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Machine Learning Applied to Composite Materials [[electronic resource] /] / edited by Vinod Kushvaha, M. R. Sanjay, Priyanka Madhushri, Suchart Siengchin
| Machine Learning Applied to Composite Materials [[electronic resource] /] / edited by Vinod Kushvaha, M. R. Sanjay, Priyanka Madhushri, Suchart Siengchin |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (202 pages) |
| Disciplina | 006.31 |
| Collana | Composites Science and Technology |
| Soggetto topico |
Composite materials
Machine learning Computational intelligence Materials science - Data processing Composites Machine Learning Computational Intelligence Computational Materials Science Materials compostos Simulació per ordinador Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-19-6278-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Importance of machine learning in material science -- Machine Learning: A methodology to explain and predict material behavior -- Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network -- Methodology of K-Nearest Neighbor for predicting the fracture toughness of polymer composites -- Forward machine learning technique to predict dynamic fracture behavior of particulate composite -- Predictive modelling of fracture behavior in silica-filled polymer composite subjected to impact with varying loading rates -- Machine learning approach to determine the elastic modulus of Carbon fiber-reinforced laminates -- Effect of weight ratio on mechanical behaviour of natural fiber based biocomposite using machine learning -- Effect of natural fiber’s mechanical properties and fiber matrix adhesion strength to design biocomposite -- Comparison of various machine learning algorithms to predict material behavior in GFRP. |
| Record Nr. | UNINA-9910633937803321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning Applied to Composite Materials [[electronic resource] /] / edited by Vinod Kushvaha, M. R. Sanjay, Priyanka Madhushri, Suchart Siengchin
| Machine Learning Applied to Composite Materials [[electronic resource] /] / edited by Vinod Kushvaha, M. R. Sanjay, Priyanka Madhushri, Suchart Siengchin |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (202 pages) |
| Disciplina | 006.31 |
| Collana | Composites Science and Technology |
| Soggetto topico |
Composite materials
Machine learning Computational intelligence Materials science - Data processing Composites Machine Learning Computational Intelligence Computational Materials Science Materials compostos Simulació per ordinador Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-19-6278-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Importance of machine learning in material science -- Machine Learning: A methodology to explain and predict material behavior -- Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network -- Methodology of K-Nearest Neighbor for predicting the fracture toughness of polymer composites -- Forward machine learning technique to predict dynamic fracture behavior of particulate composite -- Predictive modelling of fracture behavior in silica-filled polymer composite subjected to impact with varying loading rates -- Machine learning approach to determine the elastic modulus of Carbon fiber-reinforced laminates -- Effect of weight ratio on mechanical behaviour of natural fiber based biocomposite using machine learning -- Effect of natural fiber’s mechanical properties and fiber matrix adhesion strength to design biocomposite -- Comparison of various machine learning algorithms to predict material behavior in GFRP. |
| Record Nr. | UNISA-996499867603316 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning for Advanced Functional Materials [[electronic resource] /] / edited by Nirav Joshi, Vinod Kushvaha, Priyanka Madhushri
| Machine Learning for Advanced Functional Materials [[electronic resource] /] / edited by Nirav Joshi, Vinod Kushvaha, Priyanka Madhushri |
| Autore | Joshi Nirav |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (306 pages) |
| Disciplina | 620.110285631 |
| Altri autori (Persone) |
KushvahaVinod
MadhushriPriyanka |
| Soggetto topico |
Optics
Machine learning Materials Detectors Tumor markers Photonics Optical engineering Optics and Photonics Machine Learning Sensors and biosensors Tumour Biomarkers Photonics and Optical Engineering Photonic Devices |
| Soggetto non controllato |
Artificial Intelligence
Materials Oncology Microwaves Optics Computers Technology & Engineering Medical Science |
| ISBN | 981-9903-93-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Solar Cells and Relevant Machine Learning -- Machine learning-driven gas identification in gas sensors -- Recent advances in Machine Learning for electrochemical, optical, and gas sensors -- Machine Learning in Wearable Healthcare Devices -- A Machine Learning approach in wearable Technologies -- The application of novel functional materials to machine learning -- Potential of Machine Learning Algorithms in Material Science: Predictions in design, properties and applications of novel functional materials -- Perovskite Based Materials for Photovoltaic Applications: A Machine Learning Approach -- A review of the high-performance gas sensors using machine learning -- Machine Learning For Next‐Generation Functional Materials -- Contemplation of Photocatalysis Through Machine Learning -- Discovery of Novel Photocatalysts using Machine Learning Approach -- Machine Learning In Impedance Based Sensors. |
| Record Nr. | UNINA-9910726286403321 |
Joshi Nirav
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||