A multi-objective hybrid machine learning approach-based optimization for enhanced biomass and bioactive phycobiliproteins production in Nostoc sp. CCC-403

2021 ◽  
Vol 329 ◽  
pp. 124908
Author(s):  
Dinesh Kumar Saini ◽  
Amit Rai ◽  
Alka Devi ◽  
Sunil Pabbi ◽  
Deepak Chhabra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Multi Objective ◽  
Machine Learning Approach ◽  
Hybrid Machine

scholarly journals A hybrid machine-learning approach for segmentation of protein localization data

2005 ◽  
Vol 21 (19) ◽  
pp. 3778-3786 ◽  
Author(s):  
P. M. Kasson ◽  
J. B. Huppa ◽  
M. M. Davis ◽  
A. T. Brunger
Keyword(s):  
Machine Learning ◽  
Protein Localization ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Hybrid Machine ◽  
Localization Data

scholarly journals COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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