top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Beginning SharePoint Communication Sites : Understanding and Managing Modern SharePoint Online / / by Charles David Waghmare
Beginning SharePoint Communication Sites : Understanding and Managing Modern SharePoint Online / / by Charles David Waghmare
Autore Waghmare Charles David
Edizione [2nd ed. 2023.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023
Descrizione fisica 1 online resource (253 pages)
Disciplina 943.005
Collana Office Essentials collection
ISBN 1-4842-8960-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: An Introduction: SharePoint Communication Sites -- Chapter 2: Effectively Communicate and Collaborate Using SharePoint Communication Sites -- Chapter 3: Build Collaborative Experiences for End Users -- Chapter 4: Creating Digital Intranets -- Chapter 5: Information Management Compliance and Governance Using SharePoint Communication Sites -- Chapter 6: Use of SharePoint Communication sites for Project Management -- Chapter 7: Integrating SharePoint Communication Sites with the Microsoft 365 Products -- Chapter 8: Create New Horizons of Digital Communication.
Record Nr. UNINA-9910739471203321
Waghmare Charles David  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational intelligence methods for bioinformatics and biostatistics : 17th international meeting, CIBB 2021, virtual event, November 15-17, 2021 : revised selected papers / / edited by Davide Chicco [and seven others]
Computational intelligence methods for bioinformatics and biostatistics : 17th international meeting, CIBB 2021, virtual event, November 15-17, 2021 : revised selected papers / / edited by Davide Chicco [and seven others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (269 pages)
Disciplina 943.005
Collana Lecture Notes in Computer Science
Soggetto topico Data mining
ISBN 3-031-20837-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to Chemical AI -- 1 Can ``Synthetic Cell'' Biotechnology Become a Useful Platform for Chemical AI? -- 2 Scientific Background - What Exactly are SCs? -- 2.1 Computer Gestalt ch1varelabook vs. Autopoiesis & -- Autonomy -- 3 Bio-Chemical Neural Network -- 3.1 Selected Examples of Potentially Interesting CNNs for SCs -- 4 Concepts and Experimental Perspectives on Chemical Neural Networks and Synthetic Cells -- 4.1 Machine Learning -- 4.2 Meaning -- 4.3 Embodiment -- References -- Development of Bayesian Network for Multiple Sclerosis Risk Factor Interaction Analysis -- 1 Introduction -- 2 Previous Work -- 2.1 Artificial Intelligence (AI) and Machine Learning (ML) in MS Research -- 2.2 Alignment with Epidemiology -- 3 BN Development -- 3.1 Relevant Risk Factors -- 3.2 Structure -- 3.3 Measurements -- 4 Results and Discussion -- 4.1 Interaction, Sufficiency, Necessity -- 4.2 Equivalent Odds Ratios -- 5 Conclusions -- References -- Real-Time Automatic Plankton Detection, Tracking and Classification on Raw Hologram -- 1 Introduction -- 2 Materials and Methods -- 2.1 Hologram Formation -- 2.2 Dataset -- 2.3 Object Detection Models and Tracking -- 2.4 Metrics -- 3 Results -- 3.1 Detection Performances -- 3.2 Tracking Performances -- 4 Conclusion and Perspectives -- References -- The First in-silico Model of Leg Movement Activity During Sleep -- 1 Scientific Background -- 2 Materials and Methods -- 2.1 The LMA Model -- 2.2 Model Calibration -- 3 Results and Discussion -- 4 Conclusion -- References -- Transfer Learning and Magnetic Resonance Imaging Techniques for the Deep Neural Network-Based Diagnosis of Early Cognitive Decline and Dementia -- 1 Introduction -- 2 Deep Learning for Medical Diagnosis.
