جستجوی مقالات مرتبط با کلیدواژه « Dataset » در نشریات گروه « برق »

Although the lack of data is one of the important challenges for research in the field of natural language processing, but this challenge is more acute in the Persian language, so finding a high-quality and comprehensive dataset in the Persian language is a difficult task. In addition to that, having some problems such as the ability to categorize and not complying with the storage standard are among the problems of the existing datasets, each of which can affect the learning rate of the model, the results, and the error rate in the experiments. For this reason, all these reasons made us seek to collect and prepare a dataset that covers all such problems and reduces the amount of error when using data in different models. In this research, we have designed and used a crawler to collect textual data. By crawling on one of the news bases, it has been able to collect data sets in five columns: title, summary, text, tag, and publication date. The textual data has been normalized with the help of one of the Persian language libraries in the Python programming language and stored in csv and xml formats and made available to fellow researchers. The tags in this dataset include 13 main tags of sports, art and media, culture, science and progress, political, foreign policy, life, family, society, education and training, international, economic and provinces. Among the tasks that can be done on this data set are text classification, text extraction, text summarization and title recognition. Also, one of the prominent features of this data set is its comprehensiveness, the amount of suitable data, the existence of useful features, having unique features, as well as storage in a standard format. This dataset is a product of the Language Processing Department of Imam Hossein Comprehensive University and can be downloaded and used through the link mentioned in the footnote of the next page and with respect to copyright.

Most Datasets related to data mining and machine learning contain data with missing values. How to deal with missing values and to provide solutions based on estimating missing values lead to a very important issue in the field of machine learning and data mining. Among data mining algorithm, the C4.5 algorithm has been used repeatedly because of performance being used in various applications and also ability in working and estimating missing values in data sets. Researchers have presented various methods for deal with missing values and estimating it’s amount in a C4.5 data sets which any of their method causes an increase in accuracy of decision tree and there for produce a more effective and efficient decision. In this paper, for estimating missing values in data sets, at the first, we review the previous methods then the proposed approach as a displacement properties method and in the end the accuracy of proposed methods for deletion and average will be comparing.

Knowledge of the styles of writing letters in the word is necessary in recognition of online (or offline) handwriting. In this paper, we have chosen Tarbiat Modarres University (TMU) dataset which includes about 10,000 samples from the 1000 Farsi useful subwords. Different grouping based on the main body and the small signs of the subwords have been introduced. Diverse styles of Farsi handwriting were investigated and the most important forms of letters writing were found. Then, the subwords were classified according to their styles. Frequency of letters that are written in different forms was discussed and analyzed. The results showed effective improvement in recognition rate using grouping based on the writing styles of letters.