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Analysis of an Intelligence Dataset
| Analysis of an Intelligence Dataset |
| Autore | Myszkowski Nils |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (166 p.) |
| Soggetto topico | Psychology |
| Soggetto non controllato |
ability-based guessing
Bayesian statistics bi-factor brms classical test theory dimensionality distractor analysis distractors E-assessment exploratory graph analysis fused grouped regularization fused regularization general mental ability intelligence intelligence tests interaction model invariant item ordering IRT item analysis Item Response Theory item-response theory Mokken scale analysis n/a nested logit models non-parametric item response theory parallel analysis psychometrics R Raven matrices Raven's progressive matrices regularization regularized latent class analysis Stan Standard Progressive Matrices Standard Progressive Matrices test target rotation test-item regression |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557153803321 |
Myszkowski Nils
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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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Bayes Factors for Forensic Decision Analyses with R [[electronic resource] /] / by Silvia Bozza, Franco Taroni, Alex Biedermann
| Bayes Factors for Forensic Decision Analyses with R [[electronic resource] /] / by Silvia Bozza, Franco Taroni, Alex Biedermann |
| Autore | Bozza Silvia |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (XII, 187 p. 22 illus., 5 illus. in color.) |
| Disciplina | 519.5 |
| Collana | Springer Texts in Statistics |
| Soggetto topico |
Statistics
Mathematical statistics—Data processing Forensic sciences Medical jurisprudence Forensic psychology Social sciences—Statistical methods Statistical Theory and Methods Statistics and Computing Forensic Science Forensic Medicine Forensic Psychology Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy Estadística bayesiana Processament de dades Criminalística R (Llenguatge de programació) |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
Bayes factor
scientific evidence decision making forensic science uncertainty management probability theory forensic decision analysis Bayesian modeling R Bayesian statistics probabilistic inference |
| ISBN | 3-031-09839-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction to the Bayes factor and decision analysis -- Chapter 2: Bayes factor for model choice -- Chapter 3: Bayes factor for evaluative purposes -- Chapter 4: Bayes factor for investigative purposes. |
| Record Nr. | UNISA-996495166503316 |
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Bozza Silvia
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| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Natural Language Processing Using R : Pocket Primer / / Oswald Campesato
| Natural Language Processing Using R : Pocket Primer / / Oswald Campesato |
| Autore | Campesato Oswald |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Dulles, VA : , : Mercury Learning and Information, , 2022 |
| Descrizione fisica | 1 online resource (xviii, 246 pages) |
| Disciplina | 006.35 |
| Collana | Pocket Primer |
| Soggetto topico | Natural language processing |
| Soggetto non controllato |
Computer Science
Data Analytics Machine Learning Natural Language Processing R |
| ISBN | 1-68392-729-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1: Introduction to R. -- 2: Conditionals, Loops, and Data Frames. -- 3: Working with Functions in R. -- 4. NLP Concepts (I). -- 5. NLP Concepts (II). -- 6: NLP and R. -- 7: Transformer, BERT, and GPT. -- Appendices. -- Index. |
| Record Nr. | UNINA-9910861958303321 |
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Campesato Oswald
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| Dulles, VA : , : Mercury Learning and Information, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sensors for Ultrasonic NDT in Harsh Environments
| Sensors for Ultrasonic NDT in Harsh Environments |
| Autore | Sinclair Anthony N |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (120 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
dry coupling
elevated temperature EMAT sensor FBR field-deployable sensor gallium phosphate guided wave guided-wave send-receive harsh environment high temperature high-temperature monitoring high-temperature ultrasonic testing imaging in-service inspection inspection ISI& L-waves liquid sodium lithium niobate NDE NDE (Non Destructive Evaluation) NDT NDT (Non Destructive Testing) neutron irradiation non-destructive evaluation nondestructive testing nuclear power plants Phased Array piezocomposites piezoelectric piezoelectric wafer active sensor PMN-PT pressurized water reactor fuel rods R radiation radiation resistance reactor SFR sodium spray-on transducers structural health monitoring thickness shear TUCSS ultrasonic ultrasonic transducer ultrasound |
| ISBN | 3-03928-423-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404083303321 |
