Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

The line between human and artificial intelligence is growing ever more blurry. Since 2021, AI has deciphered ancient texts ...

Financial word of the day: Heteroscedasticity describes a situation where risk (variance) changes with the level of a ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Traumatic spinal cord injury (SCI) is a serious neurologic disorder that can result in long-term motor and sensory impairments after external damage to the spinal cord. Although anatomic MRI offers ...

Introduction Duplicate medical records occur when a single patient is assigned multiple medical record numbers within an ...

Objectives To examine how the population composition, practice organisation and geographical context of general practice clinics are associated with unwarranted variation in prescribing patterns ...

AI systems now operate on a very large scale. Modern deep learning models contain billions of parameters and are trained on large datasets. Therefore, they produce strong accuracy. However, their ...

This paper presents a novel framework for optimizing Carbon Release (CR) through an AI-driven approach to Fossil Fuel Intake (FFI) management. We propose a new training methodology for AI models to ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...