Background and Objectives: Twitter is a microblogging platform for expressing assessments, opinions, and sentiments on different topics and events. While there have been several studies around sentiment analysis of tweets and their popularity in the form of the number of retweets, predicting the sentiment of first-order replies remained a neglected challenge. Predicting the sentiment of tweet replies is helpful for both users and enterprises. In this study, we define a novel problem; given just a tweet's text, the goal is to predict the overall sentiment polarity of its upcoming replies.Methods: To address this problem, we proposed a Graph Convolutional Neural network model that exploits the text's dependencies. The proposed model contains two parallel branches. The first branch extracts the contextual representation of the input tweets. The second branch extracts the structural and semantic information from tweets. Specifically, a Bi-LSTM network and a self-attention layer are used in the first layer for extracting syntactical relations, and an affective knowledge-enhanced dependency tree is used in the second branch for extracting semantic relations. Moreover, a Graph Convolutional network is used on the top of these branches to learn the joint feature representation. Finally, a retrieval-based attention mechanism is used on the output of the Graph Convolutional network for learning essential features from the final affective picture of tweets.Results: In the experiments, we only used the original tweets of the RETWEET dataset for training the models and ignored the replies of the tweets in the training process. The results on three versions of the RETWEET dataset showed that the proposed model outperforms the LSTM-based models and similar state-of-the-art Graph Convolutional network models. Conclusion: The proposed model showed promising results in confirming that by using only the content of a tweet, we can predict the overall sentiment of its replies. Moreover, the results showed that the proposed model achieves similar or comparable results with simpler deep models when trained on a public tweet dataset such as ACL 2014 dataset while outperforming both simple deep models and state-of-the-art Graph Convolutional deep models when trained on the RETWEET dataset. This shows the proposed model's effectiveness in extracting structural and semantic relations in the tweets.

This paper explores Graph embedding techniques for effectively analyzing large, heterogeneous Graphs with complex and noisy patterns. Graphs represent data through nodes (entities) and edges (relationships), and when dealing with large-scale data, effective search methods are crucial. Graph embedding helps evaluate node significance and transforms data into latent space representations. It also addresses challenges like handling multi-label data in heterogeneous Networks, where nodes may have multiple labels describing complex concepts. Traditional methods struggle with such multi-label scenarios and fail to capture label dependencies. The paper introduces a Graph Neural Network (GCN)-based node embedding method, which extends traditional Neural Networks to Graph data. GCNs allow the extraction of local features from nodes and their neighbors, making them useful for heterogeneous Networks. By integrating label information into the embedding process, the method improves relationships between labels. The proposed approach transforms neighboring labels into continuous vectors, structured into a matrix for learning. This enhances the overall network embedding. The method outperforms previous techniques, demonstrating improved performance on real-world datasets, such as a 2.4% improvement on the IMDB dataset and 9.3% on the DBLP dataset. The paper discusses Graph embedding techniques in the first section and explores the potential of multi-label embedding in non-uniform Graphs, suggesting future research directions in the final section. The article's code link on GitHub can also be found at the following: https://github.com/frshkara/EGSA.

Speech recognition representing a communication between computers and human as a sub eld of computational linguistics or natural language processing has a long history. Automatic Speech Recognition (ASR), Text To Speech (TTS), speech to text, Continuous Speech Recognition (CSR), and interactive voice response systems are dierent approaches to solving problems in this area. The performance improvement is partially attributed to the ability of the Deep Neural Network (DNN) to model complex correlations in speech features. In this paper, unlike the use of conventional model for sequential data like voice that employs Recurrent Neural Networks (RNNs) with the emergence of dierent architectures in deep Networks and good performance of Conventional Neural Networks (CNNs) in image processing and feature extraction, the application of CNNs was developed in other domains. It was shown that prosodic features for Persian language could be extracted via CNNs for segmentation and labeling speech for short texts. By using 128 and 200 lters for CNN and special architectures, 19. 46 error in detection rate and better time consumption than RNNs were obtained. In addition, CNN simplies the learning procedure. Experimental results show that CNN Networks can be a good feature extractor for speech recognition in various languages.

Diabetic Retinopathy (DR) is one of the major complications of Diabetes, which is the injury to the retina of the diabetic patient and causes blindness if not diagnosed in early stages. Various machine learning classification and clustering approaches have been studied in literature with the purpose of improving the accuracy of the screening methods. Some of machine learning classification and clustering approaches are based on manually feature extraction of fundus images by image processing experts. In recent years, a new approach for image classification and diagnosis without using any manual feature extraction is proposed based on Convolutional Neural network (CNN). In medical imaging and diagnosis, training a deep CNN from scratch is difficult because it requires a large amount of labeled training data and the training procedure is a time consuming task to ensure proper convergence. Therefore, a very common method to train CNNs for medical diagnosis is fine-tuning a pre-trained CNN. In this paper, the pre-trained GoogleNet as a powerful CNN is employed on the Kaggle database for DR diagnosis from retinal images. To assess the efficacy of the clinical results, the proposed CNN algorithm is performed to diagnose DR from the images that are gathered from the the Navid-Didegan ophthalmology clinic.

In the past three decades, the use of smart methods in medical diagnostic systems has attracted the attention of many researchers. However, no smart activity has been provided in the  eld of medical image processing for diagnosis of bladder cancer through cystoscopy images despite the high prevalence in the world. In this paper, two well-known Convolutional Neural Networks (CNNs) and a multilayer Neural network was applied to classify bladder cystoscopy images. One of the most im-portant issues in training phase of Neural Networks is determining the learning rate because selecting too small or large learning rate leads to slow convergence, volatility and divergence, respectively. Therefore, an algorithm is required to dynamically change the convergence rate. In this respect, an adaptive method was presented for determining the learning rate so that the multilayer Neural network could be improved. In this method, the learning rate is determined using a coe cient based on the di erence between the accuracy of training and validation according to the output error. In addition, the rate of changes is updated according to the level of weight changes and output error. The proposed method was evaluated on 720 bladder cystoscopy images in four classes of blood in urine, benign and malignant masses. Based on the simulated results, the second proposed method (CNNs) achieved at least 17% decrease in error and increased the convergence speed of the proposed method in the classi cation of cystoscopy images, compared to the other competing methods.