Artificial Neural Network for Civil Engineering - Dehghan, Shahide;Norouzi, Hossein;Gholami, Hossein
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In recent years, artificial neural networks (ANN) and artificial intelligence (AI), in general, have garnered significant attention with respect to their applications in several scientific fields, varying from big data management to medical diagnosis. ANN techniques are already used in everyday applications, such as personalized advertisements, virtual assistants, autonomous driving, etc. The start of regeneration breakthroughs in ANNs can be traced back to the year 2005 and can be attributed to the development of novel learning architectures such as convolutional neural networks (CNN) and…mehr

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Produktbeschreibung
In recent years, artificial neural networks (ANN) and artificial intelligence (AI), in general, have garnered significant attention with respect to their applications in several scientific fields, varying from big data management to medical diagnosis. ANN techniques are already used in everyday applications, such as personalized advertisements, virtual assistants, autonomous driving, etc. The start of regeneration breakthroughs in ANNs can be traced back to the year 2005 and can be attributed to the development of novel learning architectures such as convolutional neural networks (CNN) and deep belief networks (DBN), with significant progress having been achieved so far and new methodologies having been proposed, such as generative adversarial networks (GAN). At present, ANN-based models are widely used in several forms of engineering applications.
Autorenporträt
Doktor Shahide Dehghan, doktor filosofii, fakul'tet geografii, Nadzhafabadskij filial, Islamskij uniwersitet Azad, Nadzhafabad, Iran. Tehnicheskie nawyki: klimatologiq, statisticheskij analiz, geografiq, geoprirodnye opasnosti, geomorfologiq, faktornyj analiz, analiz dannyh, izmenenie klimata, atmosfera, global'noe poteplenie, klimatologiq, strahowye inwesticii i wodnye resursy.