Classification of Lung CT Scan Images using Machine Learning

Timely discovery of COVID-19 may safeguard numerous diseased people. Several such lung diseases can turn to be life threatening. Early detection of these diseases can help in treating them at an early stage before it becomes threatening. In this paper, the proposed 3D CNN model helps in classifying the CT scans as normal and abnormal, which can then be used to treat the patients after recognizing the diseases. Chest X-ray is fewer commanding in the initial phases of the sickness, while a CT scan of the chest is advantageous even formerly symptoms seem, and CT scan accurately identify the anomalous features which are recognized in images. Besides this, using the two forms of images will raise the database. This will enhance the classification accuracy. In this paper the model used is a 3D CNN model;using this model the predictions are done. The dataset used is acquired from NKP Salve Medical Institute, Nagpur. This acquired dataset is used for prediction while an open source database is used for training the CNN model. After training the model the prediction were successfully completed, with these proposed 3D CNN model total accuracy of 87.86% is achieved. This accuracy can further be increased by using larger dataset. © 2022 IEEE.

A Multivariate Deep Learning Model with Coupled Human Intervention Factors for COVID-19 Forecasting

Artificial intelligence (AI) technology plays a crucial role in infectious disease outbreak prediction and control. Many human interventions can influence the spread of epidemics, including government responses, quarantine, and economic support. However, most previous AI-based models have failed to consider human interventions when predicting the trend of infectious diseases. This study selected four human intervention factors that may affect COVID-19 transmission, examined their relationship to epidemic cases, and developed a multivariate long short-term memory network model (M-LSTM) incorporating human intervention factors. Firstly, we analyzed the correlations and lagged effects between four human factors and epidemic cases in three representative countries, and found that these four factors typically delayed the epidemic case data by approximately 15 days. On this basis, a multivariate epidemic prediction model (M-LSTM) was developed. The model prediction results show that coupling human intervention factors generally improves model performance, but adding certain intervention factors also results in lower performance. Overall, a multivariate deep learning model with coupled variable correlation and lag outperformed other comparative models, and thus validated its effectiveness in predicting infectious diseases.

Food Enterprises- Profit Growth Rate Prediction Based on LSTM from the Perspective of the Supply Chain

Food is the fundamental guarantee of people's lives, and the food industry has always occupied an essential position in the national economy. Profit growth rate, as a measure of an enterprise's development ability, can intuitively reflect the change in operating profit for food enterprises. The accurate prediction of profit growth rate can provide a decision-making reference for enterprises in planning business objectives in the next stage. However, many factors affect the profit variation of a company, and it is hard to make accurate predictions using traditional statistical economics forecasting methods. Since the Long-Short Term Memory (LSTM) model can capture nonlinear relationships in time series analysis, we propose an LSTM-based model to predict the profit growth rate of enterprises by using the operational data of four seasons ahead. Moreover, due to the COVID-19 pandemic, the impact of supply chain integrity on enterprise operations is increasing. We introduce the information of the supply chain owned by the enterprise to predict the profit growth rate of the enterprise. The result of our model exhibits high prediction accuracy, which indicates that our model could provide practical guidance for companies' production and operation activities. © 2022 The authors and IOS Press.

Prediction model of urban epidemic prevention and control based on multivariate Bi-A-LSTM

The Novel Coronavirus outbreak has since spread rapidly across the globe, seriously affecting the quality of life and economic development of all countries. By predicting the epidemic situation in a certain area, government departments can take corresponding measures to prevent and control the epidemic according to the forecast results. However, with the implementation of various prevention and control measures, vaccination and the impact of virus mutation, the traditional epidemic model and regression model have limitations in the prediction performance, there is a large error in the prediction accuracy. To improve the prediction accuracy, this paper proposes an Attentional mechanism-based LSTM network (A-LSTM), which takes the multiple factors affecting the epidemic trend as the input of the model. Bidirectional A-LSTM is constructed by a-LSTM neural network unit, and the best fitting degree is obtained by training in bidirectional A-LSTM network. Multivariate Bi-A-LSTM epidemic prevention and control prediction model was obtained. In this paper, the actual data as reference, the average absolute error, average absolute percentage error, root mean square error as the evaluation index of the model, and the improved model and other models are compared, experimental results show that the improved model is more accurate than the traditional model in prediction accuracy. © 2022 IEEE.

The effect of COVID-19 on garlic prices

This paper studied the impact of COVID-19 on garlic price and found a model with high accuracy to predict garlic price to provide reference for relevant personnel in the garlic industry. Through the analysis of the average weekly price of garlic over the years, and analysis of garlic prices at specific time points since the outbreak in 2020.It was found that the outbreak had a relatively large impact on garlic prices，which kept garlic prices low relative to previous years. In order to better respond to emergencies.Therefore, it is particularly important to find a better forecasting model for garlic price prediction. It can provide a reference for people engaged in garlic industry.The CEEDMAN-LSTM combined model is used to forecast the average weekly garlic price in 2020, and the prediction results show that the model is suitable for the prediction of garlic price. © 2021 IEEE.

