جلال الدین نصیری

Human users can easily divide a bibliographic reference to its constructing fields such as authors, title, journal, year, etc. However, due to the variations in formats and errors made by the authors in citing documents, it is difficult to automate this task. There exist many solutions for this problem, known as citation parsing problem in the literature, however, none of them is compatible with Persian language. This is mainly due to high language-sensitivity of these solutions. Considering the important role of citation parsing in tasks such as autonomous citation indexing and information retrieval, in this paper, we propose an intelligent method for citation parsing in Persian language. The proposed method uses the support vector machine (SVM) classification method as its core. The results of testing the proposed method using a dataset designed for this task show 95% in average for precision, recall and F1 measures for extracting different fields from a bibliographic reference, which is quite plausible.

In this paper, a new method for annotating images in Persian scientific documents is suggested.  Images in scientific documents contain valuable information. In many cases, by analyzing images one can understand the main idea and important results of the document. Due to the explosive growth of image data, automatic image annotation has attracted extensive attention and become one of the growing subjects in the literature. Image annotation is the first step in image retrieval methods, in which descriptive tags are assigned to each image. Here, for image annotation the associated text is used. The caption and the part of the document that includes the reference to the image are considered. None phrases in the associated text are ranked based on five different methods; term frequency, inverse document frequency, term frequency–inverse document frequency, cosine similarity between word embedding of noun phrases in the text and the caption and using both term frequency–inverse document frequency and cosine similarity methods. Image tags in every method are the noun phrases with the highest rank. Suggested methods are evaluated on the test data from Iran scientific information database (Ganj), the main database of Persian scientific documents. Term frequency–inverse document frequency method gives the best results.

Keyword extraction aims to extract words that are able to represent the corpus meaning. Keyword extraction has a crucial role in information retrieval, recommendation systems and corpora classification. In Persian language, keyword extraction is known as hard task due to Persian’s inherent complication. In this research work, we aim to address keyword extraction with a combination of statistical and Machine Learning as a novel approach to this problem. First the required preprocessing is applied to the corpora. Then three statistical methods and Bayesian classifier was utilized to the corpora to extract the keywords pattern. Also, a post processing methods was used to decrease the number of True Positive outputs. It should be pointed out that the built model can extract up to 20 keywords and they will be compared with keywords in the corresponding corpus. The evaluation results indicate that the proposed method, could extract keywords from scientific corpora (Specifically Thesis and Dissertations) with a good accuracy.

Metadata extraction facilitates the process of indexing and improves information retrieval. Also automation of this process increases efficiency more than manual extraction. The example of the thesis metadata are names of students, professors, title, field, degree, abstract, keywords, etc. In this paper the aim is automatic boundary detection of metadata from the main body in Persian theses. Therefore, 250 theses collected from IRANDOC system. Features were extracted from paragraphs of each thesis then paragraphs were classified using support vector machine into 2 classes: metadata and body. In this study, Bat algorithm is used to set the parameter of SVM. The result reveals that the proposed method predicts type of paragraphs with 96.6 percent accuracy.

Recent researches suggest that data is known as one of the most valuable resources of any organization. On the other hand, today science and technology data have become a key to the growth and development of each country. Treasure system plays a key role in advancing science and technology in the country and providing researchers with the services of effective and efficient research. Accordingly, the performance of this system is one of the factors influencing this mission. On the other hand, the major part of the observed quality problems in the area of information dissemination by the system is rooted radically in the output of another system called the registration system, which is at the very beginning of the process of recording, organizing and disseminating information. Determining the desirable status (or level of quality) in each field of the metadata from a variety of quality aspects such as accuracy, inconsistency, completeness, etc. is one of the most vital steps in the field of quality of the registration system. In this study, after reviewing the standard
data quality management models, a comprehensive framework was developed to determine the quality dimensions of the registration system The TDQM model was selected as one of the most widely used data quality model to evaluate the components of the system from its main four-dimensional core, accessibility, terrain and display. Then, in the form of a Quality Management System, key factors will be determined in the quality control system of the registration system. The data quality plan presented after the validation by the team of experts as the reference point for determining the non-conformities of the data. At the end of the present study, practical steps were taken to improve the quality of this system.

Internet and computer access have created opportunities for e-learning. Easier access to resources and freedom of action for users is one of the benefits of e-learning. However, e-learning is not as attractive and dynamic as traditional or face-to-face instruction, and in these systems the user's condition, such as learning rate and motivation, is not taken into account. Therefore, the developers of e-learning systems can help to solve the problems mentioned in these systems by considering the learning style and design of interactive user relationships. Automated identification of learning style not only increases the attractiveness of e-learning, but also increases the efficiency and motivation of learners in e-learning environments. Research shows that people differ in decision making, problem solving, and learning. Learning style makes people understand a story differently. For example, people with good visual memory prefer to present topics visually rather than orally. Applying a proper teaching method improves the learner's performance in the learning environment. Lack of attention to students' learning style reduces their motivation and interest in studying and engagement in educational courses. Students’ success is one of the prominent goals in the learning environments. In order to achieve this goal, paying attention to students’ learning style is essential. Being aware of students’ learning style helps to design an appropriate education method which improves student’s performance in the learning environments. In this paper, the aim is to create a model for automatic prediction of learning styles.

Therefore, two real datasets collected from an e-learning environment which consists of 202 electrical and computer engineering students. Behavioral features were extracted from users’ interaction with e-learning system and then learning styles were classified using twin support vector machine. Twin support vector machine is an extension of SVM which aims at generating two non-parallel hyperplanes. This classifier is not sensitive to imbalanced datasets and its training speed is fast.

In this study, increasing the attractiveness of e-learning is emphasized and the issue of automatic recognition of students' learning style has been investigated by MBTI model. Two data sets from the interaction of 202 electrical and computer engineering students with the Moodle e-learning system have been collected. The collected data set is very unbalanced, which has a negative effect on the accuracy of the categories. With this in mind, the twin support vector machine uses the least squares as a binder. The distinctive feature of this category is the low sensitivity to data balance and very high speed. The results show that the proposed method, despite the inconsistency of the data, has performed very well in the classification of students' learning style and accurately recognizes 95% of learning styles.

Due to the excellent performance of the proposed method, a new component can be added to e-learning systems such as Moodle by identifying the learning style, content and appropriate teaching method for the learner. Future research could also gather more data from an e-learning environment and categorize learning styles with cognitive characteristics from the learner.