2.1 Convolutional Neural Network for Image Classification -- 2.2 Pretrained Convolutional Neural Network -- 3 Imaging Data Repositories -- 4 Proposed Transfer Learning Pipeline -- 5 Experiments and Results -- 6 Discussion and Conclusions -- References -- Improving Bacterial sRNA Identification By Combining Genomic Context and Sequence-Derived Features -- 1 Background -- 2 Materials and Methods -- 2.1 Data -- 2.2 Feature Sets -- 2.3 Model Generation -- 2.4 Comparative Assessment -- 3 Results and Discussion -- 3.1 Model Selection -- 3.2 Variable Importance Analysis -- 3.3 Comparative Assessment -- 4 Conclusion -- References -- High-Dimensional Multi-trait GWAS By Reverse Prediction of Genotypes Using Machine Learning Methods -- 1 Background -- 2 Methods -- 2.1 Reverse Genotype and Trans-eQTL Prediction -- 2.2 Datasets -- 2.3 Experimental Settings -- 2.4 Code and Supplementary Information -- 3 Results -- 3.1 Reverse Genotype Prediction and Trans-EQTL Analysis in Simulated Data -- 3.2 Reverse Genotype Prediction and Trans-EQTL Analysis in Yeast -- 4 Discussion -- References -- A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity -- 1 Background -- 2 Material and Methods -- 2.1 Datasets -- 2.2 Model -- 2.3 Method -- 2.4 Prediction of Novel Associations -- 2.5 Prediction of the Whole Drug Profile for a New Cell Line -- 3 Results -- 3.1 Prediction of Novel Associations -- 3.2 Prediction of the Whole Drug Profile for a New Cell Line -- 4 Discussion and Concluding Remarks -- References -- A Rule-Based Approach for Generating Synthetic Biological Pathways -- 1 Scientific Background -- 1.1 Introduction -- 1.2 Related Work -- 2 Materials and Methods -- 2.1 Synthetic Data Generation -- 2.2 Implementation Details -- 3 Experimental Setup -- 3.1 Model -- 3.2 Data -- 4 Results -- 4.1 Synthetic Data for Mixed-Batches.
4.2 When to Use Synthetic Data -- 4.3 Generalizing to New Tasks -- 4.4 Computational Time -- 5 Conclusion -- References -- Machine Learning Classifiers Based on Dimensionality Reduction Techniques for the Early Diagnosis of Alzheimer's Disease Using Magnetic Resonance Imaging and Positron Emission Tomography Brain Data -- 1 Scientific Background -- 2 Methods -- 2.1 Dataset Description -- 2.2 Image Preprocessing -- 2.3 Feature Extraction -- 2.4 Dimensionality Reduction Techniques -- 2.5 Machine Learning Classifiers -- 2.6 Description of resampling Method and Performance Metrics -- 3 Result and Discussion -- 4 Conclusion -- References -- Text Mining Enhancements for Image Recognition of Gene Names and Gene Relations -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Dataset -- 3.2 OCR Tool -- 3.3 Gene Name Enhancements -- 3.4 Gene Relation Enhancements -- 4 Results -- 4.1 Gene Name Enhancement Results -- 4.2 Gene Relation Enhancement Results -- 4.3 Use Cases -- 5 Discussion -- 6 Conclusion -- References -- Sentence Classification to Detect Tables for Helping Extraction of Regulatory Interactions in Bacteria -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Set -- 2.2 Feature Extraction and Vectorization -- 2.3 Supervised Learning -- 3 Results -- 3.1 Best Model -- 3.2 Best Features -- 4 Conclusion -- References -- RF-Isolation: A Novel Representation of Structural Connectivity Networks for Multiple Sclerosis Classification -- 1 Introduction -- 2 Materials and Methods -- 2.1 Study Population -- 2.2 MRI Acquisition and Processing -- 2.3 RF-Isolation Extraction -- 2.4 Classification Analysis -- 3 Results -- 3.1 Analysis of the MS-ProxIF Model -- 3.2 Comparison to Standard Network Measures -- 4 Conclusion -- References -- Summarizing Global SARS-CoV-2 Geographical Spread by Phylogenetic Multitype Branching Models -- 1 Introduction.
2 Data and Methods -- 3 Results and Discussion -- 4 Conclusions -- References -- Explainable AI Models for COVID-19 Diagnosis Using CT-Scan Images and Clinical Data -- 1 Scientific Background -- 2 Materials and Methods -- 2.1 Datasets Description and Preprocessing -- 2.2 Models Design -- 2.3 Explainability and Interpretability -- 3 Results -- 3.1 Deep CNN for Image-Data Experimentation Results -- 3.2 Classifiers for Bio-Data Experimentation Results -- 3.3 Comparison Study -- 3.4 Explainability/Interpretability Results -- 4 Conclusion -- References -- The Need of Standardised Metadata to Encode Causal Relationships: Towards Safer Data-Driven Machine Learning Biological Solutions -- 1 Introduction -- 2 Considerations for the Development and Reporting of ML Solutions -- 2.1 The Desirable Properties of ML Models -- 2.2 Current Limitations in Biomedical ML Solutions -- 2.3 Origin and Error Types -- 2.4 Limitations of the Current Evaluation System -- 2.5 Helping Methodological Tools -- 3 Relevance of Induced Bias in Biological Studies for ML Analysis -- 4 An Approach to Overcome the Limitations: Accompanying Metadata with Causal Information -- 4.1 Incorporating Causal Information -- 5 Conclusion -- References -- Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences -- 1 Introduction -- 1.1 Coronaviridae -- 1.2 Recurrent Neural Networks -- 2 Methods -- 2.1 Recurrent Neural Network (RNN) Architecture -- 2.2 Coronavirus Training Set -- 3 Results -- 3.1 Characteristics of DL Simulated Spike Proteins -- 4 Conclusions -- References -- Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data -- 1 Introduction -- 2 State-of-the-Art Dimensionality Reduction Techniques -- 3 Experimental Analysis -- 3.1 Dataset Description and Preprocessing -- 3.2 Results -- 3.3 Discussion -- 4 Conclusions.
