Artificial Neural Network

By Satchidananda Dehuri, Susmita Ghosh, Sung-Bae Cho. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2020

1,356 citations recorded by the source corpus.

: An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the biological nervous systems, such as the brain, which process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working together to solve specific problems. Artificial Neural Network was used in following Application Areas: Handwriting Recognition , Image Compression, Stock Exchange Predictions etc.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/9a6a6ac5350023766504c042c8edbb18ba927024

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In Artificial Intelligence

Citations in the corpus

Explainable artificial intelligence: a comprehensive review (2021, methodology): “• Feed-forward neural networks Since its first appearance (Wang 2003), ANN has been accepted by the research community and has become the foundation of various deep learning models (OstadAli-Askari et al.”
Data-driven modelling: some past experiences and new approaches (2008, background): “…of successful applications in river basin management and related problems, for example: † Modelling rainfall–runoff processes: Hsu et al. (1995); Minns & Hall (1996); Dawson & Wilby (1998); Dibike et al. (1999); Abrahart & See (2000); Govindaraju & Ramachandra Rao (2001); Hu et al. (2007);…”
A CNN-RNN Framework for Crop Yield Prediction (2019, methodology): “Jiang et al. (2004) applied artificial neural network and multiple linear regression for estimating winter wheat yields based on the remotely sensed and climate data, and found that artificial neural network model outperformed the multiple linear regression.”
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning (2019, methodology): “38 In Hydrology, the first demonstration of ML out-performing a process-based model that 39 we are aware of was by Hsu et al. (1995), who compared a calibrated Sacramento Soil 40 Moisture Accounting Model (SAC-SMA) against a feed-forward artificial neural network 41 across a range of flow regimes. M”