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• Chapter 1. Introduction to Analytics and Data Science.
• Chapter 2. Data Types Structure & Data Preparation Process.
• Chapter 3. Data Exploration and Data Visualization.
• Chapter 4. Evaluating Predictive Performance.
• Chapter 5. Decision Trees & Ensemble.
• Chapter 6. Regression Models.
• Chapter 7. Neural Networks.
• Chapter 8. Model Deployment.
• .
• (source: Nielsen Book Data)

This text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they can be applied and explained within improving decisions.
(source: Nielsen Book Data)

The benchmarking effort within the Extreme Scale Systems Center at Oak Ridge National Laboratory seeks to provide High Performance Computing benchmarks and test suites of interest to the DoD sponsor. The work described in this report is a part of the effort focusing on graph generation. A previously developed benchmark, SystemBurn, allowed the emulation of dierent application behavior profiles within a single framework. To complement this effort, similar capabilities are desired for graph-centric problems. This report examines existing synthetic graph generator implementations in preparation for further study on the properties of their generated synthetic graphs.

Overview of current state-of-the-art of social media for NTI Working Group III: Societal Verification

• Table of Contents Opportunities and Challenges Platform Components and Key Concepts Fundamental Workflow - Data to Deployable Model H2O Model Building at Scale - Capability Articulation Advanced Model Building - Part I Advanced Model Building - Part II Understanding ML Models Putting It All Together Production Scoring and the H2O MOJO H2O Model Deployment Patterns The Administrator and Operations Views The Enterprise Architect and Security Views Introducing the H2O AI Cloud H2O at Scale in a Larger Platform Context Appendix - Alternative Methods to Launch H2O Clusters.
• (source: Nielsen Book Data)

Build predictive models using large data volumes and deploy them to production using cutting-edge techniques Key Features Build highly accurate state-of-the-art machine learning models against large-scale data Deploy models for batch, real-time, and streaming data in a wide variety of target production systems Explore all the new features of the H2O AI Cloud end-to-end machine learning platform Book DescriptionH2O is an open source, fast, and scalable machine learning framework that allows you to build models using big data and then easily productionalize them in diverse enterprise environments. Machine Learning at Scale with H2O begins with an overview of the challenges faced in building machine learning models on large enterprise systems, and then addresses how H2O helps you to overcome them. You'll start by exploring H2O's in-memory distributed architecture and find out how it enables you to build highly accurate and explainable models on massive datasets using your favorite ML algorithms, language, and IDE. You'll also get to grips with the seamless integration of H2O model building and deployment with Spark using H2O Sparkling Water. You'll then learn how to easily deploy models with H2O MOJO. Next, the book shows you how H2O Enterprise Steam handles admin configurations and user management, and then helps you to identify different stakeholder perspectives that a data scientist must understand in order to succeed in an enterprise setting. Finally, you'll be introduced to the H2O AI Cloud platform and explore the entire machine learning life cycle using multiple advanced AI capabilities. By the end of this book, you'll be able to build and deploy advanced, state-of-the-art machine learning models for your business needs. What you will learn Build and deploy machine learning models using H2O Explore advanced model-building techniques Integrate Spark and H2O code using H2O Sparkling Water Launch self-service model building environments Deploy H2O models in a variety of target systems and scoring contexts Expand your machine learning capabilities on the H2O AI Cloud Who this book is forThis book is for data scientists and machine learning engineers who want to gain hands-on machine learning experience by building and deploying state-of-the-art models with advanced techniques using H2O technology. An understanding of the data science process and experience in Python programming is recommended. This book will also benefit students by helping them understand how machine learning works in real-world enterprise scenarios.
(source: Nielsen Book Data)

• Table of Contents Understanding H2O AutoML Basics Working with H2O Flow (H2O's Web UI) Understanding Data Processing Understanding H2O AutoML Training and Architecture Understanding AutoML Algorithms Understanding H2O AutoML Leaderboard and Other Performance Metrics Working with Model Explainability Exploring Optional Parameters for H2O AutoML Exploring Miscellaneous Features in H2O AutoML Working with Plain Old Java Objects (POJOs) Working with Model Object, Optimized (MOJO) Working with H2O AutoML and Apache Spark Using H2O AutoML with Other Technologies.
• (source: Nielsen Book Data)

