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Forecasting

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Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing.

Neural networks : the official journal of the International Neural Network Society
Recent work has shown that machine learning (ML) models can skillfully forecast the dynamics of unknown chaotic systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics ("climate") c...

A cluster-based ensemble approach for congenital heart disease prediction.

Computer methods and programs in biomedicine
BACKGROUND: One of the most prevalent birth disorders is congenital heart diseases (CHD). Although CHD risk factors have been the subject of numerous studies, their propensity to cause CHD has not been tested. Particularly few research has attempted ...

What More Can We Ask of Robotics?

European urology
The future of robotics relies heavily on the ongoing synergy between robotic surgery and artificial intelligence. To unlock their full potential, we should address issues such as accessibility, education, data privacy, and ethics.

Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm.

PloS one
Copper is an important mineral and fluctuations in copper prices can affect the stable functioning of some countries' economies. Policy makers, futures traders and individual investors are very concerned about copper prices. In a recent paper, we use...

Sliding Window-Based Machine Learning for Environmental Inspection Resource Allocation.

Environmental science & technology
Environmental regulation is pivotal in mitigating environmental risks and promoting sustainable development, yet regulators frequently encounter resource constraints when inspecting enterprises. To address this limitation, we employed four sliding wi...

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting.

Scientific reports
COVID-19 has resulted in significant morbidity and mortality globally. We develop a model that uses data from thirty days before a fixed time point to forecast the daily number of new COVID-19 cases fourteen days later in the early stages of the pand...

Ensemble Approach to Combining Episode Prediction Models Using Sequential Circadian Rhythm Sensor Data from Mental Health Patients.

Sensors (Basel, Switzerland)
Managing mood disorders poses challenges in counseling and drug treatment, owing to limitations. Counseling is the most effective during hospital visits, and the side effects of drugs can be burdensome. Patient empowerment is crucial for understandin...