avaz naghipour

Because of the non-linear fluctuations of the stock price, it is challenging to predict. Therefore, it is important to identify the characteristics that can be used to predict market behavior. Both traditional and modern methods are very important for these features. Among these features, we can mention the feelings and emotions of consumers. In this research, the effect of consumer sentiment factor in predicting market fluctuations has been investigated. For this purpose, the S&P500 index has been considered for forecasting first without considering the characteristics of consumer sentiments using BiLSTM deep neural network and then by combining the UMCSENT consumer sentiment factor. The experiments and results of this research show that the use of consumers' feelings and emotions increases the accuracy of forecasting the S&P500 index.

The COVID-19 pandemic has highlighted the urgent need for rapid and accurate diagnostic methods. In this study, we evaluate three machine learning models—Random Forest (RF), Logistic Regression (LR) and Decision Tree (DT)—for detecting COVID-19 trained on preprocessed imbalanced datasets with 5086 negative and 558 positive cases. To this end, we demonstrate the capability of two advanced data synthesis algorithms, Conditional Tabular Generative Adversarial Network (CTGAN) and Tabular Variational Autoencoder (TVAE), in addressing the class imbalance inherent in the dataset. The classifiers trained on the original as well as the balanced datasets were evaluated for comparison. Our findings reveal that RF obtains the highest accuracy of 98.83% on the CTGAN-balanced dataset. In conclusion, our results verify the potential of coupling data synthesis with traditional machine learning for the diagnosis of COVID-19. We hope that this research will make a significant contribution to the current AI (Artificial Intelligence) efforts in combating the pandemic.

Corona virus, Severe Acute Respiratory virus and swine flu is a disease caused by acute respiratory syndrome. These viruses require advanced tools to identify dangerous mortality factors with high accuracy due to their immediate spread among humans. Machine learning methods directly address this issue and are essential tools for understanding and guiding public health interventions. In this article, machine learning is used to investigate demographic and clinical significance. The investigated characteristics include age, gender, fever, countries and clinical details such as cough, shortness of breath, etc. Several machine learning algorithms have been implemented and applied on the collected data, the K-Nearest Neighbor algorithm works with the highest accuracy (more than 97%) to predict and select features that correctly represent the status of viruses.