Using a Layered Ensemble of Physics-Guided Graph Attention Networks to Predict COVID-19 Trends

The COVID-19 pandemic has spread rapidly and significantly impacted most countries in the world. Providing an accurate forecast of COVID-19 at multiple scales would help inform public health decisions, but recent forecasting models are typically used at the state or country level. Furthermore, traditional mathematical models are limited by simplifying assumptions, while machine learning algorithms struggle to generalize to unseen trends. This motivates the need for hybrid machine learning models that integrate domain knowledge for accurate long-term prediction. We propose a three-layer, geographically informed ensemble, an extensive peer-learning framework, for predicting COVID-19 trends at the country, continent, and global levels. As the base layer, we develop a country-level predictor using a hybrid Graph Attention Network that incorporates a modified SIR model, adaptive loss function, and edge weights informed by mobility data. We aggregated 163 country GATs to train the continent and world MLP layers of the ensemble. Our results indicate that incorporating quantitatively accurate equations and real-world data to model inter-community interactions improves the performance of spatio-temporal machine learning algorithms. Additionally, we demonstrate that integrating geographic information (continent composition) improves the performance of the world predictor in our layered architecture. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Big data and machine learning driven bioprocessing - Recent trends and critical analysis.

Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.

Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020-2022.

Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average-least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.

COVID-19 mRNA Vaccine Degradation Prediction By Using Deep Learning Algorithms

The worldwide coronavirus (COVID-19) pandemic has accelerated substantially in the 2020, necessitating a global collaborative from various entities to create and speed vaccine development to prevent illnesses and deaths. Because of its fast development, high efficiently, safe administration, and low-cost production, messenger RNA (mRNA) has emerged as a significant technology in this epidemic. However, due of the inadequate in vivo distribution of mRNA, its chemical qualities make it difficult to use the vaccine. As a result, the goal of this study is to create and construct a sequence deep model that will be used to predict the degradation rate of the COVID-19 mRNA vaccine using five reactivity values for each place in the mRNA sequence. The probability degradation rate with/without magnesium at pH10 and 50°C was one of four of these values. The fifth reactivity value shows the likelihood of the RNA sample's secondary structure. The numerical and categorical properties of the deep learning model are the most important. Categorical features are referred from the structures, sequences, and predicted loop of the mRNA sequence, while numerical features are extracted via mathematical computations. 6 models of bidirectional layers models (LSTM, GRU, LSTM+GRU (L_GRU), GRU+LSTM (G_LSTM), LSTM+GRU+LSTM (L_G_LSTM), and GRU+LSTM+GRU (G_L_GRU) give trustworthy projected outcomes because it comprises five reactivity values and validate by mean columnwise root mean square error (MCRMSE). The MCRMSE results are then used to evaluate the performance. The stronger the prediction model, the smaller the values are. The best-fitting model is L_G_LSTM with the MCRMSE difference of 0.007 will be implemented into a Graphical User Interface (GUI) prediction system. © 2022 IEEE.

An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning

In this work investigate the use of stochastic hybrid models, statistical model checking and machine learning to analyze, predict and control the rapid spreading of Covid-19. During the pandemic numerous studies using stochastic models have been produced. Most of these studies are used to predict the effect of some restrictions. In contrast, in this paper we focus on the synthesis of strategies which prevent Covid-19 spreading. The computed strategies provide valuable information which can be used by the authorities to design new and more specific restrictions. We consider two large case studies that develop in the Copenhagen area in Denmark. Our experiments show that the computed strategies significantly prevent Covid-19 spreading, and thus provide valuable information e.g. expected social distance to minimize Covid-19 spreading. On the technical side, we demonstrate the applicability of analytical methods for preventing the spreading of Covid-19 in large scenarios.

