In the field of cardiovascular health, the need to make quick decisions in emergency situations is mandatory to save one’s life. During cardiovascular abnormalities, often the patients become unresponsive as the physical and mental conditions become unstable. A meticulous approach that considers various aspects of emergency circumstances is crucial to address these challenges effectively. This paper proposes an effective emergency decision-making method for cardiovascular health using a new T-spherical q-rung linear diophantine fuzzy set (TSqLDFS), logistic differential evolution optimization, and evidential reasoning methodologies. TSqLDFS is employed with a broader scope of bounds for the experts to assess the evaluation values for alternatives corresponding to specified attributes without any restriction. The optimal weights of each attribute are obtained using logistic differential evolution optimization. Then, the aggregated T-spherical q-rung linear diophantine fuzzy values (TSqLDFVs) of each alternative are calculated using evidential reasoning. Subsequently, the score values are evaluated, facilitating the selection of the optimal choice with the highest score. The outcomes of the proposed approach in the context of cardiovascular health have been compared with the existing methods, ensuring its robustness and better performance in medical scenarios.

In this paper, a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set (q-RLDFS), differential evolutionary (DE) optimization principles, and evidential reasoning (ER) methodology. The proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the attributes. DE optimization is used to obtain the optimal weights of the attributes, and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values (q-RLDFVs) of each alternative. Then the score values of alternatives are computed based on the aggregated q-RLDFVs. An alternative with the maximum score value is selected as a better one. The applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning management. Moreover, we have validated the proposed approach with a numerical example. Finally, a comparative study is provided with the existing models, where the proposed approach is found to be robust to perform better and consistent in uncertain environments.

The distribution of vaccines poses a critical challenge, particularly during public emergencies. This paper introduces a decision support framework to address the requirement of vaccine supply distribution in medical emergencies by optimizing an artificial neural network (ANN) model with hyperbolic secant grey wolf optimization (HSGWO) techniques. The proposed hyperbolic HSGWO-ANN model offers a method for optimizing vaccine requirement by efficiently allocating resources, thereby improving decision-making processes in vaccine supply and enhancing healthcare outcomes. Utilizing HSGWO-ANN model, our research aids decision makers or governmental bodies in strategically allocating vaccines to high-risk locations, mitigating the impact of pandemics. Initially, HSGWO employs a hyperbolic secant function to explore search space and enhances exploration and exploitation capabilities. Subsequently, HSGWO is integrated with ANN to optimize weights and minimize training error for accurate prediction. Furthermore, we compute normalized regression residuals for each location with the highest risk for vaccine deployment. We evaluate the performance of the proposed HSGWO-ANN model on two numerical datasets using error measures such as Root mean squared and Mean absolute percentage errors, and correlation coefficients such as R2 and Pearson’s. The results demonstrate that the HSGWO-ANN model outperforms existing models, exhibiting lower error rates and stronger correlations, thus offering a more efficient solution for vaccine distribution.

Global infrastructure project (GIP) selection for any organization is considered to be a complex decision-making process as it involves multiple conflicting criteria and uncertainties. To resolve the issues concerned with GIP selection, in this paper, we propose an improved chaotic Rao3 (CRao3 ) based adaptive neuro-fuzzy inference system (ANFIS), which assists organizations in terms of expected economic benefits and return on investments. Our proposal is mainly two-fold. Firstly, we propose chaotic Rao3 (CRao3 ) optimization by integrating chaotic theory with Rao3 optimization technique to enhance the exploration and exploitation capabilities of Rao3. Next, the proposed CRao3 is integrated with ANFIS to optimize the premise and consequent parameters to reduce the training error for providing the predicted values of the projects. Then, we rank the projects based on the uncertainty bands and a ranking measure to select the best global infrastructure projects. For the demonstration purpose, this study has considered two GIP related datasets and evaluated the performance of the proposed CRao3based ANFIS model (CRao3-ANFIS) by various error measures such as root mean squared error (RMSE), mean absolute percentage error (MAPE), R2 correlation coefficient and pearson's correlation coefficient. Finally, the results of the proposed model are compared with the existing models, where the proposed CRao3-ANFIS model outperforms the existing ones in terms of accuracy and efficiency with a low error rate and better correlation. Therefore, the proposed model is found to be suitable for GIP selection process.

