Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods.

The global economy has suffered losses as a result of the COVID-19 epidemic. Accurate and effective predictive models are necessary for the governance and readiness of the healthcare system and its resources and, ultimately, for the prevention of the spread of illness. The primary objective of the project is to build a robust, universal method for predicting COVID-19-positive cases. Collaborators will benefit from this while developing and revising their pandemic response plans. For accurate prediction of the spread of COVID-19, the research recommends an adaptive gradient LSTM model (AGLSTM) using multivariate time series data. RNN, LSTM, LASSO regression, Ada-Boost, Light Gradient Boosting and KNN models are also used in the research, which accurately and reliably predict the course of this unpleasant disease. The proposed technique is evaluated under two different experimental conditions. The former uses case studies from India to validate the methodology, while the latter uses data fusion and transfer-learning techniques to reuse data and models to predict the onset of COVID-19. The model extracts important advanced features that influence the COVID-19 cases using a convolutional neural network and predicts the cases using adaptive LSTM after CNN processes the data. The experiment results show that the output of AGLSTM outperforms with an accuracy of 99.81% and requires only a short time for training and prediction.

To Act Towards Alarming Situation: A Huge Gap Between Food Supply & Demand

The United Nations Sustainable Development Goals also talk about the ZERO HUNGER by 2030, which itself shows that we must think about this alarming situation, the situation is becoming worse due to the Covid -19 pandemic, which has increased the level of stress. As the recent trend, the number of people affected by hunger will surpass 840 million by 2030, or 9.8 percent of the global population [16]. With soaring food prices pushing millions of people into food insecurity, the United Nations International Fund for Agricultural Development (IFAD) is calling on governments and the private sector to urgently step up their investments in small-scale agriculture focused on locally produced, nutrition-rich food [14].This move is very essential and will be effective too, as if local food production will increase, the food will be made easily available to the people & at a much lower rate, this in turn will reduce the insecurity of the food at a much higher level. After realizing the United Nations Goal 2 -ZERO HUNGER and looking at the announcement of the International Fund for Agriculture Development ahead of Tokyo Nutrition for Growth (N4G) Summit on 7-8 of December, 2021, we may conclude that to overcome the fear of hunger in the near future, we must rebuild our agriculture system to produce the maximum output with the available resources & for this we must use the latest technological tools and ICT also.