References -- Soft Brain Ageing Indicators Based on Light-Weight LeNet-Like Neural Networks and Localized 2D Brain Age Biomarkers -- 1 Introduction -- 2 Methods -- 2.1 Data Extraction, Preprocessing and Labeling -- 2.2 2D-CNN Models for Brain Age Classification and Regression -- 2.3 2D Brain-Age Biomarkers Model Explanation -- 3 Results -- 3.1 Classification Results -- 3.2 Linear Regression Results -- 4 Discussion -- 5 Architectural, Qualitative and Performance Comparisons -- 6 Conclusion -- References -- Author Index.
Record Nr. UNINA-9910632491003321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational intelligence methods for bioinformatics and biostatistics : 17th international meeting, CIBB 2021, virtual event, November 15-17, 2021 : revised selected papers / / edited by Davide Chicco [and seven others]
Computational intelligence methods for bioinformatics and biostatistics : 17th international meeting, CIBB 2021, virtual event, November 15-17, 2021 : revised selected papers / / edited by Davide Chicco [and seven others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (269 pages)
Disciplina 943.005
Collana Lecture Notes in Computer Science
Soggetto topico Data mining
ISBN 3-031-20837-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to Chemical AI -- 1 Can ``Synthetic Cell'' Biotechnology Become a Useful Platform for Chemical AI? -- 2 Scientific Background - What Exactly are SCs? -- 2.1 Computer Gestalt ch1varelabook vs. Autopoiesis & -- Autonomy -- 3 Bio-Chemical Neural Network -- 3.1 Selected Examples of Potentially Interesting CNNs for SCs -- 4 Concepts and Experimental Perspectives on Chemical Neural Networks and Synthetic Cells -- 4.1 Machine Learning -- 4.2 Meaning -- 4.3 Embodiment -- References -- Development of Bayesian Network for Multiple Sclerosis Risk Factor Interaction Analysis -- 1 Introduction -- 2 Previous Work -- 2.1 Artificial Intelligence (AI) and Machine Learning (ML) in MS Research -- 2.2 Alignment with Epidemiology -- 3 BN Development -- 3.1 Relevant Risk Factors -- 3.2 Structure -- 3.3 Measurements -- 4 Results and Discussion -- 4.1 Interaction, Sufficiency, Necessity -- 4.2 Equivalent Odds Ratios -- 5 Conclusions -- References -- Real-Time Automatic Plankton Detection, Tracking and Classification on Raw Hologram -- 1 Introduction -- 2 Materials and Methods -- 2.1 Hologram Formation -- 2.2 Dataset -- 2.3 Object Detection Models and Tracking -- 2.4 Metrics -- 3 Results -- 3.1 Detection Performances -- 3.2 Tracking Performances -- 4 Conclusion and Perspectives -- References -- The First in-silico Model of Leg Movement Activity During Sleep -- 1 Scientific Background -- 2 Materials and Methods -- 2.1 The LMA Model -- 2.2 Model Calibration -- 3 Results and Discussion -- 4 Conclusion -- References -- Transfer Learning and Magnetic Resonance Imaging Techniques for the Deep Neural Network-Based Diagnosis of Early Cognitive Decline and Dementia -- 1 Introduction -- 2 Deep Learning for Medical Diagnosis.
