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Biblioteca

MARC Lista (tabellare)
ACM/IMS transactions on data science
ACM/IMS transactions on data science
Pubbl/distr/stampa New York, NY : , : Association for Computing Machinery, , [2020]-
Descrizione fisica 1 online resource
Disciplina 006
Soggetto topico Data mining - Statistical methods
Big data - Statistical methods
Quantitative research
Données volumineuses - Méthodes statistiques
Recherche quantitative
Soggetto genere / forma Periodicals.
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti TDS
Transactions on data science
ACM transactions on data sciencec
Record Nr. UNISA-996548964503316
New York, NY : , : Association for Computing Machinery, , [2020]-
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
ACM/IMS transactions on data science
ACM/IMS transactions on data science
Pubbl/distr/stampa New York, NY : , : Association for Computing Machinery, , [2020]-
Descrizione fisica 1 online resource
Disciplina 006
Soggetto topico Data mining - Statistical methods
Big data - Statistical methods
Quantitative research
Données volumineuses - Méthodes statistiques
Recherche quantitative
Soggetto genere / forma Periodicals.
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti TDS
Transactions on data science
ACM transactions on data sciencec
Record Nr. UNINA-9910412143603321
New York, NY : , : Association for Computing Machinery, , [2020]-
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining and data visualization / edited by C. R. Rao, E. J. Wegman, J. L. Solka
Data mining and data visualization / edited by C. R. Rao, E. J. Wegman, J. L. Solka
Pubbl/distr/stampa Amsterdam : Elsevier North Holland, 2005
Descrizione fisica xiv, 643 p. : ill. (some col.), maps ; 25 cm
Disciplina 005.74
Altri autori (Persone) Rao, Calyampudi Radhakrishna
Wegman, Edward J.
Solka, Jeffrey L.
Collana Handbook of statistics, 0169-7161 ; 24
Soggetto topico Data mining
Data mining - Statistical methods
ISBN 0444511415
Classificazione AMS 62-06
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991001662499707536
Amsterdam : Elsevier North Holland, 2005
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Data mining methods for the content analyst [[electronic resource] ] : an introduction to the computational analysis of content / / Kalev Hannes Leetaru
Data mining methods for the content analyst [[electronic resource] ] : an introduction to the computational analysis of content / / Kalev Hannes Leetaru
Autore Leetaru Kalev
Pubbl/distr/stampa New York, : Routledge, 2012
Descrizione fisica 1 online resource (121 p.)
Disciplina 006.3/12
006.312
Collana Routledge communication series
Soggetto topico Data mining
Data mining - Statistical methods
Soggetto genere / forma Electronic books.
ISBN 0-203-14938-6
1-283-84361-7
1-136-51459-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto DATA MINING METHODS FOR THE CONTENT ANALYST An Introduction to the Computational Analysis of Content; Copyright; Contents; List of Tables and Figures; Acknowledgments; 1 Introduction; What Is Content Analysis?; Why Use Computerized Analysis Techniques?; Standalone Tools or Integrated Suites; Transitioning from Theory to Practice; Chapter in Summary; 2 Obtaining and Preparing Data; Collecting Data from Digital Text Repositories; Are the Data Meaningful?; Using Data in Unintended Ways; Analytical Resolution; Types of Data Sources; Finding Sources; Searching Text Collections
