Biometric and Intelligent Decision Making Support (Intelligent Systems Reference Library Book 81)

This book presents different methods for analyzing the body language (movement, position, use of personal space, silences, pauses and tone, the eyes, pupil dilation or constriction, smiles, body temperature and the like) for better understanding people’s needs and actions, including biometric data gathering and reading. Different studies described in this book indicate that sufficiently much data, information and knowledge can be gained by utilizing biometric technologies. This is the first, wide-ranging book that is devoted completely to the area of intelligent decision support systems, biometrics technologies and their integrations. This book is designated for scholars, practitioners and doctoral and master’s degree students in various areas and those who are interested in the latest biometric and intelligent decision making support problems and means for their resolutions, biometric and intelligent decision making support systems and the theory and practice of their integration and the opportunities for the practical use of biometric and intelligent decision making support. Preface 6 Contents 10 1 Introduction to Intelligent Decision Support Systems 14 Abstract 14 1.1 Introduction 14 1.2 Development of Intelligent Decision Support Systems: Based on Artificial Intelligence Methods with Special Emphasis on Technology 17 1.3 Intelligent User Interface 20 1.4 Integration of Artificial Intelligent and DBMS Technologies 27 References 38 2 Intelligent Decision Support Systems 43 Abstract 43 2.1 Recommender, Advisory and Expert Systems and Their Integration with Decision Support Systems 43 2.2 Text Analytics and Mining Based DSSs 47 2.3 Data Mining as an Important Component of Intelligent Decision Support Systems 54 2.4 Integration of Data Analytics and Decision Support Systems 60 2.5 Artificial Neural Networks in Decision Support Systems and Biometrics 62 2.6 Integration of Remote Sensing into a Decision Support Systems 68 2.7 Biometrics-Based Decision Support Systems 70 2.7.1 Voice Recognition Decision Support Systems 70 2.7.2 Speech Recognition and Understanding Decision Support Systems 71 2.7.3 Adaptive Biometrics-Based Decision Support Systems 72 2.7.4 Other Biometrics-Based Decision Support Systems 73 2.8 Ambient Intelligence and the Internet of a Things-Based Decision Support Systems 74 2.9 Other Intelligent Decision Support Systems 80 2.9.1 GA-Based Decision Support Systems 80 2.9.2 Fuzzy Sets IDSS 81 2.9.3 Rough Sets 81 2.9.4 Intelligent Agent-Assisted Decision Support Systems 82 2.9.5 Process Mining Integration to Decision Support 84 2.9.6 Adaptive Decision Support Systems 85 2.9.7 Computer Vision Based DSS 87 2.9.8 Sensory Decision Support Systems 87 2.9.9 Robotic Decision Support Systems 88 References 88 3 Passive House Model for Quantitative and Qualitative Analyses and Its Intelligent System 98 Abstract 98 3.1 Introduction 98 3.2 Passive House Model for Quantitative and Qualitative Analyses and Illustration of Its Several Stages 99 3.2.1 Passive House Model for Quantitative and Qualitative Analyses 99 3.2.2 Passive House Socio-cultural Aspects 101 3.2.3 Self-expression Values, Environmentalism, Global Warming and the Passive House 107 3.2.3.1 Self-expression Values and Environmentalism 107 3.2.3.2 Passive House, Global Warming, Zero Carbon Emissions and Self-expression Values 108 3.2.4 Low Energy Dwelling Weaknesses in Lithuania 112 3.3 The Intelligent Passive House Design System 114 3.4 Case Study 117 References 121 4 Biometric and Intelligent Self-Assessment of Student Progress System 124 Abstract 124 4.1 Introduction 124 4.2 Reliability of Self-Assessment 126 4.3 Biometric and Intelligent Self-Assessment of Student Progress (BISASP) System 128 4.4 Self-Assessment Integrated Grading Model 132 4.5 Self-Assessment Integrated Grading Adjustment Model 134 4.6 Case Studies 135 4.6.1 Case Study 1: