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.

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.

With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and naive bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features space in TDC. TDC includes different actions such as text processing, feature extraction, forming feature vectors, and final classification. In the presented model, the authors formed a feature vector for each document by means of weighting features use for IWO. Then, documents are trained with NB classifier; then using the test, similar documents are classified together. FS do increase accuracy and decrease the calculation time. IWO-NB was performed on the datasets Reuters-21578, WebKb, and Cade 12. In order to demonstrate the superiority of the proposed model in the FS, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been used as comparison models. Results show that in FS the proposed model has a higher accuracy than NB and other models. In addition, comparing the proposed model with and without FS suggests that error rate has decreased.