Sources of IncompletenessLicensing Restrictions and Content Blackouts; Measuring Viewership; Accuracy and Convenience Samples; Random Samples; Multimedia Content; Converting to Textual Format; Prosody; Example Data Sources; Patterns in Historical War Coverage; Competitive Intelligence; Global News Coverage; Downloading Content; Digital Content; Print Content; Preparing Content; Document Extraction; Cleaning; Post Filtering; Reforming/Reshaping; Content Proxy Extraction; Chapter in Summary; 3 Vocabulary Analysis; The Basics; Word Histograms; Readability Indexes; Normative Comparison
Non-word AnalysisColloquialisms: Abbreviations and Slang; Restricting the Analytical Window; Vocabulary Comparison and Evolution/Chronemics; Advanced Topics; Syllables, Rhyming, and "Sounds Like"; Gender and Language; Authorship Attribution; Word Morphology, Stemming, and Lemmatization; Chapter in Summary; 4 Correlation and Co-occurrence; Understanding Correlation; Computing Word Correlations; Directionality; Concordance; Co-occurrence and Search; Language Variation and Lexicons; Non-co-occurrence; Correlation with Metadata; Chapter in Summary; 5 Lexicons, Entity Extraction, and Geocoding
LexiconsLexicons and Categorization; Lexical Correlation; Lexicon Consistency Checks; Thesauri and Vocabulary Expanders; Named Entity Extraction; Lexicons and Processing; Applications; Geocoding, Gazetteers, and Spatial Analysis; Geocoding; Gazetteers and the Geocoding Process; Operating Under Uncertainty; Spatial Analysis; Chapter in Summary; 6 Topic Extraction; How Machines Process Text; Unstructured Text; Extracting Meaning from Text; Applications of Topic Extraction; Comparing/Clustering Documents; Automatic Summarization; Automatic Keyword Generation
Multilingual Analysis: Topic Extraction with Multiple LanguagesChapter in Summary; 7 Sentiment Analysis; Examining Emotions; Evolution; Evaluation; Analytical Resolution: Documents versus Objects; Hand-crafted versus Automatically Generated Lexicons; Other Sentiment Scales; Limitations; Measuring Language Rather Than Worldview; Chapter in Summary; 8 Similarity, Categorization and Clustering; Categorization; The Vector Space Model; Feature Selection; Feature Reduction; Learning Algorithm; Evaluating ATC Results; Benefi ts of ATC over Human Categorization; Limitations of ATC
Applications of ATC
Record Nr. UNINA-9910462683603321
Leetaru Kalev  
New York, : Routledge, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining methods for the content analyst [[electronic resource] ] : an introduction to the computational analysis of content / / Kalev Hannes Leetaru
Data mining methods for the content analyst [[electronic resource] ] : an introduction to the computational analysis of content / / Kalev Hannes Leetaru
Autore Leetaru Kalev
Pubbl/distr/stampa New York, : Routledge, 2012
Descrizione fisica 1 online resource (121 p.)
Disciplina 006.3/12
006.312
Collana Routledge communication series
Soggetto topico Data mining
Data mining - Statistical methods
ISBN 0-203-14938-6
1-283-84361-7
1-136-51459-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto DATA MINING METHODS FOR THE CONTENT ANALYST An Introduction to the Computational Analysis of Content; Copyright; Contents; List of Tables and Figures; Acknowledgments; 1 Introduction; What Is Content Analysis?; Why Use Computerized Analysis Techniques?; Standalone Tools or Integrated Suites; Transitioning from Theory to Practice; Chapter in Summary; 2 Obtaining and Preparing Data; Collecting Data from Digital Text Repositories; Are the Data Meaningful?; Using Data in Unintended Ways; Analytical Resolution; Types of Data Sources; Finding Sources; Searching Text Collections
