Contribution of the artificial neural network (ANN) method to the interpolation of the Bouguer gravity anomalies in the region of Lom-Pangar (East-Cameroon)

Diffo, Steve; Lemotio, Willy; Adiang, Cyrille Mezoue; Heutchi, Évariste Ngatchou; Nguiya, Séverin; Ndjaka, Jean Marie Bienvenue

doi:10.1007/s40948-022-00341-1

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…In this multidimensional input model, neurons are responsible for docking activation functions. After processing the docking, the resulting information is passed to the next neuron [8]. After getting information from the outside world, it becomes a basic neuron.…”

Section: Investment Project Riskmentioning

confidence: 99%

Risk Management of Investment Projects Based on Artificial Neural Network

Deng

Chang

2022

Wireless Communications and Mobile Computing

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The benefit evaluation of investment projects is the key to the whole investment activities. This paper mainly describes the risk management of investment projects using an artificial neural network. It generally adopts the index system of project risk through modern scientific measurement methods, to evaluate whether the investment project of artificial neural network is feasible or not. It establishes a benefit evaluation model based on an artificial neural network, from the analysis and consideration of 4 groups of experiments, comparing four sets of data: BP network convergence rate, artificial neural network identification efficiency, enterprise risk, artificial neural network output, and error; it is concluded that the relative risk is reduced by about 20% after using the artificial neural network. This also verifies the feasibility of artificial neural networks in the application of raw materials.

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Section: Investment Project Riskmentioning

confidence: 99%