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Collaborative technologies and data science in artificial intelligence applications / / Aram Hajian, Nelson Baloian, Tomoo Inoue [and one other] (eds.)
| Collaborative technologies and data science in artificial intelligence applications / / Aram Hajian, Nelson Baloian, Tomoo Inoue [and one other] (eds.) |
| Autore | Hajian Aram |
| Pubbl/distr/stampa | Berlin/Germany, : Logos Verlag Berlin, 2020 |
| Descrizione fisica | 1 online resource (190 pages) : illustrations; digital file(s) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Information theory |
| Soggetto non controllato |
Data science
Collaborative technologies Artificial neural networks Deep learning Smart human centered computing |
| ISBN | 9783832551414 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910418343503321 |
Hajian Aram
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| Berlin/Germany, : Logos Verlag Berlin, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Science, Human-Centered Computing, and Intelligent Technologies / Aram Hajian, Nelson Baloian, Tomoo Inoue, Wolfram Luther
| Data Science, Human-Centered Computing, and Intelligent Technologies / Aram Hajian, Nelson Baloian, Tomoo Inoue, Wolfram Luther |
| Pubbl/distr/stampa | Berlin, : Logos Verlag Berlin, 2022 |
| Descrizione fisica | 1 electronic resource (130 p.) |
| Soggetto topico | Information technology: general issues |
| Soggetto non controllato |
Data science
Intelligent technologies Artificial neural networks Deep learning Smart human-centered computing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910595092103321 |
| Berlin, : Logos Verlag Berlin, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Engaging Students : Using Evidence to Promote Student Success
| Engaging Students : Using Evidence to Promote Student Success |
| Pubbl/distr/stampa | Bloemfontein, : UJ Press, 2017 |
| Descrizione fisica | 1 electronic resource (262 p.) |
| Soggetto topico | Higher & further education, tertiary education |
| Soggetto non controllato |
Academics
Academic achievement Academic advising Academic advisors Academic challenge Academic development Academic literacy Academic performance Academic support Access Academic staff (also see academics/Lecturers) Actionable Active learning Agency Aggregated Analyse Apply Ask questions Assessment Attitude Australasian Survey of Student Engagement (AUSSE) Beginning University Survey of Student Engagement (BUSSE) Benchmarking Bloom’s taxonomy Business economics and management Campus environment Capacity Career advisors Challenges Classroom activities Classroom Survey of Student Engagement (CLASSE) Co-curricular (also see extra-curricular) Cognitive Cognitive development Cognitive educational activities Cognitive functions Cognitive skills Collaborative learning Colleges Community college Comprehensive universities Conditional formatting Contextual Contextual challenges Contextualised Council on Higher Education (CHE) Course (module/subject) Critical thinking Culture Curriculum Data Data-informed Decision-making Decolonisation Deep learning Department chairs (heads of departments) Department of Higher Education and Training (DHET) Development Developmental outcomes Diagnostic Disaggregating Discussions Discussion with diverse others Dropout Education outcomes Effective educational behaviours Effective educational practices Effective leadership Effective teaching practices Empirical Engagement – also see Student Engagement Engineering Equity Equitable outcomes Evaluate Evidence Evidence-based Expectations Expected academic difficulty Expected academic perseverance Experiential learning Experience with staff Extended degree Extended curricula Extra-curricular (also see co-curricular) Financial Stress Scale First-generation First-year Food Food insecurity Frequency Freshman myth Gender Graduate attributes (Learning outcomes) Group work Heads of departments High-Impact practices Higher education outcomes Higher-Order Learning Holistic Humanities Incentive Indicators Innovation Innovative Instructional paradigm Interactions Interventions Institutional culture Institutional performance Institutional research Institutional researchers Institution-wide approaches Interpersonal relationships Interpersonal skills Intersectional Intersectionality Irish Survey of Student Engagement (ISSE) Knowledge Knowledge society Language Law Leaders Leadership (management/university leadership) Learning Learning environments Learning facilitator Learning outcomes Learning paradigm Learning strategies Learning with peers Lecturer Survey of Student Engagement (LSSE) Lecturers (also see academics/academic staff) Librarians Management (University leaders and Leadership) Mathematics Memorisation Mentor Mentoring Mentorship Mission Module (course/subject) Motivation National Benchmark Tests (NBT) National Benchmark Test Project (NBTP) National Development Plan National Survey of Student Engagement (NSSE) Natural and Agricultural Sciences Next Generation of Academics Programme (nGAP) Numeracy development Off-campus On-campus Online resources Pathways Peer learning (also see Tutor) Pedagogical approaches Pedagogical contexts Pedagogical environments Pedagogical experiences Pedagogical innovation