Neural Network Models for Feature Extraction and Empirical Thresholding

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Neural Network Models for Feature Extraction and Empirical Thresholding study the combination of neural network models and empirical thresholding methods to improve the procedure for extracting features. For researchers and practitioners working in the fields of feature extraction and machine learning, it illustrates the advantages, approaches, and difficulties connected with this integration and offers helpful insights. The basic concepts of feature extraction are covered in this book chapter, along with an overview of the several neural network models that can be used to accomplish this task, such as auto-encoders, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). This book chapter emphasizes the benefits, methodologies, and challenges associated with this integration, providing valuable insights for researchers and practitioners in the fields of feature extraction and machine learning. This book chapter is useful for statistical analysis, domain expertise-driven threshold selection, and validation metrics-based threshold choice as efficient techniques for enhancing feature quality and lowering noise.