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Advances in the medical industry has become a major trend because of the new developments in information technologies. This research offers a novel approach for estimating the smart medical devices (SMDs) selection process in a group decision making (GDM) in a vague decision environment. The complexity of the selected decision criteria for the smart medical devices is a significant feature of this analysis. To simulate these processes, a methodology that combines neutrosophics using bipolar numbers with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under GDM is suggested. Neutrosophics with TOPSIS approach is applied in the decision making process to deal with the vagueness, incomplete data and the uncertainty, considering the decisions criteria in the data collected by the decision makers (DMs). In this research, the stress is placed upon the choosing of sugar analyzing smart medical devices for diabetics' patients. The main objective is to present the complications of the problem, raising interest among specialists in the healthcare industry and assessing smart medical devices under different evaluation criteria. The problem is formulated as a multi criteria decision type with seven alternatives and seven criteria, and then edited as a multi criteria decision model with seven alternatives and seven criteria. The results of the neutrosophics with TOPSIS model are analyzed, showing that the competence of the acquired results and the rankings are sufficiently stable. The results of the suggested method are also compared with the neutrosophic extensions AHP and MOORA models in order to validate and prove the acquired results. In addition, we used the SPSS program to check the stability of the variations in the rankings by the Spearman coefficient of correlation. The selection methodology is applied on a numerical case, to prove the validity of the suggested approach.

The selection and assessment process of appropriate robots became a more complex and complicated task due to various available alternatives and conflicting attributes which must take into consideration. Also, uncertainty which exists usually in the selection process is an unavoidable component that needs to be thoughtfully measured and traditional multi-attribute decision-making approaches failed to deal precisely with it. Since almost all decisions originate from subjective ordinal preferences, handling uncertainty using linguistic variables is also not enough. Thus, the objective of the current study is to present a new extended ordinal priority approach in the neutrosophic environment for the first time to select an appropriate robot. Since neutrosophic is one of the most effective and accommodating tools for handling uncertainty, thus, this method goes to transform linguistic information into triangular neutrosophic numbers using a new presented scale. This scale was used to determine the importance degree of attributes and alternatives regarding experts' opinions. Also, the score function of the triangular neutrosophic number is used for prioritizing attributes and alternatives. The experts in our proposed method have the same degree of importance, since each expert is a person with special skills and knowledge representing mastery of a particular subject. To measure the applicability and efficiency of the proposed approach, an experimental case study has been established for the robot selection problem of a new pharmaceutical city in Egypt for the first time. The source of data in this case study is experts, interviews, and questionnaires. Also, sensitivity and comparative analysis are further made for verifying the power of the proposed approach. The outcome of this study shows that the suggested approach for robot selection is quite helpful and has a great performance under uncertainty over classical and fuzzy ordinal priority approaches. Also, the suggested approach is less consumption of time and simpler than the fuzzy ordinal priority approach. Therefore, we recommend firms and governments to apply it for increasing product quality, hence the profitability of manufacturing industries and decrease needless costs. [ABSTRACT FROM AUTHOR]

There are several multicriteria decision-making (MCDM) approaches presented in the literature with their characteristics. Although traditional MCDM approaches are considered a proper implementation to select the best alternative from available types, they failed to consider uncertainty which is quite high and desires to be thoughtfully measured in the selection process. This research focuses on extending MCDM in the neutrosophic environment using axiomatic design (AD) as a novel contribution to selecting appropriate Computed Tomography (CT) devices. We present a new linguistic scale for evaluating criteria and alternatives based on single-valued triangular neutrosophic numbers (SVTrN). The proposed approach is superior to other existing approaches due to its simplicity and ability to simulate natural human thinking via considering truth, indeterminacy, and falsity degrees. Then, applying it will increase the value of imaging for medical decision-making and decrease needless costs. So, this study can be valuable to researchers by helping them consider the appropriate medical imaging system selection problem theoretically under uncertainty, and for governments and organizations to design better satisfying medical imaging evaluation systems. [ABSTRACT FROM AUTHOR]