Pedagogical practices Pedagogical relationship Pedagogical responsiveness Pedagogies Perceived academic preparation Perceived preparedness Persistence Policies Policy Policy makers Practical significance Practical work Preparing for class Professional development Professionals Professional staff Quadrant Quality Quality assurance Quality of interactions Quantitative reasoning Reflection Reflective and integrative learning Relationships Research Responsiveness Resources Retention Science engineering and technology Self-reflection Senior students Service learning Social sciences Socio-economic South African Survey(s) of Student Engagement (SASSE) Staff development (also academic development and lecturer development) Stakeholder Strategies Statistical Student affairs Student behaviour Student bodies Student data Student development Student engagement Student evaluation Student financial aid Student involvement Student learning Student life Student needs Student outcomes Student organisations Student perspective Student participation Student performance Student persistence Student retention Student responses Student societies Student-staff interaction Student success Student views Student voice Success rates Subject (course/module) Support services Support staff Supportive campus Supportive environment Synthesise Systemic perspective Systemic understanding Teaching Teaching and learning Techniques Time Time management Traditional universities Transformation Transformative Transition Tutor Tutorials Undergraduate research Underprepared United States University Capacity Development Grant (University Capacity Development Programme) Universities Universities of Technology University leaders Unrealistic Well-being |
| ISBN |
9781928424093
1928424090 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910639978103321 |
| Bloemfontein, : UJ Press, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning Techniques on Gene Function Prediction
| Machine Learning Techniques on Gene Function Prediction |
| Autore | Zou Quan |
| Pubbl/distr/stampa | Frontiers Media SA, 2019 |
| Descrizione fisica | 1 online resource (485 p.) |
| Soggetto topico |
Medical genetics
Science: general issues |
| Soggetto non controllato |
Bioinformatics
Deep learning Feature selection Genetics Machine learning |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557762403321 |
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Zou Quan
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| Frontiers Media SA, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multivariate Statistical Machine Learning Methods for Genomic Prediction
| Multivariate Statistical Machine Learning Methods for Genomic Prediction |
| Autore | Montesinos López Osval Antonio |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (707 pages) |
| Altri autori (Persone) |
Montesinos LópezAbelardo
CrossaJosé |
| Soggetto topico |
Agricultural science
Life sciences: general issues Botany & plant sciences Animal reproduction Probability & statistics Aprenentatge automàtic Genètica vegetal Estadística matemàtica Anàlisi multivariable Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
open access
Statistical learning Bayesian regression Deep learning Non linear regression Plant breeding Crop management multi-trait multi-environments models |
| ISBN | 3-030-89010-4 |
| Classificazione | MED090000SCI011000SCI070000SCI086000TEC003000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Chapter 1: General Elements of Genomic Selection and Statistical Learning -- 1.1 Data as a Powerful Weapon -- 1.2 Genomic Selection -- 1.2.1 Concepts of Genomic Selection -- 1.2.2 Why Is Statistical Machine Learning a Key Element of Genomic Selection? -- 1.3 Modeling Basics -- 1.3.1 What Is a Statistical Machine Learning Model? -- 1.3.2 The Two Cultures of Model Building: Prediction Versus Inference -- 1.3.3 Types of Statistical Machine Learning Models and Model Effects -- 1.3.3.1 Types of Statistical Machine Learning Models -- 1.3.3.2 Model Effects -- 1.4 Matrix Algebra Review -- 1.5 Statistical Data Types -- 1.5.1 Data Types -- 1.5.2 Multivariate Data Types -- 1.6 Types of Learning -- 1.6.1 Definition and Examples of Supervised Learning -- 1.6.2 Definitions and Examples of Unsupervised Learning -- 1.6.3 Definition and Examples of Semi-Supervised Learning -- References -- Chapter 2: Preprocessing Tools for Data Preparation -- 2.1 Fixed or Random Effects -- 2.2 BLUEs and BLUPs -- 2.3 Marker Depuration -- 2.4 Methods to Compute the Genomic Relationship Matrix -- 2.5 Genomic Breeding Values and Their Estimation -- 2.6 Normalization Methods -- 2.7 General Suggestions for Removing or Adding Inputs -- 2.8 Principal Component Analysis as a Compression Method -- Appendix 1 -- Appendix 2 -- References -- Chapter 3: Elements for Building Supervised Statistical Machine Learning Models -- 3.1 Definition of a Linear Multiple Regression Model -- 3.2 Fitting a Linear Multiple Regression Model via the Ordinary Least Square (OLS) Method -- 3.3 Fitting the Linear Multiple Regression Model via the Maximum Likelihood (ML) Method -- 3.4 Fitting the Linear Multiple Regression Model via the Gradient Descent (GD) Method -- 3.5 Advantages and Disadvantages of Standard Linear Regression Models (OLS and MLR).
