Results 21 to 30 of about 154,891 (246)

Improving molecular machine learning through adaptive subsampling with active learning

Digital Discovery, 2023
Active machine learning can be used to sample training data in an autonomous manner to improve machine learning performance. This approach is competitive with state-of-the-art data sampling approaches, especially on erroneous data.
Yujing Wen, Zhixiong Li, Yan Xiang, Daniel Reker   +3 more
openaire   +1 more source

Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges

Applied Sciences, 2022
Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations as ...
Mingfei Wu, Chen Li, Zehuan Yao
doaj   +1 more source

QuantuMoonLight: A low-code platform to experiment with quantum machine learning

SoftwareX, 2023
Nowadays, machine learning is being used to address multiple problems in various research fields, with software engineering researchers being among the most active users of machine learning mechanisms.
Francesco Amato, Matteo Cicalese, Luca Contrasto, Giacomo Cubicciotti, Gerardo D’Ambola, Antonio La Marca, Giuseppe Pagano, Fiorentino Tomeo, Gennaro Alessio Robertazzi, Gabriele Vassallo, Giovanni Acampora, Autilia Vitiello, Gemma Catolino, Giammaria Giordano, Stefano Lambiase, Valeria Pontillo, Giulia Sellitto, Filomena Ferrucci, Fabio Palomba   +18 more
doaj   +1 more source

Interactive labelling of a multivariate dataset for supervised machine learning using linked visualisations, clustering, and active learning

Visual Informatics, 2019
Supervised machine learning techniques require labelled multivariate training datasets. Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive visualisations. Using appropriate techniques,
Mohammad Chegini, Jürgen Bernard, Philip Berger, Alexei Sourin, Keith Andrews, Tobias Schreck   +5 more
doaj   +1 more source

Classifying Human Activities Using Machine Learning and Deep Learning Techniques

, 2023
Human Activity Recognition (HAR) describes the machines ability to recognize human actions. Nowadays, most people on earth are health conscious, so people are more interested in tracking their daily activities using Smartphones or Smart Watches, which can help them manage their daily routines in a healthy way.
Uday, Sanku Satya, Pavani, Satti Thanuja, Lakshmi, T. Jaya, Chivukula, Rohit   +3 more
openaire   +2 more sources

Prediction of GPCR activity using machine learning

Computational and Structural Biotechnology Journal, 2022
GPCRs are the target for one-third of the FDA-approved drugs, however; the development of new drug molecules targeting GPCRs is limited by the lack of mechanistic understanding of the GPCR structure-activity-function relationship. To modulate the GPCR activity with highly specific drugs and minimal side-effects, it is necessary to quantitatively ...
Prakarsh Yadav, Parisa Mollaei, Zhonglin Cao, Yuyang Wang, Amir Barati Farimani   +4 more
openaire   +3 more sources

Active Management of Operational Risk in the Regimes of the “Unknown”: What Can Machine Learning or Heuristics Deliver?

Risks, 2018
Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it
Udo Milkau, Jürgen Bott
doaj   +1 more source

Active Learning of Nondeterministic Finite State Machines [PDF]

Mathematical Problems in Engineering, 2013
We consider the problem of learning nondeterministic finite state machines (NFSMs) from systems where their internal structures are implicit and nondeterministic. Recently, an algorithm for inferring observable NFSMs (ONFSMs), which are the potentially learnable subclass of NFSMs, has been proposed based on the hypothesis that the complete testing ...
Warawoot Pacharoen, Toshiaki Aoki, Pattarasinee Bhattarakosol, Athasit Surarerks   +3 more
openaire   +2 more sources

Active machine learning for transmembrane helix prediction [PDF]

BMC Bioinformatics, 2010
Abstract Background About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in ...
Osmanbeyoglu, Hatice U, Wehner, Jessica A, Carbonell, Jaime G, Ganapathiraju, Madhavi K   +3 more
openaire   +2 more sources

Physically regularized machine learning emulators of aerosol activation [PDF]

Geoscientific Model Development, 2020
Abstract. The activation of aerosol into cloud droplets is an important step in the formation of clouds and strongly influences the radiative budget of the Earth. Explicitly simulating aerosol activation in Earth system models is challenging due to the computational complexity required to resolve the necessary chemical and physical processes and their ...
Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, Daniel Rothenberg   +3 more
openaire   +3 more sources