Tabla de Contenidos: “…-- A Financial Adult Understands the Critical Context of Equity and Personal Finance, Recognizes That Privilege Can and Should Be Used to Help Close Racial and Gender Wealth Gaps, or Realizes That They May Be Starting at a Disadvantage Due to Historic and Systemic Obstacles -- Time to Get Started -- Chapter 2 Equity and Personal Finance -- The Idea That You Can Pull Yourself Up by Your Bootstraps Is a Scam -- The Racial Wealth Gap -- A Very Quick History Lesson (That We Missed in School) -- The 1862 Homestead Act -- Freedman's Savings Bank -- Tulsa Race Massacre of 1921 -- The Federal Housing Administration (FHA) -- Redlining -- The Gender Wealth Gap -- Another Gap That Has a History -- The Equal Credit Opportunity Act (ECOA) of 1974 -- The Equal Pay Act of 1963 and the Pregnancy Discrimination Act of 1978 -- The Double X Economy -- We Need Women to Be Wealthy -- What Needs to Happen to Close These Gaps -- Raise the Minimum Wage -- Cancel Student Loan Debt -- Tax the Super-Wealthy -- Pay Reparations -- Mandate Paid Leave…”

Tabla de Contenidos: “…. -- 6.5 New and modern approaches to data -- 6.5.1 Open Data defined -- 6.5.2 Open Data's beginnings -- 6.5.3 Open Data today -- 6.5.4 Governmental Open Data policies -- 6.5.5 Open Data: US federal and state governments -- 6.6 Data exchanges27 -- 6.6.1 Types of data exchanges -- 6.7 Data intermediaries, data pools, and data unions -- 6.8 Data commons -- 6.9 Final thoughts -- Summary -- 7 Derived data, synthetic data, and analytics -- 7.1 Data lineage -- 7.2 Forms of data -- 7.2.1 Natural or raw data -- 7.2.2 Aggregated data -- 7.2.3 Derived data -- 7.2.4 Synthetic data -- 7.2.5 Simulated data -- 7.2.6 Optimization data…”

Tabla de Contenidos: “…5.1 The Nature of Information and the Rules of Collection -- 5.2 Leads and Contacts for the Private Investigator -- 5.3 Public Sources of Information -- 5.3.1 Federal Agencies -- 5.3.2 State and Local Agencies -- 5.4 Private Sources of Information -- 5.4.1 Directories, Databases, and Atlases -- 5.4.1.1 Business Databases -- 5.4.1.2 Miscellaneous Databases -- 5.4.2 Libraries -- 5.4.3 Newspapers -- 5.4.4 The Internet and Web-Based Searches -- 5.4.4.1 Social Media Searches -- 5.4.5 Professional Associations and Groups -- 5.4.6 Auto Records -- 5.5 The Background Investigation -- 5.6 Real Property Title Search -- 5.7 Conclusion -- Notes -- 6: Collection of Evidence and Preservation of the Scene -- 6.1 Investigative Technique: Initial Scene Visitation -- 6.2 Investigative Technique: Preliminary Evidence Collection Strategies -- 6.3 Investigative Technique: Scene Sketches and Other Graphic Portrayals -- 6.3.1 Rules for Sketching -- 6.3.2 Rules for Measuring -- 6.4 Investigative Technique: Photographic Evidence -- 6.4.1 Location of Crime Scene -- 6.4.2 Photography and Special Applications -- 6.4.2.1 Vehicular Accidents and Related Auto Claims -- 6.4.2.2 Bodily Injuries and Photographic Method -- 6.4.2.3 Arson/Insurance Fraud and Photographic Evidence -- 6.4.2.4 Burglary/Criminal Trespass/Forcible Theft and Photographic Evidence -- 6.4.2.5 Drug and Alcohol Abuse and Photographic Evidence -- 6.5 Investigative Technique: Documentary Evidence -- 6.5.1 Police Records -- 6.5.1.1 Alcohol or Chemical Reports -- 6.5.1.2 Accident Reports -- 6.5.1.3 Domestic Relations Documentation -- 6.5.1.4 Radio Logs -- 6.5.1.5 Arrest Reports-Narcotics -- 6.5.1.6 Violence in the Workplace -- 6.5.1.7 Missing Person Reports -- 6.5.1.8 Disposition Sheets -- 6.5.1.9 Suspect Descriptions -- 6.6 Investigative Technique: Medical Records…”

Tabla de Contenidos: “…Chapter 4 Analysis of Smart Technologies in Healthcare -- 4.1 Introduction -- 4.2 Emerging Technologies in Healthcare -- 4.2.1 Internet of Things -- 4.2.2 Blockchain -- 4.2.3 Machine Learning -- 4.2.4 Deep Learning -- 4.2.5 Federated Learning -- 4.3 Literature Review -- 4.4 Risks and Challenges -- 4.5 Conclusion -- References -- Chapter 5 Enhanced Neural Network Ensemble Classification for the Diagnosis of Lung Cancer Disease -- 5.1 Introduction -- 5.2 Algorithm for Classification of Proposed Weight-Optimized Neural Network Ensembles -- 5.2.1 Enhanced Raphson's Most Likelihood and Minimum Redundancy Preprocessing -- 5.2.2 Maximum Likelihood Boosting in a Weighted Optimized Neural Network -- 5.3 Experimental Work and Results -- 5.4 Conclusion -- References -- Chapter 6 Feature Selection for Breast Cancer Detection -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Design and Implementation -- 6.3.1 Feature Selection -- 6.4 Conclusion -- References -- Chapter 7 An Optimized Feature-Based Prediction Model for Grouping the Liver Patients -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Proposed Methodology -- 7.4 Results and Discussions -- 7.5 Conclusion -- References -- Chapter 8 A Robust Machine Learning Model for Breast Cancer Prediction -- 8.1 Introduction -- 8.2 Literature Review -- 8.2.1 Comparative Analysis -- 8.3 Proposed Mythology -- 8.4 Result and Discussion -- 8.4.1 Accuracy -- 8.4.2 Error -- 8.4.3 TP Rate -- 8.4.4 FP Rate -- 8.4.5 F-Measure -- 8.5 Concluding Remarks and Future Scope -- References -- Chapter 9 Revolutionizing Pneumonia Diagnosis and Prediction Through Deep Neural Networks -- 9.1 Introduction -- 9.2 Literature Work -- 9.3 Proposed Section -- 9.3.1 Input Image -- 9.3.2 Pre-Processing -- 9.3.3 Identification and Classification Using ResNet50 -- 9.4 Result Analysis -- 9.5 Conclusion and Future Scope -- References…”