[HTML][HTML] Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?
J Devaraj, RM Elavarasan, R Pugazhendhi… - Results in Physics, 2021 - Elsevier
The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global
economic growth and henceforth, all of society since neither a curing drug nor a preventing …
economic growth and henceforth, all of society since neither a curing drug nor a preventing …
Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images
Despite various approaches proposed to smooth the hyperspectral images (HSIs) before
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …
The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality
L Guo, W Fang, Q Zhao, X Wang - Computers & Industrial Engineering, 2021 - Elsevier
Demand forecasting is the basic aspect of supply chain management. It has important
impacts on planning, capacity and inventory control decisions. Seasonality is a common …
impacts on planning, capacity and inventory control decisions. Seasonality is a common …
[HTML][HTML] Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models
Y Wang, Z Yan, D Wang, M Yang, Z Li, X Gong… - BMC Infectious …, 2022 - Springer
Background COVID-19 poses a severe threat to global human health, especially the USA,
Brazil, and India cases continue to increase dynamically, which has a far-reaching impact on …
Brazil, and India cases continue to increase dynamically, which has a far-reaching impact on …
A time-series classification approach based on change detection for rapid land cover mapping
J Yan, L Wang, W Song, Y Chen, X Chen… - ISPRS Journal of …, 2019 - Elsevier
Abstract Land-Use/Land-Cover Time-Series Classification (LULC-TSC) is an important and
challenging problem in terrestrial remote sensing. Detecting change-points, dividing the …
challenging problem in terrestrial remote sensing. Detecting change-points, dividing the …
Taming energy and electronic waste generation in bitcoin mining: Insights from Facebook prophet and deep neural network
The Bitcoin mining hosted in the blockchain network consumes enormous amounts of
energy and generates electronic waste at an alarming rate. The paper aims to model and …
energy and generates electronic waste at an alarming rate. The paper aims to model and …
Developing a new cross-sensor calibration model for DMSP-OLS and Suomi-NPP VIIRS night-light imageries
Night-time light (NTL) data provides a great opportunity to monitor human activities and
settlements. Currently, global-scale NTL data are acquired by two satellite sensors, ie …
settlements. Currently, global-scale NTL data are acquired by two satellite sensors, ie …
[HTML][HTML] Long-term evaluation of a low-cost air sensor network for monitoring indoor and outdoor air quality at the community scale
RE Connolly, Q Yu, Z Wang, YH Chen, JZ Liu… - Science of The Total …, 2022 - Elsevier
Given the growing interest in community air quality monitoring using low-cost sensors, 30
PurpleAir II sensors (12 outdoor and 18 indoor) were deployed in partnership with …
PurpleAir II sensors (12 outdoor and 18 indoor) were deployed in partnership with …
Estimating hourly PM2. 5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study
Predicting PM 2.5 concentrations at a fine spatial and temporal resolution (ie, neighborhood,
hourly) is challenging. Recent growth in low cost sensor networks is providing increased …
hourly) is challenging. Recent growth in low cost sensor networks is providing increased …
[HTML][HTML] Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national …
N Zhao, K Charland, M Carabali… - PLoS neglected …, 2020 - journals.plos.org
The robust estimate and forecast capability of random forests (RF) has been widely
recognized, however this ensemble machine learning method has not been widely used in …
recognized, however this ensemble machine learning method has not been widely used in …