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Showing 20 of 2,356 academic articles
Charaf Ouaddi, Lamya Benaddi, Adnane Souha, Hamza Abdelmalek et al. • IEEE Access
(2025)
Conversational agents are intelligent, ubiquitous software applications widely used across various sectors, such as commerce, tourism, and more. Their key benefits include automating tasks, improving customer service, and ensuring service a...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2025.3574641
Naima Jawhar, Adnane Talha, Anas Bouayad • Discover Artificial Intelligence
(2026)
Abstract Task scheduling remains a challenge in IoT-Fog-Cloud systems due to dynamic workloads and heterogeneous resources. Ineffective scheduling strategies increase latency, energy consumption, and Service Level Agreement (SLA) violations...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44163-026-02614-0
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...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44163-024-00114-7
Michael Chary, David Yi, Alex F. Manini • Frontiers in Psychiatry
(2018)
Novel psychoactive substances (NPS) refer to synthetic compounds or derivatives of more widely known substances of abuse that have emerged over the last two decades. Case reports suggest that users combine substances to achieve desired psyc...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fpsyt.2018.00135
Field Cady • Wiley eBooks
(2017)
This chapter starts with several generic sections about natural language processing (NLP) datasets and big-picture concepts. It focuses on the core NLP concepts, and quick-and-dirty techniques to more complicated ones. The most basic concep...
OPENALEX
DOI: https://doi.org/10.1002/9781119092919.ch16
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...
OPENALEX
Open Access
DOI: https://doi.org/10.13025/17598
Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo et al. • Foundations and Trends® in Machine Learning
(2023)
Deep learning has become the dominant approach in addressing various tasks in Natural Language Processing (NLP). Although text inputs are typically represented as a sequence of tokens, there is a rich variety of NLP problems that can be bes...
OPENALEX
Open Access
DOI: https://doi.org/10.1561/2200000096
Palash Goyal, Sumit Kumar Pandey, Karan Jain • Apress eBooks
(2018)
Discover the concepts of deep learning used for natural language processing (NLP) in this practical book, with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2...
OPENALEX
DOI: https://doi.org/10.1007/978-1-4842-3685-7
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, ...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s44163-026-01962-1
Bianca Pilla, Jennifer Stone, Zoe Jordan • Archives of Public Health
(2025)
Abstract Background Understanding the alignment and contributions of research to the United Nations Sustainable Development Goals (UN SDGs) is essential for guiding global progress toward these critical targets. Several SDG mapping approach...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s13690-025-01784-0
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...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1103.0398
CollobertRonan, WestonJason, BottouLéon, KarlenMichael et al. • Journal of Machine Learning Research
(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 rec......
OPENALEX
DOI: https://doi.org/10.5555/1953048.2078186
Detmar Meurers • Wiley eBooks
(2012)
Abstract As a relatively young field of research and development that began with work on crypt‐analysis and machine translation around 50 years ago, natural language processing (NLP) is concerned with the automated processing of human langu...
OPENALEX
DOI: https://doi.org/10.1002/9781405198431.wbeal0858
Guillaume Loignon • Mesure et Évaluation en Éducation
(2021)
Estimating language complexity is an important aspect of educational measurement and assessment that can be used, for instance, to control for unwanted variance due to language, or to provide students with texts that are conducive to learni...
DOAJ
Open Access (DOAJ)
DOI: 10.7202/1095682ar
Yoav Goldberg • Journal of Artificial Intelligence Research
(2016)
Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be ap...
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DOI: https://doi.org/10.1613/jair.4992
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond et al. • arXiv (Cornell University)
(2019)
Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to ...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1910.03771
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...
OPENALEX
DOI: https://doi.org/10.1145/1390156.1390177
Alina Dushkevych • Філологічні студії
(2025)
The article is devoted to a comprehensive analysis of the resources of forming the English terminological system of inclusive education in the modern educational environment. The role of terminology as a tool for standardizing knowledge, co...
DOAJ
Open Access (DOAJ)
DOI: 10.28925/2412-2491.2025.255
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