2.1 Convolutional Neural Network for Image Classification -- 2.2 Pretrained Convolutional Neural Network -- 3 Imaging Data Repositories -- 4 Proposed Transfer Learning Pipeline -- 5 Experiments and Results -- 6 Discussion and Conclusions -- References -- Improving Bacterial sRNA Identification By Combining Genomic Context and Sequence-Derived Features -- 1 Background -- 2 Materials and Methods -- 2.1 Data -- 2.2 Feature Sets -- 2.3 Model Generation -- 2.4 Comparative Assessment -- 3 Results and Discussion -- 3.1 Model Selection -- 3.2 Variable Importance Analysis -- 3.3 Comparative Assessment -- 4 Conclusion -- References -- High-Dimensional Multi-trait GWAS By Reverse Prediction of Genotypes Using Machine Learning Methods -- 1 Background -- 2 Methods -- 2.1 Reverse Genotype and Trans-eQTL Prediction -- 2.2 Datasets -- 2.3 Experimental Settings -- 2.4 Code and Supplementary Information -- 3 Results -- 3.1 Reverse Genotype Prediction and Trans-EQTL Analysis in Simulated Data -- 3.2 Reverse Genotype Prediction and Trans-EQTL Analysis in Yeast -- 4 Discussion -- References -- A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity -- 1 Background -- 2 Material and Methods -- 2.1 Datasets -- 2.2 Model -- 2.3 Method -- 2.4 Prediction of Novel Associations -- 2.5 Prediction of the Whole Drug Profile for a New Cell Line -- 3 Results -- 3.1 Prediction of Novel Associations -- 3.2 Prediction of the Whole Drug Profile for a New Cell Line -- 4 Discussion and Concluding Remarks -- References -- A Rule-Based Approach for Generating Synthetic Biological Pathways -- 1 Scientific Background -- 1.1 Introduction -- 1.2 Related Work -- 2 Materials and Methods -- 2.1 Synthetic Data Generation -- 2.2 Implementation Details -- 3 Experimental Setup -- 3.1 Model -- 3.2 Data -- 4 Results -- 4.1 Synthetic Data for Mixed-Batches.
4.2 When to Use Synthetic Data -- 4.3 Generalizing to New Tasks -- 4.4 Computational Time -- 5 Conclusion -- References -- Machine Learning Classifiers Based on Dimensionality Reduction Techniques for the Early Diagnosis of Alzheimer's Disease Using Magnetic Resonance Imaging and Positron Emission Tomography Brain Data -- 1 Scientific Background -- 2 Methods -- 2.1 Dataset Description -- 2.2 Image Preprocessing -- 2.3 Feature Extraction -- 2.4 Dimensionality Reduction Techniques -- 2.5 Machine Learning Classifiers -- 2.6 Description of resampling Method and Performance Metrics -- 3 Result and Discussion -- 4 Conclusion -- References -- Text Mining Enhancements for Image Recognition of Gene Names and Gene Relations -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Dataset -- 3.2 OCR Tool -- 3.3 Gene Name Enhancements -- 3.4 Gene Relation Enhancements -- 4 Results -- 4.1 Gene Name Enhancement Results -- 4.2 Gene Relation Enhancement Results -- 4.3 Use Cases -- 5 Discussion -- 6 Conclusion -- References -- Sentence Classification to Detect Tables for Helping Extraction of Regulatory Interactions in Bacteria -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Set -- 2.2 Feature Extraction and Vectorization -- 2.3 Supervised Learning -- 3 Results -- 3.1 Best Model -- 3.2 Best Features -- 4 Conclusion -- References -- RF-Isolation: A Novel Representation of Structural Connectivity Networks for Multiple Sclerosis Classification -- 1 Introduction -- 2 Materials and Methods -- 2.1 Study Population -- 2.2 MRI Acquisition and Processing -- 2.3 RF-Isolation Extraction -- 2.4 Classification Analysis -- 3 Results -- 3.1 Analysis of the MS-ProxIF Model -- 3.2 Comparison to Standard Network Measures -- 4 Conclusion -- References -- Summarizing Global SARS-CoV-2 Geographical Spread by Phylogenetic Multitype Branching Models -- 1 Introduction.
2 Data and Methods -- 3 Results and Discussion -- 4 Conclusions -- References -- Explainable AI Models for COVID-19 Diagnosis Using CT-Scan Images and Clinical Data -- 1 Scientific Background -- 2 Materials and Methods -- 2.1 Datasets Description and Preprocessing -- 2.2 Models Design -- 2.3 Explainability and Interpretability -- 3 Results -- 3.1 Deep CNN for Image-Data Experimentation Results -- 3.2 Classifiers for Bio-Data Experimentation Results -- 3.3 Comparison Study -- 3.4 Explainability/Interpretability Results -- 4 Conclusion -- References -- The Need of Standardised Metadata to Encode Causal Relationships: Towards Safer Data-Driven Machine Learning Biological Solutions -- 1 Introduction -- 2 Considerations for the Development and Reporting of ML Solutions -- 2.1 The Desirable Properties of ML Models -- 2.2 Current Limitations in Biomedical ML Solutions -- 2.3 Origin and Error Types -- 2.4 Limitations of the Current Evaluation System -- 2.5 Helping Methodological Tools -- 3 Relevance of Induced Bias in Biological Studies for ML Analysis -- 4 An Approach to Overcome the Limitations: Accompanying Metadata with Causal Information -- 4.1 Incorporating Causal Information -- 5 Conclusion -- References -- Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences -- 1 Introduction -- 1.1 Coronaviridae -- 1.2 Recurrent Neural Networks -- 2 Methods -- 2.1 Recurrent Neural Network (RNN) Architecture -- 2.2 Coronavirus Training Set -- 3 Results -- 3.1 Characteristics of DL Simulated Spike Proteins -- 4 Conclusions -- References -- Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data -- 1 Introduction -- 2 State-of-the-Art Dimensionality Reduction Techniques -- 3 Experimental Analysis -- 3.1 Dataset Description and Preprocessing -- 3.2 Results -- 3.3 Discussion -- 4 Conclusions.
