Carlo giuseppe bertinetto (15 risultati)

Condizione: New. pp. 116.

Condizione: New. pp. 192.

Taschenbuch. Condizione: Neu. Prediction of Molecular Properties by Recursive Neural Networks | Application to the glass transition temperature of acrylic polymers | Carlo Giuseppe Bertinetto | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639162097 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

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Taschenbuch. Condizione: Neu. Prediction of Properties of Low and High Molecular Weight Compounds | A Structure-Based QSAR/QSPR Approach Using Recursive Neural Networks | Carlo Giuseppe Bertinetto | Taschenbuch | 192 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659271090 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Condizione: New. Print on Demand pp. 116 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bertinetto Carlo GiuseppeCarlo Giuseppe Bertinetto was born on 7-12-1981 in Torino, Italy. In October 2006 he graduated cum laude in chemistry at the University of Pisa. He is currently a PhD student in chemical sciences at the Un.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the past few years, a novel approach in cheminformatics for the Quantitative Structure-Property Relationship (QSPR) analysis of physical, chemical and biological properties of chemical compounds was developed at the University of Pisa. This methodology is based on the direct treatment of molecular structure, without using numerical descriptors, and employs recursive neural networks. In subsequent studies it was successfully used to predict various properties of different classes of compounds. It is a promising tool in the evaluation of existing substances, as well as in the design of new materials. This master thesis focuses on the prediction of the properties of polymers, a problem not easily treatable with traditional methods based on molecular descriptors. The study explores different representational issues and show the accuracy and flexibility of the structure-based QSPR approach.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This work describes and discusses an innovative approach for the prediction of physical, chemical and biological properties of compounds, ranging from small molecules to large polymers. It is based on the direct and adaptive treatment of molecular structure by means of a Recursive Neural Network (RNN) to derive Quantitative Structure-Property/Activity Relationships (QSPR/QSARs). Chemical compounds are represented through appropriate graphical tools that bypass the need for numerical descriptors. The capabilities of this methodology are investigated by applying it to different predictive problems: the melting point of ionic liquids, the glass transition temperature of polymers and the toxicity of organic molecules. The results show that the graphical molecular representation was able to effectively model each case, providing accurate predictions using practically no background knowledge. The proposed structure-based RNN approach, it is argued, can provide a simple and general prediction method with great potential in molecular design, toxicology and evaluation of other complex properties. 192 pp. Englisch.…

Condizione: New. PRINT ON DEMAND pp. 116.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work describes and discusses an innovative approach for the prediction of physical, chemical and biological properties of compounds, ranging from small molecules to large polymers. It is based on the direct and adaptive treatment of molecular structure by means of a Recursive Neural Network (RNN) to derive Quantitative Structure-Property/Activity Relationships (QSPR/QSARs). Chemical compounds are represented through appropriate graphical tools that bypass the need for numerical descriptors. The capabilities of this methodology are investigated by applying it to different predictive problems: the melting point of ionic liquids, the glass transition temperature of polymers and the toxicity of organic molecules. The results show that the graphical molecular representation was able to effectively model each case, providing accurate predictions using practically no background knowledge. The proposed structure-based RNN approach, it is argued, can provide a simple and general prediction method with great potential in molecular design, toxicology and evaluation of other complex properties.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bertinetto Carlo GiuseppeCarlo G. Bertinetto is a researcher at the Department of Forest Product Technologies of the Aalto University of Helsinki, Finland.He works mainly in the field of chemometrics, applying different mathematical .…

Condizione: New. Print on Demand pp. 192 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condizione: New. PRINT ON DEMAND pp. 192.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This work describes and discusses an innovative approach for the prediction of physical, chemical and biological properties of compounds, ranging from small molecules to large polymers. It is based on the direct and adaptive treatment of molecular structure by means of a Recursive Neural Network (RNN) to derive Quantitative Structure-Property/Activity Relationships (QSPR/QSARs). Chemical compounds are represented through appropriate graphical tools that bypass the need for numerical descriptors. The capabilities of this methodology are investigated by applying it to different predictive problems: the melting point of ionic liquids, the glass transition temperature of polymers and the toxicity of organic molecules. The results show that the graphical molecular representation was able to effectively model each case, providing accurate predictions using practically no background knowledge. The proposed structure-based RNN approach, it is argued, can provide a simple and general prediction method with great potential in molecular design, toxicology and evaluation of other complex properties.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 192 pp. Englisch.…