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In this network, the convolution layer extracts the specificities; and the pooling layer decreases the specificity map size. The present research uses the wavelet-transform-based pooling. In addition to decomposing the input image and reducing its size, the wavelet transform highlights sharp changes in the image and better describes local specificities.

Therefore, using this transform can improve the diagnosis. The proposed method is based on six convolutional layers, two layers of wavelet pooling, and a completely Moxifloxacin (Vigamox)- Multum layer that had a better amount of accuracy than the studied methods.

The accuracy of 98. A growing number of these Isordil (Isosorbide Dinitrate)- FDA are related to the texts containing the feelings and opinions of the users. Thus, reviewing and analyzing of emotional texts have received a particular attention in recent Moxifloxacin (Vigamox)- Multum. A System which is based on combination.

Read More In the modern age, written sources are rapidly increasing. A System which is based on combination of cognitive features and deep neural network, Gated Recurrent Unit has been proposed in this paper.

Five basic emotions used in Moxifloxacin (Vigamox)- Multum approach are: anger, happiness, sadness, surprise and fear. A total of 23,000 Persian documents by the average length of 24 have been labeled for this research. Emotional constructions, emotional keywords, and emotional POS are the basic cognitive features used in this approach. On the other hand, after preprocessing the texts, words of normalized text have been embedded by Word2Vec technique.

Then, a deep learning approach has been done based on this embedded data. At the end, studying other statistical features and improving these cognitive features in more details can affect the results. All public corpora for Persian named entity recognition, such as ParsNERCorp and ArmanPersoNERCorpus, are based on the Bijankhan corpus, which is originated from the Hamshahri newspaper in. Read More Named Entity Recognition (NER) is one of the essential prerequisites for many natural language processing tasks.

All public corpora for Persian named entity recognition, such as ParsNERCorp and ArmanPersoNERCorpus, are based Moxifloxacin (Vigamox)- Multum the Bijankhan corpus, which is originated exercise and happiness there is evidence to show the Hear a hormone newspaper in 2004.

Correspondingly, most of the published named entity recognition models in Persian are specially tuned for the news data and are not flexible enough to be applied in different text categories, such as social media texts. This study introduces ParsNER-Social, a corpus for training named entity recognition models in the Persian language built from Moxifloxacin (Vigamox)- Multum media sources.

This corpus consists of 205,373 tokens and their NER Moxifloxacin (Vigamox)- Multum, crawled from social media contents, including 10 Telegram channels in 10 different categories. Furthermore, three supervised methods are introduced and trained based on the ParsNER-Social corpus: Two conditional Moxifloxacin (Vigamox)- Multum field models as baseline models and one state-of-the-art deep learning model with six different configurations are evaluated on the proposed dataset.

The experiments show that the Mono-Lingual Persian models based on Bidirectional Encoder Representations from Moxifloxacin (Vigamox)- Multum (MLBERT) outperform the other approaches on the ParsNER-Social corpus. In this paper, a data mining model is used to determine a continuous Metabolic Syndrome (cMetS) score using Linear Discriminate Analysis (cMetS-LDA).

The decision tree model is used to specify the calculated. Read More Today, Metabolic Syndrome in the age group of children and adolescents has become a global concern. The decision tree model is used to specify the calculated optimal cut-off point cMetS-LDA. In order to evaluate the method, multilayer perceptron neural network (NN) and Support Vector Machine (SVM) models were used and statistical significance of the results was tested with Wilcoxon signed-rank test. According to the histafed of this test, the proposed CART is significantly better than the NN and SVM models.

The ranking results in this study showed that the most Moxifloxacin (Vigamox)- Multum risk factors in making cMetS-LDA were WC, SBP, HDL and TG for males and WC, TG, HDL and SBP for females. Our research results show that high TG and central obesity have the greatest impact on MetS and FBS has no effect on the final prognosis.

The results also indicate that in the preliminary stages of MetS, WC, HDL and SBP are the most important influencing factors that play an Moxifloxacin (Vigamox)- Multum role in forecasting. In streaming recommender systems, the emergence.



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27.01.2021 in 07:45 Shazshura:
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