Tabla de Contenidos: “…Wong, Sorin Istrail -- Extracting significant sample-specific cancer mutations using their protein interactions / Liviu Badea -- The stream algorithm: Computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity / Elias Chaibub Neto ... …”

Tabla de Contenidos: “…Session introduction / Kevin Bretonnel Cohen ... [et al.]. The extraction of pharmacogenetic and pharmacogenomic relations - A case study using PharmGKB / Ekaterina Buyko, Elena Beisswanger, and Udo Hahn. …”

Tabla de Contenidos: “…-- Packaging Options -- Building Blocks of the IoT -- Chapter 5: Small Data, Big Data, and Human Interaction -- The "Brains" of the IoT -- For Once, IP Makes Sense -- Extracting the Streams -- Analysis and Control -- Chirps to "Small Data" to Big Data: An Example -- Neighborhoods and Affinities -- Public, Private, and Some of Each -- Bias Bonus -- Searching for and Managing Agents -- High- and Low-Level "Loops " -- Human Interface and Control Points -- Machines and Metcalfe -- Collaborative Scheduling Tools -- Packaging and Provisioning -- Distribut ed Integrator Functions…”

Tabla de Contenidos: “…-- 4.2 A Simple Binary-Loading Interface -- 4.3 Implementing the Binary Loader -- 4.4 Testing the Binary Loader -- 4.5 Summary -- Exercises -- PART II: BINARY ANALYSIS FUNDAMENTALS -- 5 BASIC BINARY ANALYSIS IN LINUX -- 5.1 Resolving Identity Crises Using file -- 5.2 Using ldd to Explore Dependencies -- 5.3 Viewing File Contents with xxd -- 5.4 Parsing the Extracted ELF with readelf -- 5.5 Parsing Symbols with nm -- 5.6 Looking for Hints with strings -- 5.7 Tracing System Calls and Library Calls with strace and ltrace -- 5.8 Examining Instruction-Level Behavior Using objdump -- 5.9 Dumping a Dynamic String Buffer Using gdb -- 5.10 Summary -- Exercise -- 6 DISASSEMBLY AND BINARY ANALYSIS FUNDAMENTALS -- 6.1 Static Disassembly -- 6.2 Dynamic Disassembly -- 6.3 Structuring Disassembled Code and Data -- 6.4 Fundamental Analysis Methods -- 6.5 Effects of Compiler Settings on Disassembly…”

Tabla de Contenidos: “…Understanding index maintenance -- The REBUILD index -- The REORGANIZE index -- Caution -- Summary -- Further reading -- Part 4: Working with Your Data on the Modern Data Platform -- Chapter 11 - Handling JSON Data in SQL Server -- Technical requirements -- Introducing JSON functionality built into SQL Server -- The basics of JSON -- JSON functions in SQL Server -- JSON as data in SQL Server -- JSON path in SQL Server -- Formatting results as JSON in SQL Server -- Using FOR JSON -- Filling the table -- Working with JSON data in SQL queries -- Discovering JSON data in SQL Server -- Extracting JSON data in SQL Server -- Modifying JSON data in SQL Server -- Building tabular results from JSON data in SQL Server -- Summary -- Chapter 12 - Integrating File Data and Data Lake Content with SQL -- Technical requirements -- Understanding the OPENROWSET (BULK..) function -- Required security and storage permissions -- Understanding external tables -- Summary -- Chapter 13 - Organizing and Sharing Your Queries with Jupyter Notebooks -- Technical requirements -- Creating Jupyter notebooks in Azure Data Studio -- Understanding notebooks in Azure Data Studio -- Creating a notebook in Azure Data Studio -- Creating the Jupyter book -- Adding a notebook to your Jupyter book -- Adding queries in your Jupyter notebooks -- Creating connections in your notebook -- Creating and executing SQL code in your notebook -- Formatting results -- Exporting results -- Resetting results -- Documenting your code with markdown in your notebooks -- Adding a text block to your notebook -- Creating a header for your notebook -- Using basic markdown for documentation -- Adding images and links to your documentation -- Managing and sharing your queries with Jupyter notebooks -- Simple sharing of Jupyter books and notebooks -- Using GitHub to collaborate on your notebook…”

