Deep convolutional neural network-based effective model for 2D ear recognition using data augmentation

In the pandemic of COVID-19, identifying a person from their face became difficult due to wearing of mask. In regard to the given circumstances, the authors have remarkably put effort on identifying a person using 2D ear images based on deep convolutional neural network (CNNs). They investigated the challenges of limited data and varying environmental conditions in this regards. To deal with such challenges, the authors developed an augmentation-based light-weight CNN model using CPU enabled machine so that it can be ported into embedded devices. While applying data augmentation technique to enhance the quality and size of training dataset, the authors analysed and discussed the different augmentation parameters (rotation, flipping, zooming, and fill mode) that are effective for generating the large number of sample images of different variability. The model works well on constrained and unconstrained ear datasets and achieves good recognition accuracy. It also reduces the problem of overfitting. [ FROM AUTHOR] Copyright of Imaging Science Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Kalman filter based short term prediction model for COVID-19 spread.

Corona Virus Disease 2019 (COVID19) has emerged as a global medical emergency in the contemporary time. The spread scenario of this pandemic has shown many variations. Keeping all this in mind, this article is written after various studies and analysis on the latest data on COVID19 spread, which also includes the demographic and environmental factors. After gathering data from various resources, all data is integrated and passed into different Machine Learning Models in order to check its appropriateness. Ensemble Learning Technique, Random Forest, gives a good evaluation score on the tested data. Through this technique, various important factors are recognized and their contribution to the spread is analyzed. Also, linear relationships between various features are plotted through the heat map of Pearson Correlation matrix. Finally, Kalman Filter is used to estimate future spread of SARS-Cov-2, which shows good results on the tested data. The inferences from the Random Forest feature importance and Pearson Correlation gives many similarities and few dissimilarities, and these techniques successfully identify the different contributing factors. The Kalman Filter gives a satisfying result for short term estimation, but not so good performance for long term forecasting. Overall, the analysis, plots, inferences and forecast are satisfying and can help a lot in fighting the spread of the virus.