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Bioinformatics Applications Based On Machine Learning
| Bioinformatics Applications Based On Machine Learning |
| Autore | Chamoso Pablo |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (206 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
Affective Computing
Arabidopsis thaliana Artificial Neural Networks bi-directional LSTM bioinformatics biological functions detection classification clinical data clinical implications cluster computer vision convolutional neural network CTX-M data mining Decision table deep learning derivative-free optimization detection diabetes dynamic models ear detection epidemiology evolutionary computation evolutionary computing feature selection forecast fuel cell gene clustering genetic programming glycolysis gradient boosting Harmony Search hybrid systems hydrogen energy image recognition informative genes intelligent systems machine learning Machine Learning metabolism metagenomics metrics mitochondrial protein parameter estimation PART Particle Swarm Optimization personality assessment plasmodium falciparum power management real-life patients regression swarm intelligence transposable elements video analysis Weka yeast |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910674055503321 |
Chamoso Pablo
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Claim Models: Granular Forms and Machine Learning Forms
| Claim Models: Granular Forms and Machine Learning Forms |
| Autore | Taylor Greg |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (108 p.) |
| Soggetto topico | Pharmaceutical chemistry and technology |
| Soggetto non controllato |
actuarial
claim watching classification and regression trees gradient boosting granular models individual claims reserving individual models loss reserving machine learning n/a neural networks payments per claim incurred predictive modeling risk pricing |
| ISBN | 3-03928-665-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Claim Models |
| Record Nr. | UNINA-9910404090203321 |
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Taylor Greg
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning in Tribology
| Machine Learning in Tribology |
| Autore | Tremmel Stephan |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (208 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
amorphous carbon coatings
analysis artificial intelligence artificial neural networks bearing fault diagnosis BERT cage instability condition monitoring Convolutional Neural Network (CNN) data mining databases digital twin dynamic friction evolutionary algorithms fault data generation feature engineering Gaussian processes Generative Adversarial Network (GAN) gradient boosting laser surface texturing machine learning meta-modeling monitoring n/a natural language processing neural networks optimization PINN prediction random forest random forest classifier reduced order modelling regression remaining useful life reynolds equation rolling bearing dynamics rolling bearings rubber seal applications self-lubricating journal bearings semi-supervised learning structure-borne sound tensor decomposition texturing during moulding total knee replacement tribAIn tribo-informatics tribo-testing triboinformatics tribology UHWMPE unbalanced datasets |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576887003321 |
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Tremmel Stephan
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sustainable Agricultural Engineering Technologies and Applications
| Sustainable Agricultural Engineering Technologies and Applications |
| Autore | Sultan Muhammad |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (254 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences Technology, engineering, agriculture |
| Soggetto non controllato |
greenhouse
microclimate Bayesian optimization deep neural network roses yield Gaussian process gradient boosting pool boiling heat transfer coefficient sintered coated porous surfaces gaussian process gradient boosting regression trees response surface renovation index CFD simulation airflow evaporative cooling desiccant dehumidification agricultural storage air conditioning system performance lemon cordial microwave preservation green processing antioxidant potential renewable energy Scheffler concentrator reflector batch-type solar roaster response surface methodology coffee roasting municipal solid waste sanitary landfill open dumps waste to energy climate change yogurt processing solar energy solar-based heating and cooling thermal analysis vegetable yield nitrogen use efficiency nutrient leaching leaching-to-input ratio nitrogen fertilizer economic benefit environment eutrophication particulate fraction effluent treatment thermal screens heating demand TRNSYS greenhouse internal temperature building energy simulation longwave radiation soil total nitrogen BP neural network support vector machines spatial distribution remote sensing |
| ISBN | 3-0365-5889-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910639995503321 |
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Sultan Muhammad
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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