Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

An Investigation into Crime Forecast Using Auto ARIMA and Stacked LSTM

The severity of criminal activities which cause both physical and psychological damage has been increasing at an alarming rate across the globe. Realizing the significance of this problem, law enforcement agencies have developed several strategies to prevent crimes. Being slow-paced and ineffective in most cases, these prevention strategies are not robust enough to contribute in predicting crime trends for an early prevention. In this paper, we propose a regression-based model that incorporates temporal, statistical relationships and other relevant information about the data to forecast crime trends. Since, seasonal information is a powerful inclusion in an application of time series pattern, we use two popular regression methods, including an extended Autoregressive Integrated Moving Average (Auto ARIMA) and stacked Long Short-Term Memory (LSTM) to analyze crime patterns, specifically during the Covid-19 pandemic lockdown, and generate forecasts. We experimented our methods on London Crime Dataset and obtained some interesting results which can not only be useful to take necessary precautions, but also analyze crime patterns during the period of pandemic lockdowns for generating useful guidelines regarding citizens' life styles and hence, contribute to reducing the crime rates accordingly. © 2022 IEEE.