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Sinclair Anthony N
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Socio-Environmental Vulnerability Assessment for Sustainable Management
| Socio-Environmental Vulnerability Assessment for Sustainable Management |
| Autore | Szewrański Szymon |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (396 p.) |
| Soggetto topico | Research and information: general |
| Soggetto non controllato |
acoustic space
adaptability aging Aksu-Jabagly nature reserve analytical hierarchy process ArcGIS assessment climate climate analogues climate change cluster clustering community-based assessment cross-sectoral partnerships cycling cycling routes dataset decision support system eco-environmental risk assessment ecological restoration ecological vulnerability education energy from biomass environmental flow environmental hazards Factor Analysis on Mixed Data (FAMD) farm management flood risk fortified landscape geospatial analysis geospatial information GIS green infrastructure green roofs healthcare facilities heritage protection hydropower production impact impact perception indicators indigenous peoples integrated environmental assessment integrated planning index inter-municipal cooperation Jiuqu stream Kazakhstan land use planning local development meteorology mineral resources mountain region multidimensional statistical analysis municipal waste municipalities municipality n/a national park natural environment nature protection Nepal noise Nysa Kłodzka sub-basin open-source software pellets performance system periodization place-based and integrated development Poland political environment preventive healthcare quality of runoff water questionnaire survey R renewable energy resource-based economy SDG implementation slow cities society socio-ecological system socio-environmental vulnerability soil water retention solar energy radiation spatial policy stakeholders Support Vector Machines sustainable development sustainable economy sustainable management sustainable mobility sustainable tourism synanthropic flora SYNOP Tableau technical infrastructure technogenic soil tourism impact traffic safety urban planning urban vegetation vulnerability and adaptation assessment Ward's method water resources watershed management wood waste world heritage |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557605203321 |
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Szewrański Szymon
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Text analytics for business decisions : a case study approach / / Andres G. Fortino
| Text analytics for business decisions : a case study approach / / Andres G. Fortino |
| Autore | Fortino Andres G. |
| Pubbl/distr/stampa | Dulles : , : Mercury Learning & Information, , [2021] |
| Descrizione fisica | 1 online resource (332 pages) |
| Disciplina | 650.02855369 |
| Soggetto topico |
Business - Decision making - Computer programs
Text processing (Computer science) |
| Soggetto non controllato |
Business Communication
Computer Science Data Analytics Data Visualization Excel R |
| ISBN |
1-68392-664-1
1-68392-665-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- Preface -- On the Companion Files -- Acknowledgements -- Chapter 1 : Framing Analytical Questions -- Data is the New Oil -- The World of the Business Data Analyst -- How Does Data Analysis Relate to Decision Making? -- How Do We Frame Analytical Questions? -- What are the Characteristics of Well-framed Analytical Questions? -- Exercise 1.1 - Case Study Using Dataset K: Titanic Disaster -- What are Some Examples of Text-Based Analytical Questions? -- Additional Case Study Using Dataset J: Remote Learning Student Survey -- References -- Chapter 2 : Analytical Tool Sets -- Tool Sets for Text Analytics -- Excel -- Microsoft Word -- Adobe Acrobat -- SAS JMP -- R and RStudio -- Voyant -- Java -- Stanford Named Entity Recognizer (NER) -- Topic Modeling Tool -- References -- Chapter 3 : Text Data Sources and Formats -- Sources and Formats of Text Data -- Social Media Data -- Customer opinion data from commercial sites -- Email -- Documents -- Surveys -- Websites -- Chapter 4 : Preparing the Data File -- What is Data Shaping? -- The Flat File Format -- Shaping the Text Variable in a Table -- Bag-of-Words Representation -- Single Text Files -- Exercise 4.1 - Case Study Using Dataset L: Resumes -- Exercise 4.2 - Case Study Using Dataset D: Occupation Descriptions -- Additional Exercise 4.3 - Case Study Using Dataset I: NAICS Codes -- Aggregating Across Rows and Columns -- Exercise 4.4 - Case Study Using Dataset D: Occupation Descriptions -- Additional Advanced Exercise 4.5 - Case Study Using Dataset E: Large Data Files -- Additional Advanced Exercise 4.6 - Case Study Using Dataset F: The Federalist Papers -- References -- Chapter 5 : Word Frequency Analysis -- What is Word Frequency Analysis? -- How Does It Apply to Text Business Data Analysis? -- Exercise 5.1 - Case Study Using Dataset A: Training Survey.
