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Record Nr. |
UNINA9910456680203321 |
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Autore |
Menke William |
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Titolo |
Environmental data analysis with MatLab [[electronic resource] /] / William Menke, Joshua Menke |
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Pubbl/distr/stampa |
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Amsterdam ; ; Boston, : Elsevier, c2012 |
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ISBN |
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1-283-24992-8 |
9786613249920 |
0-12-391887-1 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (282 p.) |
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Altri autori (Persone) |
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MenkeJoshua E <1976-> (Joshua Ephraim) |
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Disciplina |
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Soggetti |
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Environmental sciences - Mathematical models |
Environmental sciences - Data processing |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Front Cover; Environmental Data Analysis with MatLab; Copyright; Dedication; Preface; Advice on scripting for beginners; Contents; Chapter 1: Data analysis with MatLab; 1.1. Why MatLab?; 1.2. Getting started with MatLab; 1.3. Getting organized; 1.4. Navigating folders; 1.5. Simple arithmetic and algebra; 1.6. Vectors and matrices; 1.7. Multiplication of vectors of matrices; 1.8. Element access; 1.9. To loop or not to loop; 1.10. The matrix inverse; 1.11. Loading data from a file; 1.12. Plotting data; 1.13. Saving data to a file; 1.14. Some advice on writing scripts; Problems |
Chapter 2: A first look at data2.1. Look at your data!; 2.2. More on MatLab graphics; 2.3. Rate information; 2.4. Scatter plots and their limitations; Problems; Chapter 3: Probability and what it has to do with data analysis; 3.1. Random variables; 3.2. Mean, median, and mode; 3.3. Variance; 3.4. Two important probability density functions; 3.5. Functions of a random variable; 3.6. Joint probabilities; 3.7. Bayesian inference; 3.8. Joint probability density functions; 3.9. Covariance; 3.10. Multivariate distributions; 3.11. The multivariate Normal distributions |
3.12. Linear functions of multivariate dataProblems; Chapter 4: The |
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power of linear models; 4.1. Quantitative models, data, and model parameters; 4.2. The simplest of quantitative models; 4.3. Curve fitting; 4.4. Mixtures; 4.5. Weighted averages; 4.6. Examining error; 4.7. Least squares; 4.8. Examples; 4.9. Covariance and the behavior of error; Problems; Chapter 5: Quantifying preconceptions; 5.1. When least square fails; 5.2. Prior information; 5.3. Bayesian inference; 5.4. The product of Normal probability density distributions; 5.5. Generalized least squares |
5.6. The role of the covariance of the data5.7. Smoothness as prior information; 5.8. Sparse matrices; 5.9. Reorganizing grids of model parameters; Problems; Chapter 6: Detecting periodicities; 6.1. Describing sinusoidal oscillations; 6.2. Models composed only of sinusoidal functions; 6.3. Going complex; 6.4. Lessons learned from the integral transform; 6.5. Normal curve; 6.6. Spikes; 6.7. Area under a function; 6.8. Time-delayed function; 6.9. Derivative of a function; 6.10. Integral of a function; 6.11. Convolution; 6.12. Nontransient signals; Problems |
Chapter 7: The past influences the present7.1. Behavior sensitive to past conditions; 7.2. Filtering as convolution; 7.3. Solving problems with filters; 7.4. Predicting the future; 7.5. A parallel between filters and polynomials; 7.6. Filter cascades and inverse filters; 7.7. Making use of what you know; Problems; Chapter 8: Patterns suggested by data; 8.1. Samples as mixtures; 8.2. Determining the minimum number of factors; 8.3. Application to the Atlantic Rocks dataset; 8.4. Spiky factors; 8.5. Time-Variable functions; Problems; Chapter 9: Detecting correlations among data |
9.1. Correlation is covariance |
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Sommario/riassunto |
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Environmental Data Analysis with MatLab is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teachi |
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2. |
Record Nr. |
UNISA996630871903316 |
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Autore |
Barhamgi Mahmoud |
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Titolo |