Accelerate the adoption of machine learning by automating away the complex parts of the ML pipeline using H2O.ai Key Features Learn how to train the best models with a single click using H2O AutoML Get a simple explanation of model performance using H2O Explainability Easily deploy your trained models to production using H2O MOJO and POJO Book DescriptionWith the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in - it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities. You'll begin by understanding how H2O's AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, you'll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, you'll find out how to leverage a Plain Old Java Object (POJO) and Model Object, Optimized (MOJO) to deploy your models to production. Throughout this book, you'll take a hands-on approach to implementation using H2O that'll enable you to set up your ML systems in no time. By the end of this H2O book, you'll be able to train and use your ML models using H2O AutoML, right from experimentation all the way to production without a single need to understand complex statistics or data science. What you will learn Get to grips with H2O AutoML and learn how to use it Explore the H2O Flow Web UI Understand how H2O AutoML trains the best models and automates hyperparameter optimization Find out how H2O Explainability helps understand model performance Explore H2O integration with scikit-learn, the Spring Framework, and Apache Storm Discover how to use H2O with Spark using H2O Sparkling Water Who this book is forThis book is for engineers and data scientists who want to quickly adopt machine learning into their products without worrying about the internal intricacies of training ML models. If you're someone who wants to incorporate machine learning into your software system but don't know where to start or don't have much expertise in the domain of ML, then you'll find this book useful. Basic knowledge of statistics and programming is beneficial. Some understanding of ML and Python will be helpful.
(source: Nielsen Book Data)

• Introduction Part I: Principles of Data Visualization 1. Visual Processing and Perceptual Rankings 2. Five Guidelines for Better Data Visualizations 3. Form and Function Part II: Chart Types 4. Comparing Categories 5. Time 6. Distribution 7. Geospatial 8. Relationship 9. Part-to-Whole 10. Qualitative 11. Tables Part III: Designing and Redesigning Your Visual 12. Developing a Data Visualization Style Guide 13. Redesigns Conclusion
• Appendix 1. Data Visualization Tools
• Appendix 2. Further Reading and Resources Acknowledgments References Index.
• (source: Nielsen Book Data)

Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do's and don'ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart's design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message.
(source: Nielsen Book Data)

• Introduction Part I: Principles of Data Visualization 1. Visual Processing and Perceptual Rankings 2. Five Guidelines for Better Data Visualizations 3. Form and Function Part II: Chart Types 4. Comparing Categories 5. Time 6. Distribution 7. Geospatial 8. Relationship 9. Part-to-Whole 10. Qualitative 11. Tables Part III: Designing and Redesigning Your Visual 12. Developing a Data Visualization Style Guide 13. Redesigns Conclusion
• Appendix 1. Data Visualization Tools
• Appendix 2. Further Reading and Resources Acknowledgments References Index.
• (source: Nielsen Book Data)

Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually. This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do's and don'ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart's design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message.
(source: Nielsen Book Data)

The need for novel data analysis is urgent in the face of a data deluge from modern applications. Traditional approaches to data analysis incur significant data movement costs, moving data back and forth between the storage system and the processor. Emerging Active Flash devices enable processing on the flash, where the data already resides. An array of such Active Flash devices allows us to revisit how analysis workflows interact with storage systems. By seamlessly blending together the flash storage and data analysis, we create an analysis workflow-aware storage system, AnalyzeThis. Our guiding principle is that analysis-awareness be deeply ingrained in each and every layer of the storage, elevating data analyses as first-class citizens, and transforming AnalyzeThis into a potent analytics-aware appliance. We implement the AnalyzeThis storage system atop an emulation platform of the Active Flash array. Our results indicate that AnalyzeThis is viable, expediting workflow execution and minimizing data movement.

