This book  presents novel research and application chapters on topics in reliability, statistics, and machine learning. It has an emphasis on analytical models and techniques and practical applications in reliability engineering, data science, manufacturing, health care, and industry using machine learning, AI, optimization, and other computational methods.

Today, billions of people are connected to each other through their mobile devices. Data is being collected and analysed more than ever before. The era of big data through machine learning algorithms, statistical inference, and reliability computing in almost all applications has resulted in a dramatic shift in the past two decades. Data analytics in business, finance, and industry is vital. It helps organizations and business to achieve better results and fact-based decision-making in all aspects of life.  

The book offers a broad picture of current research on the analytics modeling and techniques and its applications in industry. Topics include:

Reliability modeling and methods.

Software reliability engineering.

Maintenance modeling and policies.

Statistical feature selection.

Big data modeling.

Machine learning: models and algorithms.

Data-driven models and decision-making methods.

Applications and case studies in business, health care, and industrial systems.

Postgraduates, researchers, professors, scientists, engineers, and practitioners in reliability engineering and management, machine learning engineering, data science, operations research, industrial and systems engineering, statistics, computer science and engineering, mechanical engineering, and business analytics will find in this book state-of-the-art analytics, modeling and methods in reliability and machine learning.