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Introducing .Net for Apache Spark : distributed processing for massive datasets by Elliott, Ed

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Future data and security engineering : big data, security and privacy, smart city and Industry 4.0 applications : 9th International Conference, FDSE 2022, Ho Chi Minh City, Vietnam... by FDSE (Conference) Ho Chi Minh City, Vietnam), SpringerLink (Online service)

Contents: “…Invited Keynotes -- Towards a Foundation of Web3 -- In-Memory Computing Architectures for Big data and Machine Learning Applications -- Secure and Efficient Implementation of Electronic Money -- Managing the Quality of Data and Metadata for Biobanks -- Big Data Analytics and Distributed Systems -- Towards A Privacy, Secured and Distributed Clinical Data Warehouse Architecture -- Improving the Storage Utilization of 0-Complete Trees as Index Structures -- Implement the Data Conversion System by using Lightweight Coreset for Validation Process -- Content Selection Methods Using User Interest Prediction Based on Similarities of Web Activities -- ImageNet Challenging Classification with the Raspberry Pis: A Federated Learning Algorithm of Local Stochastic Gradient Descent Models -- Covid-19 Detection Based on Lung Lesion Signs in Big Data Processing Environment -- Predicting Loan Repayment Using A Hybrid of Genetic Algorithms, Logistic Regression, and Artificial Neural Networks -- Performance Evaluation of Regular Decomposition and Benchmark Clustering Methods -- Security and Privacy Engineering -- Authentication of Luxury Products Identifying Key Requirements from a Seller and Consumer Perspective -- Analysis of A New Practical SPN-based Scheme in the Luby-Rackoff Model -- Authorization and Access Control for Different Database Models: Requirements and Current State of the Art -- A Hierarchical Deterministic Wallet using Ed25519 Digital Signature Scheme -- A Secure Framework for Internet of Medical Things Security Based System using Lightweight Cryptography enabled Blockchain -- Signature Algorithms on Non-commutative Algebras over Finite Fields of Characteristic Two -- Design a Smart Lock System Using Fingerprint and Password for Enhancing Security -- Machine Learning and Artificial Intelligence for Security and Privacy -- Towards an Attention-based Threat Detection System for IoT Networks -- Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare -- pPATE: A Pragmatic Private Aggregation of Teacher Ensembles Framework by Sparse Vector Technique based Differential Privacy, Paillier Cryptosystem and Human-in-the-loop -- Vietnamese Texts Writing Styles Based Authorship Identification Model -- Application of Machine Learning in Malware Detection -- Smart City and Industry 4.0 Applications -- Text Classification Models and Topic Models: An Overall Picture and A Case Study in Vietnamese -- Deep Hybrid Models for Forecasting Stock Midprices from the High-Frequency Limit Order Book -- An Image Denoising Model based on Nonlinear Partial Diferential Equation Using Deep Learning -- Recommendations in E-Commerce Systems based on Deep Matrix Factorization -- AttendanceKit: A set of Role-based Mobile Applications for Automatic Attendance Checking with UHF RFID using Realtime Firebase and Face Recognition -- Poses Classification in a Taekwondo Lesson using Skeleton Data extracted from Videos with Shallow and Deep Learning Architectures -- Social Distancing Violation Detection in Video using ChessBoard and Bird's-eye Perspective -- 3D-FaultSeg-UNet: 3D Fault Segmentation in Seismic Data Using Bi-stream U-Net -- Applying Artificial Intelligence to Plan Flight Paths for Aircraft at Subsonic Speeds -- Capacity Building in Government: Towards Developing A Standard for A Functional Specialist in AI for Public Services -- On the Feasibility of Machine Learning Models for Customer Spending Prediction Problem -- Forecasting the Opening and Closing Price Trends of Stock Using Hybrid Models and Artificial Intelligence Algorithm -- Artificial Intelligence Use in e-Government Services: A Systematic Interdisciplinary Literature Review -- Face Recognition based on Deep Learning and Data Augmentation -- An Approach for Similarity Vietnamese Documents Detection from English Documents -- Combine Clasification Algorithm and Centernet Model to Predict Trafic Density -- Data Analytics and Healthcare Systems -- Comparison of Health Indicators Construction for Concrete Structure Using Acoustic Emission Hit and Kullback-Leibler Divergence -- Lung and Colon Tumor Classification Based on Transfer Learning-Based Techniques -- An Enhanced Diabetes Mellitus Prediction using Feature Selection-Based Type-2 Fuzzy Model -- A Novel Approach of Using Neural Circuit Policies for COVID-19 Classification on CT-images -- Lung Lesions Segmentation and Classification with Deep Neural Networks -- Security and Data Engineering -- Image Denoising using Fully Connected Network with Reinforcement Learning -- Energy Harvesting Aware for Delay-efficient Data Aggregation in Battery-free IoT Sensors -- Deep Models for Mispronounce Prediction for Vietnamese Learners of English -- An Approach to Hummed-tune and Song Sequences Matching -- 84 Birds Classification using Transfer Learning and EfficientNetB2 -- A Session-based Recommender System for Learning Resources -- A Drowsiness Detection System Based on Eye Landmarks Using IoT -- A Novel Approach for Vietnamese Speech Recognition Using Conformer -- Enhancing obfuscated malware detection with machine learning techniques -- Detecting Intrusion using Multiple Datasets in Software-Defined Networks -- Detecting Exams Fraud Using Transfer Learning and Fine-tuning for ResNet50 -- Shape of Pill Recognition Using Mask R-CNN.…”
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Data warehousing in the age of big data by Krishnan, Krish

