國立高雄大學圖資館 |

Context-aware machine learning and mobile data analyticsautomated rule-based services with intelligent decision-making /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Context-aware machine learning and mobile data analyticsby Iqbal Sarker ... [et al.].
Reminder of title:	automated rule-based services with intelligent decision-making /
other author:	Sarker, Iqbal H.
Published:	Cham :Springer International Publishing :2021.
Description:	xvi, 157 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-88530-4
ISBN:	9783030885304$q(electronic bk.)

Context-aware machine learning and mobile data analyticsautomated rule-based services with intelligent decision-making /
Context-aware machine learning and mobile data analyticsautomated rule-based services with intelligent decision-making /[electronic resource] :by Iqbal Sarker ... [et al.]. - Cham :Springer International Publishing :2021. - xvi, 157 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030885304$q(electronic bk.)

Standard No.: 10.1007/978-3-030-88530-4doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Context-aware machine learning and mobile data analyticsautomated rule-based services with intelligent decision-making /
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505 0 $a 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.
520 $a 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.
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