The Internet of Things (IoT) connects billions of devices to afford inventive opportunities between things and people. The rapid development of products related to the IoT is a new challenge to keep security issues, lack of confidence, and understanding of the IoT. Analytical hierarchy process (AHP) is a classic multi-criteria decision making (MCDM) method used to analyze and scale complex problems and to obtain weights for the selected criteria. The vague and inconsistent information in real situations can lead to the decision maker's confusion. The decision makers cannot determine accurate judgments for all situations due to the conditions of uncertainty factors in real life; in addition to the limited knowledge and experience of decision makers. In this research, we present a neutrosophic AHP of the IoT in enterprises to help decision makers to estimate the influential factors. The estimation of influential factors can affect the success of the IoT-related enterprise. This study combines AHP methods with neutrosophic techniques to effectively present the criteria related to influential factors. The recommended alternatives are presented based on neutrosophic techniques satisfying the estimated influential factors for a successful enterprise. A case study is applied in Smart Village, Cairo, Egypt, to show the applicability of the proposed model. The smart village' consistency rate is measured after applying neutrosophic methodologies to reach to nearest optimum results. Additional case studies on the smart city in the U.K. and China have been presented to justify that our proposal can be used and replicated in different environments.INDEX TERMS Multi-criteria decision making (MCDM), analytical hierarchal Process (AHP), neutrosophic sets, Internet of Things (IoT).

Personnel selection is a critical obstacle that influences the success of the enterprise. The complexity of personnel selection is to determine efficiently the proper applicantion to fulfill enterprise requirements. The decision makers do their best to match enterprise requirements with the most suitable applicant. Unfortunately, the numerous criterions, alternatives, and goals make the process of choosing among several applicants is very complex and confusing to decision making. The environment of decision making is a multi-criteria decision making surrounded by inconsistency and uncertainty. This paper contributes to support personnel selection process by integrating neutrosophic analytical hierarchy process (AHP) with the technique for order preference by similarity to an ideal solution (TOPSIS) to illustrate an ideal solution amongst different alternatives. A case study on smart village Cairo Egypt is developed based on decision maker's judgments recommendations. The proposed study applies neutrosophic AHP and TOPSIS to enhance the traditional methods of personnel selection to achieve the ideal solutions. By reaching the ideal solutions, the smart village will enhance the resource management for attaining the goals to be a successful enterprise. The proposed method demonstrates a great impact on the personnel selection process rather than the traditional decision-making methods.

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Non-financial analysis is one of the varied crucial directive tools of credit study that is used for judging whether the client has a genuine desire to pay the assigned amounts of the loan at its maturity dates. Fuzzy logic can help to solve the problem of dealing with factors of non-financial analysis by converting the linguistic variables to numerical variables to calculate their accuracy. This study proposes a fuzzy model that contains a complete database of non-financial factors used by the decision-maker using a fuzzy logic technique, which helps in building the fuzzy rules with great accuracy and helps in predicting the actual situation of the client. In addition, it provides constant following-up of the uses of the granted loan to guarantee that all terms set by the bank are met so that the bank can avoid future defaulting of the client. The proposed model is applied in the credit department of a private Egyptian bank (QNB), with random samples of previous real clients. Some real standards are set to calculate non-financial factors that are related to the client, management, economic situation, and project activity. The results of the proposed model revealed that the correlation factor is 95.3% between real successful payment clients and successful model clients. To guarantee the accuracy of the knowledge base quality and validation, the knowledge model was presented to the credit manager of the bank under study (expert), who provided a full evaluation of the results of the proposed model compared to the actual situation of clients.