Sources of IncompletenessLicensing Restrictions and Content Blackouts; Measuring Viewership; Accuracy and Convenience Samples; Random Samples; Multimedia Content; Converting to Textual Format; Prosody; Example Data Sources; Patterns in Historical War Coverage; Competitive Intelligence; Global News Coverage; Downloading Content; Digital Content; Print Content; Preparing Content; Document Extraction; Cleaning; Post Filtering; Reforming/Reshaping; Content Proxy Extraction; Chapter in Summary; 3 Vocabulary Analysis; The Basics; Word Histograms; Readability Indexes; Normative Comparison
Non-word AnalysisColloquialisms: Abbreviations and Slang; Restricting the Analytical Window; Vocabulary Comparison and Evolution/Chronemics; Advanced Topics; Syllables, Rhyming, and "Sounds Like"; Gender and Language; Authorship Attribution; Word Morphology, Stemming, and Lemmatization; Chapter in Summary; 4 Correlation and Co-occurrence; Understanding Correlation; Computing Word Correlations; Directionality; Concordance; Co-occurrence and Search; Language Variation and Lexicons; Non-co-occurrence; Correlation with Metadata; Chapter in Summary; 5 Lexicons, Entity Extraction, and Geocoding
LexiconsLexicons and Categorization; Lexical Correlation; Lexicon Consistency Checks; Thesauri and Vocabulary Expanders; Named Entity Extraction; Lexicons and Processing; Applications; Geocoding, Gazetteers, and Spatial Analysis; Geocoding; Gazetteers and the Geocoding Process; Operating Under Uncertainty; Spatial Analysis; Chapter in Summary; 6 Topic Extraction; How Machines Process Text; Unstructured Text; Extracting Meaning from Text; Applications of Topic Extraction; Comparing/Clustering Documents; Automatic Summarization; Automatic Keyword Generation
Multilingual Analysis: Topic Extraction with Multiple LanguagesChapter in Summary; 7 Sentiment Analysis; Examining Emotions; Evolution; Evaluation; Analytical Resolution: Documents versus Objects; Hand-crafted versus Automatically Generated Lexicons; Other Sentiment Scales; Limitations; Measuring Language Rather Than Worldview; Chapter in Summary; 8 Similarity, Categorization and Clustering; Categorization; The Vector Space Model; Feature Selection; Feature Reduction; Learning Algorithm; Evaluating ATC Results; Benefi ts of ATC over Human Categorization; Limitations of ATC
Applications of ATC
Record Nr. UNINA-9910786303103321
Leetaru Kalev  
New York, : Routledge, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining methods for the content analyst : an introduction to the computational analysis of content / / Kalev Hannes Leetaru
Data mining methods for the content analyst : an introduction to the computational analysis of content / / Kalev Hannes Leetaru
Autore Leetaru Kalev
Edizione [1st ed.]
Pubbl/distr/stampa New York, : Routledge, 2012
Descrizione fisica 1 online resource (121 p.)
Disciplina 006.3/12
006.312
Collana Routledge communication series
Soggetto topico Data mining
Data mining - Statistical methods
ISBN 0-203-14938-6
1-283-84361-7
1-136-51459-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto DATA MINING METHODS FOR THE CONTENT ANALYST An Introduction to the Computational Analysis of Content; Copyright; Contents; List of Tables and Figures; Acknowledgments; 1 Introduction; What Is Content Analysis?; Why Use Computerized Analysis Techniques?; Standalone Tools or Integrated Suites; Transitioning from Theory to Practice; Chapter in Summary; 2 Obtaining and Preparing Data; Collecting Data from Digital Text Repositories; Are the Data Meaningful?; Using Data in Unintended Ways; Analytical Resolution; Types of Data Sources; Finding Sources; Searching Text Collections