Tabla de Contenidos: “…Programming in JavaScript using node-redis -- Redis OM for JavaScript -- Programming in Go using go-redis -- Storing information in Redis Stack using Go -- Programming in C#/.NET using NRedisStack -- Storing information in Redis Stack using C#/.NET -- Redis OM for C#/.NET -- Summary -- Part 2: Data Modeling -- Chapter 5: Redis Stack as a Document Store -- Technical requirements -- Storing and querying documents in Redis Stack -- The dialect and other configuration parameters -- The query language -- Simple terms -- Using field modifiers -- Intersection of results (AND) -- Union of results (OR) -- Exact query matches -- Stop words -- Negation and purely negative queries -- Prefix, infix, and suffix queries -- Wildcard matching -- Fuzzy matching -- Numeric filters -- Tag filters -- Geospatial filters -- Aggregation and transformation -- Cursor-based requests -- Faceted search using aggregations -- Updating an index in production -- Temporary indexes -- Additional commands -- Working with Hashes -- Highlighting and summarizing -- Synonyms support -- Spellchecking -- Auto-completion -- Phonetic matching -- Working with JSON -- The JSONPath syntax -- Indexing a JSON document -- Indexing arrays and objects -- Multi-value indexing -- Extracting partial data from JSON documents -- Redis Stack as a recommendation engine -- Recommendation using scores -- Redis Stack as a session store -- Redis data structures for session data -- Additional Redis data structures -- Modeling key-value pairs -- Modeling objects -- Modeling collections -- Session management for real-time applications -- Summary -- Chapter 6: Redis Stack as a Vector Database -- Technical requirements -- Vector embeddings for unstructured data modeling -- Storing the embeddings -- Hashes -- JSON -- Indexing the embeddings -- The algorithms - FLAT and HNSW -- Type -- Index dimension…”

Tabla de Contenidos: “…Special considerations -- 6.1 Programming considerations -- 6.1.1 Exposing InteractionSpec / ConnectionSpec properties as data -- 6.1.2 Programmatically reading and setting InteractionSpec values -- 6.1.3 Programmatically reading and setting ConnectionSpec -- 6.2 Security considerations -- 6.2.1 Extracting embedded exceptions from the WSIF exceptions -- 6.3 Co-existence of CCF and J2CA applications -- 6.4 Exploring WebSphere Studio 5.1 Batch Importer -- 6.5 Migrating CMA-generated code from WebSphere Studio 5.0.x to 5.1 -- Chapter 7. …”

Tabla de Contenidos: “…. -- How it works... -- Using the Extract to User Control feature -- Getting ready -- How to do it... -- How it works... -- Using the Page Inspector feature -- Getting ready -- How to do it... -- How it works... -- Creating an asynchronous HTTP module -- Getting ready -- How to do it... -- How it works... -- There's more... -- 6. …”

Tabla de Contenidos: “…Use lazy quantifiers strategically instead of greedy quantifiers that cause excessive backtracking -- Make use of possessive quantifiers to avoid backtracking -- Extract common repeating substrings out of alternation -- Use atomic group to avoid backtracking and fail fast -- Summary -- Index…”

Tabla de Contenidos: “…&amp -- #160 -- -- Chapter 14: Pretrained Models -- Introduction -- Large-scale visual recognition with GoogLeNet/Inception -- How to do it... -- Extracting bottleneck features with ResNet -- How to do it... -- Leveraging pretrained VGG models for new classes -- How to do it... -- Fine-tuning with Xception -- How to do it... -- Index…”

Tabla de Contenidos: “…Transcultural Materialism and the Literatures of Extraction in Contemporary Latin America. New York: Columbia University Press, 2019. …”

Tabla de Contenidos: “…Transcultural Materialism and the Literatures of Extraction in Contemporary Latin America. New York: Columbia University Press, 219. …”

