Vision-based collective motion: A locust-inspired reductionist model DOI Creative Commons
David L. Krongauz, Amir Ayali, Gal A. Kaminka

et al.

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(1), P. e1011796 - e1011796

Published: Jan. 29, 2024

Naturally occurring collective motion is a fascinating phenomenon in which swarming individuals aggregate and coordinate their motion. Many theoretical models of assume idealized, perfect perceptual capabilities, ignore the underlying perception processes, particularly for agents relying on visual perception. Specifically, biological vision many animals, such as locusts, utilizes monocular non-stereoscopic vision, prevents acquisition distances velocities. Moreover, peers can visually occlude each other, further introducing estimation errors. In this study, we explore necessary conditions emergence ordered under restricted conditions, using non-stereoscopic, vision. We present model vision-based locust-like agents: elongated shape, omni-directional sensor parallel to horizontal plane, lacking stereoscopic depth The addresses (i) distance velocity, (ii) presence occlusions field. consider compare three strategies that an agent may use interpret partially-occluded information at cost computational complexity required processes. Computer-simulated experiments conducted various geometrical environments (toroidal, corridor, ring-shaped arenas) demonstrate result or near-ordered state. At same time, they differ rate order achieved. results are sensitive elongation agents. Experiments geometrically constrained reveal differences between elucidate possible tradeoffs them control These suggest avenues study biology robotics.

Language: Английский

Coronavirus disease (COVID-19) cases analysis using machine-learning applications DOI Creative Commons
Ameer Sardar Kwekha Rashid, Heamn Noori Abduljabbar, Bilal Alhayani

et al.

Applied Nanoscience, Journal Year: 2021, Volume and Issue: 13(3), P. 2013 - 2025

Published: May 21, 2021

Language: Английский

Citations

309
Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions DOI Creative Commons
Saleh I. Alzahrani, Ibrahim Aljamaan, Ebrahim Al-Fakih

et al.

Journal of Infection and Public Health, Journal Year: 2020, Volume and Issue: 13(7), P. 914 - 919

Published: June 8, 2020

The substantial increase in the number of daily new cases infected with coronavirus around world is alarming, and several researchers are currently using various mathematical machine learning-based prediction models to estimate future trend this pandemic. In work, we employed Autoregressive Integrated Moving Average (ARIMA) model forecast expected COVID-19 Saudi Arabia next four weeks. We first performed different models; Model, Average, a combination both (ARMA), integrated ARMA (ARIMA), determine best fit, found out that ARIMA outperformed other models. forecasting results showed will continue growing may reach up 7668 per day over 127,129 cumulative matter weeks if stringent precautionary control measures not implemented limit spread COVID-19. This indicates Umrah Hajj Pilgrimages two holy cities Mecca Medina supposedly scheduled be by nearly 2 million Muslims mid-July suspended. A set extreme preventive proposed an effort avoid such situation.

Language: Английский

Citations

251
Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study DOI Open Access
Sourabh Shastri, Kuljeet Singh, Sachin Kumar

et al.

Chaos Solitons & Fractals, Journal Year: 2020, Volume and Issue: 140, P. 110227 - 110227

Published: Aug. 20, 2020

Language: Английский

Citations

244
A review on COVID-19 forecasting models DOI Creative Commons
Iman Rahimi, Chen Fang, Amir H. Gandomi

et al.

Neural Computing and Applications, Journal Year: 2021, Volume and Issue: 35(33), P. 23671 - 23681

Published: Feb. 4, 2021

The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis most important forecasting against COVID-19. presented in this study possesses two parts. In first section, detailed scientometric an influential tool for bibliometric analyses, which were performed on COVID-19 data from Scopus Web Science databases. For above-mentioned analysis, keywords subject areas are addressed, while classification models, criteria evaluation, comparison solution approaches discussed second section work. conclusion discussion provided final sections study.

Language: Английский

Citations

220
Applications of artificial intelligence in battling against covid-19: A literature review DOI Open Access

Mohammad-H. Tayarani N.

Chaos Solitons & Fractals, Journal Year: 2020, Volume and Issue: 142, P. 110338 - 110338

Published: Oct. 3, 2020

Language: Английский

Citations

196
COVID-19 Prediction Models and Unexploited Data DOI Creative Commons
K. C. Santosh

Journal of Medical Systems, Journal Year: 2020, Volume and Issue: 44(9)

Published: Aug. 13, 2020

Language: Английский

Citations

150
Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial DOI Creative Commons
Hoyt Burdick, Carson Lam, Samson Mataraso

et al.

Computers in Biology and Medicine, Journal Year: 2020, Volume and Issue: 124, P. 103949 - 103949

Published: Aug. 6, 2020

Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective identifying patient decompensation. Machine learning (ML) may offer alternative strategy. A prospectively validated method predict the need ventilation patients is essential help triage patients, allocate resources, and prevent emergency intubations associated risks. In a multicenter clinical trial, we evaluated performance of machine algorithm prediction invasive mechanical within 24 h initial encounter. We enrolled with diagnosis who were admitted five United States health between March May 4, 2020. 197 REspirAtory Decompensation model covid-19 patients: prospective studY (READY) trial. The had higher diagnostic odds ratio (DOR, 12.58) predicting than comparator early warning system, Modified Early Warning Score (MEWS). also achieved significantly sensitivity (0.90) MEWS, which 0.78, while maintaining specificity (p < 0.05). first trial needs among demonstrated h. This care teams effectively resources. Further, capable accurately 16% more widely used system minimizing false results.

Language: Английский

Citations

146
A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the COVID-19 outbreak DOI Open Access
Abbas Mardani, Mahyar Kamali Saraji, Arunodaya Raj Mishra

et al.

Applied Soft Computing, Journal Year: 2020, Volume and Issue: 96, P. 106613 - 106613

Published: Aug. 6, 2020

Language: Английский

Citations

142
Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics DOI Creative Commons
Jacques Demongeot,

Yannis Flet-Berliac,

Hervé Seligmann

et al.

Biology, Journal Year: 2020, Volume and Issue: 9(5), P. 94 - 94

Published: May 3, 2020

(1) Background: The virulence of coronavirus diseases due to viruses like SARS-CoV or MERS-CoV decreases in humid and hot weather. putative temperature dependence infectivity by the new SARS-CoV-2 covid-19 has a high predictive medical interest. (2) Methods: External cases 21 countries French administrative regions were collected from public data. Associations between epidemiological parameters case dynamics examined using an ARIMA model. (3) Results: We show that, first stages epidemic, velocity contagion with country- region-wise temperature. (4) Conclusions: Results indicate that temperatures diminish initial rates, but seasonal effects at later epidemy remain questionable. Confinement policies other eviction rules should account for climatological heterogeneities, order adapt health decisions possible geographic gradients.

Language: Английский

Citations

141
Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases DOI Creative Commons
Suyel Namasudra,

S. Dhamodharavadhani,

R. Rathipriya

et al.

Neural Processing Letters, Journal Year: 2021, Volume and Issue: 55(1), P. 171 - 191

Published: April 1, 2021

Language: Английский

Citations

118