Sources of IncompletenessLicensing Restrictions and Content Blackouts; Measuring Viewership; Accuracy and Convenience Samples; Random Samples; Multimedia Content; Converting to Textual Format; Prosody; Example Data Sources; Patterns in Historical War Coverage; Competitive Intelligence; Global News Coverage; Downloading Content; Digital Content; Print Content; Preparing Content; Document Extraction; Cleaning; Post Filtering; Reforming/Reshaping; Content Proxy Extraction; Chapter in Summary; 3 Vocabulary Analysis; The Basics; Word Histograms; Readability Indexes; Normative Comparison
Non-word AnalysisColloquialisms: Abbreviations and Slang; Restricting the Analytical Window; Vocabulary Comparison and Evolution/Chronemics; Advanced Topics; Syllables, Rhyming, and "Sounds Like"; Gender and Language; Authorship Attribution; Word Morphology, Stemming, and Lemmatization; Chapter in Summary; 4 Correlation and Co-occurrence; Understanding Correlation; Computing Word Correlations; Directionality; Concordance; Co-occurrence and Search; Language Variation and Lexicons; Non-co-occurrence; Correlation with Metadata; Chapter in Summary; 5 Lexicons, Entity Extraction, and Geocoding
LexiconsLexicons and Categorization; Lexical Correlation; Lexicon Consistency Checks; Thesauri and Vocabulary Expanders; Named Entity Extraction; Lexicons and Processing; Applications; Geocoding, Gazetteers, and Spatial Analysis; Geocoding; Gazetteers and the Geocoding Process; Operating Under Uncertainty; Spatial Analysis; Chapter in Summary; 6 Topic Extraction; How Machines Process Text; Unstructured Text; Extracting Meaning from Text; Applications of Topic Extraction; Comparing/Clustering Documents; Automatic Summarization; Automatic Keyword Generation
Multilingual Analysis: Topic Extraction with Multiple LanguagesChapter in Summary; 7 Sentiment Analysis; Examining Emotions; Evolution; Evaluation; Analytical Resolution: Documents versus Objects; Hand-crafted versus Automatically Generated Lexicons; Other Sentiment Scales; Limitations; Measuring Language Rather Than Worldview; Chapter in Summary; 8 Similarity, Categorization and Clustering; Categorization; The Vector Space Model; Feature Selection; Feature Reduction; Learning Algorithm; Evaluating ATC Results; Benefi ts of ATC over Human Categorization; Limitations of ATC
Applications of ATC
Record Nr. UNINA-9910971263003321
Leetaru Kalev  
New York, : Routledge, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data science : theory, analysis, and applications / / edited by Qurban A Memon, Shakeel Ahmed Khoja
Data science : theory, analysis, and applications / / edited by Qurban A Memon, Shakeel Ahmed Khoja
Pubbl/distr/stampa Boca Raton : , : CRC Press, , [2020]
Descrizione fisica 1 online resource (345 pages)
Disciplina 006.312
Soggetto topico Data mining - Statistical methods
ISBN 0-429-55882-1
0-429-26379-1
0-429-55435-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910793884903321
Boca Raton : , : CRC Press, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data science : theory, analysis, and applications / / edited by Qurban A Memon, Shakeel Ahmed Khoja
Data science : theory, analysis, and applications / / edited by Qurban A Memon, Shakeel Ahmed Khoja
Pubbl/distr/stampa Boca Raton : , : CRC Press, , [2020]
Descrizione fisica 1 online resource (345 pages)
Disciplina 006.312
Soggetto topico Data mining - Statistical methods
ISBN 0-429-55882-1
0-429-26379-1
0-429-55435-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910799926403321
Boca Raton : , : CRC Press, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data science : theory, analysis, and applications / / edited by Qurban A Memon, Shakeel Ahmed Khoja
Data science : theory, analysis, and applications / / edited by Qurban A Memon, Shakeel Ahmed Khoja
Edizione [1st ed.]
