Forecasting Global Monkeypox Infections Using LSTM: A Non-Stationary Time Series Analysis DOI

Omnia M. Osama,

Khder Alakkari, Mostafa Abotaleb

et al.

Published: Oct. 7, 2023

This study leverages the capabilities of Long Short-Term Memory (LSTM) models in forecasting global Monkeypox infections, thereby demonstrating significant potential advanced machine learning techniques epidemiological forecasting. Our LSTM model effectively navigates challenges posed by non-stationary time-series data, a common issue studies. It successfully captures underlying patterns producing reliable forecasts. The model's performance was evaluated using several metrics, including RMSE, MSE, MAE, and R 2 , all which pointed to its robust satisfactory predictive capabilities. findings underscore role can play informing development timely effective disease control prevention strategies. They contribute enhancing public health responses emerging infectious diseases such as Monkeypox. However, despite promising results, highlights ongoing challenge interpretability models, an area that warrants further research. As future direction, efforts should focus on refining bolster their interpretability, ensuring broader adoption utility practice.

Language: Английский

The application of integrated deep learning models with the Assistance of meteorological factors in forecasting major tobacco diseases DOI
Chen Yuan, Changcheng Li, Can Wang

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 236, P. 110429 - 110429

Published: April 24, 2025

Language: Английский

Citations

0
A privacy-enhanced framework for collaborative Big Data analysis in healthcare using adaptive federated learning aggregation DOI Creative Commons

R Haripriya,

Nilay Khare, Manish Pandey

et al.

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: May 6, 2025

Language: Английский

Citations

0
The Role of Machine Learning in Infectious Disease Early Detection and Prediction in the MENA Region: A Systematic Review DOI Creative Commons
Radwan Qasrawi, Ghada Issa,

Suliman Thwib

et al.

Informatics in Medicine Unlocked, Journal Year: 2025, Volume and Issue: unknown, P. 101651 - 101651

Published: May 1, 2025

Language: Английский

Citations

0
Pre-trained Deep learning model for Monkeypox Prediction using Dermoscopy Images in Healthcare DOI
Shikha Prasher, Leema Nelson,

S. Gomathi

et al.

Published: July 14, 2023

Monkeypox is a medical skin problem that can be transferred from animals to humans and then one person other. Its species Otho poxvirus. The manifestations of monkeypox smallpox are virtually identical thus, antiviral medication developed prevent the virus may used for despite absence effective therapy. Infected individuals, vaccination, prevention infection, use personal Protective Equipment (PPE) kits all part control monkey pox. In this study, deep learning-based convolution neural network (CNN) detect monkeypoxes. research, three optimizers namely SGD, RMSProp Adam employed predict monkeypox. From optimizers, best optimizer selected based on accuracy. SGD achieves highest accuracy 93.39% in 100 epochs. Other were Adam, with scores 91.30% 93.22%, respectively. Using single image an infected person, CNN model easily predicts virus. This as second source opinion practitioners identify

Language: Английский

Citations

5
Forecasting Global Monkeypox Infections Using LSTM: A Non-Stationary Time Series Analysis DOI

Omnia M. Osama,

Khder Alakkari, Mostafa Abotaleb

et al.

Published: Oct. 7, 2023

This study leverages the capabilities of Long Short-Term Memory (LSTM) models in forecasting global Monkeypox infections, thereby demonstrating significant potential advanced machine learning techniques epidemiological forecasting. Our LSTM model effectively navigates challenges posed by non-stationary time-series data, a common issue studies. It successfully captures underlying patterns producing reliable forecasts. The model's performance was evaluated using several metrics, including RMSE, MSE, MAE, and R 2 , all which pointed to its robust satisfactory predictive capabilities. findings underscore role can play informing development timely effective disease control prevention strategies. They contribute enhancing public health responses emerging infectious diseases such as Monkeypox. However, despite promising results, highlights ongoing challenge interpretability models, an area that warrants further research. As future direction, efforts should focus on refining bolster their interpretability, ensuring broader adoption utility practice.

Language: Английский

Citations

5