Covid 19 Prediction from X Ray Images Using Fully Connected Convolutional Neural Network

Bhoumik, Sanghamita; Chatterjee, Sayantan; Sarkar, A.; Kumar, Avneesh; Joseph, Ferdin Joe John

doi:10.1145/3429210.3429233

Cited by 14 publications

(1 citation statement)

References 4 publications

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“…e ARMA model and ARIMA models have many similarities. However, the general autoregressive model estimates predictions using previous values of the dependent variable [40,41].…”

Section: 11mentioning

confidence: 99%

Prediction of Fault Occurrences in Smart City Water Distribution System Using Time-Series Forecasting Algorithm

Chenniappan

Gnanavel

Gunasekaran

et al. 2022

Mathematical Problems in Engineering

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The proposed research work is focused on forecasting the future requirements of water supply based on the current requirement of water and also identifying the possibility of occurrences of cracks and leaks using the ARIMA (autoregressive integrated moving average) model. The experiments were conducted using real-time experimental hardware. The pressure data obtained and their p -value is less than 0.05, which represents the stability of the data in the ARIMA model. The forecasted pressure data range between 0.451379 N/m2 and 2.022273 N/m2. The frequency of the forecasted pressure ranges between 1.706869 N/m2 and 3.065836 N/m2 (maximum peak) and −0.81046 N/m2 and 1.042164 N/m2 (minimum peak). Forecasted data of pressure at damaged condition lie between 2.880788 N/m2 and 3.29797 N/m2 and frequency ranges between 4.866227 N/m2 and 5.664348 N/m2. Similarly, future forecasted data of water requirement for the next 1 year range between 614.6292 (liters/week) and 620.0099 (liters/week), the frequency of the forecast value with maximum ranging from 617.0086 (liters/week) to 628.5465 (liters/week), and the minimum peaks ranging from 611.0967 (liters/week) to 612.2914 (liters/week). The above data are for a single water distribution pipeline.

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“…e ARMA model and ARIMA models have many similarities. However, the general autoregressive model estimates predictions using previous values of the dependent variable [40,41].…”

Section: 11mentioning

confidence: 99%