An Epidemic Spread Risk Prediction Model Coupled with LSTM Algorithm and Cloud Model

The COVID-19 epidemic poses a great threat to public health and people's lives, which has initiated new challenges to the prevention and control system of the epidemic in China. In all efforts for epidemic control and prevention, predicting the risk of epidemic spread is of great practical importance for scientific prevention and control, and precise strategies. To predict the risk of an epidemic rapidly and quantitatively, this paper fused multi-source spatiotemporal data and established a risk prediction model for epidemic transmission by coupling LSTM algorithm and cloud model. Firstly, a simulation model of the spatiotemporal spread of infectious diseases was built based on GIS and LSTM algorithm, which simulated the infectious disease's spatiotemporal transmission process by learning rules in historical epidemic data. At the same time, to improve the simulation accuracy, this paper took 1 km × 1 km for the spatial scale, and days for the temporal scale as the study scale. Secondly, this paper applied the simulated data of infectious cases and the spatiotemporal influence factors on the spread of the epidemic to construct risk evaluation indicators. Finally, the cloud model and adaptive strategies were applied to construct an epidemic risk assessment model. In this way, the epidemic risk assessment at multiple spatial scales was achieved. In the empirical study phase, based on the Beijing COVID-19 epidemic data from 11 June 2020 to 25 June 2020, this paper simulated the process of the spatial evolution of the epidemic from 26 June 2020 to 1 July 2020. To test the advantage of the LSTM model applied to simulate spatiotemporal spread of infectious diseases, four machine learning models were introduced for comparison, including GA-BP Neural Network, Decision Regression Tree, Random Forest, and Support Vector Machine. The results were as follows: ① Compared with other conventional machine learning models, the LSTM model with time-series relationship had higher simulation accuracy (MAE=0.002 61) and better fitting degree (R-Square=0.9455). This showed that the LSTM model considering the temporal relationship between epidemic data was more suitable for epidemic spatial evolution simulation. ② The application results showed that the coupled model can not only fully consider the influence of infection source factors, weather factors, epidemic spread factors and epidemic prevention factors on the spread of transmission risk and reflect the trend of risk evolution, but also quickly quantify regional risk levels. Therefore, the coupled model based on LSTM algorithm and cloud model can effectively predict the transmission risk of epidemic, and also provide a method reference for establishing spatial-temporal transmission models and assessing epidemic risk. 2021, Science Press. All right reserved.

Analysis, visualization and forecasting of COVID-19 outbreak using LSTM model

The COVID-19 outbreak has been treated as a pandemic disease by the World Health Organization (WHO). Severe diseases like Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS) are caused by members of a large family of viruses called coronavirus (CoV). A new strain was identified in humans in December 2019, named coronavirus (COVID-19). The highest affected countries are unable to predict the pace of the outbreak of COVID-19. So, AI is helpful to analyze the COVID-19 outbreak in the world. We have used the LSTM model to predict the outbreak of COVID-19 all over the world with limited epidemiological data. A variant of recurrent neural network (RNN) which has the capability of learning long-term dependencies with feedback connections, also known as long short-term memory (LSTM), is used in resolving the problems related to time series in deep learning. LSTM is capable of processing a single data point and an entire sequence of data related to any field. We observe that the LSTM model is useful to predict the ongoing outbreak so that authorities can take preventive action earlier. The LSTM model result shows that the growth rate of infected cases of COVID-19 increased exponentially every week. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union.

Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded.

DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images.

The new coronavirus, known as COVID-19, first emerged in Wuhan, China, and since then has been transmitted to the whole world. Around 34 million people have been infected with COVID-19 virus so far, and nearly 1 million have died as a result of the virus. Resource shortages such as test kits and ventilator have arisen in many countries as the number of cases have increased beyond the control. Therefore, it has become very important to develop deep learning-based applications that automatically detect COVID-19 cases using chest X-ray images to assist specialists and radiologists in diagnosis. In this study, we propose a new approach based on deep LSTM model to automatically identify COVID-19 cases from X-ray images. Contrary to the transfer learning and deep feature extraction approaches, the deep LSTM model is an architecture, which is learned from scratch. Besides, the Sobel gradient and marker-controlled watershed segmentation operations are applied to raw images for increasing the performance of proposed model in the pre-processing stage. The experimental studies were performed on a combined public dataset constituted by gathering COVID-19, pneumonia and normal (healthy) chest X-ray images. The dataset was randomly separated into two sections as training and testing data. For training and testing, these separations were performed with the rates of 80%-20%, 70%-30% and 60%-40%, respectively. The best performance was achieved with 80% training and 20% testing rate. Moreover, the success rate was 100% for all performance criteria, which composed of accuracy, sensitivity, specificity and F-score. Consequently, the proposed model with pre-processing images ensured promising results on a small dataset compared to big data. Generally, the proposed model can significantly improve the present radiology based approaches and it can be very useful application for radiologists and specialists to help them in detection, quantity determination and tracing of COVID-19 cases throughout the pandemic.

Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model.

SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of June 15, already more than 7.9 million people have been infected and 432k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, the United States, and the rest of the World. It has been found that a huge amount of Thymine (T) and Adenine (A) are mutated to other nucleotides for all regions, but codons are not frequently mutating like nucleotides. A recurrent neural network-based Long Short Term Memory (LSTM) model has been applied to predict the future mutation rate of this virus. The LSTM model gives Root Mean Square Error (RMSE) of 0.06 in testing and 0.04 in training, which is an optimized value. Using this train and testing process, the nucleotide mutation rate of 400th patient in future time has been predicted. About 0.1% increment in mutation rate is found for mutating of nucleotides from T to C and G, C to G and G to T. While a decrement of 0.1% is seen for mutating of T to A, and A to C. It is found that this model can be used to predict day basis mutation rates if more patient data is available in updated time.