This study evaluates the performance of twenty-four machine learning models (MLMs) in forecasting SPVP in Bamenda, Cameroon. The study uses data from Photovoltaic Geographical Information System with ...

Deep learning models have shown great potential in predicting and engineering functional enzymes and proteins. Does this prowess extend to other fields of biology as well?

Learn how DenseNet works and why it’s a powerful architecture in deep learning. This tutorial breaks down DenseNet’s key concepts, including dense connections, feature reuse, and parameter ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

Google DeepMind is rolling out Gemini 2.5 Deep Think, which, the company says, is its most advanced AI reasoning model, able to answer questions by exploring and considering multiple ideas ...

Methods A multimodal deep-learning model with transformers was developed for real-time recurrence prediction using baseline clinical, pathological, and molecular data with longitudinal laboratory and ...

Community college enrollment among adult learners in Massachusetts has surged for a second consecutive year, with new data showing a nearly 40% increase in students taking advantage of the state ...

Deep learning — which uses multilayered neural networks to elicit patterns from data — was getting really hot back then. There was already a lot of excitement about applications in image ...

About this issue Abstract Assuming that a smoothness condition and a suitable restriction on the structure of the regression function hold, it is shown that least squares estimates based on multilayer ...

Tabular data is the most prevalent form of structured data, necessitating robust models for classification and regression tasks. Traditional models like eXtreme Gradient Boosting (XGBoost) have gained ...