Pubbl/distr/stampa Boca Raton : , : CRC Press, , [2020]
Descrizione fisica 1 online resource (345 pages)
Disciplina 006.312
Soggetto topico Data mining - Statistical methods
ISBN 0-429-55882-1
0-429-26379-1
0-429-55435-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Editors -- Contributors -- PART I: Data Science: Theory, Concepts, and Algorithms -- Chapter 1 Framework for Visualization of GeoSpatial Query Processing by Integrating MongoDB with Spark -- 1.1 Introduction -- 1.1.1 Integration of Spark and MongoDB -- 1.2 Literature Survey -- 1.3 Proposed System -- 1.3.1 Methodology for Processing Spatial Queries -- 1.3.2 Spark Master-Slave Framework -- 1.3.3 Algorithms for Sharding -- 1.3.3.1 Algorithm for Range Sharding -- 1.3.3.2 Algorithms for Zone Sharding -- 1.3.4 Dataset and Statistics -- 1.4 Results and Performance Evaluation -- 1.5 Conclusion -- References -- Chapter 2 A Study on Metaheuristic-Based Neural Networks for Image Segmentation Purposes -- 2.1 Introduction -- 2.2 Supervised Image Segmentation -- 2.3 Literature Review -- 2.4 Artificial Neural Networks -- 2.5 Optimization -- 2.6 Metaheuristic Algorithms -- 2.6.1 Genetic Algorithm -- 2.6.2 Particle Swarm Optimization Algorithm -- 2.6.3 Imperialist Competitive Algorithm -- 2.7 Optimization of the Neural Networks Weights Using Optimization Algorithms -- 2.8 Experimental Setup and Method Analysis -- 2.9 Conclusions -- References -- Chapter 3 A Study and Analysis of a Feature Subset Selection Technique Using Penguin Search Optimization Algorithm -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Proposed Work -- 3.3.1 Pseudocode of the Proposed FS-PeSOA Algorithm -- 3.3.2 Discussion -- 3.3.2.1 Hunting Strategy of Penguins -- 3.3.2.2 Fitness Function Evaluation -- 3.3.2.3 Position Update Logic -- 3.3.2.4 Oxygen Update Logic -- 3.4 Result Analysis -- 3.5 Conclusions -- References -- Chapter 4 A Physical Design Strategy on a NoSQL DBMS -- 4.1 Introduction -- 4.2 Motivation Example -- 4.3 Neo4j -- 4.4 Design Guidelines -- 4.5 Physical Design.
4.5.1 Query Rewriting Using Path Redundancy Pattern -- 4.5.2 Query Rewriting Using Minimal Query Pattern -- 4.5.3 Path Materialization -- 4.5.4 Index Creation -- 4.6 Experimental Study -- 4.6.1 Experimental Design -- 4.6.2 Impact of the Proposed Physical Design on Query Performance for a 1 GB Database -- 4.6.3 Impact of the Proposed Physical Design on Query Performance for a 10 GB Database -- 4.6.4 Impact of the Proposed Physical Design on Query Performance for a 100 GB Database -- 4.7 Related Work -- 4.8 Discussion -- 4.9 Future Research Directions -- 4.10 Conclusion -- References -- Chapter 5 Large-Scale Distributed Stream Data Collection Schemes -- 5.1 Introduction -- 5.2 Data Collection Scheme for Distributed TBPS -- 5.2.1 Assumed Environment -- 5.2.1.1 Assumed TBPS Architecture -- 5.2.1.2 Assumed Overlay for Distributed TBPS -- 5.2.2 Proposed Method -- 5.2.2.1 Methodology Principle -- 5.2.2.2 Collective Store and Forwarding -- 5.2.2.3 Adaptive Data Collection Tree -- 5.2.3 Evaluation -- 5.2.3.1 Simulation Parameters -- 5.2.3.2 Simulation Results -- 5.3 Data Collection Scheme Considering Phase Differences -- 5.3.1 Problems Addressed -- 5.3.1.1 Assumed Environment -- 5.3.1.2 Input Setting -- 5.3.1.3 Definition of a Load -- 5.3.2 Proposed Method -- 5.3.2.1 Skip Graphs -- 5.3.2.2 Phase Differences -- 5.3.3 Evaluation -- 5.3.3.1 Collection Target Nodes -- 5.3.3.2 Communication Loads and Hops -- 5.4 Discussion -- 5.5 Related Work -- 5.6 Conclusion -- Acknowledgements -- References -- PART II: Data Design and Analysis -- Chapter 6 Big Data Analysis and Management in Healthcare -- 6.1 Introduction -- 6.2 Preliminary Studies -- 6.3 Healthcare Data -- 6.4 Need of Big Data Analytics in Healthcare -- 6.5 Challenges in Big Data Analysis in Healthcare -- 6.5.1 Capture -- 6.5.2 Cleaning -- 6.5.3 Storage -- 6.5.4 Security -- 6.5.5 Stewardship -- 6.5.6 Querying.
