| TÃtulo : |
Context-Aware Machine Learning and Mobile Data Analytics : Automated Rule-based Services with Intelligent Decision-Making |
| Tipo de documento: |
documento electrónico |
| Autores: |
Sarker, Iqbal, ; Colman, Alan, ; Han, Jun, ; Watters, Paul, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2021 |
| Número de páginas: |
XVI, 157 p. 41 ilustraciones, 31 ilustraciones en color. |
| ISBN/ISSN/DL: |
978-3-030-88530-4 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Idioma : |
Inglés (eng) |
| Palabras clave: |
Procesamiento de datos Aprendizaje automático Informática móvil MinerÃa de datos y descubrimiento de conocimientos |
| Clasificación: |
006.312 |
| Resumen: |
Este libro ofrece una comprensión clara del concepto de aprendizaje automático consciente del contexto, incluido un marco automatizado basado en reglas dentro del área amplia de la ciencia y el análisis de datos, en particular, con el objetivo de la toma de decisiones inteligente basada en datos. Por lo tanto, hemos otorgado un estudio integral sobre este tema que explora contextos multidimensionales en el modelado de aprendizaje automático, la discretización del contexto con modelado de series de tiempo, el descubrimiento de reglas contextuales y el análisis predictivo, el modelado de comportamiento basado en reglas o patrones recientes, y su utilidad. en diversas aplicaciones y servicios inteligentes sensibles al contexto. Las técnicas presentadas basadas en aprendizaje automático se pueden emplear en una amplia gama de áreas de aplicaciones del mundo real que van desde servicios móviles personalizados hasta inteligencia de seguridad, como se destaca en el libro. Como la interpretabilidad de un sistema basado en reglas es alta, la automatización en el descubrimiento de reglas a partir de datos sin procesar contextuales puede hacer que este libro tenga más impacto tanto para los desarrolladores de aplicaciones como para los investigadores. En general, este libro proporciona una buena referencia tanto para el mundo académico como para el personal de la industria en el amplio área de la ciencia de datos, el aprendizaje automático, la informática impulsada por la IA, la informática y la personalización centradas en el ser humano, el análisis del comportamiento, la IoT y las aplicaciones móviles, y la inteligencia en ciberseguridad. |
| Nota de contenido: |
Part I Preliminaries -- 1 Introduction to Context-Aware Machine Learning and Mobile Data -- Analytics -- 1.1 Introduction -- 1.2 Context-Aware Machine Learning -- 1.3 Mobile Data Analytics -- 1.4 An Overview of this Book -- 1.5 Conclusion -- References -- 2 Application Scenarios and Basic Structure for Context-Aware -- Machine Learning Framework -- 2.1 Motivational Examples with Application Scenarios -- 2.2 Structure and Elements of Context-Aware Machine Learning -- Framework -- 2.2.1 Contextual Data Acquisition -- 2.2.2 Context Discretization -- 2.2.3 Contextual Rule Discovery -- 2.2.4 Dynamic Updating and Management of Rules -- 2.3 Conclusion -- References -- 3 A Literature Review on Context-Aware Machine Learning and -- Mobile Data Analytics -- 3.1 Contextual Information -- 3.1.1 Definitions of Contexts -- 3.1.2 Understanding the Relevancy of Contexts -- 3.2 Context Discretization -- 3.2.1 Discretization of Time-Series Data -- 3.2.2 Static Segmentation -- vii -- viii Contents -- 3.2.3 Dynamic Segmentation -- 3.3 Rule Discovery -- 3.3.1 Association Rule Mining -- 3.3.2 Classification Rules -- 3.4 Incremental Learning and Updating -- 3.5 Identifying the Scope of Research -- 3.6 Conclusion -- References -- Part II Context-Aware Rule Learning and Management -- 4 Contextual Mobile Datasets, Pre-processing and Feature Selection -- 4.1 Smart Mobile Phone Data and Associated Contexts -- 4.1.1 Phone Call Log -- 4.1.2 Mobile SMS Log -- 4.1.3 Smartphone App Usage Log -- 4.1.4 Mobile Phone Notification Log -- 4.1.5 Web or Navigation Log -- 4.1.6 Game Log -- 4.1.7 Smartphone Life Log -- 4.1.8 Dataset Summary -- 4.2 