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Academic Articles & Research Papers
Showing 20 of 1,679 academic articles
Ben D. Sawyer, Peter A. Hancock • Human Factors The Journal of the Human Factors and Ergonomics Society
(2018)
OBJECTIVE: This work assesses the efficacy of the "prevalence effect" as a form of cyberattack in human-automation teaming, using an email task. BACKGROUND: Under the prevalence effect, rare signals are more difficult to detect, even when t...
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Open Access
DOI: https://doi.org/10.1177/0018720818780472
Mohamed Amine Ferrag, Fatima Alwahedi, Ammar Ayman Battah, Bilel Cherif et al. • Internet of Things and Cyber-Physical Systems
(2025)
This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, s...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.iotcps.2025.01.001
Artie W. Ng, Benny K.B. Kwok • Journal of Financial Regulation and Compliance
(2017)
Purpose This paper aims to explore how the regulator of a global financial centre (GFC) under an international trend of adopting emerging technologies for financial services (Fintech) articulates such opportunities and risks strategically. ...
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DOI: https://doi.org/10.1108/jfrc-01-2017-0013
Shazia A. Mitha, Jessica Schwartz, Mollie Hobensack, Kenrick Cato et al. • CIN Computers Informatics Nursing
(2022)
Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in n...
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Open Access
DOI: https://doi.org/10.1097/cin.0000000000000967
PMC: 11499545
Yijun Xiao, William Yang Wang • Proceedings of the AAAI Conference on Artificial Intelligence
(2019)
Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper,...
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Open Access
DOI: https://doi.org/10.1609/aaai.v33i01.33017322
Yang Liu, Meng Zhang • Computational Linguistics
(2017)
Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...
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cc-by-nc-nd
DOI: https://doi.org/10.1162/coli_r_00312
Barry Walsh • Apress eBooks
(2022)
Any book on operationalizing Artificial Intelligence today cannot ignore growth in Natural Language Processing (NLP), with market size projected to be $43b in 2025. Ostensibly the branch of AI concerned with programming computers to underst...
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DOI: https://doi.org/10.1007/978-1-4842-8817-7_10
Mitchell P. Marcus • Proceedings of the National Academy of Sciences
(1995)
The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. R...
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Open Access
DOI: https://doi.org/10.1073/pnas.92.22.10052
PMC: 40734
Kevin Crowston, Eileen E. Allen, Robert Heckman • International Journal of Social Research Methodology
(2011)
Social researchers often apply qualitative research methods to study groups and their communications artifacts. The use of computer-mediated communications has dramatically increased the volume of text available, but coding such text requir...
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DOI: https://doi.org/10.1080/13645579.2011.625764
Helmut Schmid • International Conference on Computational Linguistics
(1994)
Text corpora which are tagged with part-of-speech information are useful in many areas of linguistic research. In this paper, a new part-of-speech tagging method based on neural networks (Net- Tagger) is presented and its performance is com...
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Open Access
DOI: https://doi.org/10.3115/991886.991915
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra • arXiv (Cornell University)
(2015)
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1505.05424
Jianlin Cheng, Zheng Wang, Gianluca Pollastri • International Joint Conference on Neural Networks (IJCNN)
(2008)
Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a gen...
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Open Access
DOI: https://doi.org/10.1109/ijcnn.2008.4633963
Vivek Prakash Nigam, Daniel Graupe • Neurological Research
(2004)
Diagnosis of epilepsy is primarily based on scalp-recorded electroencephalograms (EEG). Unfortunately the long-term recordings obtained from 'ambulatory recording systems' contain EEG data of up to one week duration, which has introduced ne...
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DOI: https://doi.org/10.1179/016164104773026534
Pierre Baldi, Yves Chauvin • Neural Computation
(1993)
After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images...
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DOI: https://doi.org/10.1162/neco.1993.5.3.402
Hongsik Jeong, Luping Shi • Journal of Physics D Applied Physics
(2018)
Abstract Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfac...
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DOI: https://doi.org/10.1088/1361-6463/aae223
Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara, Kemi Victoria Dada et al. • Heliyon
(2018)
This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and ar...
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DOI: https://doi.org/10.1016/j.heliyon.2018.e00938
PMC: 6260436
Gene A. Tagliarini, John Fury Christ, Edward W. Page • IEEE Transactions on Computers
(1991)
The design of feedback (or recurrent) neural networks to produce good solutions to complex optimization problems is discussed. The theoretical basis for applying neural networks to optimization problems is reviewed, and a design rule that s...
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DOI: https://doi.org/10.1109/12.106220
Jiahui Yu, Linjie Yang, Ning Xu, Yang, Jianchao et al. • arXiv (Cornell University)
(2018)
We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individua...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1812.08928
Byoung S. Kim, Anthony J. Calise • Journal of Guidance Control and Dynamics
(1997)
The theoretical developmentofa direct adaptivetracking controlarchitectureusingneuralnetworks ispresented. Emphasis is placed on utilization of neural networks in a ight control architecture based on feedback linearization of the aircraft...
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DOI: https://doi.org/10.2514/2.4029
Osama Moselhi, Tarek Hegazy, Paul Fazio • Journal of Construction Engineering and Management
(1991)
The competitive and risk‐averse nature of the construction industry and its heuristic problem‐solving needs, among other reasons, have contributed to the development of nontraditional decision‐making tools. Research in artificial intelligen...
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DOI: https://doi.org/10.1061/(asce)0733-9364(1991)117:4(606)