Globalization and the rapid growth of technologies are the main challenges facing the manufacturer and its sustainability and survival. Sustainability for any manufacturing plays an important role in competitive advantage which make the manufacturing firm a sustainable competitor. Sustainability in manufacturing is integrated with Industry 4.0 (I4.0) to achieve benefits of economic, environmental, and social. But it has many criteria and factors and contains incomplete and uncertain information. So, we used the neutrosophic sets to overcome this incomplete information and treat with uncertainty environment. The Single-Valued Neutrosophic Set (SVNS) is used to evaluate these criteria, which include three values (Truth, indeterminacy, and falsity). The SVNS is integrated with Multi-Criteria Decision Making (MCDM) methods. The MCDM concept is used in this paper to deal with many conflicting criteria. A Decision-making trial and evaluation laboratory (DEMATEL) is utilized for determining the relation between five main criteria and fourteen sub-criteria in this study. Analytic Hierarchy Process (AHP) is used to compute the weights of the main and sub-criteria. Our framework is applied to a real case study in Egypt to show the validity of our framework. [ABSTRACT FROM AUTHOR]

The demand for energy in Egypt has increased dramatically due to the steady increase in economic and societal development. To meet this need, the use of more renewable energy resources is an essential part of the solution to the ultimate shortage of energy. Because of the multitude of factors involved, the selection of the most suitable renewable energy systems (RESs) is a multi-criteria decision-making (MCDM) problem. There are a good number of works associated with the design of MCDM methods, particularly under uncertain and ambiguous situations. However, efficient incorporation of ambiguity and uncertainty in decision making is still a challenging task, and thus this work proposes a hybrid MCDM approach for selecting the components of a sustainable RES under uncertain environments, utilizing different triangular neutrosophic numbers to deal with unclear information. This proposed hybrid approach starts with defining the relative importance of the selected sustainable criteria by using an Analytic Hierarchy Process (AHP). Later, in the second phase, this hybrid approach combines VIKOR and the TOPSIS to rank the RESs for a real-life case study. The results obtained indicate that concentrated solar power is the most suitable source of renewable energy for Egypt, with photoelectric power the second most suitable. • Development of an evaluation model to select the most appropriate RES in Egypt. • A novel hybrid MCDM (AHP-VIKOR-TOPSIS) approach is proposed for RES selection. • Five criteria and their related sub-criteria are considered for such decision making. • The triangular neutrosophic numbers are introduced to dispose uncertainties. • A case study is proposed to illustrate the rationality of the proposed method. [ABSTRACT FROM AUTHOR]

This article proposes the Mamdani complex fuzzy inference system (Mamdani CFIS) to improve performance of the classical FIS and complex FIS. The applicability of the proposed CFIS is demonstrated by applying it to six commonly available datasets from UCI Machine Learning under the comparison with Mamdani FIS and the Adaptive Neuro Complex Fuzzy Inference System (ANCFIS). It is successfully proven that the proposed Mamdani CFIS is computationally less expensive and presents a more efficient method to handle time-series data and time-periodic phenomena, among all the fuzzy IS found thus far in the literature. Furthermore, the novelty of CFIS mainly lies in its implementation of the complex number throughout the entire procedures of computation. This gives much greater flexibility of implementing unexpected, nonlinear fluctuations. [ABSTRACT FROM AUTHOR]

