Future of Artificial Neural Networks
Published on 17. May 2024
193 pages
979-8-89113-725-7 (ISBN)
Description
This book is a compilation of eleven quality articles exploring a variety of aspects on applications of ANN. Various authors of the articles from India and abroad have presented their work around the applications of ANN in healthcare and self-medication behaviour, Stock Market Analytics, ANN integrated application for industries including regulatory complaining aspect in Banking Industry, Deep Learning Framework in Medical Diagnosis, Face Recognition, Mobile Learning in Medical Education, Process and Applications of ANN using MATLAB, etc.
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Indrajit Ghosal
The Future of Artificial Neural Networks
Book
05/2024
Nova Science Publishers Inc
€210.94
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Content
- Intro
- Contents
- Foreword
- Preface
- Acknowledgments
- Chapter 1
- The Importance of Artificial Neural Networks in Decision Making for the Field of Medicine
- Abstract
- Introduction
- A Concise Overview of Image Processing and Its Utilization
- Classical Approach
- Some Basic Operations in Image Processing
- Image Adjustment
- Adaptive Noise Removal Filtering
- Dilation and Erosion
- Traditional Artificial Neural Network (ANN)/Deep Convolutional Neural Network (DCNN) Designs
- Research in Medical Science
- Conclusion
- Disclaimer
- Acknowledgments
- References
- Chapter 2
- Real-Time Outlier Detection in Streaming Data: Deep Learning-Based Approach
- Abstract
- Introduction
- Related Work
- Proposed Model
- Preprocessing Data
- Scaling Test Set Features
- DNN Training
- Discovery Phase
- Experiment Analysis
- Dataset Description
- Annthyroid
- Musk
- Cardiotocography
- Metrics for Evaluation
- Experimental Results and Analysis
- Discussion of The Findings
- Conclusion and Further Research
- References
- Chapter 3
- Unsupervised Learning in Civil Engineering
- Abstract
- Introduction
- Supervised Learning Applications in Civil Engineering
- Network Structures
- Feedforward Networks
- Feedback Networks
- Self Organizing Maps
- Learning Rules
- Delta Rule/Gradient Descent Rule
- Hebbian Learning Rule
- Conclusion
- Disclaimer
- Acknowledgments
- References
- Chapter 4
- Investor Opinions Regarding the Use of Artificial Neural Network (ANN) Technology in Indian Investment Services
- Abstract
- Introduction
- Revolutionising Capital Markets with the Evolution of ANN
- Financial Sector Reforms: The Approach
- Changing Investor Profile to Have Huge Impact on MF Industry
- Review of Literature
- Research Methodology
- Research Design
- Objectives of the Data Collection/Structure
- Data Analysis and Interpretation
- Demographic Profile of Respondents
- Gender Profile
- Age Profile
- Annual Income of Respondents
- Annual Saving of Respondents
- Artificial Neural Network
- Artificial Neural Network for Forecasting
- Concluding Remarks
- Discussion
- Finding and Recommendation
- Young People and the Stock Market: Some Statistics
- The COVID Effect on Stock Market Investments
- Role of the Neo-Brokers
- Tech Impact
- Conclusion
- Road Ahead
- References
- Chapter 5
- Application of Deep Learning to Identify Cervical Spine Fractures Using a Modified VGG16-Based Model
- Abstract
- Introduction
- Relevant Literature
- Methodology
- Dataset
- Research Method
- Convolutional Neural Network
- VGG16
- ImageNet
- ImageDataGenerator
- Primary Work
- Confusion Matrix
- Procedure
- Algorithm
- 1. Define Model Architecture
- 2. Compile Model
- 3. Data Preprocessing
- 4. Data Loading
- 5. Model Training
- 6. Model Evaluation
- 7. Mathematical Metrics
- Flowchart
- Results and Discussion
- Comparison
- Conclusion
- Future Scope
- References
- Chapter 6
- A Study of Artificial Neural Networks Using MATLAB and Its Future Application in Forecasting
- Abstract
- Introduction
- Review of Literature
- Methodology
- Results and Discussion
- Conclusion
- Future Scope
- Acknowledgments
- Conflicts
- References
- Chapter 7
- Applications of Artificial Neural Networks in the E-Commerce Industry: A Qualitative Exploration
- Abstract
- Introduction
- State of Art
- Artificial Neural Networks
- E-Commerce
- Conceptual Framework
- Applications of ANN in E-Commerce
- Competitive Advantage
- Enhanced Operational Efficiency
- Challenges during Integration of ANN in E-Commerce
- Limitations
- Implications and Future Scope
- Conclusion
- References
- Chapter 8
- Comprehensive Review of Applications of Artificial Intelligence (AI) and Artificial Neural Network (ANN) in Healthcare Sector: Current Trends and Future Perspectives
- Abstract
- Introduction
- What Are Artificial Neural Networks?
- Review of Literature
- How Is ANN Helping in Different Streams of Healthcare Sector
- The Effects of ANN on Biomedical Engineering
- The Benefits of ANN Obtained for Gastric Cancer
- The Role of ANN in Curing Diabetes
- Detection of Breast Cancer using ANN
- The Use of ANN in the Detection of Chronic Kidney Disease
- Example of ANN Implementation: A Case Study of Self-Medication
- Methodology
- Analysis and Findings
- Need for ANN in Developing Countries
- Scope of Future Research
- Conclusion
- Annexure
- Self-Medication Behavior
- References
- Chapter 9
- Face Recognition Using Residual Neural Network in Artificial Neural Network
- Abstract
- Introduction
- Literature Review
- Challenges Facing in Face Recognition System
- Image Conditions and Imaging Acquisition
- Evaluation Protocols and Benchmark Datasets
- General Overview
- Face Recognition Process
- Artificial Neural Network
- Convolutional Neural Network (CNN)
- ResNet-34
- Dlib
- Material and Methodology
- Result and Analysis
- Conclusion
- Disclaimer
- References
- Chapter 10
- Gradient Boosting Machine Combine Model Create Robust and Accurate Model of Regulatory Complaining Aspect in the Banking Industry
- Abstract
- Introduction
- Literature of Review
- The Bank Operations Compilation
- Research Methodology to Produce Smart Tool Products and Bank Services
- Second Step Categorical Variables
- Discussion and Dataset
- Gradient Boosting ML Algorithm
- Conclusion
- Implication and Future Scope
- References
- Chapter 11
- Segmenting Brain Tumors in MRI Scans: An Advance U-Net-based Deep Learning Framework
- Abstract
- Introduction
- Literature Review
- Dataset
- Results
- Conclusion
- References
- About the Editors
- Index
- Blank Page
