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.

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.

Touch, one of the fundamental human senses, is essential for understanding the environment by enabling object identification and stable movements. This ability has inspired significant advancements in artificial neural Networks for object recognition, texture identification, and slip detection applications. However, despite their remarkable capacity to simulate tactile perception, artificial neural Networks consume considerable energy, limiting their broader adoption. Recent developments in electronic skin technology have brought robots closer to achieving human-like tactile perception by enabling asynchronous responses to temperature and pressure changes, thereby enhancing robotic precision in tasks like object manipulation and grasping. This research presents a Spiking Graph Convolutional Network (SGCN) designed for processing tactile data in object recognition tasks. The model addresses the redundancy in spiking-format input data by employing two key techniques: (1) data compression to reduce the input size and (2) batch normalization to standardize the data. Experimental results demonstrated a 93.75% accuracy on the EvTouch-Objects dataset, reflecting a 4.31% improvement, and a 78.33% accuracy on the EvTouch-Containers dataset, representing an 18% improvement. These results underscore the SGCN's effectiveness in reducing data redundancy, decreasing required time steps, and optimizing tactile data processing to enhance robotic performance in object recognition.

Kidney stones are solid crystals made of minerals and salts that form within the kidney, often creating a sharp, hard mass. These stones can block urine flow as they move into the urinary tract, making early detection crucial. Although deep neural Networks (DNNs) have been used to diagnose kidney stones with some success, they still face performance and standardization issues. A new approach combines Graph Convolutional Networks (GCNs) with DNNs to address these challenges. This method extracts orb features from images, converts them into Graphs, and embeds nodes using a Graph Convolutional network, which includes a message-passing layer and node feature aggregation. The GCN updates node properties, enhancing efficiency and performance when integrated into a deep network. This approach enables more comprehensive and precise feature extraction from images, improving kidney stone diagnosis. The study highlights GCNs' potential in analyzing medical images for diagnosing kidney stones. The proposed architecture was tested using publicly available CT scan images and demonstrated outstanding accuracy, correctly identifying kidney stones or healthy conditions in 98.6% of cases. It outperformed other advanced techniques, especially in detecting stones of various sizes, including very small ones, proving its effectiveness in medical image analysis.

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.

Background: The most common types of non-melanoma skin cancer are basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). AKIEC-Actinic keratoses (Solar keratoses) and intraepithelial carcinoma (Bowen’ s disease)-are common noninvasive precursors of SCC, which may progress to invasive SCC, if left untreated. Due to the importance of early detection in cancer treatment, this study aimed to propose a computer-based model for identification non-melanoma malignancies. Methods: In this analytic study, 327 AKIEC, 513 BCC, and 840 benign keratosis images from human against machine with 10000 training dermoscopy images (HAM10000) were extracted. From each of these three types, 90% of the images were designated as the training set and the remaining images were considered as the test set. A deep learning Convolutional neural network (CNN) was developed for skin cancer detection by using AlexNet (Krizhevsky, et al., 2012) as a pretrained network. First, the model was trained on the training images to discriminate between benign and malignant lesions. In comparison with conventional methods, the main advantage of the proposed approach is that it does not need cumbersome and time-consuming procedures of lesion segmentation and feature extraction. This is because CNNs have the capability of learning useful features from the raw images. Once the system was trained, it was validated with test data to assess the performance. Study was carried out at Shahid Beheshti University of Medical Sciences, Tehran, Iran, in January and February, 2020. Results: The proposed deep learning network achieved an AUC (area under the ROC curve) of 0. 97. Using a confidence score threshold of 0. 5, a classification accuracy of 90% was attained in the classification of images into malignant and benign lesions. Moreover, a sensitivity of 94% and specificity of 86% were obtained. It should be noted that the user can change the threshold to adjust the model performance based on preference. For example, reducing the threshold increase sensitivity while decreasing specificity. Conclusion: The results highlight the efficacy of deep learning models in detecting non-melanoma skin cancer. This approach can be employed in computer-aided detection systems to assist dermatologists in identification of malignant lesions.