deep learning

For investors, predicting stock market changes has always been attractive and challenging because it helps them accurately identify profits and reduce potential risks. Deep learning-based models, as a subset of machine learning, receive attention in the field of price prediction through the improvement of traditional neural network models. In this paper, we propose a model for predicting stock prices of Tehran Stock Exchange companies using a long-short-term memory (LSTM) deep neural network. The model consists of two LSTM layers, one Dense layer, and two DropOut layers. In this study, using our studies and evaluations, the adjusted stock price with 12 technical index variables was taken as an input for the model. In assessing the model's predictive outcomes, we considered RMSE, MAE, and MAPE as criteria. According to the results, integrating technical indicators increases the model's accuracy in predicting the stock price, with the LSTM model outperforming the RNN model in this task.

The capital market plays a crucial role within a country's financial structure and is instrumental in funding significant, long-term projects. Investments in the railway transport industry are vital for boosting other economic areas and have a profound impact on macroeconomic dynamics. Nonetheless, the potential for delayed or uncertain returns may deter investors. Accurate predictions of rail company stock prices on exchanges are therefore vital for making informed investment choices and securing sustained investment. This study employs deep learning techniques to forecast the closing prices of MAPNA and Toucaril shares on the Tehran Stock Exchange. It utilizes deep neural networks, specifically One-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM) networks, and a combined CNN-LSTM model, for stock price prediction. The effectiveness of these models is measured using various metrics, including MAE, MSE, RMSE, MAPE, and R2. Findings indicate that deep learning methods can predict stock prices effectively, with the CNN-LSTM model outperforming others in this research. According to the results, The CNN-LSTM model reached the highest R2 of 0.992. Also, based on criteria such as MAE, MSE, RMSE, and MAPE the best results belong to LSTM (Kaggle-modified) with 521.715, 651119.194, 806.920, and 0.028, respectively.

Lung infection represents one of the most perilous indicators of Covid-19. The most efficient diagnostic approach entails the analysis of CT scan images. Utilizing deep learning algorithms and machine vision, computer scientists have devised a method for automated detection of this disease. This study proposes a two-stage approach to identifying lung infection. In the initial stage, image features are extracted through a transfer learning framework employing ResNet50, with the last two layers being fixed. Subsequently, a CNN neural network is constructed for image detection and categorization in the second stage. By employing superior image feature selection and minimizing non-informative features, this proposed method achieves impressive accuracy metrics: 98.99% accuracy, 98.91% sensitivity, and 99.10% specificity. Furthermore, a comparative analysis is conducted between this method and six other architectures (Inception, InceptionResNetV2, ResNet101, ResNet152, VGG16, VGG19), with and without transfer learning. The findings demonstrate that the proposed method attains 98% accuracy on test data, without succumbing to overfitting.

Over the past few years, there has been a significant increase in patent applications, which has resulted in a heavier workload for examination offices in examining and prosecuting these inventions. To adequately perform this legal process, examiners must thoroughly analyze patents by manually identifying the semantic information such as problem description and solutions. The process of manually annotating is both tedious and time-consuming. To solve this issue, we have introduced a deep ensemble model for semantic paragraph-level pattern classification based on the semantic content of patents. Specifically, our proposed model classifies the paragraphs into semantic categories to facilitate the annotation process. The proposed model employs stack generalization as an ensemble method for combining various deep models such as Long Short-Term Memories (LSTM), bidirectional LSTM (BiLSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and the pre-trained BERT model. We compared the proposed model with several baselines and state-of-the-art deep models on the PaSA dataset containing 150000 USPTO patents classified into three classes of 'technical advantages', 'technical problems', and 'other boilerplate text'. The results of extensive experiments show that the proposed model outperforms both traditional and state-of-the-art deep models significantly.