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.

SCImago Journal Rank (SJR) is one indicator of a journal's reputation. The value is calculated based on several published journals, such as scholarly journals' scientific impact, representing the number of quotes sent to a journal and the relevance or reputation of journals from which the quotations originate. A high SJR value means that the corresponding journal has a high reputation. This study aims to approach the SJR classification by implementing a machine learning approach. A simple yet powerful method Naï ve bayes Classifier (NBC), is selected. NBC utilizes probability calculations based on bayes' theorem. However, NBC has an assumption that the attribute values do not depend on each other. This method is optimized using Particle Swarm Optimization (PSO) to overcome this weakness. This study used SJR data of the computer science domain from 2014 to 2017. Publication without Q rank is filtered for better performance. As a result, the accuracy of the proposed method is higher than the baseline. The use of PSO significantly improves the NBC performance based on the performed T-test. The PSO-NBC selects four of eight features: H index, Cites/ Doc (2 Years), and Ref. / Doc. Overall results show that using PSO-NBC is closer to SJR rather than using mere NBC.

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.

Machine translation is considered as a branch of machine intelligence with about fifty years background. Ambiguity of language is the most problematic issue in machine translation systems, which may lead to unclear or wrong translation. One of the problems involved in natural language processing is the semantic and structural ambiguity of the words. The objective of this paper to focused on the word sense disambiguation. In here, the existing algorithms for word sense disambiguation are evaluated and a method which is proposed based on the concept, structure and meaning of the words. The experimental results are promising and indicate that this proposed approach significantly outperform its counterparts in terms of disambiguation accuracy.