Index
A
- activation functions
- about / Activation functions
- sigmoid function / Sigmoid function
- hyperbolic tangent (tanh) function / Tanh function
- ReLu function / ReLu
- AI-based defense / AI-based defense
- ALU (Arithmetic and Logic Unit) / Evolution from dumb to intelligent machines
- ant colony optimization (ACO) model / Ant colony optimization model
- Apache Hadoop / Batch processing
- Apache Hive / Hive
- Apache Pig / Apache Pig
- ArcSight ESM / ArcSight ESM
- ARFF (Attribute-Relation File Format) / Introduction to the Weka framework
- artificial intelligence (AI)
- about / Best of both worlds
- big data / Big Data
- natural language processing / Areas of AI
- fuzzy logic systems / Areas of AI
- intelligent robotics / Areas of AI
- expert systems / Areas of AI
- applied AI / Cognitive science
- cognitively simulated AI / Cognitive science
- strong AI / Cognitive science
- artificial neural networks (ANN)
- Artificial Neuro-Fuzzy Inference Systems (ANFIS)
- about / ANFIS network
- adaptive network / Adaptive network
- architecture / ANFIS architecture and hybrid learning algorithm
- hybrid learning algorithm / ANFIS architecture and hybrid learning algorithm
B
- backpropagation / Backpropagation
- backpropagation model / Backpropagation model
- big data
- volume / Big Data
- velocity / Big Data
- variety / Big Data
- value / Big Data
- for critical infrastructure protection / Big Data for critical infrastructure protection
- big data frameworks
- about / Big data frameworks
- batch processing / Batch processing
- real-time processing / Real-time processing
C
- capabilities, Cognitive Systems
- exploration / Goals of Cognitive Systems
- retrieval / Goals of Cognitive Systems
- semantic search / Goals of Cognitive Systems
- physical activity / Goals of Cognitive Systems
- state manipulation / Goals of Cognitive Systems
- information enrichment / Goals of Cognitive Systems
- navigation / Goals of Cognitive Systems
- control / Goals of Cognitive Systems
- decision support / Goals of Cognitive Systems
- natural language interface / Goals of Cognitive Systems
- cluster manager
- reference link / The Spark programming model
- Coarse to Fine search / Hyperparameter tuning
- cognitive intelligence
- as service / Cognitive intelligence as a service
- cognitive science
- about / Cognitive science
- vision / Cognitive science
- audition / Cognitive science
- gustation / Cognitive science
- olfaction / Cognitive science
- somatosensation / Cognitive science
- Cognitive Systems (CS)
- about / Cognitive Systems
- history / A brief history of Cognitive Systems
- goals / Goals of Cognitive Systems
- big data analytics, application / Application in Big Data analytics
- collective intelligent systems
- advantages / Advantages of collective intelligent systems
- components, conceptual data flow
- Hadoop Distributed Filesystem (HDFS) / Hadoop Distributed File System
- NoSQL (Not only SQL) / NoSQL databases
- MapReduce (MR) / MapReduce
- Apache Pig / Apache Pig
- Apache Hive / Hive
- components, Ontologies
- concepts / Components of Ontologies
- slots / Components of Ontologies
- relationships / Components of Ontologies
- axioms / Components of Ontologies
- instances / Components of Ontologies
- operations / Components of Ontologies
- components, velocity function
- inertia / The particle swarm optimization model
- self-knowledge / The particle swarm optimization model
- social-knowledge / The particle swarm optimization model
- concepts, matrix theory
- transpose of a matrix / Matrix theory and linear algebra overview
- matrix addition / Matrix theory and linear algebra overview
- matrix multiplication / Matrix theory and linear algebra overview
- identity matrices / Matrix theory and linear algebra overview
- diagonal matrix / Matrix theory and linear algebra overview
- concepts, Spark ML API
- transformer function / The transformer function
- estimator algorithm / The estimator algorithm
- pipeline / Pipeline
- conceptual data flow / Conceptual Data Flow
- conditional probability / Introduction to Naive Bayes' algorithm
- content-based recommendation systems / Content-based recommendation systems
- continuous bag of words model (CBOW) / Word2Vec, CBOW
- convolution neural networks (CNNs) / Deep reinforcement learning
