Results 71 to 80 of about 1,710,361 (207)

Prediction of remaining useful life and downtime of induction motors with supervised machine learning

Applied Computer Science
This research aims to use a vibration monitoring system along with machine learning techniques to predict the downtime and Remaining Useful Life (RUL) of three-phase induction motors in the manufacturing sector.
Muhammad Dzulfiqar ANINDHITO, SUHARJITO
doaj   +1 more source

MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

, 2018
With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API.
Augustin, Max, Backes, Michael, Fritz, Mario, Grosse, Kathrin, Hanzlik, Lucjan, Salem, Ahmed, Zhang, Yang   +6 more
core  

Evaluation metrics and statistical tests for machine learning

Scientific Reports
Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and ...
O. Rainio, J. Teuho, R. Klén
semanticscholar   +1 more source

Integrating longitudinal mental health data into a staging database: harnessing DDI-lifecycle and OMOP vocabularies within the INSPIRE Network Datahub

Frontiers in Big Data
BackgroundLongitudinal studies are essential for understanding the progression of mental health disorders over time, but combining data collected through different methods to assess conditions like depression, anxiety, and psychosis presents significant ...
Bylhah Mugotitsa, Bylhah Mugotitsa, Tathagata Bhattacharjee, Michael Ochola, Dorothy Mailosi, David Amadi, Pauline Andeso, Joseph Kuria, Reinpeter Momanyi, Evans Omondi, Evans Omondi, Dan Kajungu, Jim Todd, Agnes Kiragga, Agnes Kiragga, Jay Greenfield   +15 more
doaj   +1 more source

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Journal of Cardiovascular Magnetic Resonance, 2019
Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction.
Alex Bratt, Jiwon Kim, Meridith Pollie, Ashley N. Beecy, Nathan H. Tehrani, Noel Codella, Rocio Perez-Johnston, Maria Chiara Palumbo, Javid Alakbarli, Wayne Colizza, Ian R. Drexler, Clerio F. Azevedo, Raymond J. Kim, Richard B. Devereux, Jonathan W. Weinsaft   +14 more
doaj   +1 more source

On Leveraging Machine Learning in Sport Science in the Hypothetico-deductive Framework

Sports Medicine - Open
Supervised machine learning (ML) offers an exciting suite of algorithms that could benefit research in sport science. In principle, supervised ML approaches were designed for pure prediction, as opposed to explanation, leading to a rise in powerful, but ...
Jordan Rodu, Alexandra F. DeJong Lempke, Natalie Kupperman, Jay Hertel   +3 more
doaj   +1 more source

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Heliyon
Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the.
Ismail Essamlali, Hasna Nhaila, Mohamed El Khaili   +2 more
semanticscholar   +1 more source

Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach

IEEE Access
With the emergence of 5G technology, congestion control has become a vital challenge to be addressed in order to have efficient communication. There are several congestion control models that have been proposed to control and predict the possible ...
Mohammed B. Alshawki, Ihab Ahmed Najm, Alaa Khalaf Hamoud   +2 more
doaj   +1 more source

AI without borders: The rise of cross-disciplinary machine learning

Telematics and Informatics Reports
This literature review thoroughly analyzes Machine Learning (ML) algorithms, their applications in many fields, current developments, and interdisciplinary viewpoints.
Aji Prasetya Wibawa, Roni Herdianto, Anik Nur Handayani, Agung Bella Putra Utama, Aripriharta, Felix Andika Dwiyanto, Rafal Drezewski   +6 more
doaj   +1 more source

Prediction of Creep Rupture Life of 5Cr-0.5Mo Steel Using Machine Learning Models

Metals
The creep rupture life of 5Cr-0.5Mo steels used in high-temperature applications is significantly influenced by factors such as minor alloying elements, hardness, austenite grain size, non-metallic inclusions, service temperature, and applied stress. The
Muhammad Ishtiaq, Hafiz Muhammad Rehan Tariq, Devarapalli Yuva Charan Reddy, Sung-Gyu Kang, Nagireddy Gari Subba Reddy   +4 more
doaj   +1 more source