Machine Learning

This paper describes different machine learning (ML) algorithms to predict heart disease incorporating a Cardiovascular Disease dataset.

This study has analyzed the views and opinions that they have expressed on different social media platforms about the vaccines and the ongoing vaccination program.

This paper proposed an optimal solution to identify diseased carrots. The Random Forest classifier with the top nine features has outperformed all the applied classifiers with the highest accuracy of 94.17%. Lastly, we have found that the features selection technique has helped to minimize the computational cost.

Analyze peoples’ reactions about COVID-19 vaccines and vaccination from the social media data to understand the sentiment.

This paper proposed an approach for sunflower disease recognition. The highest average accuracy of 90.68% has been obtained for the Random Forest classifier.

A multilayer stacking model has been employed in this study to predict the appearance of insomnia in a person.

This study has investigated six different machine learning classifiers using various socio-demographic and psychosocial information to detect whether a person is depressed or not.

Applied six machine learning classifiers with feature selection methods on imbalanced socio-demographic and psychosocial data for predicting depression.

Various machine learning regression models are used to forecast the trends and patterns of crime in Bangladesh.