A Scoping Review of Current Developments in the Field of Machine Learning and Artificial Intelligence

Abstract

This chapter gives a broad outline of machine learning and artificial intelligence, and introduces the reader to many novel and latest developments in the field of machine learning. The first half of this compilation provides a comprehensive view of the classical concepts of machine learning. Subsequently, examples of machine learning frameworks are discussed. Deep learning, concepts, models, types, and algorithms in machine learning are elaborated in the subsequent section, followed by a detailed introduction to neural networks, concepts of weights, propagation, and initialization. The final section of this chapter introduces the reader to the fascinating and latest world of cutting-edge applications of machine learning like convolutional neural networks (CNNs), bidirectional long short-term memory (BLSTM), artistic, image-generating AI engines like DALL-E and stable diffusion, music and drama writing AI engines, human-like chatbot ChatGPT, art generation with AI, generative neural network concepts, regenerative neural network, and natural language processing (NLP).

Enhancing Outlier Detection in Healthcare Data through Mahalanobis Distance Metric Analysis

“…The `scipy.spatial.distance` module is a powerful tool for performing various distance computations and is often used in conjunction with other scientific computing libraries like NumPy and scikit-learn [11].…”

Enhancing Outlier Detection in Healthcare Data through Mahalanobis Distance Metric Analysis

“…The `scipy.spatial.distance` m o d u l e i s a p o w e r f u l t o o l f o r performing various distance computations and is often used in conjunction with other scientific computing libraries like NumPy and scikit-learn [11].…”