Our research in this project focused on creating and evaluating an I/O infrastructure and tools for extreme-scale applications and machines so that scientists can reduce their time to discovery at small cost in machine resources and consequent power consumption. We wanted to provide tools that are highly scalable, portable, and easy-to-use, so that scientists can gain control of their science, and concentrate on producing important scientific discovery in their own domain. Accelerating the rate of insight and scientific productivity, therefore, demands new solutions to managing the avalanche of data expected at extreme scale.

• Introduction to Multivariate Network Visualization
• Multivariate Networks in Software Engineering.-Multivariate Social Network Visual Analytics
• Multivariate Networks in the Life Sciences.-Tasks for Multivariate Network Analysis.-Interaction in the Visualization of Multivariate Networks.-Novel Visual Metaphors for Multivariate Networks.-Temporal Multivariate Networks
• Heterogeneous Networks on Multiple Levels.-Scalability Considerations for Multivariate Graph Visualization.

This book is the outcome of the Dagstuhl Seminar 13201 on Information Visualization - Towards Multivariate Network Visualization, held in Dagstuhl Castle, Germany in May 2013. The goal of this Dagstuhl Seminar was to bring together theoreticians and practitioners from Information Visualization, HCI and Graph Drawing with a special focus on multivariate network visualization, i.e., on graphs where the nodes and/or edges have additional (multidimensional) attributes. The integration of multivariate data into complex networks and their visual analysis is one of the big challenges not only in visualization, but also in many application areas. Thus, in order to support discussions related to the visualization of real world data, also invited researchers from selected application areas, especially bioinformatics, social sciences and software engineering. The unique "Dagstuhl climate" ensured an open and undisturbed atmosphere to discuss the state-of-the-art, new directions and open challenges of multivariate network visualization

• Intro
• Title
• Copyright
• Contents
• About the Editors
• 1 COVID-19 detection in X-ray images using customized CNN model
• 1.1 Introduction
• 1.2 Related work
• 1.2.1 Key contributions and proposed work
• 1.3 Materials and methods
• 1.3.1 Feature extraction and selection
• 1.4 Results and discussion
• 1.5 Conclusion and future scope
• References
• 2 Introducing deep learning in medical diagnosis
• 2.1 Introduction
• 2.2 Literature survey
• 2.3 Overview of DL algorithms
• 2.3.1 Convolutional neural network
• 2.3.2 Recurrent neural network
• 2.3.3 Long short-term memory

• 2.3.4 Restricted Boltzmann machine
• 2.3.5 Deep belief networks
• 2.4 Proposed DL framework for neuro disease diagnosis
• 2.4.1 FAST-RCNN
• 2.4.2 Ten fully connected layer
• 2.5 Preprocessing of dataset
• 2.6 Implementation and results
• 2.7 Conclusion
• References
• 3 Intelligent approach for network intrusion detection system (NIDS) utilizing machine learning (ML)
• 3.1 Introduction
• 3.1.1 DoS and DDoS attacks
• 3.1.2 Man-in-the-middle (MitM) attack
• 3.1.3 Phishing and spear-phishing attacks
• 3.1.4 Password attack
• 3.1.5 Eavesdropping attack
• 3.1.6 Malware attack

• 3.2 Related work
• 3.3 Cloud computing
• 3.3.1 Machine learning
• 3.3.2 Exploratory data analysis
• 3.4 Results
• References
• 4 Classification methodologies in healthcare
• 4.1 Introduction
• 4.2 Classification algorithms
• 4.2.1 Statistical data
• 4.2.2 Discriminant analysis
• 4.2.3 Decision tree
• 4.2.4 K-nearest neighbor (KNN)
• 4.2.5 Logistic regression (LR)
• 4.2.6 Bayesian classifier
• 4.2.7 Support vector machine (SVM)
• 4.3 Parameter identification
• 4.3.1 Feature selection for classi cation
• 4.4 Real-time applications