- CPU (Central Processing Unit) / Evolution from dumb to intelligent machines
- crisp sets
- attributes / Attributes and notations of crisp sets
- notations / Attributes and notations of crisp sets
- about / Attributes and notations of crisp sets
- operations / Operations on crisp sets
- properties / Properties of crisp sets
- critical infrastructure (CI)
- about / Big Data for critical infrastructure protection
- data collection / Data collection and analysis
- data analysis / Data collection and analysis
- structured data / Data collection and analysis
- unstructured data / Data collection and analysis
- semi-structured data / Data collection and analysis
- anomaly detection / Anomaly detection
- corrective actions / Corrective and preventive actions
- conceptual data flow / Conceptual Data Flow
D
- databases
- versus Ontologies / Advantages of Ontologies
- data clustering
- about / Data clustering
- fixed clustering / Data clustering
- probabilistic clustering / Data clustering
- connectivity models / Data clustering
- centroid models / Data clustering
- distribution models / Data clustering
- density models / Data clustering
- Data Dimensionality Reduction (DDR) / Data dimensionality reduction
- data preparation pipelines
- building / Building data preparation pipelines
- Dawson stemming
- above / Dawson stemming
- advantages / Dawson stemming
- deep learning
- basics / Deep learning basics and the building blocks
- building blocks / Deep learning basics and the building blocks
- gradient-based learning / Gradient-based learning
- backpropagation / Backpropagation
- non-linearities / Non-linearities
- dropout / Dropout
- deeplearning4j (DL4J)
- experimenting / Experimenting with hyperparameters with Deeplearning4j
- deep neural networks (DNNs) / Deep reinforcement learning
- deep Q-network (DQN) / Deep reinforcement learning
- defuzzification
- about / Defuzzification
- methods / Defuzzification methods
- degree of membership / Fuzzy sets and membership functions
- derivative / Gradient-based learning
- design principles, for SI system development
- proximity principle / Design principles for developing SI systems
- quality principle / Design principles for developing SI systems
- diverse response principle / Design principles for developing SI systems
- adaptability principle / Design principles for developing SI systems
- distributed computing
- about / Distributed computing
- model distribution / Distributed computing
- data distribution / Distributed computing
- distributed deep learning
- about / Distributed deep learning
- DL4J / DL4J and Spark
- Spark / DL4J and Spark
- TensorFlow / TensorFlow
- Keras / Keras
- dropout / Dropout
- dynamical data
- handling / Handling dynamical data
- dynamic programming
- about / Dynamic programming and reinforcement learning
- deterministic environment, with policy iteration / Learning in a deterministic environment with policy iteration
E
- electronic brain, capabilities
- speed information storage / Speed information storage
- brute force / Processing by brute force
- enablers, Cognitive Systems (CS)
- data / Cognitive Systems enablers
- computation / Cognitive Systems enablers
- connectivity / Cognitive Systems enablers
- sensors / Cognitive Systems enablers
- theories in human brain / Cognitive Systems enablers
- nature / Cognitive Systems enablers
- Encog API structure / Encog API structure
- Encog framework
- about / Encog machine learning framework
- development environment setup / Encog development environment setup
- Enterprise Data Hub (EDH) / Application in Big Data analytics
- epoch / Number of epochs
- evolutionary algorithms (EAs) / Genetic algorithms structure
- Extract Load and Transform (ELTTT) / Building data preparation pipelines
- Extract Transform and Load (ETL) / Building data preparation pipelines
F
- feature extraction
- about / Feature extraction
- one hot encoding / One hot encoding
- TF-IDF method / TF-IDF
- Word2Vec / Word2Vec
- Feed Forward Neural Networks / Feed-forward neural networks, Natural language processing basics
- fundamentals, Security Incident and Event Management (SIEM)
- data collection / Understanding SIEM
- data persistence / Understanding SIEM
- data normalization / Understanding SIEM
- data visualization / Understanding SIEM