Tabla de Contenidos: “…-- Licensing prerequisites -- Configuring solution prerequisites -- Creating a shared mailbox -- Creating a Microsoft Teams team -- Configuring a sentiment analysis flow -- Testing the flow -- Further exploration -- Summary -- Chapter 7: Using Power Automate and AI to Build PowerPoint Presentations -- Licensing prerequisites -- Learning about the Encodian Flowr connector -- Input formatting -- Tokens -- Populate PowerPoint -- Merge Presentations -- Interacting with Wikipedia articles -- Creating a PowerPoint template -- Creating the flow -- Creating the Generate Content Summaries scope -- Configuring the JSON parameters -- Customizing the GPT prompt -- Creating the Generate Slides scope -- Working with Encodian Flowr -- Testing the flow -- Further exploration -- Summary -- Chapter 8: Building an Event Registration App with Identity Verification -- Designing a solution -- Licensing prerequisites -- Configuring solution prerequisites -- Configuring SharePoint Online -- Establishing a Teams meeting -- Building an input form -- Creating flows -- Configuring a flow to handle form submission -- Processing the identity document -- Sending confirmation messages -- Testing the flow -- Further exploration -- Summary -- Chapter 9: Implementing an AI-Enabled Resume Screener -- Designing a solution -- Licensing prerequisites -- Configuring solution prerequisites -- Creating a shared mailbox -- Creating a team -- Configuring SharePoint Online -- Configuring the AI model -- Enabling the Cloudmersive connector -- Creating a flow -- Configuring the trigger and variables -- Processing the attachment and candidate record -- Extracting information from a resume and updating a candidate record -- Evaluating the resume with a prompt -- Updating the candidate record -- Sending confirmation messages -- Testing the flow…”

Tabla de Contenidos: “…Cover -- Title Name -- Copyright and Credits -- Dedicated -- Contributors -- Table of Contents -- Preface -- Part 1: Begin with NGINX -- Chapter 1: Downloading and Installing NGINX -- Installing NGINX via package managers -- NGINX-provided packages -- Compiling NGINX from source -- Installing GNU Compiler Collection -- The PCRE library -- The zlib library -- OpenSSL -- Downloading and compiling the NGINX source code -- Websites and resources -- Version branches -- Features -- Downloading and extracting -- Exploring the options for configuring the compilation -- The easy way -- Path options -- Build configuration issues -- Compiling and installing -- Controlling the NGINX service -- Daemons and services -- Users and groups -- NGINX command-line switches -- Starting and stopping the daemon -- Testing the configuration -- Other switches -- Adding NGINX as a system service -- systemd unit file -- Handling system errors -- A quick overview of the possibilities offered by NGINX Plus -- Summary -- Chapter 2: Basic NGINX Configuration -- Delving into the configuration file syntax -- Configuration directives -- Organization and inclusions -- Directive blocks -- Advanced language rules -- Looking at the base module directives -- What are base modules? …”

Tabla de Contenidos: “…5.1.2.10 Remote Sensing and Image Analysis -- 5.2 History of Surveillance Systems -- 5.3 Literature Review -- 5.4 Mathematical Models for Surveillance Systems -- 5.4.1 Overview of Mathematical Modeling in Surveillance -- 5.4.2 Role of Probability and Statistics in Surveillance -- 5.4.2.1 Anomaly Detection -- 5.4.2.2 Predictive Analytics -- 5.4.2.3 Risk Assessment -- 5.4.2.4 Decision Support -- 5.4.2.5 Data Fusion and Integration -- 5.4.3 Modeling Human Behavior in Surveillance Scenario -- 5.4.3.1 Behavioral Patterns -- 5.4.3.2 Machine Learning -- 5.4.3.3 Social Dynamics -- 5.4.3.4 Continuous Learning and Adaptation -- 5.4.3.5 Cognitive Modeling -- 5.4.4 Mathematical Modeling for Tracking and Motion Analysis -- 5.4.4.1 Object Tracking -- 5.4.4.2 Motion Prediction -- 5.4.4.3 Motion Analysis -- 5.4.4.4 Motion Representation -- 5.4.4.5 Trajectory Analysis -- 5.4.4.6 Data Fusion -- 5.4.4.7 Continuous Learning and Adaptation -- 5.5 Artificial Intelligence in Surveillance Systems -- 5.5.1 Object Recognition and Detection -- 5.5.2 Behavior Analysis -- 5.5.3 Facial Recognition -- 5.5.4 Video Analytics -- 5.5.5 Real-Time Alert Generation -- 5.5.6 Predictive Analytics -- 5.5.7 Data Management and Analytics -- 5.6 Use of Mathematical Models for Pre-Processing Image Data -- 5.6.1 Filtering and Smoothing -- 5.6.2 Image Enhancement -- 5.6.3 Edge Detection -- 5.6.4 Image Restoration -- 5.6.5 Feature Extraction -- 5.6.6 Dimensionality Reduction -- 5.7 Future Directions and Challenges -- 5.7.1 Deep Learning and Neural Networks -- 5.7.2 Real-Time Processing -- 5.7.3 Multi-Modal Data Fusion -- 5.7.4 Privacy-Preserving Techniques -- 5.7.5 Human-Centric Surveillance -- 5.7.6 Robustness to Adversarial Attacks -- 5.7.7 Interoperability and Scalability -- 5.7.8 Ethical and Legal Considerations -- 5.8 Conclusion -- 5.8.1 Summary of the Chapter…”

