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 ...
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 ...
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 ...
This repository provides an efficient binary video classification pipeline using PyTorch, optimized for local GPU-enabled PCs. It includes preprocessing and model inference tools for classifying ...
Abstract: In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to ...
ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...
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