6.5.7 Reporting -- 6.5.8 Visualization -- 6.5.9 Updating -- 6.5.10 Sharing -- 6.6 Collection of Healthcare Data -- 6.6.1 Importance in Healthcare Data Collection -- 6.6.2 Complications and Clarifications of Healthcare Data Collection -- 6.6.3 Current Data Collection Methods -- 6.6.4 Advanced Data Collection Tools -- 6.6.5 Healthcare Data Standards -- 6.6.6 Inferences of Patient Data Collection in Healthcare -- 6.7 Analysis of Healthcare Data -- 6.8 Healthcare Data Management -- 6.8.1 Big Data and Care Management -- 6.8.2 Advantages of Healthcare Data Management -- 6.9 Big Data in Healthcare -- 6.9.1 Big Data and IoT -- 6.9.2 Patient Prophecies for Upgraded Staffing -- 6.9.3 Electronic Health Records -- 6.9.4 Real-Time Warning -- 6.9.5 Augmenting Patient Engagement -- 6.9.6 Using Health Data for Informed Strategic Planning -- 6.9.7 Extrapolative Analytics in Healthcare -- 6.9.8 Diminish Fraud and Enrich Security -- 6.9.9 Telemedicine -- 6.9.10 Assimilating Big Data per Medical Imaging -- 6.9.11 A Method to Avert Pointless ER (Emergency Room) Visits -- 6.10 Future for Big Data in Healthcare -- 6.11 Conclusion -- References -- Chapter 7 Healthcare Analytics: A Case Study Approach Using the Framingham Heart Study -- 7.1 Introduction and Background to the Case Study: Framingham Heart Study -- 7.2 Literature Review -- 7.3 Introduction to the Data Analytics Framework -- 7.3.1 Step 1. Defining the Healthcare Problem -- 7.3.2 Step 2. Explore the Healthcare Data -- 7.3.3 Step 3. Predict What Is Likely to Happen -- or Perform Classification Analysis -- 7.3.4 Step 4. Check the Modeling Results -- 7.3.5 Step 5. Optimize (Find the Best Solution) -- 7.3.6 Step 6. Derive a Clinical Strategy for Patient Care and Measure the Outcome -- 7.3.7 Step 7. Update the CDS System -- 7.4 Data Exploration and Understanding of the Healthcare Problem.
7.5 Machine-Learning Model Application -- 7.6 Evaluation of the Machine-Learning Model Results -- 7.7 Conclusion -- 7.8 Future Direction -- Acknowledgements -- References -- Chapter 8 Bioinformatics Analysis of Dysfunctional (Mutated) Proteins of Cardiac Ion Channels Underlying the Brugada Syndrome -- 8.1 Introduction -- 8.2 Results -- 8.2.1 Brief Description of Unique BrS-Related Proteins -- 8.2.2 PIM-Based Analysis of the Unique BrS-Related Proteins -- 8.2.3 Intrinsic Disorder Analysis of the BrS-Related Proteins -- 8.2.4 Kolmogorov-Smirnov Test -- 8.3 Discussion -- 8.4 Materials and Methods -- 8.4.1 Evaluation of Polar Profile -- 8.4.1.1 Weighting of Polar Profiles -- 8.4.1.2 Comparison of Polar Profiles -- 8.4.1.3 Graphics of Polar Profiles -- 8.4.2 Evaluation of Intrinsic Disorder Predisposition -- 8.4.3 Data Files -- 8.4.4 Kolmogorov-Smirnov Test -- 8.4.5 Test Plan -- 8.4.5.1 Polar Profile -- 8.5 Conclusions -- References -- Chapter 9 Discrimination of Healthy Skin, Superficial Epidermal Burns, and Full-Thickness Burns from 2D-Colored Images Using Machine Learning -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Skin Burns -- 9.2.2 Causes of Burn Injuries -- 9.2.3 Burns Category -- 9.2.4 Burn Assessment Techniques -- 9.2.4.1 Clinical Assessment -- 9.2.4.2 Blood Perfusion Measurement -- 9.3 Machine Learning -- 9.3.1 Convolutional Neural Networks -- 9.3.1.1 Convolution Layer -- 9.3.1.2 Pooling Layer -- 9.3.1.3 Output/Classification Layer -- 9.3.2 Training a ConvNet -- 9.3.3 Common ConvNet Models -- 9.3.3.1 AlexNet -- 9.3.3.2 GoogleNet -- 9.3.3.3 VGGNet -- 9.3.3.4 Residual Network -- 9.4 Goals and Methodology -- 9.4.1 Image Acquisition and Preprocessing -- 9.4.2 Feature Extraction and Classification -- 9.5 Results and Discussion -- 9.5.1 Terms Related to Contingency Table -- 9.5.2 Classifier Performance -- 9.6 Conclusions -- References.