Professional selection is a significant task for any organization that aims to select the most appropriate candidates to fill well-defined vacancies up. In the recruitment process, various individual characteristics are involved, such as leadership, analytical skills, independent thinking, innovation, stamina and personality, ambiguity and imprecision. It outlines staff contribution and therefore plays a significant part in human resources administration. Additionally, in the era of the Internet of Things and Big Data (IoTBD), professional selection would face several challenges not only to the safe selection and security but also to make wise and prompt decisions especially in the large-scale candidates and criteria from the Cloud. However, the process of professional selection is often led by experience, which contains vague, ambiguous and uncertain decisions. It is therefore necessary to design an efficient decision-making algorithm, which could be further escalated to IoTBD. In this paper, we propose a new hybrid neutrosophic multi criteria decision making (MCDM) framework that employs a collection of neutrosophic analytical network process (ANP), and order preference by similarity to ideal solution (TOPSIS) under bipolar neutrosophic numbers. The MCDM framework is applied for chief executive officer (CEO) selection in a case study at the Elsewedy Electric Group, Egypt. The proposed approach allows us to assemble individual evaluations of the decision makers and therefore perform accurate personnel selection. The outcomes of the proposed method are compared with those of the related works such as weight sum model (WSM), weight product model (WPM), analytical hierarchy process (AHP), multi-objective optimization based on simple ratio analysis (MOORA) and ANP methods to prove and validate the results. [ABSTRACT FROM AUTHOR]

In decision-making problem, fuzzy system is considered as an effective tool with access to uncertain information by fuzzy representations. Evolutionary fuzzy systems have been developed with the appearance of intuitionistic fuzzy, hesitant fuzzy, neutrosophic representations, etc. Moreover, by capturing compound features and convey multifaceted information, complex numbers are utilized to generalize fuzzy and intuitionistic fuzzy sets. However, the order relations established in these existing systems have certain limitations, such as they are not total order relations or they are defined based on intermediate functions, hence it is difficult to use in building and ensuring important properties of logical operators and distance measures in the systems. In this article, a representation of the intuitionistic fuzzy systems based on complex numbers (IFS-C) in the polar form by a new way is proposed to overcome the above restrictions. Specifically, an intuitionistic fuzzy set is characterized by the two functions of modulus and argument. A new order relation, set-theoretic operations, and a new distance measure by the polar form of IFS-C are defined and investigated. The applicability of the proposal is illustrated by a new decision-making model called P-distance measure. It is tested on the benchmark medical datasets in comparison with the existing methods. The experiments confirm the advantages of the proposal. [ABSTRACT FROM AUTHOR]

The project selection is one of the most important phases of a project life cycle. The project selection is considered as a Multi-Criteria Decision-Making (MCDM) problem. This research aims to study the integration between Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) into Decision-Making Trial and Evaluation Laboratory (DEMATEL) under neutrosophic environment to provide a new technique for making a decision regarding the choice of appropriate project (project selection) as one of the most important phases of the project life cycle. Projects are selected by comparing them against many criteria. Criteria are evaluated based on expert's opinion. Sometimes experts cannot give reliable information due to the non-deterministic environment. The neutrosophic set theory will be used to handle and overcome the ambiguity or lack of confirmation of information. The criteria are weighted by DEMATEL, then the best project alternative is selected using TOPSIS. In the proposed model, each pairwise comparison judgments is symbolized as a trapezoidal neutrosophic number. Experts will focus only on (n − 1) judgments for n alternatives to overcome the difficulties of [(n * (n − 1))/2] consistence judgments in case of increasing number of alternatives. A numerical example is developed to show the validation of the suggested model in the neutrosophic environment. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes an advanced type of neutrosophic technique, called type 2 neutrosophic numbers, and defines some of its operational rules. The type 2 neutrosophic number weighted averaging operator is determined in order to collective the type 2 neutrosophic number set, inferring some properties of the suggested operator. The operator is employed in a MADM problem to collect the type 2 neutrosophic numbers based classification values of each alternative over the features. The convergent classification values of every alternative are arranged with the assistance of score and accuracy values with the aim to detect the superior alternative. We introduce an illuminating example to confirm the suggested approach for multi attribute decision making issues, ordering the alternatives based on the accuracy function. Selecting an appropriate alternative among the selection options is a difficult activity for decision makers, since it is complicated to express attributes as crisp numbers. To tackle the problem, type 2 neutrosophic numbers can be efficiently used to estimate information in the decision making process. The type 2 neutrosophic numbers can accurately describe real cognitive information. We propose a novel T2NN-TOPSIS strategy combining type 2 neutrosophic numbers and TOPSIS under group decision making as application of T2NN, suggesting a type 2 neutrosophic number expression for linguistic terms. Finally, we provide a real case dealing with a decision making problem based on the proposed T2NN-TOPSIS methodology to prove the efficiency and the applicability of the type 2 neutrosophic number. Highlights • An advanced type of neutrosophic technique, called T2NN is proposed. • Some desirable properties and peculiar forms of operators are investigated. • The type 2 neutrosophic numbers can accurately describe real cognitive information. • A real case study based on the proposed T2NN-TOPSIS is introduced. [ABSTRACT FROM AUTHOR]

