Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19

Kavadi, Durga Prasad; Patan, Rizwan; Ramachandran, Manikandan; Gandomi, Amir H.

doi:10.1016/j.chaos.2020.110056

Cited by 76 publications

(56 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A partial derivative regression and nonlinear machine learning method is proposed in [451] to predict the global pandemic. In this algorithm, the non linear machine learning models the behavior and the partial derivative linear regression acts as the search algorithm for the optimization of the model parameters.…”

Section: Applications Of Ai In Epidemiologymentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

160

View full text Add to dashboard Cite

show abstract

Section: Applications Of Ai In Epidemiologymentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

160

View full text Add to dashboard Cite

show abstract

“…One of the first areas where AI was applied to the COVID-19 pandemic was related to the development of outbreak forecasting models, which had the potential to help decision makers understand the potential progression of COVID-19 in their area. One such approach was developed by Kavadi et al [132] who suggested a partial derivative regression and non-linear machine learning (PDR-NML) model to predict the COVID-19 transmission dynamics in India. The presented model achieved better performance in terms of accuracy and prediction time over linear regression and state-of-the-art AI-based models.…”

Section: Ai-based Approaches For Covid-19mentioning

confidence: 99%

“… Time series [133] June, 2020 K-mean with PCA Clustering countries in groups according to COVID-19 positive cases. Text [132] June, 2020 Partial derivative regression and nonlinear ML method (PDR-NML) COVID-19 pandemic outbreak prediction across globe Text [141] June, 2020 ML-based models (SVM, Linear Regression, and Polynomial Regression) COVID-19 epidemic prediction, transmission rate analysis, and growth rates and migration type analysis. Text [134] May, 2020 Modified Auto-Encoders To estimate pandemic transmission and evaluate interventions and measurements to halt COVID-19 spread Time series [142] May, 2020 Unsupervised Self-Organizing Maps Spatially grouping countries that share similar COVID-19 cases Time series [143] May, 2020 ML-based method with Cloud computing Potential threat and growth prediction of COVID-19 Time series [144] April, 2020 Linear Regression with LSTM Predicting outbreak trends and COVID-19 incidence in Iran.…”

Section: Ai-based Approaches For Covid-19mentioning

confidence: 99%

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

Rasheed

Jamıl

Hameed

et al. 2020

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

Highlights The current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak. This paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. Investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. The impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. The investigation of this paper reveals several AI-based approaches that have been proposed as potential ways to help, with the COVID-19 pandemic, covering everything from initial diagnoses via image diagnostics up to the presentation of models that help to understand the spread of COVID-19 and identify potential new outbreak areas.

show abstract

“…Penularan terjadi karena percikan-percikan droplet yang keluar dari hidung atau mulut saat orang yang telah terinfeksi covid19 tersebut batuk, bersin atau berbicara. Oleh karena itu pada masa pandemik sangat dianjurkan untuk menggunakan masker ataupun alat pelindung serta melakukan pembatasan sosial untuk mengurangi potensi penyebaran virus [4].…”

Section: Pendahuluanunclassified

Prediksi Jumlah Penderita COVID-19 di Kota Malang Menggunakan Jaringan Syaraf Tiruan Backpropagation dan Metode Conjugate Gradient

Anam

Maulana

Hidayat

et al. 2020

Prosid Sem Nas Teknoka

View full text Add to dashboard Cite

COVID-19 merupakan penyakit menular yang diakibatkan oleh infeksi virus corona baru. Penyakit ini sangat berbahaya dan menyebabkan kematian terutama bagi penderita yang memilki penyakit bawaan atau yang memiliki imunitas rendah. Penyebaran penyakit ini melalui melalui percikan-percikan dari hidung atau mulut yang keluar saat orang yang terinfeksi COVID-19 batuk, bersin atau berbicara. Prediksi jumlah penderita COVID-19 menjadi sangat penting untuk dilakukan dalam pencegahan dan penanggulangan penyebaran penyakit ini. Jaringan syaraf tiruan backpropagation merupakan salah metode yang dapat digunakan untuk menyelesaikan masalah prediksi dengan hasil yang baik, tetapi kinerjanya dipengaruhi oleh metode optimisasi yang digunakan saat pelatihan. Pada umumnya metode optimisasi yang digunakan adalah metode gradient descent, tetapi metode ini memiliki konvergensi yang lambat. Metode Conjugate Gradient memiliki konvergensi yang sangat baik jika dibandingkan dengan metode gradient descent. Pada tulisan ini akan dibahas bagaimana membuat model prediksi jumlah penderita COVID-19 di Kota Malang menggunakan jaringan syaraf backprogation dan metode conjugate gradient. Hasil eksperimen menunjukan bahwa model prediksi ini memperoleh hasil yang baik jika dibandingkan jaringan syaraf tiruan yang dioptimasi dengan metode gradient descent.

show abstract

Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19

Cited by 76 publications

References 19 publications

Applications of artificial intelligence in battling against covid-19: A literature review

Applications of artificial intelligence in battling against covid-19: A literature review

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

Prediksi Jumlah Penderita COVID-19 di Kota Malang Menggunakan Jaringan Syaraf Tiruan Backpropagation dan Metode Conjugate Gradient

Contact Info

Product

Resources

About