- visualization attributes / Visualization attributes and features
- visualization features / Visualization attributes and features
- fuzzification / Fuzzification
- fuzzy C-means clustering / Fuzzy C-means clustering
- fuzzy inference / Fuzzy inference
- fuzzy inference systems (FIS) / ANFIS network
- fuzzy logic
- fundamentals / Fuzzy logic fundamentals
- crisp sets / Attributes and notations of crisp sets
- fuzzy sets / Fuzzy sets and membership functions
G
- generalized linear model / Generalized linear model
- genetic algorithm
- structure / Genetic algorithms structure
- about / Encog machine learning framework
- attribute search, in Weka / Attribute search with genetic algorithms in Weka
- genetic programming / Encog machine learning framework
- goals, Hadoop Distributed File System (HDFS)
- hardware failure / Hadoop Distributed File System
- large datasets / Hadoop Distributed File System
- simple coherency model / Hadoop Distributed File System
- portability across heterogeneous hardware platform / Hadoop Distributed File System
- portability across heterogeneous software platform / Hadoop Distributed File System
- gradient-based learning / Gradient-based learning
- gradient descent
- about / Gradient descent and backpropagation
- pseudocode / Gradient descent pseudocode
H
- Hadoop Distributed File System (HDFS)
- about / Batch processing, Building data preparation pipelines, Hadoop Distributed File System
- InputFormat / Building data preparation pipelines
- InputSplit / Building data preparation pipelines
- RecordReader / Building data preparation pipelines
- human brain, capabilities
- sensory input / Sensory input
- storage / Storage
- processing power / Processing power
- low energy consumption / Low energy consumption
- human memory
- sensory memory / Human brain and Ontology
- short-term memory / Human brain and Ontology
- long-term memory / Human brain and Ontology
- hyperbolic tangent (tanh) function / Tanh function
- hyperparameter tuning
- about / Hyperparameter tuning
- grid search / Hyperparameter tuning
- random search / Hyperparameter tuning
- learning rate / Learning rate
- number of training iterations / Number of training iterations
- number of hidden units / Number of hidden units
- number of epochs / Number of epochs
- experimenting / Experimenting with hyperparameters with Deeplearning4j
- hypothesis / Supervised and unsupervised machine learning
I
- information technology (IT) / Cognitive Systems
- injection attack / Injection attacks
- InputFormat
- about / Building data preparation pipelines
- FileInputFormat / Building data preparation pipelines
- TextInputFormat / Building data preparation pipelines
- SequenceFileInputFormat / Building data preparation pipelines
- InputSplit / Building data preparation pipelines
- intelligence
- about / Intelligence
- tasks classification / Intelligence tasks classification
- intelligent agent
- characteristics / Building intelligent machines with Ontologies
- intelligent applications
- with big data / Intelligent applications with Big Data
- intelligent machines
- building, Ontologies used / Building intelligent machines with Ontologies
- Internet of Things (IoT) / Big Data
- Inverse Document Frequency (IDF) / Content-based recommendation systems, TF-IDF
J
- Java
- language translator application, developing / Developing a language translator application in Java
- Java Virtual Machine (JVM)
- about / The Spark programming model
- driver / The Spark programming model
- executor / The Spark programming model
- cluster manager / The Spark programming model
K
- K-means algorithm
- about / The K-means algorithm
- implementation, with Spark ML / K-means implementation with Spark ML
- Kafka / Kafka
- KEEL framework / KEEL framework
- Keras
- key considerations, stream processing
- unbounded data / Understanding stream processing
- unbounded data processing / Understanding stream processing
- low latency analysis / Understanding stream processing
- knowledge graph / The role Ontology plays in Big Data
L
- Lancaster stemming / Lancaster stemming
- language translator application
- developing, with Watson / Developing with Watson
- developing, in Java / Developing a language translator application in Java
- language translator application, developing
