AIMC Topic: Forecasting

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Using model explanations to guide deep learning models towards consistent explanations for EHR data.

Scientific reports
It has been shown that identical deep learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that are orthogonal to the task (e.g. random seed, training set order). In domains such as healthcare and...

Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network.

Sensors (Basel, Switzerland)
The current satellite network traffic forecasting methods cannot fully exploit the long correlation between satellite traffic sequences, which leads to large network traffic forecasting errors and low forecasting accuracy. To solve these problems, we...

Development of an Artificial Neural Network Algorithm Embedded in an On-Site Sensor for Water Level Forecasting.

Sensors (Basel, Switzerland)
Extreme weather events cause stream overflow and lead to urban inundation. In this study, a decentralized flood monitoring system is proposed to provide water level predictions in streams three hours ahead. The customized sensor in the system measure...

An Open Source Replication of a Winning Recidivism Prediction Model.

International journal of offender therapy and comparative criminology
We present results of our winning solution to the National Institute of Justice recidivism forecasting challenge. Our team, "MCHawks," placed highly in both terms of accuracy (as measured via the Brier score), as well as the fairness criteria (weight...

Wavelet LSTM for Fault Forecasting in Electrical Power Grids.

Sensors (Basel, Switzerland)
An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this rea...

A Robust Learning Algorithm Based on Particle Swarm Optimization for Pi-Sigma Artificial Neural Networks.

Big data
Artificial neural networks (ANNs) have been frequently used in forecasting problems in recent years. One of the most popular types of ANNs in these days is Pi-Sigma artificial neural networks (PS-ANNs). PS-ANNs have a high order ANN structure and the...

Foresight science in conservation: Tools, barriers, and mainstreaming opportunities.

Ambio
Foresight science is a systematic approach to generate future predictions for planning and management by drawing upon analytical and predictive tools to understand the past and present, while providing insights about the future. To illustrate the app...

Development and Evaluation of Machine Learning-Based High-Cost Prediction Model Using Health Check-Up Data by the National Health Insurance Service of Korea.

International journal of environmental research and public health
In this study, socioeconomic, medical treatment, and health check-up data from 2010 to 2017 of the National Health Insurance Service (NHIS) of Korea were analyzed. This year's socioeconomic, treatment, and health check-up data are used to develop a p...

PM2.5 forecasting for an urban area based on deep learning and decomposition method.

Scientific reports
Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the hea...

Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil.

International journal of environmental research and public health
Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computa...