Results 61 to 70 of about 8,368,743 (296)

Driver Drowsiness Detection using Evolutionary Machine Learning: A Survey [PDF]

BIO Web of Conferences
One of the factors that kills hundreds of people every year is driving accidents caused by drowsy drivers. There are different methods to prevent this type of accidents. Recently Machine Learning (ML) and Deep Learning (DL) have emerged as very effective
Yasir Jumhaa Maha, Majeed Osama, Taima Alaa   +2 more
doaj   +1 more source

CSF Cytokine Network Organization Predicts Progression Independent of Relapse and MRI Activity in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Progression independent of relapse activity is a major determinant of long‐term disability in multiple sclerosis, but its immunopathologic basis remains incompletely understood. We investigated whether relapse‐independent progression in radiologically stable relapsing–remitting multiple sclerosis is associated with distinct ...
Antonio Bruno, Matteo Conti, Ettore Dolcetti, Federica Azzolini, Angela Borrelli, Roberta Fantozzi, Mario Stampanoni Bassi, Giovanni Galifi, Giuseppe Maccarrone, Luca Montaguti, Marco Cervigni, Maddalena Dal Pozzo, Roberto Furlan, Annamaria Finardi, Girolama Alessandra Marfia, Marco Salvetti, Antonella Conte, Fabio Buttari, Diego Centonze, Luana Gilio   +19 more
wiley   +1 more source

Using SVM in Classification [PDF]

Database Systems Journal, 2018
Machine learning technology is very often used in present days. Exist many machine learning models, the theory has evolved in last year's due exponential use of those technologies.
Stefan PREDA
doaj   +2 more sources

Early Clinical and Cerebrospinal Fluid Predictors of 1‐Year Recurrence in Autoimmune GFAP Astrocytopathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Autoimmune glial fibrillary acidic protein astrocytopathy (GFAP‐A) is an inflammatory central nervous system disorder with variable outcomes. Relapse occurs in a subset of patients, but early predictors remain unclear. We aimed to identify admission‐available features associated with 1‐year recurrence and develop an interpretable ...
Qingting Hong, Yuping Xiao, Yuhan Wu, Ziyuan Huang, Weishan Liang, Li Xiao, Yaqing Shu, Tingting Lu, Yanyu Chang, Wei Qiu, Yuge Wang   +10 more
wiley   +1 more source

The need for open source software in machine learning [PDF]

, 2007
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse ...
Ratsch, Gunnar, Scholkopf, Bernhard, Vincent, P., LeCun, Y, Ong, C., Braun, M.L., Rasmussen, Carl Edward, Rasmussen, CE, Rätsch, G., Bengio, S., Müller, K.-R., Müller, KR, LeCunn, Yann, Muller, Klaus-Robert, Vincent , P., Pereira, Fernando, Vincent, Pascal, Bottou, Leon, Pereira, F, ,, Braun, ML, Ong, CS, Rasmussen, C.E., Smola, A, Sonnenburg, S., Holmes, G, Bengio, S, Weston, Jason, Williamson, RC, Sonnenburg, S, Williamson, Robert C., Bottou, L., Holmes, G., Ong, Cheng Soon, Pereira, F., Braun, M., Sonnenburg, Soren, Smola, Alexander, Williamson, R.C., Holmes , G., Schölkopf, B, Holmes, Geoffrey, Braun, Mikio L., Bengio, Samy, Schölkopf, B., Rasmussen, C., Williamson, R., Rätch, G, Weston, J, Smola, A., Ong, C.S., Müller, K., Bottou, L, Weston, J., Vincent, P, LeCun, Y.   +55 more
core  

Generalized Energy-Based Models

, 2021
Presented online via Bluejeans Events on October 13, 2021 at 12:00 p.m.Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit, and director of the Centre for Computational Statistics and Machine Learning (CSML) at UCL.
Gretton, Arthur
core  

Characterization and machine learning based optimization of banana and paddy straw fiber reinforced epoxy hybrid composites

Scientific Reports
This paper investigates a novel hybrid epoxy composite reinforced with cellulose-rich banana and paddy straw fibers, focusing on its fabrication and machining characteristics.
Saravanakumar Sengottaiyan, M. Muhemin Sheriff, V. S. Shaisundaram, Yuvaraj Dinakarkumar, S. Ramalakshmi, S. Rajkumar   +5 more
doaj   +1 more source

Next-Generation Machine Learning in Healthcare Fraud Detection: Current Trends, Challenges, and Future Research Directions

Information
The growing complexity and size of healthcare systems have rendered fraud detection increasingly challenging; however, the current literature lacks a holistic view of the latest machine learning (ML) techniques with practical implementation concerns. The
Kamran Razzaq, Mahmood Shah
doaj   +1 more source

ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine

Applied Sciences, 2020
Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward.
Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead   +5 more
doaj   +1 more source

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source