Examples of Contextual Mobile Phone Data -- 4.2.1 Time-Series Mobile Phone Data -- 4.2.2 Mobile phone data with multi-dimensional contexts -- 4.2.3 Contextual Apps Usage Data -- 4.3 Data Preprocessing -- 4.3.1 Data Cleaning -- 4.3.2 Data Integration -- 4.3.3 Data Transformation -- 4.3.4 Data Reduction -- 4.4 Dimensionality Reduction -- 4.4.1 Feature Selection -- 4.4.2 Feature Extraction -- 4.4.3 Dimensionality Reduction Algorithms -- 4.5 Conclusion -- References -- 5 Discretization of Time-Series Behavioral Data and Rule Generation -- based on Temporal Context -- 5.1 Introduction -- 5.2 Requirements Analysis -- 5.3 Time-series Segmentation Approach -- 5.3.1 Approach Overview -- 5.3.2 Initial Time Slices Generation -- 5.3.3 Behavior-Oriented Segments Generation -- Contents ix -- 5.3.4 Selection of Optimal Segmentation -- 5.3.5 Temporal Behavior Rule Generation using Time Segments -- 5.4 Effectiveness Comparison -- 5.5 Conclusion -- References -- 6 Discovering User Behavioral Rules based on Multi-dimensional -- Contexts -- 6.1 Introduction -- 6.2 Multi-dimensional Contexts in User Behavioral Rules -- 6.3 Requirements Analysis -- 6.4 Rule Mining Methodology -- 6.4.1 Identifying the Precedence of Context -- 6.4.2 Designing Association Generation Tree -- 6.4.3 Extracting Non-Redundant Behavioral Association Rules -- 6.5 Experimental Analysis -- 6.5.1 Effect on the Number of Produced Rules.-6.5.2 Effect of Confidence Preference the Predicted Accuracy -- 6.5.3 Effectiveness Comparison -- 6.6 Conclusion -- References -- 7 Recency-based Updating and Dynamic Management of Contextual -- Rules -- 7.1 Introduction -- 7.2 Requirements Analysis -- 7.3 An Example of Recent Data -- 7.4 Identifying Optimal Period of Recent Log Data -- 7.4.1 Data Splitting -- 7.4.2 Association Generation -- 7.4.3 Score Calculation -- 7.4.4 Data Aggregation -- 7.5 Machine Learning based Behavioral Rule Generation and Management -- 7.6 Effectiveness Comparison and Analysis -- 7.7 Conclusion -- References -- Part III Application and Deep Learning Perspective -- 8 Context-Aware Rule-based Expert System Modeling -- 8.1 Structure of a Context-Aware Mobile Expert System -- 8.2 Context-Aware Rule Generation Methods -- 8.3 Context-Aware IF-THEN Rules and Discussion -- 8.3.1 IF-THEN Classification Rules -- 8.3.2 IF-THEN Association Rules -- x Contents -- 8.4 Conclusion -- References -- 9 Deep Learning for Contextual Mobile Data Analytics -- 9.1 Introduction -- 9.2 Contextual Data -- 9.3 Deep Neural Network Modeling -- 9.3.1 Model Overview -- 9.3.2 Input Layer -- 9.3.3 Hidden Layer(s) -- 9.3.4 Output Layer -- 9.4 Prediction Results of the Model -- 9.5 Conclusion -- References -- 10 Context-Aware Machine Learning System: Applications and -- Challenging Issues -- 10.1 Rule-based Intelligent Mobile Applications -- 10.2 Major Challenges and Research Issues -- 10.3 Concluding Remarks -- References. |
| Tipo de medio : |
Computadora |
| Summary : |
This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making. Thus, we have bestowed a comprehensive study on this topic that explores multi-dimensional contexts in machine learning modeling, context discretization with time-series modeling, contextual rule discovery and predictive analytics, recent-pattern or rule-based behavior modeling, and their usefulness in various context-aware intelligent applications and services. The presented machine learning-based techniques can be employed in a wide range of real-world application areas ranging from personalized mobile services to security intelligence, highlighted in the book. As the interpretability of a rule-based system is high, the automation in discovering rules from contextual raw data can make this book more impactful for the application developers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AI-Driven computing, human-centered computing and personalization, behavioral analytics, IoT and mobile applications, and cybersecurity intelligence. |
| Enlace de acceso : |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
