| viXra.org > Artificial Intelligence > 2106 |
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| viXra.org > Artificial Intelligence > 2106 |
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[3] viXra:2106.0084 [pdf] replaced on 2021-06-17 18:25:12
Authors: Souvik Sengupta
Comments: 6 Pages.
After one year from the start of the COVID-19 pandemic in India, the country is now having a steady decay in the number of daily new cases and active cases. Although the vaccination process is about to start from mid of January 2021, it would not affect the number of daily cases at least for the next three to four months for obvious reasons like phase-wise implementation and six to eight weeks time span required from the first dosage to develop the immunity. Therefore, the prime question is now, where would we reach at the end of the first quarter of 2021, and what could be the number of new cases and active cases before the vaccination immunity starts working. This paper analyzes the growth and decay pattern of Indian COVID-19 cases with help of SEIR epidemical modeling, ARIMA statistical modeling, and time series analysis by LSTM. The models learn the parameter and hyper-parameter values that are best suited for describing the pattern for the COVID-19 pandemic in India. Then it tries to predict the numbers for India by the end of March, 2021. It is forecasted that the number of new cases would come down near 5000 per day, active cases near 40,000 and the total number of infected may reach 11.1 million if the current pattern is followed.
Category: Artificial Intelligence
[2] viXra:2106.0071 [pdf] submitted on 2021-06-12 18:39:56
Authors: Ashrith Appani
Comments: 11 Pages.
Backdrop Purging is a common pre-processing step in computer vision and video processing for object tracking, people recognition, and other tasks. Several successful background-subtraction algorithms have recently been proposed, however nearly all of the best-performing ones are supervised. The availability of some annotated frames of the test video during training is critical to their performance. As a result, there is no literature on their performance on completely "unseen" videos. We provide a new supervised background-subtraction technique for unseen films (BSUV-Net) based on a fully-convolutional neural network in this paper. The current frame and two background frames collected at various time scales, along with their semantic segmentation maps, are fed into our network. We also offer a new data-augmentation strategy that mitigates the influence of illumination differences between the background frames and the current frame in order to limit the risk of overfitting. In terms of F-measure, recall, and precision, BSUV-Net beats state-of-the-art algorithms assessed on unseen videos on the CDNet-2014 dataset.
Category: Artificial Intelligence
[1] viXra:2106.0040 [pdf] submitted on 2021-06-07 07:02:56
Authors: Jovial Joe Jayarson
Comments: 3 Pages. Best paper award in NCGCE 21. Mr. Ebin PM is the author's guide.
It is no secret that AI is an upcoming titan. Even though people are stunned to hear that AI has been here for around a century, due to the advancement in computational methods and resources, today AI peaks like never before. As a tiny glimpse into the field of Digit Recognition, this project aims to understand the underlying cogs and wheels on which the neural networks spin. This paper tries to elucidate a project which solves the Sudoku puzzle drawn and written by hand. The paraphernalia for that project includes programming language: Python3; libraries: OpenCV, Numpy, Keras; datasets: MNIST handwritten digit database. Digit recognition is a classical problem which will introduce neurons, neural networks, connections hidden layers, weights, biases, activation functions like sigmoid, back-propagation and other related topics as well. Algorithm(s) in the project employed to solve Sudoku is also explored in this paper.
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