AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Forecasting

Showing 381 to 390 of 1494 articles

Clear Filters

DAFA-BiLSTM: Deep Autoregression Feature Augmented Bidirectional LSTM network for time series prediction.

Neural networks : the official journal of the International Neural Network Society
Time series forecasting models that use the past information of exogenous or endogenous sequences to forecast future series play an important role in the real world because most real-world time series datasets are rich in time-dependent information. ...

Tropical support vector machines: Evaluations and extension to function spaces.

Neural networks : the official journal of the International Neural Network Society
Support Vector Machines (SVMs) are one of the most popular supervised learning models to classify using a hyperplane in an Euclidean space. Similar to SVMs, tropical SVMs classify data points using a tropical hyperplane under the tropical metric with...

Air Quality Index prediction using an effective hybrid deep learning model.

Environmental pollution (Barking, Essex : 1987)
Environmentalism has become an intrinsic part of everyday life. One of the greatest challenge to the environment's long-term existence is the air pollution. Delhi, the capital of India, has experienced decreasing of air quality for several years. The...

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing.

Computational intelligence and neuroscience
In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure...

Deep learning-based neural networks for day-ahead power load probability density forecasting.

Environmental science and pollution research international
Energy efficiency is crucial to greenhouse gas (GHG) emission pathways reported by the Intergovernmental Panel on Climate Change. Electrical overload frequently occurs and causes unwanted outages in distribution networks, which reduces energy utiliza...

Photovoltaic Power Generation Forecasting Using a Novel Hybrid Intelligent Model in Smart Grid.

Computational intelligence and neuroscience
The exponential growth of electrical demand and the integration of renewable energy sources (RES) brought new challenges in the traditional grid about energy quality. The transition from traditional grid to smart grid is the best solution which provi...

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network.

Sensors (Basel, Switzerland)
As the aggravation of road congestion leads to frequent traffic crashes, it is necessary to relieve traffic pressure through traffic flow prediction. As well, the traffic flow of the target road section to be predicted is also closely related to the ...

A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting.

International journal of environmental research and public health
The coronavirus disease 2019 (COVID-19) has spread rapidly around the world since 2020, with a significant fatality rate. Until recently, numerous countries were unable to adequately control the pandemic. As a result, COVID-19 trend prediction has be...

Recent advances in applications of artificial intelligence in solid waste management: A review.

Chemosphere
Efficient management of solid waste is essential to lessen its potential health and environmental impacts. However, the current solid waste management practices encounter several challenges. The development of effective waste management systems using...

Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition.

Sensors (Basel, Switzerland)
Wind energy reserves are large worldwide, but their randomness and volatility hinder wind power development. To promote the utilization of wind energy and improve the accuracy of wind power prediction, we comprehensively consider the influence of win...