3.6 Regularized Linear Multiple Regression Model -- 3.6.1 Ridge Regression -- 3.6.2 Lasso Regression -- 3.7 Logistic Regression -- 3.7.1 Logistic Ridge Regression -- 3.7.2 Lasso Logistic Regression -- Appendix 1: R Code for Ridge Regression Used in Example 2 -- References -- Chapter 4: Overfitting, Model Tuning, and Evaluation of Prediction Performance -- 4.1 The Problem of Overfitting and Underfitting -- 4.2 The Trade-Off Between Prediction Accuracy and Model Interpretability -- 4.3 Cross-validation -- 4.3.1 The Single Hold-Out Set Approach -- 4.3.2 The k-Fold Cross-validation -- 4.3.3 The Leave-One-Out Cross-validation -- 4.3.4 The Leave-m-Out Cross-validation -- 4.3.5 Random Cross-validation -- 4.3.6 The Leave-One-Group-Out Cross-validation -- 4.3.7 Bootstrap Cross-validation -- 4.3.8 Incomplete Block Cross-validation -- 4.3.9 Random Cross-validation with Blocks -- 4.3.10 Other Options and General Comments on Cross-validation -- 4.4 Model Tuning -- 4.4.1 Why Is Model Tuning Important? -- 4.4.2 Methods for Hyperparameter Tuning (Grid Search, Random Search, etc.) -- 4.5 Metrics for the Evaluation of Prediction Performance -- 4.5.1 Quantitative Measures of Prediction Performance -- 4.5.2 Binary and Ordinal Measures of Prediction Performance -- 4.5.3 Count Measures of Prediction Performance -- References -- Chapter 5: Linear Mixed Models -- 5.1 General of Linear Mixed Models -- 5.2 Estimation of the Linear Mixed Model -- 5.2.1 Maximum Likelihood Estimation -- 5.2.1.1 EM Algorithm -- E Step -- M Step -- 5.2.1.2 REML -- 5.2.1.3 BLUPs -- 5.3 Linear Mixed Models in Genomic Prediction -- 5.4 Illustrative Examples of the Univariate LMM -- 5.5 Multi-trait Genomic Linear Mixed-Effects Models -- 5.6 Final Comments -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4 -- Appendix 5 -- Appendix 6 -- Appendix 7 -- References. Chapter 6: Bayesian Genomic Linear Regression -- 6.1 Bayes Theorem and Bayesian Linear Regression -- 6.2 Bayesian Genome-Based Ridge Regression -- 6.3 Bayesian GBLUP Genomic Model -- 6.4 Genomic-Enabled Prediction BayesA Model -- 6.5 Genomic-Enabled Prediction BayesB and BayesC Models -- 6.6 Genomic-Enabled Prediction Bayesian Lasso Model -- 6.7 Extended Predictor in Bayesian Genomic Regression Models -- 6.8 Bayesian Genomic Multi-trait Linear Regression Model -- 6.8.1 Genomic Multi-trait Linear Model -- 6.9 Bayesian Genomic Multi-trait and Multi-environment Model (BMTME) -- Appendix 1 -- Appendix 2: Setting Hyperparameters for the Prior Distributions of the BRR Model -- Appendix 3: R Code Example 1 -- Appendix 4: R Code Example 2 -- Appendix 5 -- R Code Example 3 -- R Code for Example 4 -- References -- Chapter 7: Bayesian and Classical Prediction Models for Categorical and Count Data -- 7.1 Introduction -- 7.2 Bayesian Ordinal Regression Model -- 7.2.1 Illustrative Examples -- 7.3 Ordinal Logistic Regression -- 7.4 Penalized Multinomial Logistic Regression -- 7.4.1 Illustrative Examples for Multinomial Penalized Logistic Regression -- 7.5 Penalized Poisson Regression -- 7.6 Final Comments -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4 (Example 4) -- Appendix 5 -- Appendix 6 -- References -- Chapter 8: Reproducing Kernel Hilbert Spaces Regression and Classification Methods -- 8.1 The Reproducing Kernel Hilbert Spaces (RKHS) -- 8.2 Generalized Kernel Model -- 8.2.1 Parameter Estimation Under the Frequentist Paradigm -- 8.2.2 Kernels -- 8.2.3 Kernel Trick -- 8.2.4 Popular Kernel Functions -- 8.2.5 A Two Separate Step Process for Building Kernel Machines -- 8.3 Kernel Methods for Gaussian Response Variables -- 8.4 Kernel Methods for Binary Response Variables -- 8.5 Kernel Methods for Categorical Response Variables. 