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Artificial Neural Networks as Models of Neural Information Processing
| Artificial Neural Networks as Models of Neural Information Processing |
| Autore | Marcel van Gerven |
| Pubbl/distr/stampa | Frontiers Media SA, 2018 |
| Descrizione fisica | 1 electronic resource (220 p.) |
| Collana | Frontiers Research Topics |
| Soggetto non controllato |
brain imaging
artificial neural networks deep learning neural information processing backpropagation spiking neural networks |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910346748103321 |
Marcel van Gerven
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| Frontiers Media SA, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Neural Networks in Agriculture
| Artificial Neural Networks in Agriculture |
| Autore | Kujawa Sebastian |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (283 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences Technology, engineering, agriculture |
| Soggetto non controllato |
artificial neural network (ANN)
Grain weevil identification neural modelling classification winter wheat grain artificial neural network ferulic acid deoxynivalenol nivalenol MLP network sensitivity analysis precision agriculture machine learning similarity metric memory deep learning plant growth dynamic response root zone temperature dynamic model NARX neural networks hydroponics vegetation indices UAV neural network corn plant density corn canopy cover yield prediction CLQ GA-BPNN GPP-driven spectral model rice phenology EBK correlation filter crop yield prediction hybrid feature extraction recursive feature elimination wrapper artificial neural networks big data classification high-throughput phenotyping modeling predicting time series forecasting soybean food production paddy rice mapping dynamic time warping LSTM weakly supervised learning cropland mapping apparent soil electrical conductivity (ECa) magnetic susceptibility (MS) EM38 neural networks Phoenix dactylifera L. Medjool dates image classification convolutional neural networks transfer learning average degree of coverage coverage unevenness coefficient optimization high-resolution imagery oil palm tree CNN Faster-RCNN image identification agroecology weeds yield gap environment health crop models soil and plant nutrition automated harvesting model application for sustainable agriculture remote sensing for agriculture decision supporting systems neural image analysis |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557509803321 |
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Kujawa Sebastian
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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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Augmented Reality, Virtual Reality & Semantic 3D Reconstruction
| Augmented Reality, Virtual Reality & Semantic 3D Reconstruction |
| Autore | Lv Zhihan |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (304 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
feature tracking
superpixel structure from motion three-dimensional reconstruction local feature multi-view stereo construction hazard safety education photoreality virtual reality anatomization audio classification olfactory display deep learning transfer learning inception model augmented reality higher education scientific production web of science bibliometric analysis scientific mapping applications in subject areas interactive learning environments 3P model primary education educational technology mobile lip reading system lightweight neural network face correction virtual reality (VR) computer vision projection mapping 3D face model super-resolution radial curve Dynamic Time Warping semantic 3D reconstruction eye-in-hand vision system robotic manipulator probabilistic fusion graph-based refinement 3D modelling 3D representation game engine laser scanning panoramic photography super-resolution reconstruction generative adversarial networks dense convolutional networks texture loss WGAN-GP orientation positioning viewpoint image matching algorithm transformation ADHD EDAH assessment continuous performance test Photometric Stereo (PS) 3D reconstruction fully convolutional network (FCN) semi-immersive virtual reality children cooperative games empowerment perception motor planning problem-solving area of interest wayfinding spatial information one-shot learning gesture recognition GREN skeleton-based 3D composition pre-visualization stereo vision 360° video |
| ISBN | 3-0365-6062-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910639985103321 |
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Lv Zhihan
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big Data Analytics and Information Science for Business and Biomedical Applications
| Big Data Analytics and Information Science for Business and Biomedical Applications |
| Autore | Ahmed S. Ejaz |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (246 p.) |
| Soggetto topico |
Humanities
Social interaction |
| Soggetto non controllato |
high-dimensional
