Results 41 to 50 of about 87,026 (282)

Epileptic seizure detection using EEG signals and extreme gradient boosting

The Journal of Biomedical Research, 2020
The problem of automated seizure detection is treated using clinical electroencephalograms (EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus (TUSZ). Performances on this complex data set are still not encountering expectations.
Paul Vanabelle, Pierre De Handschutter, El Tahry, Riëm, Mohammed Benjelloun, Mohamed Boukhebouze   +4 more
openaire   +3 more sources

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

Buildings
The standard approach for testing ordinary concrete compressive strength (CS) is to cast samples and test them after different curing times. However, testing adds cost and time to projects, and, therefore, construction sites experience delays.
Fei Zhu, Xiangping Wu, Yijun Lu, Jiandong Huang   +3 more
doaj   +1 more source

Fast calibrated additive quantile regression

, 2020
We propose a novel framework for fitting additive quantile regression models, which provides well calibrated inference about the conditional quantiles and fast automatic estimation of the smoothing parameters, for model structures as diverse as those ...
Azzalini A., Brent R. P., Hastie T., Hothorn T., Jones M., Margaux Zaffran, Matteo Fasiolo, Raphaël Nedellec, Robert C., Simon N. Wood, Yannig Goude, Yee T. W.   +11 more
core   +1 more source

Impact of 25-Hydroxyvitamin D on the Prognosis of Acute Ischemic Stroke: Machine Learning Approach

Frontiers in Neurology, 2020
Background and Purpose: Vitamin D is a predictor of poor outcome for cardiovascular disease. We evaluated whether serum 25-hydroxyvitamin D level was associated with poor outcome in patients with acute ischemic stroke (AIS) using machine learning ...
Chulho Kim, Chulho Kim, Sang-Hwa Lee, Jae-Sung Lim, Yerim Kim, Min Uk Jang, Mi Sun Oh, San Jung, Ju-Hun Lee, Kyung-Ho Yu, Byung-Chul Lee   +10 more
doaj   +1 more source

Gradient boosting with extreme-value theory for wildfire prediction

Extremes, 2023
AbstractThis paper details the approach of the teamKohrrelationin the 2021 Extreme Value Analysis data challenge, dealing with the prediction of wildfire counts and sizes over the contiguous US. Our approach uses ideas from extreme-value theory in a machine learning context with theoretically justified loss functions for gradient boosting.
openaire   +4 more sources

Using Big Data to Enhance the Bosch Production Line Performance: A Kaggle Challenge

, 2016
This paper describes our approach to the Bosch production line performance challenge run by Kaggle.com. Maximizing the production yield is at the heart of the manufacturing industry.
Kumar, Nishant, Mangal, Ankita
core   +1 more source

Predicting time to graduation at a large enrollment American university

, 2020
The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend ...
Aiken, John M., Caballero, Marcos D., De Bin, Riccardo, Hjorth-Jensen, Morten   +3 more
core   +1 more source

AI meets economics: Can deep learning surpass machine learning and traditional statistical models in inflation time series forecasting?

Data Science in Finance and Economics
This study examined the forecasting ability of deep learning (DL) and machine learning (ML) models against benchmark traditional statistical models for the monthly inflation rates in the USA. The study compared various DL and ML models like transformers,
Ezekiel NN Nortey, Edmund F. Agyemang, Enoch Sakyi-Yeboah, Obu-Amoah Ampomah, Louis Agyekum   +4 more
doaj   +1 more source

Intelligent Fault Diagnosis of Diesel Engines via Extreme Gradient Boosting and High-Accuracy Time–Frequency Information of Vibration Signals

Sensors, 2019
Accurate and timely misfire fault diagnosis is of vital significance for diesel engines. However, existing algorithms are prone to fall into model over-fitting and adopt low energy-concentrated features.
Jianfeng Tao, Chengjin Qin, Weixing Li, Chengliang Liu   +3 more
doaj   +1 more source

Optimization by gradient boosting

, 2017
Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of simple predictors---typically decision trees---by solving an infinite-dimensional convex optimization problem.
Biau, Gérard, Cadre, Benoît
core   +2 more sources