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Review and Analysis of the Development of Artificial Neural Networks
Кібернетика та комп'ютерні технології, 2023 Introduction. The creation of intelligent cyber-physical systems is impossible without knowledge of the analysis and process of development of scientific thought regarding artificial neural networks. The main task of this article is research and analysis
Oleksandr Bilokon
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Using the Mean Absolute Percentage Error for Regression Models [PDF]
, 2015 We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE ...
De Myttenaere, Arnaud +3 more
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Nature Communications, 2018 Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in ...
Decebal Constantin Mocanu +5 more
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THE INTELLIGENT SYSTEM FOR AUTOMOTIVE FUELS QUALITY DEFINITION
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2013 An intelligent system, based on hydrodynamic method and artificial neural networks usage for automotive fuels quality definition have been developed. Artificial neural networks optimal structures for the octane number of gasoline, cetane number, cetane ...
Volodymyr Drevetskiy , Marko Klepach
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Advanced Artificial Neural Networks
Algorithms, 2018 Artificial neural networks (ANNs) have been extensively applied to a wide range of disciplines, such as system identification and control, decision making, pattern recognition, medical diagnosis, finance, data mining, visualization, and others.
Tin-Chih Toly Chen +2 more
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Observational Cosmology with Artificial Neural Networks
Universe, 2022 In cosmology, the analysis of observational evidence is very important when testing theoretical models of the Universe. Artificial neural networks are powerful and versatile computational tools for data modelling and have recently been considered in the ...
Juan de Dios Rojas Olvera +2 more
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Artificial neural networks in geospatial analysis [PDF]
, 2017 Artificial neural networks are computational models widely used in geospatial analysis for data classification, change detection, clustering, function approximation, and forecasting or prediction. There are many types of neural networks based on learning
Bishop, Carpenter, Carpenter, Ripley
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Molecular bases of circadian magnesium rhythms across eukaryotes
FEBS Letters, EarlyView.Circadian rhythms in intracellular [Mg2+] exist across eukaryotic kingdoms. Central roles for Mg2+ in metabolism suggest that Mg2+ rhythms could regulate daily cellular energy and metabolism. In this Perspective paper, we propose that ancestral prokaryotic transport proteins could be responsible for mediating Mg2+ rhythms and posit a feedback model ...
Helen K. Feord, Gerben van Ooijen
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Artificial neural networks as the data ruling components of future intelligent energy systems
MATEC Web of Conferences, 2017 The main target of this work is to study artificial neural networks and their role in the future intelligent energy systems. Artificial neural networks in the near future will be a very effective tool for data analysis and very perspective for energy ...
Buran Anna, Denisov Maksim
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STOCK CLOSING PRICE PREDICTION OF ISX-LISTED INDUSTRIAL COMPANIES USING ARTIFICIAL NEURAL NETWORKS
Jurnal Ilmu Keuangan dan Perbankan, 2022 Making stock investment decisions is a complex challenge that investors continuously face. When it comes to an uncertain future, making the wrong decision can result in massive losses.
Salim Sallal Al-Hasnawi +1 more
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