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Natural Language Processing

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...

OPENALEX DOI: https://doi.org/10.1007/978-1-4842-8817-7_10

Neural Networks in Business Forecasting

• IGI Global eBooks (2004)

Neural Network is defined as the ability of a group to solve more problems than its individual members.The idea brings that a group of people can solve problems efficiently and offer greater insight and a better answer than any one individu...

OPENALEX DOI: https://doi.org/10.4018/978-1-59140-176-6

A Survey on Neural Network Interpretability

Yu Zhang, Peter Tiňo B, Aleš Leonardis, Ke Tang • IEEE Transactions on Emerging Topics in Computational Intelligence (2021)

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g.,...

OPENALEX Open Access DOI: https://doi.org/10.1109/tetci.2021.3100641

Neural Networks in Clinical Medicine

W.D. Penny, David Frost • Medical Decision Making (1996)

Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical de...

OPENALEX DOI: https://doi.org/10.1177/0272989x9601600409

State-of-the-art in artificial neural network applications: A survey

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...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.heliyon.2018.e00938 PMC: 6260436

iNNvestigate neural networks!

Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele et al. • arXiv (Cornell University) (2018)

In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1808.04260

A neural network approach to cloud classification

J. Lee, Ronald C. Weger, S.K. Sengupta, Ronald M. Welch • IEEE Transactions on Geoscience and Remote Sensing (1990)

It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery ...

OPENALEX DOI: https://doi.org/10.1109/36.58972