Название: Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease
Автор: Manikant Roy, Lovi Raj Gupta
Издательство: IGI Global, Medical Information Science Reference
Год: 2021
Страниц: 259
Язык: английский
Формат: pdf (true), epub
Размер: 35.8 MB

Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as Machine Learning (ML) and data analytics. Covering a range of topics such as mental health analytics, data analysis and Machine Learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.

Recognizing landmarks in images with Machine Learning is an excellent topic for research today. Landmark recognition is an important field in Computer Vision. In this field, we train the Machine Learning models to identify and recognize the closed distinctly distinguishable objects in a digital image. In general, if we consider a digital image to be a set of coordinates of different pixels, a landmark is said to be enclosed in that closed polygon formed by the pixels that may be considered as a distinct and distinguishable thing in one or the other sense. Landmark recognition is an important subject area of image classification since it is considered as one of the first steps towards reaching complete computer vision. The extremely broad definition of a landmark makes it eligible to be considered as one of the leading problems in image classification tasks. Since the task is considered to be a very broad one, the solutions to the task hold no easy procedures. The Chapter 5 explores landmark recognition using ensemble-based Machine Learning models.

Image Classification Using Deep Neural Networks: Emotion Detection Using Facial Images: Facial expression recognition is an activity that is performed by every human in their day-to-day lives. Each one of us analyses the expressions of the individuals we interact with to understand how people interact and respond with us. The malicious intentions of a thief or a person to be interviewed can be recognized with the help of his facial features and gestures. Face recognition from picture or video is a well-known point in biometrics inquiry. Numerous open places, for the most part, have reconnaissance cameras, and these cameras have their noteworthy security incentives. It is generally recognized that face recognition has assumed a significant job in reconnaissance framework. The genuine favorable circumstances of face-based distinguishing proof over different biometrics are uniqueness. Since the human face is a unique item having a high level of inconstancy in its appearance, face location is a troublesome issue in computer vision. The Chapter 6 explores emotion detection using facial images. Emotion Detection has attracted significant attention in the advancement of human behavior and Machine Learning.