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An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification.

Scientific reports
Shear sonic wave velocity (Vs) has a wide variety of implications, from reservoir management and development to geomechanical and geophysical studies. In the current study, two approaches were adopted to predict shear sonic wave velocities (Vs) from ...

Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success From Video.

Neurosurgery
BACKGROUND: Deep neural networks (DNNs) have not been proven to detect blood loss (BL) or predict surgeon performance from video.

MoËT: Mixture of Expert Trees and its application to verifiable reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adoption into safety-critical settings, such as healthcare or self-drivi...

Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques.

Scientific reports
Multi-Model Ensembles (MMEs) are used for improving the performance of GCM simulations. This study evaluates the performance of MMEs of precipitation, maximum temperature and minimum temperature over a tropical river basin in India developed by vario...

Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement.

Sensors (Basel, Switzerland)
We propose a linear regression model for the estimation of human body measurements. The input to the model only consists of the information that a person can self-estimate, such as height and weight. We evaluate our model against the state-of-the-art...

Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The U.S. Body Project I.

Body image
Most body image studies assess only linear relations between predictors and outcome variables, relying on techniques such as multiple Linear Regression. These predictor variables are often validated multi-item measures that aggregate individual items...

Prediction and Elucidation of Triglycerides Levels Using a Machine Learning and Linear Fuzzy Modelling Approach.

BioMed research international
INTRODUCTION: Triglycerides are lipids composed of fatty acids that provide energy to the cell. These compounds are delivered to the body's cells via lipoproteins found in the bloodstream. Increased blood triglyceride levels have been associated with...

Predicting daily pore water pressure in embankment dam: Empowering Machine Learning-based modeling.

Environmental science and pollution research international
Dam safety assessment is important to implement the appropriate measures to avoid a dam break disaster as part of the water reservoirs management process. Prediction-based approaches are valuable to compare the actual measurements with the simulated ...

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation.

Computational and mathematical methods in medicine
Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with...

Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: A comprehensive and systematic study.

Environmental science and pollution research international
To overcome the need of the world for energy consumption, we have to find some better and stable alternate ways of renewable energy with advanced technology. The most readily available source of energy is solar energy but solar energy has nonlinear n...