FORECASTING BIST100 AND NASDAQ INDICES WITH SINGLE AND HYBRID MACHINE LEARNING ALGORITHMS

The aim of this paper is to investigate stock market return forecasting performance of single and the developed novel hybrid machine learning (ML) algorithms. Daily returns of BIST100 and NASDAQ indices are predicted by series specific GARCH and ARMA-GARCH as well as three different ML algorithms that are Random Forest, XGBoost and Artificial Neural Networks (ANN). New hybrid ML models incorporating forecasts of the traditional (ARMA-)GARCH and the three ML algorithms are developed. Accuracy of the out-of-sample predictions of the methods are reported both for the single and hybrid models including pre-COVID-19, post-COVID-19 and the full sample test periods. Moreover, a simple trading strategy is applied in order to assess the economic impact of employing a specific forecasting model. According to the obtained accuracy metrics and the results of the trading strategy, developed novel hybrid models suggest quite promising results compared to the forecasts of the other models, especially (ARMA-)GARCH. © 2022, Bucharest University of Economic Studies. All rights reserved.

Deep Learning Hybrid Models for COVID-19 Prediction

COVID-19 is a highly contagious virus. Blood test is one of effective method for COVID-19 diagnosis. However, the issues of blood test are time-consuming and lack of medical staffs. In this paper, four deep learning hybrid models are proposed to address these issues, i.e., CNN+GRU, CNN+Bi-RNN, CNN+Bi-LSTM, CNN+Bi-GRU. Besides, two best models CNN and CNN+LSTM from Turabieh et al. and Alakus et al. are implemented, respectively. Blood test data from Hospital Israelita Albert Einstein is used to train and test six models. The proposed best model CNN+Bi-GRU is accuracy of 0.9415, precision of 0.9417, recall of 0.9417, F1-score of 0.9417, AUC of 0.91, which outperforms the best models from Turabieh et al. and Alakus et al. Furthermore, the proposed model can help patients to get blood test results faster than traditional manual tests, and do not have errors caused by fatigue. We can envisage a wide deployment of proposed model in hospitals to alleviate the testing pressure from medical workers, especially in developing and underdeveloped countries.

Face Mask Detection Using Deep Hybrid Network Architectures

As the world has been severely affected by Novel Coronavirus, scientists have been working hard to study this rapidly evolving virus, its long-term and short-term implications, and how to stop its spread. As newer variants of the virus are discovered, it has become even more important to enforce the various steps required to curb its spread. We can only fight this virus by wearing masks, using sanitizers, and social distancing. This paper proposes a hybrid masked face detection model for implementing the proper use of face masks. Our study focuses on combining machine learning models and Neural Networks. Even though various models have been proposed in the past for face mask detection, we tried to change the conventional machine learning methods by creating hybrid models like ResNet50 and VGG16 and combining classical machine learning models like SVM and Gradient Booster, and Neural Networks and comparing their performance. The Hybrid model architecture consisting of ResNet50 + SVM significantly outperformed the other models, returning an accuracy and precision of more than 97 and close to 100% each respectively. © 2022, Springer Nature Switzerland AG.

COVID19-HPSMP : COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction.

The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction ( COVID19 PRIMO ) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction ( COVID19-HPSMP ), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion center that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.

The Signature Features of COVID-19 Pandemic in a Hybrid Mathematical Model-Implications for Optimal Work-School Lockdown Policy.

The new COVID-19 pandemic has challenged policymakers on key issues. Most countries have adopted "lockdown" policies to reduce the spatial spread of COVID-19, but they have damaged the economic and moral fabric of society. Mathematical modeling in non-pharmaceutical intervention policy management has proven to be a major weapon in this fight due to the lack of an effective COVID-19 vaccine. A new hybrid model for COVID-19 dynamics using both an age-structured mathematical model based on the SIRD model and spatio-temporal model in silico is presented, analyzing the data of COVID-19 in Israel. Using the hybrid model, a method for estimating the reproduction number of an epidemic in real-time from the data of daily notification of cases is introduced. The results of the proposed model are confirmed by the Israeli Lockdown experience with a mean square error of 0.205 over 2 weeks. The use of mathematical models promises to reduce the uncertainty in the choice of "Lockdown" policies. The unique use of contact details from 2 classes (children and adults), the interaction of populations depending on the time of day, and several physical locations, allow a new look at the differential dynamics of the spread and control of infection.