During the recent years the smart cities knows a great extension as a modern shape of sustainable expansion. It's a urban area that utilize various devices connected with internet and integrates them with ICTs to promote goodness and execution of services for the best interaction among citizens and city's government. The basic for smart cities is distributed and independent information infrastructure. Using information effectively is going to be a main factor for success in the smart cities. The sources of information's (models, experts, and sensors) must be reason, perfect and complete. The generated information from independent and distributed sources can be imprecise, uncertain, and/or incomplete in real life. Any deficiency in gathered information will have a negative effect on the performance of services and decision making process within smart cities. So, we need a general framework to represent all types of imperfect and incomplete information. Since the classical methods fails to deal with vague, inconsistent and incomplete information, the fuzzy set was introduced to solve this drawback. The fuzzy set was not the perfect method for dealing with these drawbacks because it considers only truthiness and fails to deal with indeterminacy. The efficient mathematical tool for dealing with uncertain, vague and inconsistent objects is rough sets theory which introduced by Pawlak. The theory of neutrosophic rough sets is powerful for dealing with incompleteness and neutrosophic set deals with indeterminate and inconsistent data efficiently through considering truthiness, indeterminacy and falsity degrees. So, in this research we will propose a general framework for dealing with imperfect and incomplete information through using single valued neutrosophic and rough set theories. The combination of two sets will deal with all aspects of vagueness, inconsistency and incompleteness of data and information, and then will enhance the quality of introduced services and decisions from smart cities to their citizens. As experimentation, we applied the proposed framework for modeling imperfect and incomplete data in healthcare field. [ABSTRACT FROM AUTHOR]

In this paper, a new modified version of the flower pollination algorithm based on the crossover for solving the multidimensional knapsack problems called (MFPA) is proposed. MFPA uses the sigmoid function as a discretization method to deal with the discrete search space. The penalty function is added to the evaluation function to recognize the infeasible solutions and assess them. A two-stage procedure is called FRIO is used to treat the infeasible solutions. MFPA uses an elimination procedure to decrease any duplication in the population in order to increase the diversity. The proposed algorithm is verified on a set of benchmark instances, and a comparison with other algorithms available in literature is shown. Several statistical and descriptive analysis was done such as recoding the results of the best, mean, worst, standard deviation, success rate, and time to prove the effectiveness and robustness of MFPA. The empirical results show that the proposed algorithm can be an effective algorithm as human-centric decision-making model for solving the multidimensional knapsack problems. [ABSTRACT FROM AUTHOR]

Currently, the incorporation of solar panels in many applications is a booming trend, which necessitates accurate simulations and analysis of their performance under different operating conditions for further decision making. In this paper, various optimization algorithms are addressed comprehensively through a comparative study and further discussions for extracting the unknown parameters. Efficient use of the iterations within the optimization process may help meta-heuristic algorithms in accelerating convergence plus attaining better accuracy for the final outcome. In this paper, a method, namely, the premature convergence method (PCM), is proposed to boost the convergence of meta-heuristic algorithms with significant improvement in their accuracies. PCM is based on updating the current position around the best-so-far solution with two-step sizes: the first is based on the distance between two individuals selected randomly from the population to encourage the exploration capability, and the second is based on the distance between the current position and the best-so-far solution to promote exploitation. In addition, PCM uses a weight variable, known also as a controlling factor, as a trade-off between the two-step sizes. The proposed method is integrated with three well-known meta-heuristic algorithms to observe its efficacy for estimating efficiently and effectively the unknown parameters of the single diode model (SDM). In addition, an RTC France Si solar cell, and three PV modules, namely, Photowatt-PWP201, Ultra 85-P, and STM6-40/36, are investigated with the improved algorithms and selected standard approaches to compare their performances in estimating the unknown parameters for those different types of PV cells and modules. The experimental results point out the efficacy of the PCM in accelerating the convergence speed with improved final outcomes. [ABSTRACT FROM AUTHOR]

