In the past, investors used their own or others’ experiences to achieve their goals. With the development of financial management, investors’ choices became more scientific. They could select the optimal choice by using different models and combining the results with their experiences. In portfolio optimization, the main issue is the optimal selection of the assets and securities that can be provided with a certain amount of capital. In the present study, the problem of optimization, i.e., maximizing stock portfolio returns and minimizing risk, has been studied. Therefore, this study discussed comprehensive modeling for the optimal selection of stock portfolios using multi-criteria decision-making methods in companies listed on the Tehran Stock Exchange. A sample of 79 companies listed on the Tehran Stock Exchange was used to conduct this research. After simulating the data and programming them with MATLAB software, the cumulative data analysis model was performed, and 24 companies were selected. This research data were collected from the financial statements of companies listed on the Tehran Stock Exchange in 2020. The primary purpose of this study was a comprehensive modeling for the optimal selection of stock portfolios using multi-criteria decision-making methods in companies listed on the Tehran Stock Exchange. The index in the Tehran Stock Exchange can be used to provide a comprehensive and optimal model for the stock portfolio; different multi-index decision-making methods (TOPSIS method), the taxonomy method (Taxonomy), ARAS method, VIKOR method, The COPRAS method and the WASPAS method can all identify the optimal stock portfolio and the best stock portfolio for the highest return.

Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm. The experimental results show the superiority of the proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path.

Pythagorean fuzzy soft set (PFSS) is the most powerful and effective extension of Pythagorean fuzzy sets (PFS) which deals with the parametrized values of the alternatives. It is also a generalization of intuitionistic fuzzy soft set (IFSS) which provides us better and precise information in the decision‐making process comparative to IFSS. The core objective of this work is to construct some algebraic operations for PFSS such as OR‐operation, AND‐operation, and necessity and possibility operations. Furthermore, some fundamental properties have been established for PFSS utilizing the developed operations. Moreover, a decision‐making technique has been offered for PFSS based on a score matrix. To demonstrate the validity of the proposed approach, a numerical example has been presented. Finally, to ensure the practicality of the established approach, a comprehensive comparative analysis has been presented. The obtained results show that our developed approach is most effective and delivers better information comparative to prevailing techniques.

In this paper, using the Mittag-Leffler-type Borel distribution, the authors introduce a new class of bi-Bazilevič functions defined in the open unit disc associated with Legendre polynomials, we find estimates for the general Taylor-Maclaurin coefficients of the functions in the subclass introduced, and the Fekete-Szego problem is solved.

Purpose: The main purpose of this paper is to create a suitable structure based on neutrosophic numbers to evaluate the safety performance in construction projects in such a way that the shortcomings can be highlighted with the reasoned measurement and possible strategies can be recommended.Design/methodology/approach: Data envelopment analysis (DEA), which is a useful tool for performance appraisal, along with neutrosophic logic, which is one of the most complete tools for handling uncertainty phenomenon, has been used to evaluate the safety performance of construction projects. With this hybrid model, a new strategy is considered as an indicator for safety performance and comparisons are made between different units.Findings: A total of 35 Chinese organizations with construction projects lasting between 1.5 and 2 years were selected for comparison. After processing the data into neutrosophic numbers and using the NN-DEA model, it can be found that projects that pay more attention to safety issues such as training and equipment are more efficient.Originality/value: Since in the real world, there are uncertainties with different contradictions, and neutrosophical data can handle many of these challenges, using DEA model with neutrosophic numbers to evaluate the performance of construction projects from a safety perspective, can provide significantly better results. Therefore, considering that no study has been presented in this field so far, the authors will deal with this topic.