nonlocal prior strong selection consistency estimation consistency generalized linear models high dimensional predictors model selection stepwise regression deep learning financial time series causal and dilated convolutional neural networks nuisance post-selection inference missingness mechanism regularization asymptotic theory unconventional likelihood high dimensional time-series segmentation mixture regression sparse PCA entropy-based robust EM information complexity criteria high dimension multicategory classification DWD sparse group lasso L2-consistency proximal algorithm abdominal aortic aneurysm emulation Medicare data ensembling high-dimensional data Lasso elastic net penalty methods prediction random subspaces ant colony system bayesian spatial mixture model inverse problem nonparamteric boostrap EEG/MEG data feature representation feature fusion trend analysis text mining |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557614803321 |
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Ahmed S. Ejaz
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big Data Analytics and Information Science for Business and Biomedical Applications II
| Big Data Analytics and Information Science for Business and Biomedical Applications II |
| Autore | Ahmed S. Ejaz |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (196 p.) |
| Soggetto topico |
Information technology industries
Computer science |
| Soggetto non controllato |
bandwidth selection
correlation edge-preserving image denoising image sequence jump regression analysis local smoothing nonparametric regression spatio-temporal data linear mixed model ridge estimation pretest and shrinkage estimation multicollinearity asymptotic bias and risk LASSO estimation high-dimensional data big data adaptation dividend estimation options markets weighted least squares online health community social support network analysis cancer functional principal component analysis functional predictor linear mixed-effects model mobile device sparse group regularization wearable device data Bayesian modeling functional regression gestational weight infant birth weight joint modeling longitudinal data maternal weight gain transfer learning deep learning pretrained neural networks chest X-ray images lung diseases causal structure learning consistency FCI algorithm high dimensionality nonparametric testing PC algorithm fMRI functional connectivity brain network Human Connectome Project statistics |
| ISBN | 3-0365-5550-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637784003321 |
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Ahmed S. Ejaz
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big data war : how to survive global big data competition / / Patrick H. Park
| Big data war : how to survive global big data competition / / Patrick H. Park |
| Autore | Park Patrick H. |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | New York, New York (222 East 46th Street, New York, NY 10017) : , : Business Expert Press, , 2016 |
| Descrizione fisica | 1 recurso en línea (x, 195 páginas) |
| Disciplina | 005.7 |
| Collana | Big data and business analytics collection |
| Soggetto topico |
Big data
Quantitative research |
| Soggetto genere / forma | Electronic books. |
| Soggetto non controllato |
Amazon
Apple big data business intelligence consulting customer analysis customer profiling CRM data deep learning IT machine learning MBA marketing product profiling problem solving strategy Tech Venture |
| ISBN | 1-63157-561-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1. Dump the data -- 1. Global data war -- 2. Why did Google TV fail? -- 3. Why do they analyze data? -- Part 2. Data is human -- 4. Think as a customer -- 5. Big data, start from human -- 6. Why does Nike compete with Nintendo? -- Part 3. Data is created by me -- 7. Knowing necessary data is everything in data analytics -- 8. Create data -- Part 4. We don't need the past -- 9. Predict human unconsciousness -- 10. Anything with a pattern can be predicted -- Part 5. What matters at the end is performance -- 11. Data is strategy -- 12. Big data, a long way to go -- Epilogue -- About the author -- Index. |
| Record Nr. | UNINA-9910465673703321 |
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Park Patrick H.
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| New York, New York (222 East 46th Street, New York, NY 10017) : , : Business Expert Press, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big data war : how to survive global big data competition / / Patrick H. Park
| Big data war : how to survive global big data competition / / Patrick H. Park |
| Autore | Park Patrick H. |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | New York, New York (222 East 46th Street, New York, NY 10017) : , : Business Expert Press, , 2016 |
| Descrizione fisica | 1 recurso en línea (x, 195 páginas) |
| Disciplina | 005.7 |
| Collana | Big data and business analytics collection |
| Soggetto topico |
Big data
Quantitative research |
| Soggetto non controllato |
Amazon
Apple big data business intelligence consulting customer analysis customer profiling CRM data deep learning IT machine learning MBA marketing product profiling problem solving strategy Tech Venture |
| ISBN | 1-63157-561-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1. Dump the data -- 1. Global data war -- 2. Why did Google TV fail? -- 3. Why do they analyze data? -- Part 2. Data is human -- 4. Think as a customer -- 5. Big data, start from human -- 6. Why does Nike compete with Nintendo? -- Part 3. Data is created by me -- 7. Knowing necessary data is everything in data analytics -- 8. Create data -- Part 4. We don't need the past -- 9. Predict human unconsciousness -- 10. Anything with a pattern can be predicted -- Part 5. What matters at the end is performance -- 11. Data is strategy -- 12. Big data, a long way to go -- Epilogue -- About the author -- Index. |
| Record Nr. | UNINA-9910798776403321 |
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Park Patrick H.
