Machine Learning in Healthcare and Security:
Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions by Prashant Pranav, Archana Patel, Sarika Jain

- Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions
- Prashant Pranav, Archana Patel, Sarika Jain
- Page: 224
- Format: pdf, ePub, mobi, fb2
- ISBN: 9781032483993
- Publisher: CRC Press
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This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms. Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describes the predictive analysis and forecasting techniques in different emerging and classical areas using the approaches of ML and AI. It discusses the application of ML and AI in medical diagnostic systems and deals with the security prevention aspects of ML and how it can be used to tackle various emerging security issues. This book also focuses on NLP and understanding the techniques, obstacles, and possible solutions. This is a valuable reference resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.
Artificial Intelligence and Privacy – Issues and Challenges
This resource serves as an introduction to a wider conversation regarding information privacy and AI. It is written for a non-technical audience.
[PDF] Machine Learning in Healthcare: Advancements, Applications, and .
as the discovery of novel insights and solutions to complex healthcare challenges. 5.4 Addressing the Regulatory and Policy Challenges: The adoption of ML .
Re-Thinking Data Strategy and Integration for Artificial Intelligence
AI has been transforming various sectors, including healthcare, finance, and transportation, with significant advancements in machine learning and deep learning .
Ethical and regulatory challenges of AI technologies in healthcare
Deep Learning (DL), a subfield of ML, has ushered in new breakthroughs in information technology. DL may study underlying features in data from .
Challenges for AI in Healthcare Systems - SpringerLink
advances in machine learning, especially in deep learning. Moreover . Security and privacy-preserving challenges of e-health solutions .
The Role of Machine Learning in Cybersecurity - ACM Digital Library
Then, in Section 3, we present the most emblematic application of ML in security: cyberthreat detection. We distinguish between three broad areas: network .
The Pros and Cons of AI in Healthcare
learning, solving problems, and making decisions. Central to the . Machine learning is a subset of AI that uses algorithms to analyze .
Machine Learning Applications, Challenges, and Securities for .
Pramanik , P.K.D. , Pareek , G. , Nayyar , A. , Security and privacy in remote healthcare: Issues, solutions, and standards , in: Telemedicine .
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