Offloading machine learning to programmable data planes: A systematic survey

R Parizotto, BL Coelho, DC Nunes, I Haque… - ACM Computing …, 2023 - dl.acm.org
The demand for machine learning (ML) has increased significantly in recent decades,
enabling several applications, such as speech recognition, computer vision, and …

[PDF][PDF] Meta-learning

J Vanschoren - Automated machine learning: methods, systems …, 2019 - library.oapen.org
Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE‐ENN Oversampling Technique and Hyperparameter …

M Muntasir Nishat, F Faisal, I Jahan Ratul… - Scientific …, 2022 - Wiley Online Library
Heart failure is a chronic cardiac condition characterized by reduced supply of blood to the
body due to impaired contractile properties of the muscles of the heart. Like any other …

Amlb: an automl benchmark

P Gijsbers, MLP Bueno, S Coors, E LeDell… - Journal of Machine …, 2024 - jmlr.org
Comparing different AutoML frameworks is notoriously challenging and often done
incorrectly. We introduce an open and extensible benchmark that follows best practices and …

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

R Kumar, B Rai, P Samui - Structural Concrete, 2023 - Wiley Online Library
Concrete which is the most commercialized construction material and thus it plays a key role
in this era of development and hence its evolution is of utmost importance and therefore the …

EEG signal processing and supervised machine learning to early diagnose Alzheimer's disease

D Pirrone, E Weitschek, P Di Paolo, S De Salvo… - Applied sciences, 2022 - mdpi.com
Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible
technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) …

A survey of machine learning techniques for video quality prediction from quality of delivery metrics

O Izima, R de Fréin, A Malik - Electronics, 2021 - mdpi.com
A growing number of video streaming networks are incorporating machine learning (ML)
applications. The growth of video streaming services places enormous pressure on network …

Intelligent fault diagnosis of rotary machinery by convolutional neural network with automatic hyper-parameters tuning using bayesian optimization

D Kolar, D Lisjak, M Pająk, M Gudlin - Sensors, 2021 - mdpi.com
Intelligent fault diagnosis can be related to applications of machine learning theories to
machine fault diagnosis. Although there is a large number of successful examples, there is a …

From data to harvest: Leveraging ensemble machine learning for enhanced crop yield predictions across Canada amidst climate change

NM Gharakhanlou, L Perez - Science of The Total Environment, 2024 - Elsevier
Accurate crop yield predictions are crucial for farmers and policymakers. Despite the
widespread use of ensemble machine learning (ML) models in computer science, their …

Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection

S Jain, A Saha - Science of Computer Programming, 2021 - Elsevier
Maintaining large and complex software is a significant task in IT industry. One reason for
that is the development of code smells which are design flaws that lead to future bugs and …