- prerequisites, setting up / Setting up the prerequisites
- LASSO regression / LASSO regression
- lateral movement / Lateral movement
- Least Absolute Shrinkage Selection Operator (LASSO) / LASSO regression
- least square method / Least square method
- lemmatization / Lemmatization
- linear algebra
- linear model / Perceptron and linear models
- linear regression
- about / Linear regression
- least square method / Least square method
- example / Gradient descent and backpropagation
- logistic regression
- about / Logistic regression classification technique
- with Spark / Logistic regression with Spark
- Lovins stemming / Lovins stemming
M
- machines evolution / Evolution from dumb to intelligent machines
- MapReduce (MR) / MapReduce
- Markov decision processes (MDPs)
- about / Markov decision processes
- deterministic environment / Markov decision processes
- stochastic environment / Markov decision processes
- Markov process / Markov decision processes
- matrix theory
- about / Matrix theory and linear algebra overview
- inverse matrices / Matrix theory and linear algebra overview
- symmetric matrix / Matrix theory and linear algebra overview
- linear regression in matrix form / Matrix theory and linear algebra overview
- metadata / Ontology of information science
- model training / Supervised and unsupervised machine learning
- modules, KEEL framework
- data management / KEEL framework
- experiments / KEEL framework
- Multi-Agent Simulation Of Neighborhoods or Networks (MASON) library
- about / MASON Library
- layered architecture / MASON Layered Architecture
- multi-objective optimization / Multi-objective optimization
- multicollinearity / Ridge regression
- multiple linear regression / Linear regression
N
- N-grams / N-grams
- Naive Bayes' algorithm / Introduction to Naive Bayes' algorithm
- Naive Bayes' text classification
- code example / Naive Bayes' text classification code example
- Naive Bayes (NB) / The estimator algorithm, Introduction to Naive Bayes' algorithm
- Natural language processing (NLP)
- about / Natural language processing basics
- hierarchy / Natural language processing basics
- neural net architectures
- implementing / Practical approach to implementing neural net architectures
- neural network
- fundamentals / Fundamentals of neural networks and artificial neural networks
- component notations / Component notations of the neural network
- neuro-fuzzy-classifier (NEFCLASS) / NEFCLASS
- NLP techniques
- applying / Applying NLP techniques
- text classification / Text classification
- nodes / Applications in big data analytics
- non-linearities
- about / Non-linearities
- sigmoid function / Non-linearities
- tanh function / Non-linearities
- rectified linear unit (RELU) / Non-linearities
- nonlinearities model / Nonlinearities model
- NoSQL (Not only SQL) / NoSQL databases
O
- one hot encoding / One hot encoding
- Ontologies
- properties / Ontology properties
- advantages / Advantages of Ontologies
- versus databases / Advantages of Ontologies
- components / Components of Ontologies
- in big data / The role Ontology plays in Big Data
- goals, in big data / Goals of Ontology in big data
- challenges, in big data / Challenges with Ontology in Big Data
- Resource Description Framework (RDF) / RDF—the universal data format
- used, for building intelligent machines / Building intelligent machines with Ontologies
- Ontology alignment / Ontology alignment
- Ontology learning
- about / Ontology learning
- approaches / Ontology learning
- challenges / Ontology learning
- process / Ontology learning process
- Ontology of information science / Ontology of information science
- Opt4J library / Opt4J library
- overfitting / Overfitting
P
- particle swarm optimization (PSO)
- about / The particle swarm optimization model
- implementation considerations / PSO implementation considerations
- PCA algorithm, using singular value decomposition
- mean normalization / The PCA algorithm using SVD
- feature scaling / The PCA algorithm using SVD
- perceptron / Perceptron and linear models
- pheromones / Self-organization
- phishing / Phishing
- platforms as a service (PAAS) / Application in Big Data analytics
- polynomial regression / Polynomial regression
- porter stemming
- about / Porter stemming
- reference link / Porter stemming