References -- Soft Brain Ageing Indicators Based on Light-Weight LeNet-Like Neural Networks and Localized 2D Brain Age Biomarkers -- 1 Introduction -- 2 Methods -- 2.1 Data Extraction, Preprocessing and Labeling -- 2.2 2D-CNN Models for Brain Age Classification and Regression -- 2.3 2D Brain-Age Biomarkers Model Explanation -- 3 Results -- 3.1 Classification Results -- 3.2 Linear Regression Results -- 4 Discussion -- 5 Architectural, Qualitative and Performance Comparisons -- 6 Conclusion -- References -- Author Index.
Record Nr. UNISA-996500061403316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Dance, Place, and Poetics : Site-specific Performance as a Portal to Knowing / / by Celeste Nazeli Snowber
Dance, Place, and Poetics : Site-specific Performance as a Portal to Knowing / / by Celeste Nazeli Snowber
Autore Snowber Celeste Nazeli
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Palgrave Macmillan, , 2022
Descrizione fisica 1 online resource (130 pages)
Disciplina 943.005
792.801
Collana Palgrave Studies in Movement across Education, the Arts and the Social Sciences
Soggetto topico Art - Study and teaching
Site-specific theater
Dance
Poetry
Ecology
Creativity and Arts Education
Site-Specific Performance
Poetry and Poetics
Dansa
Literatura
Poesia
Soggetto genere / forma Llibres electrònics
ISBN 9783031097164
9783031097157
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Coming to our Senses: The Body’s Capacity for Creation -- Chapter 2. Place, Ecology and the Poetic -- Chapter 3. Water, Tides and Heron Lessons -- Chapter 4. Lessons from a Botanical Garden – Fall and Winter -- Chapter 5. Lessons from a Botanical Garden – Spring and Summer -- Chapter 6. Dance in COVID times: Site-Specific Art in the In-between -- Chapter 7. The Body as Portal. .
Record Nr. UNINA-9910632478403321
Snowber Celeste Nazeli  
Cham : , : Springer International Publishing : , : Imprint : Palgrave Macmillan, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining : 19th Australasian conference on data mining, AusDM, Brisbane, QLD, Australia, December 14-15, 2021 : proceedings / / edited by Yue Xu [and five others]
Data mining : 19th Australasian conference on data mining, AusDM, Brisbane, QLD, Australia, December 14-15, 2021 : proceedings / / edited by Yue Xu [and five others]
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (244 pages)
Disciplina 943.005
Collana Communications in Computer and Information Science
Soggetto topico Data mining
ISBN 981-16-8531-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996464430003316
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Data mining : 19th Australasian conference on data mining, AusDM, Brisbane, QLD, Australia, December 14-15, 2021 : proceedings / / edited by Yue Xu [and five others]
Data mining : 19th Australasian conference on data mining, AusDM, Brisbane, QLD, Australia, December 14-15, 2021 : proceedings / / edited by Yue Xu [and five others]
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (244 pages)
Disciplina 943.005
Collana Communications in Computer and Information Science
Soggetto topico Data mining
ISBN 981-16-8531-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910513692403321
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data science revealed : with feature engineering, data visualization, pipeline development, and hyperparameter tuning / / Tshepo Chris Nokeri
Data science revealed : with feature engineering, data visualization, pipeline development, and hyperparameter tuning / / Tshepo Chris Nokeri
Autore Nokeri Tshepo Chris
Edizione [1st ed. 2021.]
Pubbl/distr/stampa California : , : Apress L. P., , [2021]
Descrizione fisica 1 online resource (XX, 252 p. 95 illus.)
Disciplina 943.005
Soggetto topico Data mining
ISBN 1-4842-6870-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: An Introduction to Simple Linear Regression Analysis -- Chapter 2: Advanced Parametric Methods -- Chapter 3: Time Series Analysis -- Chapter 4: High-Quality Time Series Analysis -- Chapter 5: Logistic Regression Analysis -- Chapter 6: Dimension Reduction and Multivariate Analysis Using Linear Discriminant Analysis -- Chapter 7: Finding Hyperplanes Using Support Vectors -- Chapter 8: Classification Using Decision Trees -- Chapter 9: Back to the Classics -- Chapter 10: Cluster Analysis -- Chapter 11: Survival Analysis -- Chapter 12: Neural Networks -- Chapter 13: Machine Learning Using H2O.
Record Nr. UNINA-9910483608103321
Nokeri Tshepo Chris  
California : , : Apress L. P., , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Database systems for advanced applications : 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, proceedings, part IV / / edited by Xin Wang [and seven others]
Database systems for advanced applications : 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, proceedings, part IV / / edited by Xin Wang [and seven others]
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (XXVII, 756 p. 241 illus., 229 illus. in color.)
Disciplina 943.005
Collana Lecture Notes in Computer Science
Soggetto topico Database management
Databases
ISBN 3-031-30678-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Query Processing -- Data Management -- Graph and Network -- Knowledge Graph -- Recommendation -- Privacy computing -- Text Processing -- Information Retrieval -- Systems and Optimization -- Spatial Data -- Time Series Data -- Applications of Machine learning.
Record Nr. UNISA-996525669703316
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Database Systems for Advanced Applications : 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV / / edited by Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao, Hongzhi Yin
Database Systems for Advanced Applications : 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV / / edited by Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao, Hongzhi Yin
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (XXVII, 756 p. 241 illus., 229 illus. in color.)
Disciplina 943.005
Collana Lecture Notes in Computer Science
Soggetto topico Machine learning
Machine Learning
ISBN 3-031-30678-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Query Processing -- Data Management -- Graph and Network -- Knowledge Graph -- Recommendation -- Privacy computing -- Text Processing -- Information Retrieval -- Systems and Optimization -- Spatial Data -- Time Series Data -- Applications of Machine learning.
Record Nr. UNINA-9910698644103321
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Database Systems for Advanced Applications : DASFAA 2022 International Workshops : BDMS, BDQM, GDMA, IWBT, MAQTDS, and PMBD, Virtual event, April 11-14, 2022, proceedings / / edited by Uday Kiran Rage, Vikram Goyal, P. Krishna Reddy
Database Systems for Advanced Applications : DASFAA 2022 International Workshops : BDMS, BDQM, GDMA, IWBT, MAQTDS, and PMBD, Virtual event, April 11-14, 2022, proceedings / / edited by Uday Kiran Rage, Vikram Goyal, P. Krishna Reddy
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (446 pages)
Disciplina 943.005
Collana Lecture Notes in Computer Science
Soggetto topico Database management
ISBN 3-031-11217-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- PMDB -- An Algorithm for Mining Fixed-Length High Utility Itemsets -- 1 Introduction -- 2 Background -- 2.1 Problem Description and Definitions -- 2.2 Related Work -- 3 Algorithm HUIKM -- 3.1 Create a Tree and a Header Table -- 3.2 Ming HUIK from a Tree -- 4 Experimental Results -- 5 Conclusion -- References -- A Novel Method to Create Synthetic Samples with Autoencoder Multi-layer Extreme Learning Machine -- 1 Introduction -- 2 SMOTE Method -- 3 Proposed AE-MLELM-SynMin Method -- 3.1 Training AE-MLELM -- 3.2 Conducting Crossover and Mutation Operations -- 3.3 Creating Synthetic Samples -- 4 Experiments -- 4.1 Experiment Setting -- 4.2 Information Amount Analysis of SMOTE and AE-MLELM-SynMin -- 4.3 Comparison Among AE-MLELM-SynMin, SMOTE, Borderline-SMOTE, Random-SMOTE, and SMOTE-IPF -- 5 Conclusion -- References -- Pattern Mining: Current Challenges and Opportunities -- 1 Introduction -- 2 C1: Mining Patterns in Complex Graph Data -- 3 C2: Targeted Pattern Mining -- 4 C3: Repetitive Sequential Pattern Mining -- 5 C4: Incremental, Stream and Interactive Pattern Mining -- 6 C5: Heuristic Pattern Mining -- 7 C6: Mining Interesting Patterns -- 8 Conclusion -- References -- Why Not to Trust Big Data: Discussing Statistical Paradoxes -- 1 Introduction -- 2 Why Not to Trust on Data Science, AI, ML and Big Data -- 3 Statistical Paradoxes -- 3.1 Berkson Paradox -- 3.2 Yule-Simpson's Paradox -- 4 Existence of Simpson's Paradox in Big Data -- 4.1 Datasets -- 5 Analysis Simpson's Paradox in Real Life: A Case Study -- 5.1 The Dataset -- 5.2 Data Analysis -- 6 Discussion -- 7 Conclusion -- References -- Localized Metric Learning for Large Multi-class Extremely Imbalanced Face Database -- 1 Introduction -- 2 Metric Learning as an Antidote for the Class Imbalance Problem.
