Results 91 to 100 of about 1,130,914 (300)

Precision‐Optimised Post‐Stroke Prognoses

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Current medicine cannot confidently predict who will recover from post‐stroke impairments. Researchers have sought to bridge this gap by treating the post‐stroke prognostic problem as a machine learning problem, reporting prediction error metrics across samples of patients whose outcomes are known.
Thomas M. H. Hope, Howard Bowman, Rachel M. Bruce, Alex P. Leff, Cathy J. Price   +4 more
wiley   +1 more source

Machine learning for computational science and engineering models

, 2017
Whitepaper submitted to the 2017 DOE ASCR Applied Math MeetingMachine learning for computational science and engineering modelsPaul Constantine, University of Colorado ...
openaire   +1 more source

A bright future for financial agent-based models

, 2018
The history of research in finance and economics has been widely impacted by the field of Agent-based Computational Economics (ACE). While at the same time being popular among natural science researchers for its proximity to the successful methods of ...
Belianin, A., Bourgeois-Gironde, S., Gutkin, B., Lussange, J.   +3 more
core   +1 more source

Genetic Diversity and Expanded Phenotypes in Dystonia: Insights From Large‐Scale Exome Sequencing

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Dystonia is one of the most prevalent movement disorders, characterized by significant clinical and etiological heterogeneity. Despite considerable heritability (~25%), the etiology in most patients remains elusive. Moreover, understanding correlations between clinical manifestations and genetic variants has become increasingly ...
Mirja Thomsen, Fabian Ott, Sebastian Loens, Gamze Kilic‐Berkmen, Ai Huey Tan, Shen‐Yang Lim, Ebba Lohmann, Kaja M. Schröder, Lea Ipsen, Lena A. Nothacker, Linn Welzel, Alexandra S. Rudnik, Frauke Hinrichs, Thorsten Odorfer, Kirsten E. Zeuner, Friederike Schumann, Andrea A. Kühn, Simone Zittel, Marius Moeller, Robert Pfister, Christoph Kamm, Anthony E. Lang, Yi Wen Tay, Ana Luísa de Almeida Marcelino, Marie Vidailhet, Emmanuel Roze, Joel S. Perlmutter, Jeanne S. Feuerstein, Victor S. C. Fung, Florence Chang, Richard L. Barbano, Steven Bellows, Aparna A. Wagle Shukla, Alberto J. Espay, Mark S. LeDoux, Brian D. Berman, Stephen Reich, Andres Deik, Andre Franke, Michael Wittig, Sören Franzenburg, Jens Volkmann, Norbert Brüggemann, H. A. Jinnah, Tobias Bäumer, Christine Klein, Hauke Busch, Katja Lohmann   +47 more
wiley   +1 more source

A Statistical and Machine Learning Approach for Summarising Computer Science Research Papers [PDF]

, 2023
Sheik Muhammad Wakeel Bauboorally, Sameerchand Pudaruth   +1 more
openalex   +1 more source

Cycles in adversarial regularized learning [PDF]

, 2017
Regularized learning is a fundamental technique in online optimization, machine learning and many other fields of computer science. A natural question that arises in these settings is how regularized learning algorithms behave when faced against each ...
Mertikopoulos, Panayotis, Papadimitriou, Christos, Piliouras, Georgios   +2 more
core   +1 more source

Heart Disease Prediction using Machine Learning Techniques

SN Computer Science, 2020
Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades.
Devansh Shah, Samir B. Patel, S. Bharti
semanticscholar   +1 more source

Time‐Frequency Fingerprint Analysis in SEEG Source‐Space to Identify the Epileptogenic Zone

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT This case study highlights the application of seizure fingerprint analysis in the source‐space of stereo‐EEG (SEEG) data to accurately localize the epileptogenic zone (EZ) in patients with complex cortical malformations. A 25‐year‐old female with extensive bilateral perisylvian polymicrogyria (PMG) presented with intractable focal seizures ...
Yash Shashank Vakilna, Deniz Atilgan, Johnson Hampson, Chinmay Chinara, Takfarinas Medani, Richard M. Leahy, Nuria Lacuey, Samden D. Lhatoo, Sandipan Pati, John C. Mosher, Jay R. Gavvala   +10 more
wiley   +1 more source

Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition

International Journal of Adaptive Control and Signal Processing, EarlyView.
Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez, Mauro Gaggero, Andrea Serani   +2 more
wiley   +1 more source

Performance Triggered Adaptive Model Reduction for Soil Moisture Estimation in Precision Irrigation

International Journal of Adaptive Control and Signal Processing, EarlyView.
ABSTRACT Accurate soil moisture information is essential for precise irrigation to enhance water use efficiency. Estimating soil moisture based on limited soil moisture sensors is especially critical for obtaining comprehensive soil moisture information when dealing with large‐scale agricultural fields.
Sarupa Debnath, Bernard Twum Agyeman, Soumya Ranjan Sahoo, Xunyuan Yin, Jinfeng Liu   +4 more
wiley   +1 more source