Machine Learning and Deep Learning in Real-Time Applications

Book Description Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm.
Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

Content

Chapter 1. Obtaining Deep Learning Models for Automatic Classification of Leukocytes
Chapter 2. Deep Leaning Using Keras
Chapter 3. Deep Learning With PyTorch 
Chapter 4. Deep Learning With TensorFlow
Chapter 5. Employee’s Attrition Prediction Using Machine Learning Approaches
Chapter 6. A Novel Deep Learning Method for Identification of Cancer Genes From Gene Expression Dataset
Chapter 7. Machine Learning in Authentication of Digital Audio Recordings
Chapter 8. Deep Convolutional Neural Network-Based Analysis for Breast Cancer Histology Images
Chapter 9. Deep Learning in Engineering Education: Performance Prediction Using Cuckoo-Based Hybrid Classification
Chapter 10. Malaria Detection System Using Convolutional Neural Network Algorithm
Chapter 11. An Introduction to Deep Convolutional Neural Networks With Keras
Chapter 12. Emotion Recognition With Facial Expression Using Machine Learning for Social Network and Healthcare
Chapter 13. Text Separation From Document Images: A Deep Learning Approach