Exercise 5.2 - Case Study Using Dataset D: Job Descriptions -- Exercise 5.3 - Case Study Using Dataset C: Product Reviews -- Additional Exercise 5.4 - Case Study Using Dataset B: Consumer Complaints -- Chapter 6 : Keyword Analysis -- Exercise 6.1 - Case Study Using Dataset D: Resume and Job Description -- Exercise 6.2 - Case Study Using Dataset G: University Curriculum -- Exercise 6.3 - Case Study Using Dataset C: Product Reviews -- Additional Exercise 6.4 - Case Study Using Dataset B: Customer Complaints -- Chapter 7 : Sentiment Analysis -- What is Sentiment Analysis? -- Exercise 7.1 - Case Study Using Dataset C: Product Reviews - Rubbermaid -- Exercise 7.2 - Case Study Using Dataset C: Product Reviews-Windex -- Exercise 7.3 - Case Study Using Dataset C: Product Reviews-Both Brands -- Chapter 8 : Visualizing Text Data -- What Is Data Visualization Used For? -- Exercise 8.1 - Case Study Using Dataset A: Training Survey -- Exercise 8.2 - Case Study Using Dataset B: Consumer Complaints -- Exercise 8.3 - Case Study Using Dataset C: Product Reviews -- Exercise 8.4 - Case Study Using Dataset E: Large Text Files -- References -- Chapter 9 : Coding Text Data -- What is a Code? -- What are the Common Approaches to Coding Text Data? -- What is Inductive Coding? -- Exercise 9.1 - Case Study Using Dataset A: Training -- Exercise 9.2 - Case Study Using Dataset J: Remote Learning -- Exercise 9.3 - Case Study Using Dataset E: Large Text Files -- Affinity Diagram Coding -- Exercise 9.4 - Case Study Using Dataset M: Onboarding Brainstorming -- References -- Chapter 10 : Named Entity Recognition -- Named Entity Recognition -- What is a Named Entity? -- Common Approaches to Extracting Named Entities -- Classifiers - The Core NER Process -- What Does This Mean for Business? -- Exercise 10.1 - Using the Stanford NER -- Exercise 10.2 - Example Cases. Exercise 10.2 - Case Study Using Dataset H: Corporate Financial Reports -- Additional Exercise 10.3 - Case Study Using Dataset L: Corporate Financial Reports -- Exercise 10.4 - Case Study Using Dataset E: Large Text Files -- Additional Exercise 10.5 - Case Study Using Dataset E: Large Text Files -- References -- Chapter 11 : Topic Recognition in Documents -- Information Retrieval -- Document Characterization -- Topic Recognition -- Exercises -- Exercise 11.1 - Case Study Using Dataset G: University Curricula -- Exercise 11.2 - Case Study Using Dataset E: Large Text Files -- Exercise 11.3 - Case Study Using Dataset E: Large Text Files -- Exercise 11.4 - Case Study Using Dataset E: Large Text Files -- Exercise 11.5 - Case Study Using Dataset E: Large Text Files -- Additional Exercise 11.6 - Case Study Using Dataset P: Patents -- Additional Exercise 11.7 - Case Study Using Dataset F: Federalist Papers -- Additional Exercise 11.8 - Case Study Using Dataset E: Large Text Files -- Additional Exercise 11.9- Case Study Using Dataset N: Sonnets -- References -- Chapter 12 : Text Similarity Scoring -- What is Text Similarity Scoring? -- Text Similarity Scoring Exercises -- Exercise 12.1 - Case Study Using Dataset D: Occupation Description -- Analysis using R -- Exercise 12.2 - Case D: Resume and Job Description -- Reference -- Chapter 13 : Analysis of Large Datasets by Sampling -- Using Sampling to Work with Large Data Files -- Exercise 13.1 - Big Data Analysis -- Additional Case Study Using Dataset E: BankComplaints Big Data File -- Chapter 14 : Installing R and RStudio -- Installing R -- Install R Software for a Mac System -- Installing RStudio -- Reference -- Chapter 15 : Installing the Entity Extraction Tool -- Downloading and Installing the Tool -- The NER Graphical User Interface -- Reference -- Chapter 16 : Installing the Topic Modeling Tool. Installing and Using the Topic Modeling Tool -- Install the tool -- For Macs -- For Windows PCs -- UTF-8 caveat -- Setting up the workspace -- Workspace Directory -- Using the Tool -- Select metadata file -- Selecting the number of topics -- Analyzing the Output -- Multiple Passes for Optimization -- The Output Files -- Chapter 17 : Installing the Voyant Text Analysis Tool -- Install or Update Java -- Installation of Voyant Server -- The Voyant Server -- Downloading VoyantServer -- Running Voyant Server -- Controlling the Voyant Server -- Testing the Installation -- Reference -- INDEX. |
| Record Nr. | UNINA-9910794555103321 |
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Fortino Andres G.
