The need for tailored data for machine learning models is often unsatisfied, as it is considered too much of a risk in the real-world context. Synthetic data, an algorithmically birthed counterpart to operational data, is the linchpin for overcoming constraints associated with sensitive or regulated information. In high-dimensional data, where the dimensions of features and variables often surpass the number of available observations, the emergence of synthetic data heralds a transformation. Applications of Synthetic High Dimensional Data delves into the algorithms and applications underpinning the creation of synthetic data, which surpass the capabilities of authentic datasets in many cases. Beyond mere mimicry, synthetic data takes center stage in prioritizing the mathematical domain, becoming the crucible for training robust machine learning models. It serves not only as a simulation but also as a theoretical entity, permitting the consideration of unforeseen variables and facilitating fundamental problem-solving. This book navigates the multifaceted advantages of synthetic data, illuminating its role in protecting the privacy and confidentiality of authentic data. It also underscores the controlled generation of synthetic data as a mechanism to safeguard private information while maintaining a controlled resemblance to real-world datasets. This controlled generation ensures the preservation of privacy and facilitates learning across datasets, which is crucial when dealing with incomplete, scarce, or biased data. Ideal for researchers, professors, practitioners, faculty members, students, and online readers, this book transcends theoretical discourse. It accentuates practical aspects, prioritizing the basic applicability of synthetic high-dimensional data. Each chapter unveils a facet of synthetic data's prowess, from its impact on society to its role in machine learning applications. It provides a roadmap for navigating the nuanced terrain of data privacy,...

lgrsnf/Applications of Synthetic High Dimensional Data (Sobczak-Michalowska, Borah, Polkowski, Mishra).epub

Marzena Sobczak-Michalowska

IGI Global

United States, United States of America

premier reference source, 2024

Cover
Title Page
Copyright Page
Book Series
Mission
Coverage
Preface
AN OVERVIEW
A DESCRIPTION OF THE IMPORTANCE OF EACH OF THE CHAPTERS
CONCLUSION
Chapter 1: A Novel Approach Towards Regeneration and Constitution of Data Linked to Distributed Databases
ABSTRACT
1. INTRODUCTION
2. RELATED WORK
3. METHOD OF REGENERATION OF DATA
4. BASIC APPLICABILITY AND CONCEPTUALIZATION
5. RETRIEVAL AND REGENERATION OF GLOBAL SCHEMA
6. STEPS FOR RETRIEVAL AND REGENERATION OF LARGE SCALED DATA FROM DATABASES
7. PRIORITIZATION OF MULTI AGENT CONCEPT
8. EXPERIMENTAL ANALYSIS
9. DISCUSSION AND FUTURE DIRECTION
10. CONCLUSION
REFERENCES
Chapter 2: Data Visualization in Large Scale Based on Trained Data
ABSTRACT
INTRODUCTION
LITERATURE REVIEW
DATA VISUALIZATION TOOLS
CONCLUSION
REFERENCES
Chapter 3: Deep Machine Learning
ABSTRACT
1. INTRODUCTION
2. RELATED WORKS
3. DEEP MACHINE LEARNING
4. SEVERAL EXISTING STRONG MACHINE LEARNING TECHNIQUES
5. GENERALITY VERSUS SPECIFICITY TRADE-OFF
6. DEEP MACHINE LEARNING AND HUMAN INTELLIGENCE
7. DEEP MACHINE LEARNING
8. CONCLUSION
REFERENCES
Chapter 4: Digitalization and Its Impact on the Development of Society
ABSTRACT
INTRODUCTION
MAIN FOCUS OF THE ARTICLE
PRESENTATION OF THE MAIN RESEARCH MATERIAL
CONCLUSION
REFERENCES
Chapter 5: E-Learning as a Training Concept for Staff
ABSTRACT
1. INTRODUCTION
2. REVIEW OF THE LITERATURE
3. RESEARCH METHODOLOGY
4. RESULTS
5. FUTURE RESEARCH
6. CONCLUSION
REFERENCES
Chapter 6: Factor Analysis of the Intercultural Sensitivity, Ethnocentrism, Social Media by the Means of Structural Equation Modelling
ABSTRACT
INTRODUCTION
CONCLUSION
REFERENCES
Chapter 7: Feature Selection Using Correlation Analysis for Accurate Breast Cancer Diagnosis
ABSTRACT
INTRODUCTION
LITERATURE REVIEW
METHODOLOGY
RESULT
FEATURE SELECTION USING GENETIC ALGORITHM
HEAT MAP OF COPY NUMBER AND PROTEIN LEVEL
FUTURE WORK
REFERENCES
Chapter 8: Developing Peacemaking Soft Skills of Managers as a Method of Preventing Professional Burnout and Restoring Work-Life Balance
ABSTRACT
METHODS
DEVELOPING PEACEMAKING SOFT SKILLS OF MANAGERS AS A METHOD OF PREVENTING PROFESSIONAL BURNOUT AND RESTORING WORK-LIFE BALANCE: A UKRAINIAN CASE STUDY
LITERATURE REVIEW
DISCUSSION
CONCLUSION
REFERENCES
Chapter 9: Prospects for the Implementation of Practice-Based Learning for Students of Managerial Profile
ABSTRACT
INTRODUCTION
LITERATURE REVIEW
DISCUSSION
CONCLUSION
REFERENCES
Chapter 10: The Privacy-Preserving High-Dimensional Synthetic Data Generation and Evaluation in the Healthcare Domain
ABSTRACT
1. INTRODUCTION
2. LITERATURE REVIEW
3. BACKGROUNDS
4. THE PROPOSED FRAMEWORK
5. CONCLUSION AND FUTURE WORK
REFERENCES
Chapter 11: Tools to Create Synthetic Data for Brain Images
ABSTRACT
1. INTRODUCTION
2. USES OF SYNTHETIC IMAGE DATASET
3. DISADVANTAGES OF THE TECHNOLOGY FOR CREATING SYNTHETIC BRAIN IMAGES
4. TOOLS FOR CREATING THE SYNTHETIC IMAGE DATASET TO TRAIN THE MODEL
5. DATA AUGMENTATION USING THE GENERATED SYNTHETIC BRAIN IMAGE DATA SET
6. TRAINING AND VALIDATING THE SYNTHETIC DATASET
7. CONCLUSION
REFERENCES
Chapter 12: Transformation of Industrial Production
ABSTRACT
INTRODUCTION
BACKGROUND
EFFECTS OF BREAKTHROUGH TECHNOLOGIES FOR INDUSTRIAL DEVELOPMENT
CONCLUSION
REFERENCES
Chapter 13: To the Question of Design and Manufacturing of Special Equipment for Mechanism of Pneumatic Power Receiving Mechanism
ABSTRACT
INTRODUCTION
STATEMENT OF MATERIAL
CONCLUSION
REFERENCES
Compilation of References
Related References
About the Contributors

Asymptotic Methods for Engineers is based on the authors’ many years of practical experience in the application of asymptotic methods to solve engineering problems.
This book is devoted to modern asymptotic methods (AM), which is widely used in engineering, applied sciences, physics, and applied mathematics. Avoiding complex formal calculations and justifications, the book’s main goal is to describe the main ideas and algorithms. Moreover, not only is there a presentation of the main AM, but there is also a focus on demonstrating their unity and inseparable connection with the methods of summation and asymptotic interpolation.
The book will be useful for students and researchers from applied mathematics and physics and of interest to doctoral and graduate students, university and industry professors from various branches of engineering (mechanical, civil, electro-mechanical, etc.).

2024-04-18