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Emerging Methods in Predictive Analytics

Book Description

Decision making tools are essential for the successful outcome of any organization. Recent advances in predictive analytics have aided in identifying particular points of leverage where critical decisions can be made. Emerging Methods in Predictive Analytics: Risk Management and Decision Making provides an interdisciplinary approach to predictive analytics; bringing together the fields of business, statistics, and information technology for effective decision making. Managers, business professionals, and decision makers in diverse fields will find the applications and cases presented in this text essential in providing new avenues for risk assessment, management, and predicting the future outcomes of their decisions.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
  5. Dedication
  6. Preface
    1. OBJECTIVES, SCOPE, AND TARGET AUDIENCE OF THE BOOK
    2. THE FUTURE OF TIME SERIES, REDUX
    3. AN OVERVIEW OF THE CONTENTS
  7. Section 1: Decision-Making and Control Systems
    1. Chapter 1: Prescriptive Analytics Using Synthetic Information
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. SYNTHETIC INFORMATION
      4. 3. CASE STUDY: A HYPOTHETICAL NUCLEAR DETONATION IN WASHINGTON DC
      5. 4. IMPLICATIONS FOR POLICY
      6. ACKNOWLEDGMENT
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
      9. ENDNOTES
      10. APPENDIX
    2. Chapter 2: A Recommendation System for Scientific Papers through Bayesian Nonparametric Hybrid Filtering
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. A HYBRID APPROACH TO RECOMMENDATION SYSTEMS
      4. 3. COMPUTATION
      5. 4. ILLUSTRATIONS
      6. 5. DISCUSSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    3. Chapter 3: Applications in Predictive Analytics
      1. ABSTRACT
      2. 1. CLINICAL ENGINEERING AND TECHNOLOGICAL LIFE CYCLE OF MEDICAL EQUIPMENT
      3. 2. PREDICTIVE MAINTENANCE OR CONDITION BASED MAINTENANCE
      4. 3. INTEGRATION OF INFORMATION AND COMMUNICATIONS TECHNOLOGIES WITH FCPB IN CLINICAL ENGINEERING
      5. 4. UBIQUITOUS MANAGEMENT METHODOLOGY
      6. 5. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    4. Chapter 4: Application of Spatial and Temporal Predictive Analysis for Energy Network Optimization
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. TECHNIQUES OF SPATIAL AND TEMPORAL PREDICTIVE ANALYSIS
      4. 3. APPLICATION OF SPATIAL AND TEMPORAL ANALYSIS FOR RISK ASSESSMENT, OPTIMIZATION AND DECISION MAKING ON ENERGY NETWORK OPERATION
      5. 4. APPLICATION DEMONSTRATION
      6. 5. CONCLUDING REMARKS
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    5. Chapter 5: Achieving RF Jamming with DSA-Enabled Cognitive Radio Swarms
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. COGNITIVE RADIO SWARM SUBSYSTEM ARCHITECTURE
      5. 4. SCENARIO
      6. 5. APPLICATIONS AND FUTURE WORK
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
  8. Section 2: Anomaly Detection and Risk Management
    1. Chapter 6: Non-Parametric Stakeholder Discovery
      1. ABSTRACT
      2. 1. THE CHALLENGES POSED TO RISK GOVERNANCE
      3. 2. THE APPROACH TO ANALYZING THE ELEMENTS OF RISK PROBLEMS
      4. 3. A PROCESS FOR INDUCTIVELY ADDRESSING RISK GOVERNANCE DEFICITS
      5. 4. CONCLUSION
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
      8. ENDNOTES
    2. Chapter 7: Predictive Network Defense
      1. ABSTRACT
      2. 1. TRAINING FIREWALLS TO FILTER PACKETS WITH ID3 DECISION TREES
      3. 2. DETECTING BOTNET MALWARE WITH -MEANS CLUSTERING
      4. REFERENCES
      5. KEY TERMS AND DEFINITIONS
    3. Chapter 8: Computing Skills in Forecasting for Liquidity Risk Management in the Indian Banking Industry
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. MAIN FOCUS OF THE CHAPTER
      5. 4. RECOMMENDATIONS
      6. 5. FUTURE RESEARCH DIRECTIONS
      7. 6. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
      10. ENDNOTES
  9. Section 3: Understanding, Modeling, and Forecasting
    1. Chapter 9: Continuous-Time Infinite Dynamic Topic Models
      1. ABSTRACT
      2. 1. DYNAMIC TOPIC MODELING AND EVENT STREAM MINING
      3. 2. BACKGROUND AND RELATED WORK
      4. 3. HYBRID SOLUTION: THE DIM SUM PROCESS FOR DYNAMIC TOPIC MODELING
      5. 4. CORPORA FOR SIMULTANEOUS TOPIC ENUMERATION AND FORMATION (STEF)
      6. REFERENCES
      7. ENDNOTES
      8. APPENDIX
    2. Chapter 10: Predictive Analytics in Digital Signal Processing
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. CONCLUSION
      4. REFERENCES
      5. APPENDIX
    3. Chapter 11: Biometric Authentication
      1. ABSTRACT
      2. 1. INTRODUCTION AND BACKGROUND
      3. 2. BUILDING AN INDIVIDUAL KEYSTROKE PROFILE
      4. 3. USING THE GENERATED KEYSTROKE PROBABILITY MODEL
      5. REFERENCES
      6. KEY TERMS AND DEFINITIONS
    4. Chapter 12: Exploration of Soft Computing Approaches in Itemset Mining
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. DATA MINING DEFINITION OF TERMS
      4. 3. PRELIMINARIES AND LITERATURE SURVEY OF SOFT COMPUTING
      5. 4. FINDINGS OF THE SURVEY
      6. 5. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    5. Chapter 13: Predictive Analytics of Social Networks
      1. ABSTRACT
      2. 1. INTRODUCTION: PREDICTION IN SOCIAL NETWORKS
      3. 2. BACKGROUND
      4. 3. TECHNIQUES
      5. 4. APPLICATIONS
      6. 5. SYSTEMS
      7. REFERENCES
    6. Chapter 14: Money Supply
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. MAIN FOCUS OF THE CHAPTER
      5. 4. DEFINITION AND MEASURES OF MONEY SUPPLY
      6. 5. AN OUTLINE OF EXISTING LITERATURE ON MONEY SUPPLY
      7. 6. THE GREAT DEBATE AS TO DETERMINATION OF MONEY SUPPLY IN INDIA
      8. 7. ECONOMETRIC TOOLS IN UNDERSTANDING THE RELATIONSHIP BETWEEN MONEY, OUTPUT AND PRICES
      9. 8. THE ISSUE OF ENDOGENEITY
      10. 9. FORECASTING MONEY SUPPLY IN INDIA IN THE POST CRISIS PERIOD: EMERGING ISSUES
      11. 10. SOLUTIONS AND RECOMMENDATIONS
      12. 11. FUTURE RESEARCH DIRECTIONS
      13. 12. CONCLUSION
      14. REFERENCES
      15. KEY TERMS AND DEFINITIONS
      16. ENDNOTES
    7. Chapter 15: Overview of Predictive Modeling Approaches in Health Care Data Mining
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. DATA MINING METHOD AND ITS CLASSIFICATION
      4. 3. PRELIMINARIES AND LITERATURE SURVEY OF PREDICTIVE DATA MINING METHODS
      5. 4. CONCLUSION AND FINDINGS
      6. REFERENCES
      7. KEY TERMS AND DEFINITIONS
  10. Compilation of References
  11. About the Contributors