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| Dulles : , : Mercury Learning & Information, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Text analytics for business decisions : a case study approach / / Andres G. Fortino
| Text analytics for business decisions : a case study approach / / Andres G. Fortino |
| Autore | Fortino Andres G. |
| Pubbl/distr/stampa | Dulles : , : Mercury Learning & Information, , [2021] |
| Descrizione fisica | 1 online resource (332 pages) |
| Disciplina | 650.02855369 |
| Soggetto topico |
Business - Decision making - Computer programs
Text processing (Computer science) |
| Soggetto non controllato |
Business Communication
Computer Science Data Analytics Data Visualization Excel R |
| ISBN |
1-68392-664-1
1-68392-665-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- Preface -- On the Companion Files -- Acknowledgements -- Chapter 1 : Framing Analytical Questions -- Data is the New Oil -- The World of the Business Data Analyst -- How Does Data Analysis Relate to Decision Making? -- How Do We Frame Analytical Questions? -- What are the Characteristics of Well-framed Analytical Questions? -- Exercise 1.1 - Case Study Using Dataset K: Titanic Disaster -- What are Some Examples of Text-Based Analytical Questions? -- Additional Case Study Using Dataset J: Remote Learning Student Survey -- References -- Chapter 2 : Analytical Tool Sets -- Tool Sets for Text Analytics -- Excel -- Microsoft Word -- Adobe Acrobat -- SAS JMP -- R and RStudio -- Voyant -- Java -- Stanford Named Entity Recognizer (NER) -- Topic Modeling Tool -- References -- Chapter 3 : Text Data Sources and Formats -- Sources and Formats of Text Data -- Social Media Data -- Customer opinion data from commercial sites -- Email -- Documents -- Surveys -- Websites -- Chapter 4 : Preparing the Data File -- What is Data Shaping? -- The Flat File Format -- Shaping the Text Variable in a Table -- Bag-of-Words Representation -- Single Text Files -- Exercise 4.1 - Case Study Using Dataset L: Resumes -- Exercise 4.2 - Case Study Using Dataset D: Occupation Descriptions -- Additional Exercise 4.3 - Case Study Using Dataset I: NAICS Codes -- Aggregating Across Rows and Columns -- Exercise 4.4 - Case Study Using Dataset D: Occupation Descriptions -- Additional Advanced Exercise 4.5 - Case Study Using Dataset E: Large Data Files -- Additional Advanced Exercise 4.6 - Case Study Using Dataset F: The Federalist Papers -- References -- Chapter 5 : Word Frequency Analysis -- What is Word Frequency Analysis? -- How Does It Apply to Text Business Data Analysis? -- Exercise 5.1 - Case Study Using Dataset A: Training Survey.
