The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.

One of the most successM applications of image analysis and undastandimg. facc recognition bas received significant attention. There are at least two reaSOns for the trend the first is the wide range of commercial and law enforcement application and the second is the availability of feasible technologies. In general, few methods of face recognition are in practice, Feature based face recognition m e t h a Eigen Face Based, Line Based, Elastic Bunch Graph method and Neural Network based methods A11 have their possibilities and features. In Neural Network approach automatic detection of eyes and mouth is followed by a spatial normalization of the images. The classification of the no& images is carried out by hybrid Neural Network that combines unsupervised and superVisd methods for finding structures and reducing classification ermn respectively. The linsbased is a type of imagsbased a p p m h . It does not use any detailed biometric knowledge of the human facc. These techniques use either the pixel-based bi-diiensional m y representation of the entire face image or a set of transformed imagea or template sub-images of facial features as the image representation. An imagsbased metric such as correlation is then used to match the resulting image with the set of model images. In the context of imagsbaxd techniqueg two approaches are there namely template-based and neural networks. In the templatsbased approach, the face is represented as a set of templates of the major facial features, which are then matched with the p t o t y + d model face templates. Neural network-based image techniques use an input image representation that is the gray-level pixel-based image or bansformed image which is used as an input to one of a variety of neural network architectures, including multilayer, radial basis hctions and autcxwciative networks

No abstract

No abstract