ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...

Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important: BCE weighs mistakes from both ...

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

Abstract: Efficient and accurate small molecule classification methods can significantly improve the efficiency of scientific research and industrial applications, but in real scenarios, many datasets ...

i am running binary classification report. my "target" column is binary 0,1 values, "pred_lablel" is binary 01, values and "prediction" is probabilities between 0-1 i get auc/roc, log loss but ...

Binary options let investors predict asset price movements for a fixed payout. Investors know potential gain or loss upfront, simplifying risk management. Example: Predicting a stock price increase ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The application of artificial neural network (ANN) techniques to spectroscopy has ...

Physical frailty is a pressing public health issue that significantly increases the risk of disability, hospitalization, and mortality. Early and accurate detection of frailty is essential for timely ...

Revised: This Reviewed Preprint has been revised by the authors in response to the previous round of peer review; the eLife assessment and the public reviews have been updated where necessary by the ...

Abstract: This study explores the use of a hybrid machine learning model that combines support vector machines (SVMs) and convolutional neural networks (CNNs) for the diagnosis of lung disorders in ...