Context-Aware Machine Learning and Mobile Data Analytics : Automated Rule-based Services with Intelligent Decision-Making [documento electrónico] / Sarker, Iqbal, ; Colman, Alan, ; Han, Jun, ; Watters, Paul, . - 1 ed. . - [s.l.] : Springer, 2021 . - XVI, 157 p. 41 ilustraciones, 31 ilustraciones en color. ISBN : 978-3-030-88530-4 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés ( eng)
| Palabras clave: |
Procesamiento de datos Aprendizaje automático Informática móvil MinerÃa de datos y descubrimiento de conocimientos |
| Clasificación: |
006.312 |
| Resumen: |
Este libro ofrece una comprensión clara del concepto de aprendizaje automático consciente del contexto, incluido un marco automatizado basado en reglas dentro del área amplia de la ciencia y el análisis de datos, en particular, con el objetivo de la toma de decisiones inteligente basada en datos. Por lo tanto, hemos otorgado un estudio integral sobre este tema que explora contextos multidimensionales en el modelado de aprendizaje automático, la discretización del contexto con modelado de series de tiempo, el descubrimiento de reglas contextuales y el análisis predictivo, el modelado de comportamiento basado en reglas o patrones recientes, y su utilidad. en diversas aplicaciones y servicios inteligentes sensibles al contexto. Las técnicas presentadas basadas en aprendizaje automático se pueden emplear en una amplia gama de áreas de aplicaciones del mundo real que van desde servicios móviles personalizados hasta inteligencia de seguridad, como se destaca en el libro. Como la interpretabilidad de un sistema basado en reglas es alta, la automatización en el descubrimiento de reglas a partir de datos sin procesar contextuales puede hacer que este libro tenga más impacto tanto para los desarrolladores de aplicaciones como para los investigadores. En general, este libro proporciona una buena referencia tanto para el mundo académico como para el personal de la industria en el amplio área de la ciencia de datos, el aprendizaje automático, la informática impulsada por la IA, la informática y la personalización centradas en el ser humano, el análisis del comportamiento, la IoT y las aplicaciones móviles, y la inteligencia en ciberseguridad. |
| Nota de contenido: |
Part I Preliminaries -- 1 Introduction to Context-Aware Machine Learning and Mobile Data -- Analytics -- 1.1 Introduction -- 1.2 Context-Aware Machine Learning -- 1.3 Mobile Data Analytics -- 1.4 An Overview of this Book -- 1.5 Conclusion -- References -- 2 Application Scenarios and Basic Structure for Context-Aware -- Machine Learning Framework -- 2.1 Motivational Examples with Application Scenarios -- 2.2 Structure and Elements of Context-Aware Machine Learning -- Framework -- 2.2.1 Contextual Data Acquisition -- 2.2.2 Context Discretization -- 2.2.3 Contextual Rule Discovery -- 2.2.4 Dynamic Updating and Management of Rules -- 2.3 Conclusion -- References -- 3 A Literature Review on Context-Aware Machine Learning and -- Mobile Data Analytics -- 3.1 Contextual Information -- 3.1.1 Definitions of Contexts -- 3.1.2 Understanding the Relevancy of Contexts -- 3.2 Context Discretization -- 3.2.1 Discretization of Time-Series Data -- 3.2.2 Static Segmentation -- vii -- viii Contents -- 3.2.3 Dynamic Segmentation -- 3.3 Rule Discovery -- 3.3.1 Association Rule Mining -- 3.3.2 Classification Rules -- 3.4 Incremental Learning and Updating -- 3.5 Identifying the Scope of Research -- 3.6 Conclusion -- References -- Part II Context-Aware Rule Learning and Management -- 4 Contextual Mobile Datasets, Pre-processing and Feature Selection -- 4.1 Smart Mobile Phone Data and Associated Contexts -- 4.1.1 Phone Call Log -- 4.1.2 Mobile SMS Log -- 4.1.3 Smartphone App Usage Log -- 4.1.4 Mobile Phone Notification Log -- 4.1.5 Web or Navigation Log -- 4.1.6 Game Log -- 4.1.7 Smartphone Life Log -- 4.1.8 Dataset Summary -- 4.2 Examples of Contextual Mobile Phone Data -- 4.2.1 Time-Series Mobile Phone Data -- 4.2.2 