Corporate directors are influenced by overconfidence, which is one of the personality traits of individuals; it may take irrational decisions that will have a significant impact on the company's performance in the long run. The purpose of this paper is to validate and compare the Naive Bayesian Classification Algorithm and probit regression in the prediction of Management's overconfident at present and in the future. Financial during the years are 2012 to 2017. To support the theoretical results, the samples were the companies admitted to the Tehran Stock Exchange, (financial data of 1292 companies/year in total). Data collection in the theoretical part of the study benefitted from the library method, and for calculating data, Excel software was used, and in order to test the research hypotheses Matlab 2017 and Eviews10. 0 were used. The empirical fi ndings demonstrate that, Gained nonlinear prediction model of the Naive Bayes Classification Algorithm, has high ability to predict, and the Probit regression model, has limited ability to predict the over-confidence of management. Finally, the artificial intelligence prediction model (Naive Bayesian Classification Algorithm) has better result compared with statistical binary regression prediction model (probit regression).

Introduction: Due to the improvement of technology during the last decade, using machine learning Algorithms for predicting diseases has found great importance. The goal of this research was to investigate the importance of Naïve Bayesian network as the most applied Algorithm in predicting diseases and classifying relevant articles related to disease prediction with data mining Algorithms.Methods: This was a systematic review study. A comprehensive search was performed from 2007 to 2017 in online databases and search engines including Scopus, Science Direct, web of science and MEDLINE.Results: From a total of 90 identified abstracts through the research, 27 ones were compatible with inclusion and exclusion criteria. Naive Bayesian network was compared with other Algorithms and in 92% of articles (25 articles out of 27), it had better accuracy in disease prediction. Results of this research showed effectiveness of Naïve Bayesian Algorithm in disease prediction.Conclusion: Naïve Bayesian network is one of the best Algorithms for disease prediction in comparison with experts’ decision and other Algorithms. This Algorithm can be used beside physicians’ decision to improve the accuracy of disease prediction.

Nowadays, the most important feature of entrepreneurship behavior is known as the form of intellectual architecture named effectuation. In this regard, current empirical research attempts to use a machine Algorithm to classify the levels of effectuation thinking among rural small business owners in the field of local poultry breeding in Sistan and Baluchestan province. To this end, 191 units out of 360 were sampled and studied. The main objective of this research was to find a combination of different demographic and behavioral variables/components of entrepreneurial ecosystems affecting the Classification level of effectuation among rural business owners. The research tool for measuring the research variables was a standard questionnaire that its validity and reliability were tested and confirmed. The main method of data analysis was Naive Bayes Algorithm of the machine learning which was implemented using R software. The results of the Classification of effectuation as the main indicator of entrepreneurial thinking showed that the variables, including prior experience, focusing on poultry breeding as the main job, capacity of the production unit, access to cooperation and market network, entrepreneurial self-efficacy, motivation, innovation in the market, human capital, and financial capital at their high levels resulted in high level of effectuation thinking. Therefore, it can be concluded that a combination of behavioral and demographic variables as well as ecosystem components can capture the entrepreneurial behavior. Based on research findings, some suggestions were proposed to develop the supportive climate of rural nascent entrepreneurs.

Background and Aim: Despite the implementation of effective preventive and therapeutic programs, no significant success has been achieved in the reduction of tuberculosis. One of the reasons is the delay in diagnosis. Therefore, the creation of a diagnostic aid system can help to diagnose early Tuberculosis. The purpose of this research was to evaluate the role of the Naive Bayes Algorithm as a tool for the diagnosis of pulmonary Tuberculosis. Materials and Methods: In this practical study, the study population included Patients with TB symptoms, the study sample is recorded data of 582 individuals with primary Tuberculosis symptoms in Tehran's Masih Daneshvari Hospital. The data of samples were investigated in two classes of pulmonary Tuberculosis and non-Tuberculosis. A Naive Bayes Algorithm for screening pulmonary Tuberculosis using primary symptoms of patients has been used in Python software version 3. 7. Results: Accuracy, sensitivity and specificity after the implementation of the Naive Bayes Algorithm for diagnosis of pulmonary Tuberculosis were %95. 89, %93. 59 and %98. 53, respectively, and the Area under curve was calculated %98. 91. Conclusion: The performance of a Naive Bayes model for diagnosis of pulmonary Tuberculosis is accurate. This system can be used to help the patient and manage illness in remote areas with limited access to laboratory resources and healthcare professional and cause to diagnose early Tuberculosis. It can also lead to timely and appropriate proceedings to control the transmission of TB to other people and to accelerate the recovery of the disease.

‎The recent advancements in technology have faced an increase in the growth rate of data‎.‎According to the amount of data generated‎, ‎ensuring effective analysis using traditional approaches becomes very complicated‎.‎One of the methods of managing and analyzing big data is Classification‎.‎%One of the data mining methods used commonly and effectively to classify big data is the MapReduce‎‎In this paper‎, ‎the feature weighting technique to improve Bayesian Classification Algorithms for big data is developed based on Correlative Naive Bayes classifier and MapReduce Model‎.‎%Classification models include Naive Bayes classifier‎, ‎correlated Naive Bayes and correlated Naive Bayes with feature weighting‎.‎Correlated Naive Bayes Classification is a generalization of the Naive Bayes Classification model by considering the dependence between features‎.‎%This paper uses the feature weighting technique and Laplace calibration to improve the correlated Naive Bayes Classification‎.‎The performance of all described methods are evaluated by considering accuracy‎, ‎sensitivity and specificity‎, ‎accuracy‎, ‎sensitivity and specificity metrics.

In this paper، using pattern recognition method all fault type is classified. Firstly، feature vectors obtained from sequence components of current and/or voltage signals are normalized by efficient technique. Afterwards، the proposed supervising function applies Kernel Naive Bayes classifier. The Classification method through tuning of kernel function bandwidth s suitable for a complex and non-linear feature spaces. The signal processing procedures is done by using minimum sampling frequency hence the output of conventional current and voltage transformers can be utilized. Moreover، the performance of proposed pattern recognition methodology is evaluated from different point of views. The achieved results indicate that the proposed fault classifier has acceptable performance even in the noisy conditions.

Supervised clustering is a data mining technique that assigns a set of data to predefined classes by analyzing dataset attributes. It is considered as an important technique for information retrieval, management, and mining in information systems. Since customer satisfaction is the main goal of organizations in modern society, to meet the requirements, 137 call center of Tehran city council is planning to reduce the waiting time of customers, forwarding their messages to the appropriate service manager to increase service quality. The city council is currently using a manual approach dispatching textual request messages. Since this process is very similar to supervised clustering concept, in this study, we applied the Naive Bayes Algorithm, which is one of the most common Algorithms in the supervised clustering arena to classify received messages regarding their subjects. The performance results of the proposed technique indicate its high efficiency in clustering the received messages with 98% accuracy.