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Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast
| Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast |
| Autore | Gómez Vela Francisco A |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (100 p.) |
| Soggetto topico |
Research & information: general
Technology: general issues |
| Soggetto non controllato |
autoregression
clustering data filtration data processing decision tree deep learning electricity demand energy demand energy efficiency evolutionary computation exponential smoothing forecasting k-nearest neighbors n/a neuroevolution photovoltaic power plant regression residential building short-term forecasting temporal convolutional network time series time series forecasting time-series forecasting |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557776003321 |
Gómez Vela Francisco A
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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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Computational Methods for the Analysis of Genomic Data and Biological Processes
| Computational Methods for the Analysis of Genomic Data and Biological Processes |
| Autore | Gómez Vela Francisco A |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (222 p.) |
| Soggetto topico |
Biology, life sciences
Research & information: general |
| Soggetto non controllato |
binding sites
bioinformatics bioinformatics analysis CAMTA1 cancer CBF chilling stress Chou's 5-steps rule chromatin interactions classification clustering computational biology computational intelligence Convolution Neural Network (CNN) CRISPR-Cas9 data mining deep learning differential genes expression differentiation DNA methylation DNA N6-methyladenine DREB ensembles eQTL exercise fine-mapping gene co-expression network Gene Ontology gene-set enrichment genome architecture genomics hepatocellular carcinoma HIGD2A high-fat diet hypoxia immune response infiltration infiltration tactics optimization algorithm Long Short-Term Memory (LSTM) machine learning machine-learning meta-analysis methylation microarray miRNA mRNA expression murine coronavirus n/a obesity pathway pathways potential therapeutic targets power prediction proteomics quercetin Reactome Pathways RNA N6-methyladenosine site single-cell clone systems biology text mining transcription factor transcriptomics viral infection yeast genome |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557129603321 |
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Gómez Vela Francisco A
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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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Genetic Programming [[electronic resource] ] : 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings / / edited by Ting Hu, Nuno Lourenço, Eric Medvet, Federico Divina
| Genetic Programming [[electronic resource] ] : 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings / / edited by Ting Hu, Nuno Lourenço, Eric Medvet, Federico Divina |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (306 pages) |
| Disciplina | 006.31 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Computer science
Computer systems Computers, Special purpose Computer networks Artificial intelligence Computer engineering Theory of Computation Computer System Implementation Special Purpose and Application-Based Systems Computer Communication Networks Artificial Intelligence Computer Engineering and Networks |
| ISBN | 3-030-44094-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Hessian Complexity Measure for Genetic Programming-based Imputation Predictor Selection in Symbolic Regression with Incomplete Data -- Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing -- Incremental Evolution and Development of Deep Artificial Neural Networks -- Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming -- Comparing Genetic Programming Approaches for Non-Functional Genetic Improvement -- Automatically Evolving Lookup Tables for Function Approximation -- Optimising Optimisers with Push GP -- An Evolutionary View on Reversible Shift-invariant Transformations -- Benchmarking Manifold Learning Methods on a Large Collection of Datasets -- Ensemble Genetic Programming -- SGP-DT: Semantic Genetic Programming Based on Dynamic Targets -- Effect of Parent Selection Methods on Modularity -- Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models -- Challenges of Program Synthesis with Grammatical Evolution -- Detection of Frailty Using Genetic Programming : The Case of Older People in Piedmont, Italy -- Is k Nearest Neighbours Regression Better than GP -- Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling -- Classification of Autism Genes using Network Science and Linear Genetic Programming. |
| Record Nr. | UNISA-996418219703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Genetic Programming : 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings / / edited by Ting Hu, Nuno Lourenço, Eric Medvet, Federico Divina
| Genetic Programming : 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings / / edited by Ting Hu, Nuno Lourenço, Eric Medvet, Federico Divina |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (306 pages) |
| Disciplina |
006.31
006.3823 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Computer science
Computer systems Computers, Special purpose Computer networks Artificial intelligence Computer engineering Theory of Computation Computer System Implementation Special Purpose and Application-Based Systems Computer Communication Networks Artificial Intelligence Computer Engineering and Networks |
| ISBN | 3-030-44094-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Hessian Complexity Measure for Genetic Programming-based Imputation Predictor Selection in Symbolic Regression with Incomplete Data -- Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing -- Incremental Evolution and Development of Deep Artificial Neural Networks -- Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming -- Comparing Genetic Programming Approaches for Non-Functional Genetic Improvement -- Automatically Evolving Lookup Tables for Function Approximation -- Optimising Optimisers with Push GP -- An Evolutionary View on Reversible Shift-invariant Transformations -- Benchmarking Manifold Learning Methods on a Large Collection of Datasets -- Ensemble Genetic Programming -- SGP-DT: Semantic Genetic Programming Based on Dynamic Targets -- Effect of Parent Selection Methods on Modularity -- Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models -- Challenges of Program Synthesis with Grammatical Evolution -- Detection of Frailty Using Genetic Programming : The Case of Older People in Piedmont, Italy -- Is k Nearest Neighbours Regression Better than GP -- Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling -- Classification of Autism Genes using Network Science and Linear Genetic Programming. |
| Record Nr. | UNINA-9910409674103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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