To address these important challenges, we propose a novel Multi-edge Cooperative universal framework for load Prediction with Personalized Federated deep learning (MC-2PF), enabling multi-edge ...

Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced ...

The rapid development of enzyme mining and de novo design has produced a large number of new enzymes, making it impractical to measure their pHopt in the wet laboratory. Consequently, in-silico ...

Keywords: deep learning, power load forecasting, smart energy, sustainable urban growth, LSTM, load distribution Citation: Byeon H, AlGhamdi A, Keshta I, Soni M, Mekhmonov S and Singh G (2025) Deep ...

It is challenging to build a deep learning predictive model using traditional data mining methods due to the scarcity of available data, and the model’s internal decision-making process is often ...

Keywords: building eletric load forecastng, global time series forecasting, multivariate, deep transfer learning, pre-trained models, foundation models This repository implements the experiments ...

For Internet Service Providers (ISPs), network traffic load prediction enables various practical applications such as load balancing, network planning, and network maintenance. With these applications ...

Transfer learning is commonly used in deep learning applications. You can take a pretrained network and use it as a starting point to learn a new task. Fine-tuning a network with transfer learning is ...