Many countries that derive their energy from fossil fuel sources are turning to renewable, and environmentally friendly energy sources to alleviate the growing concerns about global warming and environmental issues. Bioenergy technology is one of the potential alternatives for renewable energy systems. This paper evaluates sustainable bioenergy production technologies through a case study in Egypt. A comprehensive methodology has been developed in which experts and decision makers are able to use linguistic terms to express their opinions and participate in the decision making required to prioritize the dimensions that affect the sustainability of bioenergy production technologies. Identification of the optimum bioenergy production technology and setting its priorities is a difficult task; many dimensions must be taken into account in the evaluation process, such as the environmental, technical, economic, and social dimensions and their sub-indicators. This paper therefore applies a hybrid multi-criteria decision-making (MCDM) approach that takes into account many conflicting dimensions. In addition, handling of uncertainty was conducted under a neutrosophic environment using trapezoidal neutrosophic numbers (TNNs). Initially, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was employed to identify the relative importance of the dimensions and their sub-indicators. Then, Evaluation based on Distance from Average Solution (EDAS) was employed to rank alternatives. An illustrative case considering seven bioenergy production technologies was studied to confirm the feasibility of the suggested approach, and comparisons were performed to show the advantages of the hybrid MCDM techniques. Sensitivity analysis is conducted to prove the validity and stability of the developed framework with variations in weights. The results of this work provide useful information for energy policy decision makers and the results of the case study indicate that the conversion of agricultural and municipal wastes to biogas is the most suitable sustainable bioenergy technology with a weight of 0.996 followed by oil crops to biodiesel technology with weight 0.539. Image 1 • A new MCDM is proposed for the assessment of bioenergy production technologies. • The proposed approach can address uncertainties and imprecision. • 4 main dimensions and 14 sub-indicators are used in the sustainability assessment. • DEMATEL ranked the identified dimensions and EDAS prioritized the alternatives. • A case study is proposed to illustrate the rationality of the proposed approach. [ABSTRACT FROM AUTHOR]

Developments in wind energy projects have received much interest in the last decade due to the encouragement of sustainable development policies. Consequently, the number of wind energy projects has rapidly increased so that wind energy is an important part of an integrated power system. The success of an offshore wind energy project depends on the selection of the optimal offshore wind power station (OWPS) location, which is often determined through the use of multi-criteria decision-making (MCDM). There are, however, a number of shortcomings in the use of MCDM methods for site selection: 1) the incomplete utilization of information and loss of data in the decision-making process; and 2) the interaction issue in the neutrosophic vicinity is neglected. In this study, to address those shortcomings, we propose a new hybrid methodology for the selection of offshore wind power station location combining the Analytical Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)-II methods in the neutrosophic environment. First, a comprehensive index system of evaluation criteria is constructed of OWPS site selection. Then, the neutrosophic set is utilized in the specialist committee decision to express incomplete information. Furthermore, by gathering opinions of specialists, we take into consideration the interaction problem. Through the development of the hybrid method, this research presents rigorous methodological support for site selection in order to achieve benefits in coastal management. The proposed methodology for OWPS site selection is validated through the use of a case study from Egypt. • A new hybrid MCDM (AHP-PROMETHEE II) approach is proposed for OWPS location selection. • Seven criteria and their related subcriteria are considered for such decision making. • The trapezoidal neutrosophic numbers are introduced to dispose uncertainties. • A case study is carried out based on five locations in the Red Sea region of Egypt. • Model's effectiveness is verified by rigorous experimentations and comparisons against different MCDM approaches. [ABSTRACT FROM AUTHOR]