Contents: “…Front Cover -- Data Warehousing in the Age of Big Data -- Copyright Page -- Contents -- Acknowledgments -- About the Author -- Introduction -- Part 1: Big Data -- Part 2: The Data Warehousing -- Part 3: Building the Big Data -- Data Warehouse -- Appendixes -- Companion website -- 1 BIG DATA -- 1 Introduction to Big Data -- Introduction -- Big Data -- Defining Big Data -- Why Big Data and why now? …”
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Big data and the internet of things : enterprise information architecture for a new age by Stackowiak, Robert, Licht, Art, Mantha, Venu, Nagode, Louis

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Guide to big data applications by SpringerLink (Online service)

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The data science handbook by Cady, Field, 1984-

Contents: “…-- 16.2 The Great Divide: Language versus Statistics -- 16.3 Example: Sentiment Analysis on Stock Market Articles -- 16.4 Software and Datasets -- 16.5 Tokenization -- 16.6 Central Concept: Bag-of-Words -- 16.7 Word Weighting: TF-IDF -- 16.8 n-Grams -- 16.9 Stop Words -- 16.10 Lemmatization and Stemming 16.11 Synonyms -- 16.12 Part of Speech Tagging -- 16.13 Common Problems -- 16.14 Advanced NLP: Syntax Trees, Knowledge, and Understanding -- 16.15 Further Reading -- 16.16 Glossary -- Chapter 17 Time Series Analysis -- 17.1 Example: Predicting Wikipedia Page Views -- 17.2 A Typical Workflow -- 17.3 Time Series versus Time-Stamped Events -- 17.4 Resampling an Interpolation -- 17.5 Smoothing Signals -- 17.6 Logarithms and Other Transformations -- 17.7 Trends and Periodicity -- 17.8 Windowing -- 17.9 Brainstorming Simple Features -- 17.10 Better Features: Time Series as Vectors -- 17.11 Fourier Analysis: Sometimes a Magic Bullet -- 17.12 Time Series in Context: The Whole Suite of Features -- 17.13 Further Reading -- 17.14 Glossary -- Chapter 18 Probability -- 18.1 Flipping Coins: Bernoulli Random Variables -- 18.2 Throwing Darts: Uniform Random Variables -- 18.3 The Uniform Distribution and Pseudorandom Numbers -- 18.4 Nondiscrete, Noncontinuous Random Variables -- 18.5 Notation, Expectations, and Standard Deviation -- 18.6 Dependence, Marginal and Conditional Probability -- 18.7 Understanding the Tails -- 18.8 Binomial Distribution -- 18.9 Poisson Distribution -- 18.10 Normal Distribution -- 18.11 Multivariate Gaussian -- 18.12 Exponential Distribution -- 18.13 Log-Normal Distribution -- 18.14 Entropy 18.15 Further Reading -- 18.16 Glossary -- Chapter 19 Statistics -- 19.1 Statistics in Perspective -- 19.2 Bayesian versus Frequentist: Practical Tradeoffs and Differing Philosophies -- 19.3 Hypothesis Testing: Key Idea and Example -- 19.4 Multiple Hypothesis Testing -- 19.5 Parameter Estimation -- 19.6 Hypothesis Testing: t-Test -- 19.7 Confidence Intervals -- 19.8 Bayesian Statistics -- 19.9 Naive Bayesian Statistics -- 19.10 Bayesian Networks -- 19.11 Choosing Priors: Maximum Entropy or Domain Knowledge -- 19.12 Further Reading -- 19.13 Glossary -- Chapter 20 Programming Language Concepts -- 20.1 Programming Paradigms -- 20.2 Compilation and Interpretation -- 20.3 Type Systems -- 20.4 Further Reading -- 20.5 Glossary -- Chapter 21 Performance and Computer Memory -- 21.1 Example Script -- 21.2 Algorithm Performance and Big-O Notation -- 21.3 Some Classic Problems: Sorting a List and Binary Search -- 21.4 Amortized Performance and Average Performance -- 21.5 Two Principles: Reducing Overhead and Managing Memory -- 21.6 Performance Tip: Use Numerical Libraries When Applicable -- 21.7 Performance Tip: Delete Large Structures You Don't Need -- 21.8 Performance Tip: Use Built-In Functions When Possible -- 21.9 Performance Tip: Avoid Superfluous Function Calls -- 21.10 Performance Tip: Avoid Creating Large New Objects -- 21.11 Further Reading -- 21.12 Glossary -- Part III Specialized or Advanced Topics -- Chapter 22 Computer Memory and Data Structures -- 22.1 Virtual Memory, the Stack, and the Heap -- 22.2 Example C Program -- 22.3 Data Types and Arrays in Memory -- 22.4 Structs -- 22.5 Pointers, the Stack, and the Heap -- 22.6 Key Data Structures -- 22.7 Further Reading -- 22.8 Glossary -- Chapter 23 Maximum Likelihood Estimation and Optimization -- 23.1 Maximum Likelihood Estimation -- 23.2 A Simple Example: Fitting a Line -- 23.3 Another Example: Logistic Regression -- 23.4 Optimization -- 23.5 Gradient Descent and Convex Optimization -- 23.6 Convex Optimization -- 23.7 Stochastic Gradient Descent -- 23.8 Further Reading -- 23.9 Glossary -- Chapter 24 Advanced Classifiers -- 24.1 A Note on Libraries -- 24.2 Basic Deep Learning -- 24.3 Convolutional Neural Networks -- 24.4 Different Types of Layers.…”
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Recent innovations in computing : proceedings of ICRIC 2020 ICRIC 2020. by International Conference on Recent Innovations in Computing Jammu, India, SpringerLink (Online service)

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IoT solutions in Microsoft's Azure IoT suite : data acquisition and analysis in the real world by Klein, Scott, 1966-

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