Reset
Showing 20 of 227 academic articles

Deep learning for electroencephalogram (EEG) classification tasks: a review

Alexander Craik, Yongtian He, Jose Luis Contreras-Vidal • Journal of Neural Engineering (2019)

OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analyti...

OPENALEX cc-by DOI: https://doi.org/10.1088/1741-2552/ab0ab5

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud, M. Shamim Kaiser, Amir Hussain, Stefano Vassanelli • IEEE Transactions on Neural Networks and Learning Systems (2018)

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body...

OPENALEX Open Access DOI: https://doi.org/10.1109/tnnls.2018.2790388

Opportunities and obstacles for deep learning in biology and medicine

Travers H. Ching, Daniel Scott Himmelstein, Brett K. Beaulieu‐Jones, Alexandr A. Kalinin et al. • Journal of The Royal Society Interface (2018)

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and m...

OPENALEX cc-by DOI: https://doi.org/10.1098/rsif.2017.0387 PMC: 5938574

Deep Learning in Ultrasound Imaging

Ruud J. G. van Sloun, Regev Cohen, Yonina C. Eldar • Proceedings of the IEEE (2019)

In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on ...

OPENALEX Open Access DOI: https://doi.org/10.1109/jproc.2019.2932116

CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets

Orges Koci, Richard K. Russell, M. Guftar Shaikh, Christine Edwards et al. • Microbiome (2024)

Abstract Background Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, t...

DOAJ Open Access (DOAJ) DOI: 10.1186/s40168-024-01834-9

Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials

Iason Batzianoulis, Fumiaki Iwane, Shupeng Wei, Carolina Gaspar Pinto Ramos Correia et al. • Communications Biology (2021)

Teaching an assistive robotic manipulator to move objects in a cluttered table requires demonstrations from expert operators, but what if the experts are individuals with motor disabilities? Batzianoulis et al. propose a learning approach w...

DOAJ Open Access (DOAJ) DOI: 10.1038/s42003-021-02891-8

The challenge of studying perovskite solar cells’ stability with machine learning

Paolo Graniero, Paolo Graniero, Mark Khenkin, Hans Köbler et al. • Frontiers in Energy Research (2023)

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technolog...

DOAJ Open Access (DOAJ) DOI: 10.3389/fenrg.2023.1118654

Interactive machine learning

Jerry Alan Fails, Dan R. Olsen • ACM International Conference on Intelligent User Interfaces (IUI) (2003)

Perceptual user interfaces (PUIs) are an important part of ubiquitous computing. Creating such interfaces is difficult because of the image and signal processing knowledge required for creating classifiers. We propose an interactive machine...

OPENALEX DOI: https://doi.org/10.1145/604045.604056

An ensemble approach for imbalanced multiclass malware classification using 1D-CNN

Binayak Panda, Sudhanshu Shekhar Bisoyi, Sidhanta Panigrahy • PeerJ Computer Science (2023)

Dependence on the internet and computer programs demonstrates the significance of computer programs in our day-to-day lives. Such demands motivate malware developers to create more malware, both in terms of quantity and variety. Researchers...

DOAJ Open Access (DOAJ) DOI: 10.7717/peerj-cs.1677

Memristor Synapse—A Device-Level Critical Review

Sridhar Chandrasekaran, Yao-Feng Chang, Firman Mangasa Simanjuntak • Nanomaterials (2026)

The memristor has long been known as a nonvolatile memory technology alternative and has recently been explored for neuromorphic computing, owing to its capability to mimic the synaptic plasticity of the human brain. The architecture of a m...

DOAJ Open Access (DOAJ) DOI: 10.3390/nano16030179

Machine Learning for Neural Decoding

Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich et al. • eNeuro (2020)

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve deco...

OPENALEX cc-by DOI: https://doi.org/10.1523/eneuro.0506-19.2020 PMC: 7470933

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space

Emily K. Makowski, Patrick C. Kinnunen, Jie Huang, Lina Wu et al. • Nature Communications (2022)

Optimising antibody properties such as affinity can be detrimental to other key properties. Here the authors use machine learning to simplify the identification of antibodies with co-optimal levels of affinity and specificity for a clinical...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41467-022-31457-3

mlr: Machine Learning in R

Bernd Bischl, Michel Lang, Lars Kotthoff, Patrick Schratz et al. (2013)

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensiti...

OPENALEX Open Access DOI: https://doi.org/10.32614/cran.package.mlr

Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber

T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale et al. • Journal of Natural Fibers (2025)

Machine learning model coupled with graphical user interface was developed to predict mechanical properties of flax fiber. The experiment was conducted using test setup which applies constant rate of loading (CRL). Flax fiber was tested und...

DOAJ Open Access (DOAJ) DOI: 10.1080/15440478.2025.2502662