Similarity plays a significant implicit or explicit role in various fields. In some real applications in decision making, similarity may bring counterintuitive outcomes from the decision maker's standpoint. Therefore, in this research, we propose some novel similarity measures for bipolar and interval-valued bipolar neutrosophic set such as the cosine similarity measures and weighted cosine similarity measures. The propositions of these similarity measures are examined, and two multi-attribute decision making techniques are presented based on proposed measures. For verifying the feasibility of proposed measures, two numerical examples are presented in comparison with the related methods for demonstrating the practicality of the proposed method. Finally, we applied the proposed measures of similarity for diagnosing bipolar disorder diseases. [ABSTRACT FROM AUTHOR]

This research suggests an approach constructed on the connotation of plithogenic theory and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) technique to come up with a methodical procedure to assess the infirmary serving under a framework of plithogenic theory, where the ambiguity, incomplete information, qualitative information, approximate evaluation, imprecision and uncertainty are addressed with semantic expressions determined by plithogenic numbers and computing of contradiction degrees of attribute values. This research stratifies the plithogenic multi criteria decision making (MCDM) strategy for defining the significant weights of assessing standards, and the VIKOR technique is applied for enhancing the serving efficiency classifications of the possible substitutes. An experimental issue, including 11 assessing standards, 3 private and 2 general hospitals in Zagazig, has been evaluated by 3 assessors from several areas of medical activities, asked to validate the suggested strategy. In this research, we give some definitions of the plithogenic environment, which is more general and comprehensive than fuzzy, intuitionistic fuzzy and neutrosophic ones. The plithogeny is interested in the contradiction degrees between attribute values that help in better calculating the aggregations. We conducted the data analysis and the results showed us that the serving efficiency of private medical centers is superior than that of general medical centers due to the fact that public medical centers are scarcely supported by governmental institutions. The private medical centers have to ward themselves to keep possession of bringing patients or attract patients. We conducted the sensitivity analysis of the achieved results, to verify their validity, and to find out to what extent the different values affect the ranking of available alternatives. [ABSTRACT FROM AUTHOR]

Supply chain sustainability has become one of the most attractive decision management topics. There are many articles that have focused on this field presenting many different points of view. This research is centred on the evaluation of supply chain sustainability based on two critical dimensions. The first is the importance of evaluation metrics based on economic, environmental and social aspects, and the second is the degree of difficulty of information gathering. This paper aims to increase the accuracy of the evaluation. The proposed method is a combination of quality function deployment (QFD) with plithogenic aggregation operations. The aggregation operation is applied to aggregate: Firstly, the decision maker's opinions of requirements that are needed to evaluate the supply chain sustainability; secondly, the evaluation metrics based on the requirements; and lastly, the evaluation of information gathering difficulty. To validate the proposed model, this study presented a real world case study of Thailand's sugar industry. The results showed the most preferred and the lowest preferred metrics in order to evaluate the sustainability of the supply chain strategy. [ABSTRACT FROM AUTHOR]

Many companies have observed the significant benefits they can get via using internet. Since then, large companies have been able to develop business transactions with customers at anytime, anywhere, and in relation to anything, so that we now need a more comprehensive concept than the internet. This concept is the Internet of Things (IoT). IoT will influence decision making style in various phases of selling, buying and marketing process. Therefore, every individual and company should know precisely what IoT is, and how and why they should incorporate it in their operations. This motivated us to propose a smart system based on IoT to help companies and marketers make a powerful marketing strategy via utilizing obtained data from IoT devices. Not only this, but the proposed system can also solve the problems which face companies and customers in online shopping. Since there are different types of the same product, and also different criteria for purchasing which can be different between individuals, customers will need a decision support system to recommend them with the best selection. This motivates us to also propose a neutrsophic technique to deal with unclear and conflicting information which exists usually in the purchasing process. Therefore, the smart system and neutrosophic technique is considered as a comprehensive system which links between customers, companies, marketers to achieve satisfaction for each of them. [ABSTRACT FROM AUTHOR]