• 4.4.1 Classification of patients based on medical record
• 4.4.2 Predictive analytics and diagnostic analytics based on medical records
• 4.4.3 Classification of diseases based on medical imaging
• 4.4.4 Mixed reality-based automation to help aid aging society
• 4.4.5 Tiny ML-based classification systems for medical gadgets
• 4.4.6 Classification systems for insurance claim management
• 4.4.7 Case study: Inspectra from Perceptra
• 4.4.8 Deep learning for beginners
• References
• 5 Introducing deep learning in medical domain
• 5.1 Introduction
• 5.1.1 DL in a nutshell

• 5.1.2 History of DL in the medical field
• 5.1.3 Benefits of DL in the medical domain
• 5.1.4 Challenges and obstacles of DL in the medical domain
• 5.1.5 Opportunities of DL in the medical field
• 5.2 DL applications in the medical domain
• 5.2.1 Drug discovery and medicine precision
• 5.2.2 Detection of diseases
• 5.2.3 Diagnosing patients
• 5.2.4 Healthcare administration
• 5.3 DL for medical image analysis
• 5.3.1 Medical image detection
• 5.3.2 Medical image recognition
• 5.3.3 Medical image segmentation
• 5.3.4 Medical image registration

This book examines machine learning trends in predictive technology to solve real-time healthcare problems. By using real-time data inputs to build predictive models, this new technology can model disease progression, assist with interventions or predict patient outcomes.

This seminal work tackles how to communicate data. There are plenty of books on visualizing data, but never one on how to communicate it to speed up decision making and drive results.
(source: Nielsen Book Data)

• Federated Time-dependent GNN Learning from Brain Connectivity Data with Missing Timepoints.- Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing.- Multi-Tracer PET Imaging Using Deep Learning: Applications in Patients with High-Grade Gliomas.- Multiple Instance Neuroimage Transformer.- Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach.- Mixup augmentation improves age prediction from T1-weighted brain MRI scans.- Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning.- MISS-Net: Multi-view contrastive transformer network for MCI stages prediction using brain 18F-FDG PET imaging.- TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation.- Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study.- Weakly-Supervised TILs Segmentation based on Point Annotations using Transfer Learning with Point Detector and Projected-Boundary Regressor.- Discriminative Deep Neural Network for Predicting Knee OsteoArthritis in Early Stage.- Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-Task Learning on Imaging and Tabular Data.- Quantifying the Predictive Uncertainty of Regression GNN Models Under Target Domain Shifts.- Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets.- Learning subject-specific functional parcellations from cortical surface measures.- A Triplet Contrast Learning of Global and Local Representations for Unannotated Medical Images.- Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification.- Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with MICCAI 2022 as a hybrid event in Singapore, in September 2022.The 19 papers presented in this volume were carefully reviewed and selected for inclusion in this book. The contributions describe new cutting-edge predictive models and methods that solve challenging problems in the medical field for a high-precision predictive medicine.
(source: Nielsen Book Data)

• Federated Time-dependent GNN Learning from Brain Connectivity Data with Missing Timepoints.- Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing.- Multi-Tracer PET Imaging Using Deep Learning: Applications in Patients with High-Grade Gliomas.- Multiple Instance Neuroimage Transformer.- Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach.- Mixup augmentation improves age prediction from T1-weighted brain MRI scans.- Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning.- MISS-Net: Multi-view contrastive transformer network for MCI stages prediction using brain 18F-FDG PET imaging.- TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation.- Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study.- Weakly-Supervised TILs Segmentation based on Point Annotations using Transfer Learning with Point Detector and Projected-Boundary Regressor.- Discriminative Deep Neural Network for Predicting Knee OsteoArthritis in Early Stage.- Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-Task Learning on Imaging and Tabular Data.- Quantifying the Predictive Uncertainty of Regression GNN Models Under Target Domain Shifts.- Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets.- Learning subject-specific functional parcellations from cortical surface measures.- A Triplet Contrast Learning of Global and Local Representations for Unannotated Medical Images.- Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification.- Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with MICCAI 2022 as a hybrid event in Singapore, in September 2022.The 19 papers presented in this volume were carefully reviewed and selected for inclusion in this book. The contributions describe new cutting-edge predictive models and methods that solve challenging problems in the medical field for a high-precision predictive medicine.
(source: Nielsen Book Data)