Mobile phone data with multi-dimensional contexts -- 4.2.3 Contextual Apps Usage Data -- 4.3 Data Preprocessing -- 4.3.1 Data Cleaning -- 4.3.2 Data Integration -- 4.3.3 Data Transformation -- 4.3.4 Data Reduction -- 4.4 Dimensionality Reduction -- 4.4.1 Feature Selection -- 4.4.2 Feature Extraction -- 4.4.3 Dimensionality Reduction Algorithms -- 4.5 Conclusion -- References -- 5 Discretization of Time-Series Behavioral Data and Rule Generation -- based on Temporal Context -- 5.1 Introduction -- 5.2 Requirements Analysis -- 5.3 Time-series Segmentation Approach -- 5.3.1 Approach Overview -- 5.3.2 Initial Time Slices Generation -- 5.3.3 Behavior-Oriented Segments Generation -- Contents ix -- 5.3.4 Selection of Optimal Segmentation -- 5.3.5 Temporal Behavior Rule Generation using Time Segments -- 5.4 Effectiveness Comparison -- 5.5 Conclusion -- References -- 6 Discovering User Behavioral Rules based on Multi-dimensional -- Contexts -- 6.1 Introduction -- 6.2 Multi-dimensional Contexts in User Behavioral Rules -- 6.3 Requirements Analysis -- 6.4 Rule Mining Methodology -- 6.4.1 Identifying the Precedence of Context -- 6.4.2 Designing Association Generation Tree -- 6.4.3 Extracting Non-Redundant Behavioral Association Rules -- 6.5 Experimental Analysis -- 6.5.1 Effect on the Number of Produced Rules.-6.5.2 Effect of Confidence Preference the Predicted Accuracy -- 6.5.3 Effectiveness Comparison -- 6.6 Conclusion -- References -- 7 Recency-based Updating and Dynamic Management of Contextual -- Rules -- 7.1 Introduction -- 7.2 Requirements Analysis -- 7.3 An Example of Recent Data -- 7.4 Identifying Optimal Period of Recent Log Data -- 7.4.1 Data Splitting -- 7.4.2 Association Generation -- 7.4.3 Score Calculation -- 7.4.4 Data Aggregation -- 7.5 Machine Learning based Behavioral Rule Generation and Management -- 7.6 Effectiveness Comparison and Analysis -- 7.7 Conclusion -- References -- Part III Application and Deep Learning Perspective -- 8 Context-Aware Rule-based Expert System Modeling -- 8.1 Structure of a Context-Aware Mobile Expert System -- 8.2 Context-Aware Rule Generation Methods -- 8.3 Context-Aware IF-THEN Rules and Discussion -- 8.3.1 IF-THEN Classification Rules -- 8.3.2 IF-THEN Association Rules -- x Contents -- 8.4 Conclusion -- References -- 9 Deep Learning for Contextual Mobile Data Analytics -- 9.1 Introduction -- 9.2 Contextual Data -- 9.3 Deep Neural Network Modeling -- 9.3.1 Model Overview -- 9.3.2 Input Layer -- 9.3.3 Hidden Layer(s) -- 9.3.4 Output Layer -- 9.4 Prediction Results of the Model -- 9.5 Conclusion -- References -- 10 Context-Aware Machine Learning System: Applications and -- Challenging Issues -- 10.1 Rule-based Intelligent Mobile Applications -- 10.2 Major Challenges and Research Issues -- 10.3 Concluding Remarks -- References. |
| Tipo de medio : |
Computadora |
| Summary : |
This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making. Thus, we have bestowed a comprehensive study on this topic that explores multi-dimensional contexts in machine learning modeling, context discretization with time-series modeling, contextual rule discovery and predictive analytics, recent-pattern or rule-based behavior modeling, and their usefulness in various context-aware intelligent applications and services. The presented machine learning-based techniques can be employed in a wide range of real-world application areas ranging from personalized mobile services to security intelligence, highlighted in the book. As the interpretability of a rule-based system is high, the automation in discovering rules from contextual raw data can make this book more impactful for the application developers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AI-Driven computing, human-centered computing and personalization, behavioral analytics, IoT and mobile applications, and cybersecurity intelligence. |
| Enlace de acceso : |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
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