- principal component analysis method / The principal component analysis method
- principles, particle swarm optimization (PSO)
- separation / The particle swarm optimization model
- alignment / The particle swarm optimization model
- Programmable Logic Controllers (PLCs) / Big Data for critical infrastructure protection
- properties, swarm intelligence (SI)
- self-organization / Self-organization
- stigmergy / Stigmergy
- division of labor / Division of labor
- pruning / Removing stop words
Q
- Q-Learning / Q-Learning
R
- Random Forest / Random Forest
- RDDs (Resilient Distributed Datasets) / Real-time processing
- real-time processing frameworks
- Apache Spark / Real-time processing
- Apache Storm / Real-time processing
- Apache Flink / Real-time processing
- Recurrent Neural Network (RNN)
- about / Recurrent neural networks, Natural language processing basics
- need for / The need for RNNs
- structure / Structure of an RNN
- training / Training an RNN
- recursive least square estimator (RLSE) / ANFIS architecture and hybrid learning algorithm
- regression / Supervised and unsupervised machine learning
- regression analysis
- about / Regression analysis
- linear regression / Linear regression
- generalized linear model / Generalized linear model
- logistic regression / Logistic regression classification technique
- polynomial regression / Polynomial regression
- stepwise regression / Stepwise regression
- ridge regression / Ridge regression
- LASSO regression / LASSO regression
- reinforcement learning
- techniques / Reinforcement learning techniques
- Markov decision processes (MDPs) / Markov decision processes
- about / Dynamic programming and reinforcement learning
- reinforcement learning algorithms
- concept / Reinforcement learning algorithms concept
- finite horizon model / Reinforcement learning algorithms concept
- infinite horizon model / Reinforcement learning algorithms concept
- average reward model / Reinforcement learning algorithms concept
- about / Deep reinforcement learning
- ReLu function / ReLu
- Resilient Distributed Datasets / The Spark programming model
- Resource Description Framework (RDF)
- about / RDF—the universal data format
- containers / RDF containers
- classes / RDF classes
- properties / RDF properties
- attributes / RDF attributes
- results pyramid / Results pyramid
- ridge regression / Ridge regression
S
- SCADA (Supervisory Control and Data Acquisition) / Big Data for critical infrastructure protection
- Security Incident and Event Management (SIEM) / Understanding SIEM
- self-organization (SO) / Swarm intelligence
- semantics, stream processing
- at least once / Stream processing semantics
- at most once / Stream processing semantics
- exactly once / Stream processing semantics
- sentiment analysis
- implementing / Implementing sentiment analysis
- sigmoid function / Sigmoid function
- simple linear regression / Linear regression
- simple perceptron model
- mathematical representation / Mathematical representation of the simple perceptron model
- singular value decomposition
- about / Singular value decomposition
- properties / The important properties of singular value decomposition
- with Spark ML / SVD with Spark ML
- implementing, with Spark ML / Implementing SVD with Spark ML
- Skip-Gram model / Word2Vec, Skip-Gram model
- Snowball stemming
- about / Snowball stemming
- reference link / Snowball stemming
- Spark
- about / The Spark programming model
- logistic regression / Logistic regression with Spark
- Spark, using DL4J
- API overview / API overview
- Spark ML
- K-means algorithm implementation / K-means implementation with Spark ML
- singular value decomposition / SVD with Spark ML
- singular value decomposition, implementing / Implementing SVD with Spark ML
- Spark ML API / The Spark MLlib library
- Spark MLlib library / The Spark MLlib library
- Spark programming model / The Spark programming model
- Spark RDDs
- reference link / The Spark programming model
- Spark Streaming / Spark Streaming
- SPARQL
- about / SPARQL query language
- generic structure / Generic structure of an SPARQL query
- features / Additional SPARQL features
- SPARQL Protocol and RDF Query Language / SPARQL query language
- Splunk / Splunk
- Splunk Enterprise Security / Splunk Enterprise Security