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| New York, New York (222 East 46th Street, New York, NY 10017) : , : Business Expert Press, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big data war : how to survive global big data competition / / Patrick H. Park
| Big data war : how to survive global big data competition / / Patrick H. Park |
| Autore | Park Patrick H. |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | New York, New York (222 East 46th Street, New York, NY 10017) : , : Business Expert Press, , 2016 |
| Descrizione fisica | 1 recurso en línea (x, 195 páginas) |
| Disciplina | 005.7 |
| Collana | Big data and business analytics collection |
| Soggetto topico |
Big data
Quantitative research |
| Soggetto non controllato |
Amazon
Apple big data business intelligence consulting customer analysis customer profiling CRM data deep learning IT machine learning MBA marketing product profiling problem solving strategy Tech Venture |
| ISBN | 1-63157-561-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1. Dump the data -- 1. Global data war -- 2. Why did Google TV fail? -- 3. Why do they analyze data? -- Part 2. Data is human -- 4. Think as a customer -- 5. Big data, start from human -- 6. Why does Nike compete with Nintendo? -- Part 3. Data is created by me -- 7. Knowing necessary data is everything in data analytics -- 8. Create data -- Part 4. We don't need the past -- 9. Predict human unconsciousness -- 10. Anything with a pattern can be predicted -- Part 5. What matters at the end is performance -- 11. Data is strategy -- 12. Big data, a long way to go -- Epilogue -- About the author -- Index. |
| Record Nr. | UNINA-9910813748203321 |
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Park Patrick H.
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| New York, New York (222 East 46th Street, New York, NY 10017) : , : Business Expert Press, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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BIM in the Construction Industry
| BIM in the Construction Industry |
| Autore | Cha Hee Sung |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (414 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
green construction
code checking mvdXML semantic technology SMEs BIM construction management system steel frame construction safety path planning A-Star Searching evacuation MEP logic chain Industry 4.0 construction industry building information modeling cyber-planning-physical system open BIM mobile BIM mobile application technology acceptance model (TAM) building information modeling (BIM) building project hindrance factor analysis structural equation modeling (SEM) managerial strategies Singapore Building Information Modeling process improvement construction management information and communication technologies Augmented Reality building design building performance simulation energy conservation fire safety inspection real-time location system smartphone crowdsourcing clash detection supervised machine learning openBIM information interoperability standards software fire disaster facility management lean construction production planning and control data-driven construction concrete formwork concrete maturity interoperability real-time monitoring fire safety rule visual language portable firefighting equipment Building Information Modeling (BIM) Industry Foundation Classes (IFC) partial model extraction query language selection set 3D Reconstruction 2D structural drawing object detection deep learning YOLO log data mining modeling performance collaborative environment behavioral patterns |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557468203321 |
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Cha Hee Sung
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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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Bioinformatics and Machine Learning for Cancer Biology
| Bioinformatics and Machine Learning for Cancer Biology |
| Autore | Wan Shibiao |
| Pubbl/distr/stampa | Basel, : MDPI Books, 2022 |
| Descrizione fisica | 1 electronic resource (196 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences |
| Soggetto non controllato |
tumor mutational burden
DNA damage repair genes immunotherapy biomarker biomedical informatics breast cancer estrogen receptor alpha persistent organic pollutants drug-drug interaction networks molecular docking NGS ctDNA VAF liquid biopsy filtering variant calling DEGs diagnosis ovarian cancer PUS7 RMGs CPA4 bladder urothelial carcinoma immune cells T cell exhaustion checkpoint architectural distortion image processing depth-wise convolutional neural network mammography bladder cancer Annexin family survival analysis prognostic signature therapeutic target R Shiny application RNA-seq proteomics multi-omics analysis T-cell acute lymphoblastic leukemia CCLE sitagliptin thyroid cancer (THCA) papillary thyroid cancer (PTCa) thyroidectomy metastasis drug resistance biomarker identification transcriptomics machine learning prediction variable selection major histocompatibility complex bidirectional long short-term memory neural network deep learning cancer incidence mortality modeling forecasting Google Trends Romania ARIMA TBATS NNAR |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910595077403321 |
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Wan Shibiao
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| Basel, : MDPI Books, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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