Web Information Systems Engineering – WISE 2024 : 25th International Conference, Doha, Qatar, December 2–5, 2024, Proceedings, Part V / / edited by Mahmoud Barhamgi, Hua Wang, Xin Wang |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (531 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 15440 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Information technology - Management |
Computer Application in Administrative Data Processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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-- Security, Privacy and Trust. -- Privacy-Preserving k-core Decomposition For Graphs. -- Anomaly Detection in Log Streams based on Time-Contextual Models. -- Enhancing Open-Set Recognition with Global Feature Representation. -- Dynamic-Parameter Genetic Algorithm for Multi-objective Privacy-Preserving Trajectory Data Publishing. -- A Graph-Based Approach for Software Functionality Classification on the Web. -- FUD-LDP: Fully User Driven Local Differential Privacy. -- A Privacy-Preserving Encryption framework for Big data analysis. -- Location nearest neighbor query scheme in edge computing based on differential privacy. -- Open Research Challenges for Private Advertising Systems under Local Differential Privacy. -- Industry-Specific Vulnerability Assessment. -- Blockchain-Driven Medical Data Shamir Threshold Encryption with Attribute-Based Access Control Scheme. -- Smart Contracts Vulnerability Detection Using Transformers. -- Weibo-FA: A Benchmark Dataset for Fake Account Detection in Weibo Platform. -- More Than Just a Random Number Generator! Unveiling the Security and Privacy Risks of Mobile OTP Authenticator Apps. -- Synthetic Data Generation: Limits and Improvement of Avatar Data. -- A Lightweight Detection of Sequential Patterns in File System Events During |
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Ransomware Attacks. -- Detection and Mitigation of Backdoor Attacks on x-Apps. -- Cohesive database neighborhoods for differential privacy: mapping relational databases to RDF. -- i-Right: Identifying and classifying GDPR user rights in fitness tracker and smart home privacy policie. -- AttackER: Towards Enhancing Cyber-Attack Attribution with a Named Entity Recognition Dataset. -- R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional Gradients. -- Privacy Preserving Behavioral Anomaly Detection in Dynamic Graphs for Card Transactions. -- Online Safety and Wellbeing through AI. -- DisCo-FEND: Social Context Veracity Dissemination Consistency-Guided Case Reasoning for Few-Shot Fake News Detection. -- NLWM: a Robust, Efficient and High-quality Watermark for Large Language Models. -- Improving the Robustness of Rumor Detection Models with Metadata-Augmented Evasive Rumor Datasets. -- Rumor Alteration for Improving Rumor Generation. -- Generating Effective Answers to People’s Everyday Cybersecurity Questions: An Initial Study. -- Propaganda to Hate: A Multimodal Analysis of Arabic Memes with Multi-Agent LLMs. -- Did You Tell a Deadly Lie? Evaluating Large Language Models for Health Misinformation Identification. -- Native vs Non-Native Language Prompting: A Comparative Analysis. -- DisFact: Fact-Checking Disaster Claims. -- Web Technologies. -- Multi-Perspective Conformance Checking For Email-driven Processes. -- Progressive Server-Side Rendering with Suspendable Web Templates. -- HMSC-LLMs: A Hierarchical Multi-Agent Service Composition Method Based on Large Language Models. -- Enhancing Web Spam Detection through a Blockchain-Enabled Crowdsourcing Mechanism. -- WNSWE: Web-based Network Simulator for Web Engineering Education. |
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Sommario/riassunto |
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This five-volume set LNCS 15436 -15440 constitutes the proceedings of the 25th International Conference on Web Information Systems Engineering, WISE 2024, held in Doha, Qatar, in December 2024. The 110 full papers and 55 short papers were presented in these proceedings were carefully reviewed and selected from 368 submissions. The papers have been organized in the following topical sections as follows: Part I : Information Retrieval and Text Processing; Text and Sentiment Analysis; Data Analysis and Optimisation; Query Processing and Information Extraction; Knowledge and Data Management. Part II: Social Media and News Analysis; Graph Machine Learning on Web and Social; Trustworthy Machine Learning; and Graph Data Management. Part III: Recommendation Systems; Web Systems and Architectures; and Humans and Web Security. Part IV: Learning and Optimization; Large Language Models and their Applications; and AI Applications. Part V: Security, Privacy and Trust; Online Safety and Wellbeing through AI; and Web Technologies. |
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