Моделирование и прогнозирование индексов производства при помощи искусственных нейронных сетей с учетом межотраслевых связей и сравнение прогностических качеств различных архитектур (Modeling and Forecasting Production Indices Using Artificial Neural Networks, Taking Into Account Intersectoral Relationships and Comparing the Predictive Qualities of Various Architectures)

2021 ◽  
Author(s):  
A Kaukin ◽  
Vladimir Kosarev
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modeling And Forecasting ◽  
Production Indices ◽  
Artificial Neural

scholarly journals Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach

FinTech ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 47-62
Author(s):  
Sanjib Kumar Nayak ◽  
Sarat Chandra Nayak ◽  
Subhranginee Das
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Financial Time Series ◽  
Optimal Parameters ◽  
Financial Time ◽  
Good Investment ◽  
Machine Learning Approach ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Artificial Neural

Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural

Artificial neural networks for diagnosis and prognosis in prostate cancer

2002 ◽  
Vol 20 (2) ◽  
pp. 89-95 ◽  
Author(s):  
Guido Schwarzer ◽  
Martin Schumacher
Keyword(s):  
Prostate Cancer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Diagnosis And Prognosis ◽  
Artificial Neural

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural

Differentiation Between Mass-forming Type Peripheral Cholangiocarcinoma and Hepatic Abscesses: Application of Artificial Neural Networks to CT Images

2005 ◽  
Vol 53 (5) ◽  
pp. 343 ◽  
Author(s):  
Nak Jong Seong ◽  
Jeong Min Lee ◽  
Se Hyung Kim ◽  
Joon Koo Han ◽  
Young Jun Kim ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ct Images ◽  
Peripheral Cholangiocarcinoma ◽  
Artificial Neural
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