Tabla de Contenidos: “…7.3.3 Methodology Flow Chart -- 7.3.4 Data Pre-Processing -- 7.3.5 Models Deployed -- 7.3.6 Training and Testing -- 7.4 Analysis -- 7.4.1 Datasets -- 7.4.2 Feature Extraction -- 7.4.3 Splitting of Data into Samples -- 7.4.4 Algorithms Used -- 7.4.4.1 Multinomial Logistic Regression -- 7.4.4.2 K-Nearest Neighbors -- 7.4.4.3 Decision Tree -- 7.4.4.4 Support Vector Machine (SVM) -- 7.4.4.5 Random Forest -- 7.5 Results and Discussion -- 7.5.1 Importance of Classification Reports -- 7.5.2 Importance of Confusion Matrices -- 7.5.3 Decision Tree -- 7.5.4 Random Forest -- 7.5.5 K-Nearest Neighbors -- 7.5.6 Logistic Regression -- 7.5.7 Support Vector Machine -- 7.5.8 Comparison of the Algorithms -- 7.5.8.1 Accuracies -- 7.5.8.2 Precision and Recall -- 7.6 Conclusions -- 7.7 Scope of Future Work -- References -- Chapter 8 Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment -- 8.1 Introduction -- 8.1.1 Color Image Processing -- 8.1.2 Motivation -- 8.1.3 Objectives -- 8.2 Literature Review -- 8.2.1 Gas Turbine Power Plants -- 8.2.2 Artificial Intelligent Methods -- 8.3 Materials and Methods -- 8.3.1 Feature Extraction -- 8.3.2 Classification -- 8.4 Results and Discussion -- 8.4.1 Fisher's Linear Discriminant Function (FLDA) and Curvelet -- 8.5 Conclusion -- 8.5.1 Future Scope of Work -- References -- Chapter 9 Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate -- Abbreviations -- 9.1 Introduction -- 9.2 Numerical Experimentation Program -- 9.3 Discussion of the Results -- 9.4 Conclusion -- Acknowledgements -- References -- Part II: Integration of Digital Technologies to Operations -- Chapter 10 Edge Computing-Based Conditional Monitoring -- 10.1 Introduction -- 10.1.1 Problem Statement -- 10.2 Literature Review -- 10.3 Edge Computing -- 10.4 Methodology…”

Tabla de Contenidos: “…The Forward Curve -- 5.1.3. Extracting the Spot Rate Curve from Forward Rates -- 5.2. …”

Tabla de Contenidos: “…-- 5.2 Literature Review -- 5.3 Architecture of Blockchain-Enabled IoMT -- 5.3.1 Opportunities of Blockchain-Enabled IoMT -- 5.3.2 Security Improvement of IoMT -- 5.3.3 Privacy Preservation of IoMT Data -- 5.3.4 Traceability of IoMT Data -- 5.4 Proposed Methodology -- 5.4.1 Overview of the Proposed Architecture -- 5.4.2 Blockchain-Enabled IoMT Architecture -- 5.5 Conclusion and Future Work -- References -- Chapter 6 A Review on the Role of Blockchain Technology in the Healthcare Domain -- 6.1 Introduction -- 6.2 Systematic Literature Methodology -- 6.2.1 Data Sources -- 6.2.2 Selection of Studies -- 6.2.3 Data Extraction and Mapping Process -- 6.2.4 Results -- 6.3 Applications of Blockchain in the Healthcare Domain -- 6.3.1 Blockchains in Electronic Health Records (EHRs) -- 6.3.2 Blockchains in Clinical Research -- 6.3.3 Blockchains in Medical Fraud Detection -- 6.3.4 Blockchains in Neuroscience -- 6.3.5 Blockchains in Pharmaceutical Industry and Research -- 6.3.6 Electronic Medical Records Management -- 6.3.7 Remote Patient Monitoring -- 6.3.8 Drug Traceability -- 6.3.9 Securing IoT Medical Devices -- 6.3.10 Tracking Infectious Disease -- 6.4 Blockchain Challenges…”

