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NATURAL LANGUAGE PROCESSING

V Ranjit, Suchitra Naik • International Journal of Computing Algorithm (2023)

A branch of computer science and AI called natural language processing is focused on how computers and human languages interact.In order for computers to interpret written and spoken human language without the use of computer-driven languag...

OPENALEX DOI: https://doi.org/10.20894/ijcoa.101.012.002.001

Impact of Machine Learning in Natural Language Processing: A Review

Tatwadarshi P. Nagarhalli, Vinod Moreshwar Vaze, Nirav Pravinsinh Rana • International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (2021)

The recent times have seen unprecedented growth in research and development in the field of Artificial Intelligence and its allied fields, subfields, like Machine Learning, Deep Learning and Natural Language Processing. The new found excite...

OPENALEX DOI: https://doi.org/10.1109/icicv50876.2021.9388380

A survey of uncertainty in deep neural networks

Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jong‐Seok Lee et al. • Artificial Intelligence Review (2023)

Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more a...

OPENALEX cc-by DOI: https://doi.org/10.1007/s10462-023-10562-9

Neural Networks in Structural Engineering

R. Daniel VanLuchene, ROUFEI SUN • Computer-Aided Civil and Infrastructure Engineering (1990)

In the past few years literature on computational civil engineering has concentrated primarily on artificial intelligence (Al) applications involving expert system technology. This article discusses a different Al approach involving neural ...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1111/j.1467-8667.1990.tb00377.x

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

Neural Networks and Computing

Tommy W. S. Chow, Siu‐Yeung Cho • Series in electrical and computer engineering (2007)

This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been co...

OPENALEX DOI: https://doi.org/10.1142/p487

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

Neural Networks for Fingerprint Recognition

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...

OPENALEX DOI: https://doi.org/10.1162/neco.1993.5.3.402

Memristor devices for neural networks

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...

OPENALEX cc-by DOI: https://doi.org/10.1088/1361-6463/aae223

Recurrent Neural Networks for Prediction

Danilo P. Mandic, Jonathon A. Chambers • Wiley eBooks (2001)

New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) ...

OPENALEX DOI: https://doi.org/10.1002/047084535x

Progress in neural networks

Omid M. Omidvar • Ablex Pub. Corp. eBooks (1991)

Receptive field calculus, Jan J. Koenderink visual reconstruction and data fusion, D. Suter visual perception of translational and rotational motion, Jim-Shih Liaw, Irwin K. King and Michael A. Arbib neural network for recovering observer m...

OPENALEX

Heterogeneous Graph Neural Network

Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami et al. • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2019)

Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This...

OPENALEX Open Access DOI: https://doi.org/10.1145/3292500.3330961

Artificial neural networks in process engineering

M.J. Willis, Christine Di Massimo, Gary A. Montague, Ming T. Tham et al. • IEE Proceedings D Control Theory and Applications (1991)

Artificial neural networks are made up of highly interconnected layers of simple ‘neuron-like’ nodes. The neurons act as nonlinear processing elements within the network. An attractive property of artificial neural networks is that, given t...

OPENALEX DOI: https://doi.org/10.1049/ip-d.1991.0036

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

Neural networks for classification: a survey

Gongbo Zhang • IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) (2000)

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifi...

OPENALEX DOI: https://doi.org/10.1109/5326.897072