proposed method provides stable result for the present case study and performs better as ranking order does not get changed significantly with the changes in the given conditions.

Equipment Development Scheme Decision Based on IFS‐Choquet Integral Improved Matter Element Model

An optimized matter element decision method is proposed based on intuitionistic fuzzy set (IFS) and Choquet integral to solve the problems of equipment development scheme decision (e.g., the correlation of scheme attributes and the low decision reliability of evaluation results). First, the nonlinear programming problem of expert intuition fuzzy subjective and objective decision information difference is constructed using the Choquet integral method, and the measure values and related factors of decision attributes are solved using the optimization algorithm. Second, given the comprehensive contribution of attributes, the fuzzy measure Shapley value is employed to represent the attribute weight. Last, the matter element extension model is optimized using the attribute weight information and intuitionistic fuzzy score value, and a two‐level evaluation strategy is formulated to determine the ranking and grade. On that basis, scientific decisions are made for several different schemes to be selected.

Multi-attribute group decision-making problem of medical consumption products based on extended TODIM-VIKOR approach with Fermatean fuzzy information measure

The fundamental goal of this research is to develop a MAGDM (Multi-Attribute Group Decision Making) problem of Medical Consumption Products. We propose TODIM–VIKOR approach in this paper, which combines the TODIM (an acronym in Portuguese for Interactive and Multi-criteria Decision-Making) and VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) procedures under Fermatean fuzzy information. A new Fermatean fuzzy scoring function is presented for dealing with comparison problems. In addition, we introduce a novel entropy measure for assessing the degree of fuzziness associated with an FFS. We also offer a Jensen–Shannon divergence measure for the Fermatean Fuzzy set that can be used to compare the discrimination information of two FFSs. This suggested measure meets all mathematical standards for being considered a measure. We introduced entropy and divergence measures to determine the objective weight in the TODIM–VIKOR approach. Meanwhile, to deal with multiple attribute group decision-making, a new decision procedure based on the suggested Entropy and Jensen–Shannon divergence measure was proposed in a Fermatean Fuzzy environment. In this article, TODIM has in view to find out the overall dominance degree, and VIKOR is to determine the compromise solution. Finally, we manage a supplier selection problem to verify the performance of the suggested Fermatean fuzzy TODIM–VIKOR method by comparing the ranking solution to the rankings of existing methodologies. We investigate the reliability and effectiveness of our proposed methodology.

АНАЛИЗ ТОНАЛЬНОСТИ ТЕКСТОВЫХ ОТЗЫВОВ С ПРИМЕНЕНИЕМ ТОНАЛЬНЫХ СЛОВАРЕЙ И КАРДИНАЛЬНОСТИ НЕЧЕТКОГО МНОЖЕСТВА

Sentiment or opinion analysis aims to determine the polarity of people's opinions in relationto any product, service, event or any person. One of the most common methods used in sentimentanalysis of text content is natural language processing. Sentiment analysis of natural languagetext can be assessed using numerous methodologies such as machine learning algorithms andstatistical tools, while the application of fuzzy logic is not common. The use of fuzzy logic waschosen for the following reasons. First, fuzzy logic handles linguistic uncertainty well. This way ofdefining the problem leads to a reduction in bias, both positively and negatively. Secondly, learning approaches based on fuzzy rules are fundamentally different from those learning approachesthat are widely used in sentiment classification, such as support vector machines, naive Bayes,etc., as they relate to generative learning, i.e. i.e. the goal of learning is to assess the degree towhich an instance belongs to each individual class. The proposed model for sentiment analysis oftext reviews is based on the use of tone lexicons using fuzzy logic and consists of four main stages.The steps include tokenization, word bag model formulation, sentiment fuzzy score formulation,and polarity assignment. In the proposed model, the power of the fuzzy set is used as a measure ofthe evaluation of the indicators of the polarity of words. Word polarity values are obtained byapplying two sentiment lexicons: SentiWordNet and AFINN. Two versions of the model were createddepending on the type of vocabulary used: based on SentiWordNet and AFINN. Comparisonof the presented approach based on fuzzy logic with other dictionary-based methods demonstratesthe superiority of the developed models based on the application of fuzzy logic.

Relative Density-Based Intuitionistic Fuzzy SVM for Class Imbalance Learning

The support vector machine (SVM) has been combined with the intuitionistic fuzzy set to suppress the negative impact of noises and outliers in classification. However, it has some inherent defects, resulting in the inaccurate prior distribution estimation for datasets, especially the imbalanced datasets with non-normally distributed data, further reducing the performance of the classification model for imbalance learning. To solve these problems, we propose a novel relative density-based intuitionistic fuzzy support vector machine (RIFSVM) algorithm for imbalanced learning in the presence of noise and outliers. In our proposed algorithm, the relative density, which is estimated by adopting the k-nearest-neighbor distances, is used to calculate the intuitionistic fuzzy numbers. The fuzzy values of the majority class instances are designed by multiplying the score function of the intuitionistic fuzzy number by the imbalance ratio, and the fuzzy values of minority class instances are assigned the intuitionistic fuzzy membership degree. With the help of the strong capture ability of the relative density to prior information and the strong recognition ability of the intuitionistic fuzzy score function to noises and outliers, the proposed RIFSVM not only reduces the influence of class imbalance but also suppresses the impact of noises and outliers, and further improves the classification performance. Experiments on the synthetic and public imbalanced datasets show that our approach has better performance in terms of G-Means, F-Measures, and AUC than the other class imbalance classification algorithms.

Assessing the Sustainability of the Prepandemic Impact on Fuzzy Traveling Sellers Problem with a New Fermatean Fuzzy Scoring Function

This article focused on transportation sustainability during the COVID-19 pandemic situation under the Fermatean fuzzy environment. In both developed and developing nations, sustainability has risen to the top of the priority list for transportation policies and planning. We introduce a simplified presentation of the Fermatean fuzzy traveling seller problem solved by using a new computation approach. Several approaches for solving the traveling seller problem using fuzzy parameters have been described in the literature. Even so, all the current strategies use general fuzzy numbers as the parameters for the traveling salesman problems, but his study, focused on the new Fermatean fuzzy number, is more effective for representing real-life incidents. The Fermatean fuzzy scoring functions and numerical conditions in distinct models in the Fermatean fuzzy environment were described to construct the algorithm. New solution methodology developed through scoring functions to find the best solution to fulfill our goal of sustainable transportation for traveling sellers problem. Sustainable cost and the optimal path are obtained by this study.