Introduction: Classification and prediction are two most important applications of statistical methods in the field of medicine. According to this note that the classical classification are provided due to the clinical symptom and do not involve the use of specialized information and knowledge. Therefore, using a classifier that can combine all this information, is necessary. The aim of this study was to design a decision support system for classification of thyroid disorder using fuzzy if and then classifier. Materials and Methods: The data consisted of 310 patients, including 105 healthy people, 150 hypothyroidisms and 55 hyperthyroidisms, who referred to Shahid Beheshti Hospital and Imam Khomeini Clinic of Hamadan (Iran) in order to investigate the status of their thyroid disease. In this fuzzy system variable including age and BMI, as well as laboratory tests such as TSH, T4, and T3, the score of hyperthyroid and hypothyroid symptoms used as input and the output variable includes individual health status. The max-min MAMDANI INFERENCE system along with center of gravity deffizifier have been used in the fuzzy toolbox of MATLAB software. Results: The fuzzy rule-based classification model had a great performance for predicting thyroid disorder in the both test and train sets. Conclusion: Fuzzy rules-based classifier by using overlapping sets, had a high potential for managing the uncertainty associated with medical diagnosis. Also, by enabling the use of linguistic variables in the decision making process and design, the interpretation of the results has improved for doctors who are not familiar with modeling concepts.

Internet of Things (IoT) and cloud computing technologies have connected the infrastructure of the city to make the context-aware and more intelligent city for utility its major resources. These technologies have much potential to solve the challenges of urban areas around the globe to facilitate the citizens. A framework model that enables the integration of sensor’ s data and analysis of the data in the context of smart parking is proposed. These technologies use sensors and devices deployed around the city parking areas sending real time data through the edge computers to the main cloud servers. Mobil-Apps are developed that used real time data, set from servers of the parking facilities in the city. Fuzzification is shown to be a capable mathematical approach for modeling city parking issues. To solve the city parking problems in cities a detailed analysis of fuzzy logic proposed systems is developed. This paper presents the results achieved using MAMDANI Fuzzy INFERENCE System to model complex smart parking system. These results are verified using MATLAB simulation.

The importance of human resources is an inescapable issue that lack of proper management will reduce productivity in project-based organizations. human resources assessment is one of the complex processes in organizations that require explicit analysis by strategic, key, and operational jobs in order to periodically monitor, recruit and hire it according to the organizational position of employees. In recent years, various models for measuring human resources in project-based organizations have been proposed, but the important issue is to provide an expert system that can provide results with high reliability. To this end, the fuzzy INFERENCE system is felt like a response to this need. Due to the identified study gap, the present study seeks to design an expert system consistent with MAMDANI fuzzy INFERENCE to measure manpower in a project-based organization in Iran. This system shows the final status of human resource assessment in the Iranian project-oriented organization and the status of each of its main dimensions. In order to show the potential and practical benefits of this system was implemented in a case study. Finally, by analyzing the system outputs, the optimal performance of the designed system is analyzed and finalized with the relevant information of the organization.

A fuzzy INFERENCE system is a mapping of input-to-output space implemented using membership functions and fuzzy rules. Intelligent, control and decision making is one of the most important fuzzy INFERENCE algorithms. MAMDANI and Sugno fuzzy INFERENCE algorithms are examined and their advantages and disadvantages are stated.

Introduction: Groundwater resources are the main source of fresh water in many parts of Iran. Groundwater resources are limited in quantity and recently due to increase of withdrawal, these resources are facing great stress. Considering groundwater resources scarcity, maintaining the quality of them are vital. Traditional methods to evaluate water quality insist on determining water quality parameter and comparison between them and available standards. The decisions in these methods rely on just specific parameters, in order to overcome this issue, water quality indices (WQIs) are developed. …

Introduction: Fuzzy logic and ANN were previously used in rainfall prediction. Halid and Ridd (2002) had used Fuzzy logic technique to make a model for forecasting local precipitation in June in Hazanodin airport region, the biggest rice production area in Indonesia. Maria et al. (2005) had used Neural Networks and regression models to predict the amount of rainfall in Sao Paolo regions in Brazil. Cavazos (2000) had used Neural Networks for forecasting daily precipitation. Choi (1998) had used Neural Networks and Geographic Information Systems for forecasting precipitation.

Modifications are an integrated part of economic evolution in financial section which include reforming the capital market structure, innovating in tools and an environment with more comprehensive discipline. Two dominant schools of thought in the literature on stock markets are fundamental and technical analysis. Selection the portfolio would be so important. So we use both fundamental and technical analysis to evaluate companies' stock and then in order to form a portfolio which consider different risk states and investor’s preferences utilize MAMDANI Fuzzy and mixed integer linear programming model. The reasons for the use MAMDANI Fuzzy system are its capability of working in vague environment and using human knowledge and for mixed integer linear programming model is its capability in finding the optimum solution among the several available ones. The results of evaluating the performance of formed portfolio for three cases of Risk averse, Risk neutral and Risk prone investor show that the performance of proposed portfolio is positive and proper, but in a more accurate scale the formed portfolio has a more proper condition for the risk averse investor.