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Academic Articles & Research Papers
Showing 15 of 35 academic articles
Pramod Kumar Singh • Apress eBooks
(2018)
This chapter uncovers some of the basic techniques to tackle text data using PySpark. Today's textual form of data is being generated at a lightning pace with multiple social media platforms offering users the options to share their opinion...
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DOI: https://doi.org/10.1007/978-1-4842-4131-8_9
Jiang Du, Qiang Wei, Yisen Wang, Xingyu Bai • Entropy
(2025)
Recent advances in graph neural networks have transformed structural pattern learning in domains ranging from social network analysis to biomolecular modeling. Nevertheless, practical deployments in mission-critical scenarios such as binary...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/e27040392
Aditi S. Krishnapriyan, Joseph Montoya, Maciej Haranczyk, Jens Hummelshøj et al. • Scientific Reports
(2021)
Abstract Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an ...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-021-88027-8
Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, Thomas Wolf (2019)
The classic supervised machine learning paradigm is based on learning in isolation, a single predictive model for a task using a single dataset. This approach requires a large number of training examples and performs best for well-defined a...
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DOI: https://doi.org/10.18653/v1/n19-5004
Yinsheng Zhang, Xudong Yang, Yongbo Cheng, Xiaojun Wu et al. • Current Research in Food Science
(2024)
Background: Fruit freshness detection by computer vision is essential for many agricultural applications, e.g., automatic harvesting and supply chain monitoring. This paper proposes to use the multi-task learning (MTL) paradigm to build a d...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.crfs.2024.100733
Nikita Andriyanov • Mathematics
(2022)
On the one hand, the solution of computer vision tasks is associated with the development of various kinds of images or random fields mathematical models, i.e., algorithms, that are called traditional image processing. On the other hand, no...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/math10214021
Opeyemi Bamigbade, Mark Scanlon, John Sheppard • IEEE Access
(2025)
Embeddings remain the best way to represent image features, but do not always capture all latent information. This is still a problem in representation learning, and computer vision descriptors struggle with precision and accuracy. Improvin...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3564496
John Hancock, Taghi M. Khoshgoftaar • Journal Of Big Data
(2020)
Abstract This survey investigates current techniques for representing qualitative data for use as input to neural networks. Techniques for using qualitative data in neural networks are well known. However, researchers continue to discover n...
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DOI: https://doi.org/10.1186/s40537-020-00305-w
Bethany Lusch, J. Nathan Kutz, Steven L. Brunton • Nature Communications
(2018)
Identifying coordinate transformations that make strongly nonlinear dynamics approximately linear has the potential to enable nonlinear prediction, estimation, and control using linear theory. The Koopman operator is a leading data-driven e...
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DOI: https://doi.org/10.1038/s41467-018-07210-0
PMC: 6251871
Lisi Zhang, Qiancheng Yu, Beijing Zhou, Yifan Zhang et al. • IEEE Access
(2023)
The efficacy of credit risk assessment models is pivotal to the risk management capacity of financial institutions. Traditional credit risk models often suffer from inadequate predictive accuracy due to overlooked feature combinations and w...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2023.3322429
Zijian Wang, Haimei Lu, Haixin Yan, Hongxing Kan et al. • Scientific Reports
(2023)
Abstract Diabetic Retinopathy (DR) is a major cause of blindness worldwide. Early detection and treatment are crucial to prevent vision loss, making accurate and timely diagnosis critical. Deep learning technology has shown promise in the a...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-023-38320-5
Christopher D. Manning • Computational Linguistics
(2015)
Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pun...
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Open Access
DOI: https://doi.org/10.1162/coli_a_00239
Azim Keshavarzian Nodehi, Nasrollah Moghadam Charkari • Machine Learning with Applications
(2022)
Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context. The semantic information provided by WSD systems is highly beneficial to many task...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.mlwa.2022.100369
Willian Borges De Lemos, Lucas De Angelo Martins Ribeiro, Vanessa Telles Da Silva, Alessandro De Lima Bicho et al. • IEEE Access
(2025)
Graph embeddings map graph-structured data into vector spaces for machine learning tasks. In Graph Neural Networks (GNNs), these embeddings are computed through message passing and support tasks such as node classification, link prediction ...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3643796
Nan Xiang, Wenjing Yang, Xindi Rao • Scientific Reports
(2024)
Abstract Traditional social link prediction models primarily concentrate on the adjacency features of the network, overlooking the rich high-order structural information within. Therefore, the study of effective extraction and encoding of t...
DOAJ
Open Access (DOAJ)
DOI: 10.1038/s41598-024-80509-9