An interactive toolbox for standardizing, validating, simulating, reducing, and exploring detailed biophysical models that can be used to reveal how morpho-electric properties map to dendritic and ...

With the popularity of AI coding tools rising among some software developers, their adoption has begun to touch every aspect ...

Here are 11 free NPTEL data science and analytics courses from leading IITs cover graph theory, Bayesian modelling, Python, R, databases and big-data stats. These are all free to audit, and enrolment ...

bayesian-sgdlm is a Python script for fully Bayesian SGDLMs, treating each node as a VAR( 𝑝) DLM. It leverages decouple–recouple filtering with Variational Bayes and importance sampling to estimate ...

In the swiftly evolving tech landscape, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as two of the most ...

Imagine a world where machines don’t just follow instructions but actively make decisions, adapt to new information, and collaborate to solve complex problems. This isn’t science fiction, it’s the ...

Model making is a great hobby, but knowing how and where to start is another story. It can be overwhelming for novices: Injection-molded plastic models have been on the market for close to a century, ...

Abstract: Conventional data-driven dynamic process monitoring methods usually rely on data collected at a single sampling rate. The effectiveness of these approaches typically diminishes when ...

ABSTRACT: This study investigates the relationship between public debt and economic growth in Uganda for the period 1990 to 2023 using a nonlinear autoregressive distributed lag (NARDL) model. The ...

Introduction: Accurate wheat yield estimation is crucial for efficient crop management. This study introduces the Spatio–Temporal Fusion Mixture of Experts (STF-MoE) model, an innovative deep learning ...