Results 51 to 60 of about 1,710,361 (207)
IEEE Access, 2023 AI and Machine Learning (ML) offer powerful tools to support clinical decision making in emergency situations such as the COVID-19 pandemic. In this context, the application of ML requires to design predictive systems that have adequate accuracy and can ...
Alfonso Emilio Gerevini +4 more
doaj +1 more source
Machine Learning and Cosmological Simulations II: Hydrodynamical Simulations
, 2016 We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations.
Brunner, Robert J. +2 more
core +1 more source
Machine learning-guided directed evolution for protein engineering [PDF]
, 2019 Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying ...
Arnold, Frances H. +2 more
core +1 more source
Pragmatic Study of Botnet Attack Detection In An IoT Environment [PDF]
E3S Web of ConferencesA comprehensive search for primary research published between 2014 and 2023 was carried across several databases. Studies that describe the application of machine learning (ML) and deep learning techniques for if they was carried out across several ...
Vennapureddy Rajasree, Srinivasulu T.
doaj +1 more source
Serverless on Machine Learning: A Systematic Mapping Study
IEEE Access, 2022 Machine Learning Operations (MLOps) is an approach to managing the entire lifecycle of a machine learning model. It has evolved over the last years and has started attracting many people in research and businesses in the industry.
Amine Barrak +2 more
doaj +1 more source
AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
, 2017 Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis.
Carrete, Jesús +10 more
core +1 more source
Machine Learning for Fluid Mechanics [PDF]
Annual Review of Fluid Mechanics, 2019 The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract
S. Brunton, B. R. Noack, P. Koumoutsakos
semanticscholar +1 more source
Rethinking PICO in the Machine Learning Era: ML-PICO
Applied Clinical Informatics, 2021 Abstract Background Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties.
Liu, Xinran +5 more
openaire +4 more sources
CATE Meets ML - Conditional Average Treatment Effect and Machine Learning [PDF]
SSRN Electronic Journal, 2021 AbstractFor treatment effects—one of the core issues in modern econometric analysis—prediction and estimation are two sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory, they allow us to estimate not only the average but a personalized treatment effect—the ...
openaire +3 more sources
Scientific ReportsTimely detection of crop diseases in large, heterogeneous agricultural fields is difficult, as aerial imagery is often corrupted by illumination, weather, and crop-stage variations.
H M Manoj +5 more
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