Tabla de Contenidos: “…3.3 Snowflake Cloud Data Warehouse -- 3.3.1 High-Level Architecture of Snowflake Cloud Data Warehouse -- 3.3.2 Features of Snowflake Cloud Data Warehouse -- 3.3.3 Snowflake Cloud Data Warehouse Pricing -- 3.4 Google BigQuery Cloud Data Warehouse -- 3.4.1 High-Level Architecture of Google BigQuery Cloud Data Warehouse -- 3.4.2 Features of Google BigQuery Cloud Data Warehouse -- 3.4.3 Google BigQuery Cloud Data Warehouse Pricing -- 3.5 Microsoft Azure Synapse Cloud Data Warehouse -- 3.5.1 Microsoft Azure Synapse Cloud Data Warehouse Architecture -- 3.5.2 Features of Microsoft Azure Synapse Cloud Data Warehouse -- 3.5.3 Pricing of Microsoft Azure Synapse Cloud Data Warehouse -- 3.6 Informatica Intelligent Cloud Services (IICS) -- 3.6.1 Informatica Intelligent Cloud Services Architecture -- 3.6.2 Salient Features of Informatica Intelligent Cloud Services -- 3.6.3 Informatica Intelligent Cloud Services Pricing Model -- 3.7 Conclusion -- Acknowledgements -- References -- Chapter 4 Data Mining with Cluster Analysis Through Partitioning Approach of Huge Transaction Data -- 4.1 Introduction -- 4.2 Methodology Used in Proposed Cluster Analysis System -- 4.2.1 Design of Algorithms -- 4.3 Literature Survey on Existing Systems -- 4.3.1 Experimental Results -- 4.4 Conclusion -- References -- Chapter 5 Application of Data Science in Macromodeling of Nonlinear Dynamical Systems -- 5.1 Introduction -- 5.2 Nonlinear Autonomous Dynamical System -- 5.3 Nonlinear System - MOR -- 5.3.1 Proper Orthogonal Decomposition -- 5.4 Data Science Life Cycle -- 5.4.1 Problem Identification -- 5.4.2 Identifying Available Data Sources and Data Collection -- 5.4.3 Data Processing -- 5.4.4 Data Exploration -- 5.4.5 Feature Extraction -- 5.4.6 Modeling -- 5.4.7 Model Performance Evaluation -- 5.5 Artificial Neural Network in Modeling -- 5.5.1 Machine Learning…”

Tabla de Contenidos: “…Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- 1 Introduction to Cognitive Computing -- 1.1 Introduction: Definition of Cognition, Cognitive Computing -- 1.2 Defining and Understanding Cognitive Computing -- 1.3 Cognitive Computing Evolution and Importance -- 1.4 Difference Between Cognitive Computing and Artificial Intelligence -- 1.5 The Elements of a Cognitive System -- 1.5.1 Infrastructure and Deployment Modalities -- 1.5.2 Data Access, Metadata, and Management Services -- 1.5.3 The Corpus, Taxonomies, and Data Catalogs -- 1.5.4 Data Analytics Services -- 1.5.5 Constant Machine Learning -- 1.5.6 Components of a Cognitive System -- 1.5.7 Building the Corpus -- 1.5.8 Corpus Administration Governing and Protection Factors -- 1.6 Ingesting Data Into Cognitive System -- 1.6.1 Leveraging Interior and Exterior Data Sources -- 1.6.2 Data Access and Feature Extraction -- 1.7 Analytics Services -- 1.8 Machine Learning -- 1.9 Machine Learning Process -- 1.9.1 Data Collection -- 1.9.2 Data Preparation -- 1.9.3 Choosing a Model -- 1.9.4 Training the Model -- 1.9.5 Evaluate the Model -- 1.9.6 Parameter Tuning -- 1.9.7 Make Predictions -- 1.10 Machine Learning Techniques -- 1.10.1 Supervised Learning -- 1.10.2 Unsupervised Learning -- 1.10.3 Reinforcement Learning -- 1.10.4 The Significant Challenges in Machine Learning -- 1.11 Hypothesis Space -- 1.11.1 Hypothesis Generation -- 1.11.2 Hypotheses Score -- 1.12 Developing a Cognitive Computing Application -- 1.13 Building a Health Care Application -- 1.13.1 Healthcare Ecosystem Constituents -- 1.13.2 Beginning With a Cognitive Healthcare Application -- 1.13.3 Characterize the Questions Asked by the Clients -- 1.13.4 Creating a Corpus and Ingesting the Content -- 1.13.5 Training the System…”