8.6 The Linear Mixed Model with Kernels -- 8.7 Hyperparameter Tuning for Building the Kernels -- 8.8 Bayesian Kernel Methods -- 8.8.1 Extended Predictor Under the Bayesian Kernel BLUP -- 8.8.2 Extended Predictor Under the Bayesian Kernel BLUP with a Binary Response Variable -- 8.8.3 Extended Predictor Under the Bayesian Kernel BLUP with a Categorical Response Variable -- 8.9 Multi-trait Bayesian Kernel -- 8.10 Kernel Compression Methods -- 8.10.1 Extended Predictor Under the Approximate Kernel Method -- 8.11 Final Comments -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4 -- Appendix 5 -- Appendix 6 -- Appendix 7 -- Appendix 8 -- Appendix 9 -- Appendix 10 -- Appendix 11 -- References -- Chapter 9: Support Vector Machines and Support Vector Regression -- 9.1 Introduction to Support Vector Machine -- 9.2 Hyperplane -- 9.3 Maximum Margin Classifier -- 9.3.1 Derivation of the Maximum Margin Classifier -- 9.3.2 Wolfe Dual -- 9.4 Derivation of the Support Vector Classifier -- 9.5 Support Vector Machine -- 9.5.1 One-Versus-One Classification -- 9.5.2 One-Versus-All Classification -- 9.6 Support Vector Regression -- Appendix 1 -- Appendix 2 -- Appendix 3 -- References -- Chapter 10: Fundamentals of Artificial Neural Networks and Deep Learning -- 10.1 The Inspiration for the Neural Network Model -- 10.2 The Building Blocks of Artificial Neural Networks -- 10.3 Activation Functions -- 10.3.1 Linear -- 10.3.2 Rectifier Linear Unit (ReLU) -- 10.3.3 Leaky ReLU -- 10.3.4 Sigmoid -- 10.3.5 Softmax -- 10.3.6 Tanh -- 10.4 The Universal Approximation Theorem -- 10.5 Artificial Neural Network Topologies -- 10.6 Successful Applications of ANN and DL -- 10.7 Loss Functions -- 10.7.1 Loss Functions for Continuous Outcomes -- 10.7.2 Loss Functions for Binary and Ordinal Outcomes -- 10.7.3 Regularized Loss Functions -- 10.7.4 Early Stopping Method of Training. 10.8 The King Algorithm for Training Artificial Neural Networks: Backpropagation -- 10.8.1 Backpropagation Algorithm: Online Version -- 10.8.1.1 Feedforward Part -- 10.8.1.2 Backpropagation Part -- 10.8.2 Illustrative Example 10.1: A Hand Computation -- 10.8.3 Illustrative Example 10.2-By Hand Computation -- References -- Chapter 11: Artificial Neural Networks and Deep Learning for Genomic Prediction of Continuous Outcomes -- 11.1 Hyperparameters to Be Tuned in ANN and DL -- 11.1.1 Network Topology -- 11.1.2 Activation Functions -- 11.1.3 Loss Function -- 11.1.4 Number of Hidden Layers -- 11.1.5 Number of Neurons in Each Layer -- 11.1.6 Regularization Type -- 11.1.7 Learning Rate -- 11.1.8 Number of Epochs and Number of Batches -- 11.1.9 Normalization Scheme for Input Data -- 11.2 Popular DL Frameworks -- 11.3 Optimizers -- 11.4 Illustrative Examples -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4 -- Appendix 5 -- References -- Chapter 12: Artificial Neural Networks and Deep Learning for Genomic Prediction of Binary, Ordinal, and Mixed Outcomes -- 12.1 Training DNN with Binary Outcomes -- 12.2 Training DNN with Categorical (Ordinal) Outcomes -- 12.3 Training DNN with Count Outcomes -- 12.4 Training DNN with Multivariate Outcomes -- 12.4.1 DNN with Multivariate Continuous Outcomes -- 12.4.2 DNN with Multivariate Binary Outcomes -- 12.4.3 DNN with Multivariate Ordinal Outcomes -- 12.4.4 DNN with Multivariate Count Outcomes -- 12.4.5 DNN with Multivariate Mixed Outcomes -- Appendix 1 -- Appendix 2 -- Appendix 3 -- Appendix 4 -- Appendix 5 -- References -- Chapter 13: Convolutional Neural Networks -- 13.1 The Importance of Convolutional Neural Networks -- 13.2 Tensors -- 13.3 Convolution -- 13.4 Pooling -- 13.5 Convolutional Operation for 1D Tensor for Sequence Data -- 13.6 Motivation of CNN. 13.7 Why Are CNNs Preferred over Feedforward Deep Neural Networks for Processing Images?. |
| Record Nr. | UNINA-9910522999103321 |
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Montesinos López Osval Antonio
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| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Personalized deeper learning : blueprints for teaching complex cognitive, social-emotional, and digital skills / / James A. Bellanca
| Personalized deeper learning : blueprints for teaching complex cognitive, social-emotional, and digital skills / / James A. Bellanca |
| Autore | Bellanca James A. <1937-> |
| Pubbl/distr/stampa | Bloomington, Indiana : , : Solution Tree Press, , [2021] |
| Descrizione fisica | 1 online resource (xi, 258 pages) : illustrations |
| Disciplina | 371.394 |
| Collana | Gale eBooks |
| Soggetto topico |
Individualized instruction
Self-managed learning Machine learning |
| Soggetto non controllato | Deep learning |
| ISBN | 1-951075-42-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | chapter 1. Personalized learning plans -- chapter 2. Engagement and trust -- chapter 3. Outcome-driven instruction and assessment -- chapter 4. Student agency -- chapter 5. Skill transfer -- chapter 6. The complex cognitive skill set -- chapter 7. The social-emotional skill set -- chapter 8. The digital skill set. |
| Record Nr. | UNINA-9910795397203321 |
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Bellanca James A. <1937->
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| Bloomington, Indiana : , : Solution Tree Press, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Personalized deeper learning : blueprints for teaching complex cognitive, social-emotional, and digital skills / / James A. Bellanca
| Personalized deeper learning : blueprints for teaching complex cognitive, social-emotional, and digital skills / / James A. Bellanca |
| Autore | Bellanca James A. <1937-> |
| Pubbl/distr/stampa | Bloomington, Indiana : , : Solution Tree Press, , [2021] |
| Descrizione fisica | 1 online resource (xi, 258 pages) : illustrations |
| Disciplina | 371.394 |
| Collana | Gale eBooks |
| Soggetto topico |
Individualized instruction
Self-managed learning Machine learning |
| Soggetto non controllato | Deep learning |
| ISBN | 1-951075-42-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | chapter 1. Personalized learning plans -- chapter 2. Engagement and trust -- chapter 3. Outcome-driven instruction and assessment -- chapter 4. Student agency -- chapter 5. Skill transfer -- chapter 6. The complex cognitive skill set -- chapter 7. The social-emotional skill set -- chapter 8. The digital skill set. |
| Record Nr. | UNINA-9910813819503321 |
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Bellanca James A. <1937->
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| Bloomington, Indiana : , : Solution Tree Press, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Quantitative PET and SPECT
| Quantitative PET and SPECT |
| Autore | de Geus-Oei Lioe-Fee |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (212 p.) |
| Soggetto topico |
Medicine
Clinical & internal medicine |
| Soggetto non controllato |
PET
SPECT PET/CT SPECT/CT Absolute quantification Quantitative accuracy Dynamic PET Phantoms Repeatability Tumor delineation Prognosis Dosimetry Radiomics Artificial intelligence Deep learning Imaging biomarkers Tumor segmentation Harmonization |
| ISBN | 3-0365-5616-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637780503321 |
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de Geus-Oei Lioe-Fee
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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