Hesitant fuzzy set based linear regression model is used to solve decision making problems, where each element is assessed with multiple membership grades. However, in order to handle uncertainty properly, non-membership and abstinence degrees of each elements are also considered as equally significant, which are not captured by hesitant fuzzy set. To resolve these limitations of hesitant fuzzy set based model, this paper proposes hesitant t-spherical fuzzy linear regression model, where hesitant t-spherical fuzzy set can efficiently handle both non-membership and abstinence degrees along with membership degree. The regression model is used to determine the imprecise functional relationship between the dependent and independent variables which are represented using hesitant t-spherical fuzzy set. We apply gradient descent technique to estimate the optimal coefficients of the variables used in the proposed regression model. Finally, the residuals of the regression are computed for the purpose of decision making. The proposed approach has been illustrated using three real life applications (robot selection, revenue generation, and material selection) and compared with the existing approaches. Moreover, we have used sensitivity analysis and weighted spearman's rank correlation coefficient metrics to demonstrate that the outcome of the proposed model is more consistent and reliable to rank its best alternative compared to existing approaches, which shows its capability to solve real life uncertain decision-making problems.

Handling uncertainty is an important factor in decision-making. To handle uncertainty, various methodologies and approaches have been introduced using fuzzy sets and its extensions. However, these are limited to the structural space bounds in representing the information given by the experts or decision-makers. On the other hand, linear diophantine fuzzy sets (LDFSs) eradicate this limitation of the fuzzy set theories and handle uncertainty even more precisely with the inclusion of reference parameters. In this paper, a consistent multi-attribute decision-making (MADM) approach is proposed based on linear diophantine fuzzy soft set (LDFSSs) and revised max-min average composition method. Firstly, we propose LDFSS as a hybridization of LDFS and soft set, and then we use it in the revised max-min average composition method. Linear diophantine fuzzy soft matrix (LDFSM) is used to represent the evaluating values of the alternatives corresponding to the attributes. Next, the LDFSMs given by various decision-makers are aggregated using the max-min average composition method. Then the score values of the alternatives are computed from the aggregated LDFSM. An alternative with the maximum score value is selected as the better one. Finally, applicability and consistency of the proposed approach have been illustrated using practical example.

With the growing businesses, the dependency on software technology has constantly been increasing. In order to handle more a customer base, companies constantly have been working to make the systems up and running. As changes to systems are frequent and have to be made in less time, companies have moved from standalone service oriented architecture to microservices architecture, where each functionality has been developed and maintained as a separate service. Microservices architecture style of developing software has many benefits such as scalability, ease of deployment, etc. But developer and support teams are facing issues with monitoring the application, which is an important and challenging task. Manual monitoring of such a large number of services is a tedious job. So far, the existing work on monitoring in the literature is focused only on the application layer. In this paper, we propose an approach for building a monitoring mechanism for the microservices architecture based software systems. We have used distributed tracing along with process mining techniques to analyze and monitor both the business layer and application layer of enterprise architecture model layers to get a holistic view of the entire system.

This paper presents a multi-attribute decision-making approach based on interval-valued pythagorean fuzzy set and differential evolutionary algorithm. Interval-valued pythagorean fuzzy set has always been effective in representing real world complex problems due to its large scope of bound compared to fuzzy sets. Differential evolutionary algorithm is the most widely and successfully used optimization algorithms for obtaining optimal results. In this paper, initially, we use an accuracy function to transform the input decision matrix represented by interval-valued pythagorean fuzzy values into a transformed decision matrix. Next, for the corresponding attributes, we obtain the optimal weight vector using differential evolutionary algorithm and transformed decision matrix. We apply interval-valued pythagorean fuzzy weighted geometric operator to compute the aggregated interval-valued pythagorean fuzzy values for the various alternatives. Subsequently, we find the final accuracy values of the aggregated interval-valued pythagorean fuzzy values to decide the ranking of the alternatives. Moreover, we perform the said multi-attribute decision-making approach using particle swarm optimization replacing differential evolutionary algorithm. The proposed approaches are explained using numerical examples. Finally, the results of the differential evolutionary based approach have been compared with the existing approaches and particle swarm optimization based approach, where differential evolutionary based decision-making approach is found to perform better in imprecise environments.