Analysis on the Interdependencies Between Microtremors, Stress and Student Marks 136 4.6.2 Case Study 2: Comparison of Marks Assigned to Students During the Psychological Examination, Prior to the e-Test and During the e-Test 140 References 145 5 Web-based Biometric Computer Mouse Advisory System to Analyze a User’s Emotions and Work Productivity 148 Abstract 148 5.1 Introduction 149 5.2 Dependency of Human Blood Pressures, Heart Rate, Skin Conductance and Temperature on Experienced Stress and Emotions 150 5.2.1 Effect of Experienced Emotions on Blood Pressure, Heart Rate, Skin Conductance and Body Temperature 152 5.2.2 Dependence of Blood Pressures and Heart Rate on a Person’s Experienced Stress 155 5.3 Web-based Biometric Computer Mouse Advisory System to Analyze a User’s Emotions and Work Productivity 156 5.3.1 e-Self-assessment Subsystem 157 5.3.2 Biometric Computer Mouse 158 5.3.3 Mouse Events Capture, Collection and Feature Extraction Subsystem 163 5.3.4 Biometric Finger 165 5.3.5 User’s Biometric Database 166 5.3.6 Maslow’s Pyramid Tables 167 5.3.7 Model-Base Management System and Model Base 169 5.4 Case Study: Determining Stress Level and Providing Recommendations 172 5.5 Scenario Used to Test and Validate the Advisory System and Its Composite Parts 177 5.5.1 Statistical Analysis of Average Temperature Dependency on Anxiety 179 5.6 Calculating Reliability of Stress Dependencies on Diastolic and Systolic Blood Pressures and Finger Temperature by Analyzing the Entire User’s Biometric Database 180 References 181 6 Student Progress Assessment with the Help of an Intelligent Pupil Analysis System 185 Abstract 185 6.1 Introduction 185 6.2 Intelligent Pupil Analysis System 187 6.2.1 Database Management System and Intelligent Database 188 6.2.2 Model-base Management Subsystem and Model-bases 189 6.2.3 Student’s Answer Correctness Estimate per Pupillary Response Model 193 6.2.3.1 Analysis of the General Trends in the Research Performed 193 Correct Answers to Multiple Choice Questions Correlate with Dilated Pupils 193 Large Pupils on Happy Faces and Small Pupils on Sad Faces 194 6.2.3.2 Analysis of Specific Dependencies in the Research Performed 195 6.3 Case Studies 197 6.3.1 Case Study 1: A Sample of IPA System’s Recommendations to a Tutor 197 6.3.2 Case Study 2: Study of the Dependence Linking a Student’s Pupil Size to the Student’s Psychological and Emotional State During an Examination 198 References 201 7 Recommender System to Analyze Student’s Academic Performance 204 Abstract 204 7.1 Introduction 204 7.2 Analysis of the Interdependence Linking Physiological Parameters of Students to Their Learning Productivity and Interest in Learning 207 7.3 Recommender System to Analyze Student’s Academic Performance 212 7.3.1 Introduction 212 7.3.2 Equipment Subsystem 212 7.3.3 Intelligent Database and Database Management System 213 7.3.4 Model-base Management System and Model Base 219 7.4 Development of Learning Materials on a Students’ Learning Productivity and the Level of Interestingness 219 7.5 Case Study: The Recommender System as a Means to Increase Student Productivity in Learning and to Improve Their Achievements 221 7.6 Reliability Analysis of the Influence of Physiological Parameters on Interest in Learning Using the Entire Student’s Physiological Database 224 References 226 Front Matter....Pages i-xii Introduction to Intelligent Decision Support Systems....Pages 1-29 Intelligent Decision Support Systems....Pages 31-85 Passive House Model for Quantitative and Qualitative Analyses and Its Intelligent System....Pages 87-112 Biometric and Intelligent Self-Assessment of Student Progress System....Pages 113-136 Web-based Biometric Computer Mouse Advisory System to Analyze a User’s Emotions and Work Productivity....Pages 137-173 Student Progress Assessment with the Help of an Intelligent Pupil Analysis System....Pages 175-193 Recommender System to Analyze Student’s Academic Performance....Pages 195-220