Exercise 5.2 - Case Study Using Dataset D: Job Descriptions -- Exercise 5.3 - Case Study Using Dataset C: Product Reviews -- Additional Exercise 5.4 - Case Study Using Dataset B: Consumer Complaints -- Chapter 6 : Keyword Analysis -- Exercise 6.1 - Case Study Using Dataset D: Resume and Job Description -- Exercise 6.2 - Case Study Using Dataset G: University Curriculum -- Exercise 6.3 - Case Study Using Dataset C: Product Reviews -- Additional Exercise 6.4 - Case Study Using Dataset B: Customer Complaints -- Chapter 7 : Sentiment Analysis -- What is Sentiment Analysis? -- Exercise 7.1 - Case Study Using Dataset C: Product Reviews - Rubbermaid -- Exercise 7.2 - Case Study Using Dataset C: Product Reviews-Windex -- Exercise 7.3 - Case Study Using Dataset C: Product Reviews-Both Brands -- Chapter 8 : Visualizing Text Data -- What Is Data Visualization Used For? -- Exercise 8.1 - Case Study Using Dataset A: Training Survey -- Exercise 8.2 - Case Study Using Dataset B: Consumer Complaints -- Exercise 8.3 - Case Study Using Dataset C: Product Reviews -- Exercise 8.4 - Case Study Using Dataset E: Large Text Files -- References -- Chapter 9 : Coding Text Data -- What is a Code? -- What are the Common Approaches to Coding Text Data? -- What is Inductive Coding? -- Exercise 9.1 - Case Study Using Dataset A: Training -- Exercise 9.2 - Case Study Using Dataset J: Remote Learning -- Exercise 9.3 - Case Study Using Dataset E: Large Text Files -- Affinity Diagram Coding -- Exercise 9.4 - Case Study Using Dataset M: Onboarding Brainstorming -- References -- Chapter 10 : Named Entity Recognition -- Named Entity Recognition -- What is a Named Entity? -- Common Approaches to Extracting Named Entities -- Classifiers - The Core NER Process -- What Does This Mean for Business? -- Exercise 10.1 - Using the Stanford NER -- Exercise 10.2 - Example Cases. Exercise 10.2 - Case Study Using Dataset H: Corporate Financial Reports -- Additional Exercise 10.3 - Case Study Using Dataset L: Corporate Financial Reports -- Exercise 10.4 - Case Study Using Dataset E: Large Text Files -- Additional Exercise 10.5 - Case Study Using Dataset E: Large Text Files -- References -- Chapter 11 : Topic Recognition in Documents -- Information Retrieval -- Document Characterization -- Topic Recognition -- Exercises -- Exercise 11.1 - Case Study Using Dataset G: University Curricula -- Exercise 11.2 - Case Study Using Dataset E: Large Text Files -- Exercise 11.3 - Case Study Using Dataset E: Large Text Files -- Exercise 11.4 - Case Study Using Dataset E: Large Text Files -- Exercise 11.5 - Case Study Using Dataset E: Large Text Files -- Additional Exercise 11.6 - Case Study Using Dataset P: Patents -- Additional Exercise 11.7 - Case Study Using Dataset F: Federalist Papers -- Additional Exercise 11.8 - Case Study Using Dataset E: Large Text Files -- Additional Exercise 11.9- Case Study Using Dataset N: Sonnets -- References -- Chapter 12 : Text Similarity Scoring -- What is Text Similarity Scoring? -- Text Similarity Scoring Exercises -- Exercise 12.1 - Case Study Using Dataset D: Occupation Description -- Analysis using R -- Exercise 12.2 - Case D: Resume and Job Description -- Reference -- Chapter 13 : Analysis of Large Datasets by Sampling -- Using Sampling to Work with Large Data Files -- Exercise 13.1 - Big Data Analysis -- Additional Case Study Using Dataset E: BankComplaints Big Data File -- Chapter 14 : Installing R and RStudio -- Installing R -- Install R Software for a Mac System -- Installing RStudio -- Reference -- Chapter 15 : Installing the Entity Extraction Tool -- Downloading and Installing the Tool -- The NER Graphical User Interface -- Reference -- Chapter 16 : Installing the Topic Modeling Tool. Installing and Using the Topic Modeling Tool -- Install the tool -- For Macs -- For Windows PCs -- UTF-8 caveat -- Setting up the workspace -- Workspace Directory -- Using the Tool -- Select metadata file -- Selecting the number of topics -- Analyzing the Output -- Multiple Passes for Optimization -- The Output Files -- Chapter 17 : Installing the Voyant Text Analysis Tool -- Install or Update Java -- Installation of Voyant Server -- The Voyant Server -- Downloading VoyantServer -- Running Voyant Server -- Controlling the Voyant Server -- Testing the Installation -- Reference -- INDEX. |
| Record Nr. | UNINA-9910806102003321 |
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Fortino Andres G.
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| Dulles : , : Mercury Learning & Information, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||