3 Localized Metric Learning - The Proposed Approach -- 3.1 Division of Dataset Into Subsets -- 3.2 Localized Metric Learning -- 3.3 Algorithm -- 4 Results and Discussions -- 5 Conclusions -- References -- Top-k Dominating Queries on Incremental Datasets -- 1 Introduction -- 2 Literature Review -- 2.1 Top-k Dominance Query -- 2.2 Dynamic Update of Data Mining -- 3 Query Base Preparation -- 4 Algorithm Description -- 5 Experiment and Analysis -- 6 Conclusion -- References -- IWBT -- Collaborative Blockchain Based Distributed Denial of Service Attack Mitigation Approach with IP Reputation System -- 1 Introduction -- 2 Theoretical Background -- 2.1 Blockchain Technology - Brief -- 2.2 Overview of DDoS Mitigation Approaches and Techniques -- 2.3 IP Reputation Scheme -- 3 Problem Description -- 4 Related Work -- 5 Research Questions -- 6 Approach and Next Step -- 6.1 Proposed Architecture -- 6.2 Integration of IP Reputation System with Proposed Architecture -- 6.3 Implementation Strategy -- 6.4 Advantages of Proposed System Over Existing Implementation -- 7 Conclusion -- References -- Model-Driven Development of Distributed Ledger Applications -- 1 Introduction -- 1.1 Running Example -- 2 Background -- 2.1 Distributed Ledger Technology and Hybrid DLT/DB Applications -- 2.2 MDD with the Interaction Flow Modeling Language -- 3 Development of Hybrid DLT/DB Applications -- 3.1 Requirement Specification -- 3.2 Data Design -- 3.3 Interface Design -- 3.4 Operation Design -- 3.5 Architecture Design -- 4 Implementation -- 5 Related Work -- 6 Conclusion -- References -- Towards a Blockchain Solution for  Customs Duty-Related Fraud -- 1 Introduction -- 2 Related Literature -- 3 Types of Blockchains for Customs Enforcement -- 4 Hyperledger Fabric -- 4.1 Transaction Overview -- 4.2 Ensuring Transaction Order.
5 Detecting and Mitigating Customs Duty Fraud Using Fabric: Three Scenarios -- 5.1 Underreporting the Cargo Weight or Quantity -- 5.2 Relying on a Bill of Lading for Customs Audits -- 5.3 Misrepresenting the Country of Origin: Shipping Goods to an Intermediate Country to Avoid Import Tariffs -- 6 Benchmarking Scalability -- 6.1 Benchmarking Setup -- 6.2 Performance Benchmarking Results and Analysis -- 7 Other Implementation Considerations -- 8 Conclusion -- References -- Securing Cookies/Sessions Through Non-fungible Tokens -- 1 Introduction -- 2 Background -- 3 Literature Review -- 3.1 Blockchain -- 3.2 Cookies -- 4 The Proposed Model -- 4.1 Properties -- 4.2 Preliminaries -- 4.3 Model Description -- 5 Security Analysis and Discussion -- 6 Conclusions and Future Works -- References -- GDMA -- Chinese Spelling Error Detection and Correction Based on Knowledge Graph -- 1 Introduction -- 2 Model -- 2.1 Knowledge Network -- 2.2 Detection and Correction Network -- 2.3 Filter -- 2.4 Loss -- 3 Experiments and Evaluation -- 3.1 Datasets and Baseline -- 3.2 Evaluation -- 3.3 Results -- 4 Related Work -- 5 Conclusion -- References -- Construction and Application of Event Logic Graph: A Survey -- 1 Introduction -- 2 Event Extraction -- 2.1 Methods Based on Pattern Matching -- 2.2 Methods Based on Machine Learning -- 2.3 Methods Based on Deep Learning -- 3 Event Relation Extraction -- 4 Applications -- 4.1 Detect Hot Events -- 4.2 Analyze the Event Lineage -- 4.3 Predict Future Events -- 5 Summary and Prospect -- References -- Enhancing Low-Resource Languages Question Answering with Syntactic Graph -- 1 Introduction -- 2 Syntactic Information Evolvement in mBERT -- 3 Related Work -- 4 Method -- 4.1 Syntactic Graph -- 4.2 Syntactic Graph Prediction Task -- 5 Experiment -- 5.1 Datasets and Baseline Models -- 5.2 Setup and Evaluation Metric.