- Splunk Light / Splunk Light
- Splunk Search Processing Language (SPL) / Splunk Light
- standard error / Least square method
- State-Action-Reward-State-Action (SARSA) / SARSA learning
- state transition rule / Ant colony optimization model
- stemming
- about / Stemming
- porter stemming / Porter stemming
- Snowball stemming / Snowball stemming
- Lancaster stemming / Lancaster stemming
- Lovins stemming / Lovins stemming
- Dawson stemming / Dawson stemming
- stepwise regression
- about / Stepwise regression
- forward selection / Forward selection
- backward elimination / Backward elimination
- stigmergy / Stigmergy
- stop words / Removing stop words
- stream processing
- about / Understanding stream processing
- Spark Streaming / Spark Streaming
- Kafka / Kafka
- supervised machine learning / Supervised and unsupervised machine learning
- swarm intelligence (SI)
- about / Swarm intelligence
- big data analytics, applications / Applications in big data analytics
T
- TensorFlow
- about / TensorFlow
- downloading, reference link / TensorFlow
- Term Frequency (TF) / Content-based recommendation systems, TF-IDF
- text classification
- about / Applying NLP techniques, Text classification
- Naive Bayes' algorithm / Introduction to Naive Bayes' algorithm
- Random Forest / Random Forest
- text preprocessing
- about / Text preprocessing
- stop words, removing / Removing stop words
- stemming / Stemming
- lemmatization / Lemmatization
- N-grams / N-grams
- TF-IDF
- references / Content-based recommendation systems
- types, cyber security attack
- phishing / Phishing
- lateral movement / Lateral movement
- injection attack / Injection attacks
- AI-based defense / AI-based defense
- types, decision support system (DSS)
- model / Goals of Cognitive Systems
- data / Goals of Cognitive Systems
- communication / Goals of Cognitive Systems
- document / Goals of Cognitive Systems
- knowledge / Goals of Cognitive Systems
- types, intelligence
- linguistic intelligence / Types of intelligence
- logical intelligence / Types of intelligence
- interpersonal intelligence / Types of intelligence
- emotional intelligence / Types of intelligence
- types, NoSQL (Not only SQL)
- document databases / NoSQL databases
- graph databases / NoSQL databases
- columnar databases / NoSQL databases
U
- Universal Resource Identifiers (URIs) / RDF—the universal data format
- Universal Resource Locators (URLs) / RDF—the universal data format
- universe of discourse / Fuzzy sets and membership functions
- unsupervised machine learning / Supervised and unsupervised machine learning
- user defined function (UDF) / KEEL framework
V
- vector space models (VSM) / Word2Vec
- velocity function / The particle swarm optimization model
W
- Waikato Environment for Knowledge Analysis (Weka) / Introduction to the Weka framework
- Watson
- IBM cognitive toolkit / IBM cognitive toolkit based on Watson
- language translator application, developing / Developing with Watson
- Watson-based cognitive apps
- about / Watson-based cognitive apps
- Watson assistant / Watson-based cognitive apps
- discovery / Watson-based cognitive apps
- knowledge catalog / Watson-based cognitive apps
- language translator / Watson-based cognitive apps
- machine learning / Watson-based cognitive apps
- natural language understanding / Watson-based cognitive apps
- personality insights / Watson-based cognitive apps
- speech to text and text to speech / Watson-based cognitive apps
- tone analyzer / Watson-based cognitive apps
- visual recognition / Watson-based cognitive apps
- Watson Studio / Watson-based cognitive apps
- Web Ontology Language (OWL)
- using / Using OWL, the Web Ontology Language
- OWL DL / Using OWL, the Web Ontology Language
- OWL Full / Using OWL, the Web Ontology Language
- OWL Lite / Using OWL, the Web Ontology Language
- Weka
- attribute search, with genetic algorithms / Attribute search with genetic algorithms in Weka
- Weka Explorer
- features / Weka Explorer features
- Preprocess / Preprocess
- Classify / Classify
- Weka framework
- about / Introduction to the Weka framework
- downloading, reference link / Introduction to the Weka framework
- Word2Vec
- about / Word2Vec
- continuous bag of words model (CBOW) / CBOW
- Skip-Gram model / Skip-Gram model