Journal: Results in Physics

Article Title: Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

doi: 10.1016/j.rinp.2021.103817

Figure Lengend Snippet: Comparison of existing works on COVID-19 prediction methodologies with the proposed work.

Article Snippet: Car et al. , Multilayer Perceptron (MLP) artificial neural network (ANN) , 30 days ahead forecast , 22nd January 2020 to 12th March 2020 Infected, recovered and deceased data , Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL) , Higher accuracy for confirmed cases with 0.986R 2 Value , To predict the spread of pandemic world-wide..

Techniques: Comparison, Control, Plasmid Preparation, Infection, Virus

Journal: AMIA Annual Symposium Proceedings

Article Title: Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare

doi:

Figure Lengend Snippet: Machine learning algorithms’ performance

Article Snippet: The following machine learning algorithms were applied using KNIME: (a) Multilayer Perceptron, applied with maximum iterations = 50, hidden layer = 1, and number of hidden neurons per layer = 10 (b) Decision Tree, pruned with minimum description length using gain ratio, number of records per node = 2, records to store for view = 10,000, and number of threads = 12 (c) Random Forest, applied with number of models = 100, minimum number of instances = 1, and number of levels = 10 (d) SVM, applied with overlapping penalty = 1, and using the polynomial kernel (e) Gradient Boosted Tree, applied with tree depth = 4, number of models = 100, and a learning rate = 0.1 and (f) Logistic Regression applied with Stochastic average gradient, a technique used to find model parameters that are most appropriate for fitting the predicted and actual outcomes.

Techniques: Plasmid Preparation