Apple's autocorrect on iPhone and iPad always aims to help when you're typing a message, but it's by no means perfect, and some of the replacements it continually spews out can be frustrating.

Text-based depression estimation using natural language processing has emerged as a feasible approach for early mental health screening. However, most existing reviews often included studies with weak ...

Abstract: Precise malware identification is essential for enhancing cybersecurity frameworks against advancing threats. This study introduces an enhanced malware detection framework utilizing machine ...

Discord servers can be pretty crowded, and it’s easy for your messages to not get attention. Hence, many Discord users use text formatting options to make their messages stand out from the crowd. If ...

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 rely on ...

Quadratic regression extends linear regression by adding squared terms and pairwise interaction terms, enabling the model to capture non-linear structure and predictor interactions. The article ...

Commonly used linear regression focuses only on the effect on the mean value of the dependent variable and may not be useful in situations where relationships across the distribution are of interest.

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. For anyone versed in the technical underpinnings of LLMs, this ...

Abstract: This paper presents a comprehensive comparison of various text representation methods, including Word2Vec, Doc2Vec, Phrase Regression, and Multinomial Inverse Regression (MNIR), applied to ...

We developed a classifier to infer acute ischemic stroke severity from Medicare claims using the modified Rankin Scale at discharge. The classifier can be used to improve stroke outcomes research and ...

An end-to-end machine learning project for predicting patient triage priority from free-text symptom descriptions using both traditional ML models and modern transformer-based approaches. Triage Text ...