Chapter 10 A Study and Analysis of an Emotion Classification and State-Transition System in Brain Computer Interfacing -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Proposed Work -- 10.3.1 Classification Processes -- 10.3.1.1 SVM Classifier -- 10.3.1.2 KNN Classifier -- 10.3.1.3 Random Forest Classifier -- 10.3.2 State-Transition Machine -- 10.3.2.1 Proposed Algorithm of Emotional State Transition Based on Channel Value for a Fixed Time Interval -- 10.4 Result Analysis -- 10.4.1 Requirement -- 10.4.2 Result Comparisons of SVM, KNN, and Random Forest Classifiers -- 10.4.3 SVM Polynomial Kernel Performance Analysis -- 10.4.4 Analysis of the State-Transition Machine -- 10.4.5 Comparison with Previous Works -- 10.4.6 Computational Complexity -- 10.5 Conclusion -- Acknowledgment -- References -- PART III: Applications and New Trends in Data Science -- Chapter 11 Comparison of Gradient and Textural Features for Writer Retrieval in Handwritten Documents -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Adopted Features -- 11.3.1 Local Binary Pattern -- 11.3.2 Histogram of Oriented Gradients -- 11.3.3 Gradient Local Binary Pattern -- 11.3.4 Pixel Density -- 11.3.5 Run Length Feature -- 11.4 Matching Step -- 11.5 Experimental Evaluation -- 11.5.1 Evaluation Criteria -- 11.5.2 Experimental Setup -- 11.5.3 Retrieval Results -- 11.6 Discussion and Comparison -- 11.7 Conclusion -- References -- Chapter 12 A Supervised Guest Satisfaction Classification with Review Text and Ratings -- 12.1 Introduction -- 12.2 Related Literature -- 12.2.1 Guest Satisfaction and Online Reviews -- 12.3 Methodology -- 12.3.1 Data Description and Analysis -- 12.3.2 Data Cleaning -- 12.3.3 Latent Semantic Analysis -- 12.3.4 Classifiers and Performance Measures -- 12.4 Experimental Results -- 12.4.1 Features Related to Guest Satisfaction.
12.4.2 Hotel Guest Satisfaction Prediction.
Record Nr. UNINA-9910954327303321
Boca Raton : , : CRC Press, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of statistical analysis and data mining applications / / authors, Robert Nisbet, Gary Miner, Ken Yale ; guest authors of selected chapters, John Elder, Andy Peterson
Handbook of statistical analysis and data mining applications / / authors, Robert Nisbet, Gary Miner, Ken Yale ; guest authors of selected chapters, John Elder, Andy Peterson
Autore Nisbet Robert
Edizione [Second edition.]
Pubbl/distr/stampa London, England : , : Academic Press, , 2018
Descrizione fisica 1 online resource (824 pages) : illustrations (some color)
Disciplina 006.312
Soggetto topico Data mining - Statistical methods
ISBN 0-12-416645-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto History of phases of data analysis, basic theory, and the data mining process -- The algorithms and methods in data mining and predictive analytics and some domain areas -- Tutorials and case studies -- Models ensembles, model complexity; using the right model for the right use, significance, ethics, and the future and advanced processes.
Record Nr. UNINA-9910583039803321
Nisbet Robert  
London, England : , : Academic Press, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui