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Showing 20 of 1,679 academic articles
Audrey Karungi Ngambeki, Halimu Chongomweru, Florence N. Kivunike • Information
(2026)
Reputational risk reflected in digital public discourse can significantly influence stakeholder perceptions, public trust, and decision-making surrounding government megaprojects. Existing computational approaches primarily focus on sentime...
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Open Access (DOAJ)
DOI: 10.3390/info17100987
Mariya Toneva, Leila Wehbe • arXiv (Cornell University)
(2019)
Neural networks models for NLP are typically implemented without the explicit encoding of language rules and yet they are able to break one performance record after another. This has generated a lot of research interest in interpreting the ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1905.11833
C Friedman, George M. Hripcsak • Academic Medicine
(1999)
If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be struct...
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DOI: https://doi.org/10.1097/00001888-199908000-00012
Charlotte Caucheteux, Jean-Rémi King • Communications Biology
(2022)
Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to those of the human brain. However, what drives this similarity remains currently unknown. Here, w...
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DOI: https://doi.org/10.1038/s42003-022-03036-1
PMC: 8850612
Dejun Zhang, Mingbo Hong, Lu Zou, Fei Han et al. • International Journal of Computational Intelligence Systems
Recurrent neural network (RNN) is one of the most popular architectures for addressing variable sequence text, and it shows outstanding results in many natural language processing (NLP) tasks and remarkable performance in capturing long-ter...
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Open Access (DOAJ)
DOI: 10.2991/ijcis.d.190710.001
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...
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Open Access (DOAJ)
DOI: 10.3390/e27040392
Xinhui Liu, Jifa Chen, Qiubo Huang • Applied Sciences
(2025)
With the explosion of information, the amount of text data has increased significantly, making text categorization a central area of research in natural language processing (NLP). Traditional machine learning methods are effective, but deep...
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Open Access (DOAJ)
DOI: 10.3390/app15084236
Ye Wang, Yaling Deng, Ge Wang, Tong Li et al. • Computers in Human Behavior: Artificial Humans
(2025)
Modern Large Language Models (LLMs) exhibit complexity and granularity similar to humans in the field of natural language processing, challenging the boundaries between humans and machines in language understanding and creativity. However, ...
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Open Access (DOAJ)
DOI: 10.1016/j.chbah.2024.100103
Yao L, Chen Y, Shen J, Zhang J et al. • Advances in Medical Education and Practice
(2026)
Lifeng Yao, Yijun Chen, Jing Shen, Junge Zhang, Yiwei Zhang, Guojin Liang, Yiqin Ji Department of Anesthesiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, People’s Republic of ChinaCorrespondence: Yijun Chen, De...
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Open Access (DOAJ)
Maria Frasca, Davide La Torre, Gabriella Pravettoni, Ilaria Cutica • Discover Artificial Intelligence
(2024)
Abstract This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box alg...
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Open Access (DOAJ)
DOI: 10.1007/s44163-024-00114-7
Sebastian Ruder • Arrow@dit (Dublin Institute of Technology)
(2019)
The current generation of neural network-based natural language processing models excels at learning from large amounts of labelled data. Given these capabilities, natural language processing is increasingly applied to new tasks, new domain...
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Open Access
DOI: https://doi.org/10.13025/17598
Antonio Di Marino, Vincenzo Bevilacqua, Emanuel Di Nardo, Angelo Ciaramella et al. • Discover Artificial Intelligence
(2026)
Abstract Full-reference image quality assessment metrics such as PSNR and SSIM misalign with human perceptual judgments on texture and shape distortions, a limitation documented across multiple benchmarks. Deep perceptual metrics such as LP...
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Open Access (DOAJ)
DOI: 10.1007/s44163-026-02424-4
Fangyan Huo, Yihua Li • Discover Artificial Intelligence
(2026)
Abstract This study presents a Data Fusion + Random Forest (RF) framework to predict adult learners’ educational outcomes, such as dropout risk and course completion. Adult lifelong learning is crucial for personal and professional growth, ...
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Open Access (DOAJ)
DOI: 10.1007/s44163-026-01962-1
Miloš Bogdanović, Milena Frtunić Gligorijević, Jelena Kocić, Leonid Stoimenov • Applied Sciences
(2025)
Various areas of natural language processing (NLP) have greatly benefited from the development of large language models in recent years. This research addresses the challenge of developing efficient tokenizers for transformer-based domain-s...
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Open Access (DOAJ)
DOI: 10.3390/app15137491
Hang Li • National Science Review
(2017)
Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). Recently, deep ...
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DOI: https://doi.org/10.1093/nsr/nwx110
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen et al. • arXiv (Cornell University)
(2011)
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1103.0398
CHEN Ao, LI Chen, YAN Jiayun, PENG Liantie et al. • Kongzhi Yu Xinxi Jishu
(2024)
Standardization is one of the key steps to analyze locomotive overhaul data with a focus on reliability-centered maintenance (RCM). However, traditional manual methods encounter challenges such as small sample sizes, non-standardized data f...
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Open Access (DOAJ)
Ewoud Pons, Loes Braun, M. G. Myriam Hunink, Jan A. Kors • Radiology
(2016)
Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology repo...
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DOI: https://doi.org/10.1148/radiol.16142770
Ronan Collobert, Jason Weston • International Conference on Machine Learning
(2008)
We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the lik...
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DOI: https://doi.org/10.1145/1390156.1390177
Kai Wang, Kunjian Wen, Yanping Chen, Yongbin Qin • Scientific Reports
(2025)
Abstract Mapping a sentence into a two-dimensional (2D) representation can flatten nested semantic structures and build multi-granular span dependencies in named entity recognition. Existing approaches to recognizing named entities often cl...
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Open Access (DOAJ)
DOI: 10.1038/s41598-025-87722-0