Results 51 to 60 of about 2,307,608 (312)

Multi-Class Adaptive Active Learning for Predicting Student Anxiety

IEEE Access
This research delves into applying active and machine learning techniques to predict student anxiety. This research explores how these technologies can be explored to understand and predict student anxiety levels.
Ahmad Almadhor, Sidra Abbas, Gabriel Avelino Sampedro, Shtwai Alsubai, Stephen Ojo, Abdullah Al Hejaili, Lubomira Strazovska   +6 more
doaj   +1 more source

Calibration of uncertainty in the active learning of machine learning force fields

Machine Learning: Science and Technology, 2023
FFLUX is a machine learning force field that uses the maximum expected prediction error (MEPE) active learning algorithm to improve the efficiency of model training.
Adam Thomas-Mitchell, Glenn Hawe, Paul L A Popelier   +2 more
doaj   +1 more source

An Ensemble Transfer Learning Model for Detecting Stego Images

Applied Sciences, 2023
As internet traffic grows daily, so does the need to protect it. Network security protects data from unauthorized access and ensures their confidentiality and integrity.
Dina Yousif Mikhail, Roojwan Sc Hawezi, Shahab Wahhab Kareem   +2 more
doaj   +1 more source

Active Learning Approaches to Enhancing Neural Machine Translation: An Empirical Study

Findings, 2020
Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models. In this paper, we explore new training frameworks by incorporating active learning into various techniques such as transfer ...
Yuekai Zhao, H. Zhang, Shuchang Zhou, Zhihua Zhang   +3 more
semanticscholar   +1 more source

Streaming Machine Learning and Online Active Learning for Automated Visual Inspection.

IFAC-PapersOnLine, 2022
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided ...
Rožanec, Jože M., Trajkova, Elena, Dam, Paulien, Fortuna, Blaž, Mladenić, Dunja   +4 more
openaire   +2 more sources

Data leakage detection in machine learning code: transfer learning, active learning, or low-shot prompting? [PDF]

PeerJ Computer Science
With the increasing reliance on machine learning (ML) across diverse disciplines, ML code has been subject to a number of issues that impact its quality, such as lack of documentation, algorithmic biases, overfitting, lack of reproducibility, inadequate ...
Nouf Alturayeif, Jameleddine Hassine
doaj   +2 more sources

Automated discovery of a robust interatomic potential for aluminum

Nature Communications, 2021
The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for
Justin S. Smith, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin, Kipton Barros   +10 more
doaj   +1 more source

Machine learning-enabled forward prediction and inverse design of 4D-printed active plates

Nature Communications
Shape transformations of active composites (ACs) depend on the spatial distribution of constituent materials. Voxel-level complex material distributions can be encoded by 3D printing, offering enormous freedom for possible shape-change 4D-printed ACs ...
Xiaohao Sun, Liang Yue, Luxia Yu, Connor T Forte, Connor D. Armstrong, Kun Zhou, F. Demoly, Ruike Renee Zhao, H. J. Qi   +8 more
semanticscholar   +1 more source

Enzyme activity from machine learning [PDF]

Science, 2019
Enzyme Engineering Enzymes are very efficient catalysts for biochemical reactions, which are increasingly important for industrial applications. However, incomplete knowledge of the key factors that induce their catalytic properties limits our ability to engineer new enzymes with new properties. Bonk et al.
openaire   +1 more source

Using human brain activity to guide machine learning [PDF]

Scientific Reports, 2018
AbstractMachine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source ofinspirationfor machine learning, little effort has been made to
Fong, Ruth C., Scheirer, Walter, Cox, David   +2 more
openaire   +5 more sources