5.3 Experiment Results -- 6 Analysis -- 6.1 Why Use Parallel Sentence Pairs to Train Syntax Task? -- 6.2 Why the Syntactic Graph Prediction Task Works? -- 7 Conclusions -- References -- Profile Consistency Discrimination -- 1 Introduction -- 2 Relate Work -- 2.1 Natural Language Inference -- 2.2 Role Consistency -- 3 Consistency Discrimination -- 3.1 Problem Definition -- 3.2 Consistency Discriminator -- 4 Experiments Setup -- 4.1 Implementation Details and Evaluations -- 4.2 Baselines -- 5 Result -- 6 Case Study -- 7 Conclusion and Future Work -- References -- BDMS -- H-V: An Improved Coding Layout Based on Erasure Coded Storage System -- 1 Introduction -- 2 Related Work -- 3 HRS(n,k) - VRS(n',k') Encoding Method -- 4 Effect Analysis of HRS(n,k) - VRS(n',k') -- 4.1 Data Transmission Performance Analysis -- 4.2 Storage Redundancy Analysis -- 5 Conclusion -- References -- Astral: An Autoencoder-Based Model for Pedestrian Trajectory Prediction of Variable-Length -- 1 Introduction -- 2 Problem Definitions -- 3 Methodology -- 3.1 Model Framework -- 3.2 Autoencoder -- 3.3 Multi-head Attention -- 3.4 Online Model -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Evaluation of Trajectory Prediction -- 4.3 Ablation Studies -- 4.4 Case Study -- 5 Related Work -- 5.1 Crowd Interaction -- 5.2 LSTM for Sequence Prediction -- 6 Conclusion -- References -- A Survey on Spatiotemporal Data Processing Techniques in Smart Urban Rail -- 1 Introduction -- 2 Spatiotemporal Data Processing Technology -- 3 Applications of Smart Urban Rail -- 3.1 Intelligent Scheduling -- 3.2 Intelligent Operation Platform -- 3.3 Intelligent Perception -- 3.4 Intelligent Train Control -- 4 Future Work -- 5 Conclusion -- References -- Fast Vehicle Track Counting in Traffic Video -- 1 Introduction -- 2 Related Work -- 3 Preliminary and Problem Statement.
4 An Efficient Vehicle Query Counting Method -- 4.1 Adaptively Choosing Frames for Vehicle Detection -- 4.2 Vehicle Tracking Based on Location and Simple Appearance Features -- 4.3 Cascade Track Judgment and Counting -- 5 Experiments -- 5.1 Settings -- 5.2 Metrics -- 5.3 Experimental Results -- 5.4 Ablation Experiments -- 6 Conclusion -- References -- TSummary: A Traffic Summarization System Using Semantic Words -- 1 Introduction -- 2 System Overview -- 2.1 Feature Extraction -- 2.2 Preliminary Concepts -- 2.3 Structure of TSummary -- 3 Traffic Summarization -- 3.1 Periodic Feature Detection -- 3.2 Road Partitioning -- 3.3 Temporal Merge -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Evaluation Approach -- 4.3 Performance Evaluation -- 5 Related Work -- 6 Conclusions -- References -- Attention-Cooperated Reinforcement Learning for Multi-agent Path Planning -- 1 Introduction -- 2 Related Work -- 2.1 Classical Path Planning Methods -- 2.2 Learning Based Methods -- 3 Problem Formulation -- 4 Approach -- 4.1 State's Structure -- 4.2 Action Space -- 4.3 Reward Design -- 4.4 Model Architecture -- 5 Experiments -- 5.1 Experiment Setting -- 5.2 Training Details -- 5.3 Metrics -- 5.4 Baselines -- 5.5 Results -- 6 Conclusion -- References -- Big Data-Driven Stable Task Allocation in Ride-Hailing Services -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 3.1 Preliminaries and Definition -- 3.2 A Baseline Approach -- 4 Equilibrium Stable Matching and Global Distance Optimization -- 4.1 Chain Algorithm -- 4.2 The Benefit Function for Equilibrium -- 5 Experimental Study -- 6 Conclusion -- References -- Weighted Mean-Field Multi-Agent Reinforcement Learning via Reward Attribution Decomposition -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Markov Decision Process and Markov Game -- 3.2 Mean-Field Reinforcement Learning -- 4 Algorithm.
4.1 Weighted Mean-Field Approximation.
Record Nr. UNINA-9910584480803321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui