Application of machine learning in corrosion inhibition study
Authors
Thankappan Sasilatha
AMET University, Department of EEE, Kanathur, Chennai, India
Author
Susai Rajendran
St. Antony's College of Arts and Sciences for Women Thamaraipady, Department of Chemistry, Corrosion Research Centre, Tamil Nadu, India + Pondicherry University, Puducherry, India
Author
Senthil Kumaran Selvaraj
Vellore Institute of Technology (VIT), Department of Manufacturing Engineering, School of Mechanical Engineering-SMEC, Tamil Nadu, India
Author
Časlav Lacnjevac
University of Belgrade, Faculty of Agriculture, Serbia
Author
Rajendran Joseph Rathish
PSNA College of Engineering and Technology, Dindigul, India
Author
Artificial Intelligence, Machine learning, Deep learning, Neural Networks, Algorithms, The Input Layer, The Hidden Layer and The Output Layer
Abstract
Artificial intelligence is a branch of science concerned with teaching machines to think and act like humans. Machine learning is concerned with enabling computers to perform tasks without the need for explicit programming. Machine Learning enables computers to learn without the need for explicit programming. Machine Learning is a broad field that encompasses a wide range of machine learning operations such as clustering, classification, and the development of predictive models. Machine Learning (ML) and Deep Learning (DL) research is now finding a home in both industry and academia. Machine Learning technologies are increasingly being used in medical imaging. To detect tumours and other malignant growths in the human body. Deep Learning is making significant contributions to the advancement of industrial robotics. Machine learning algorithms are used in the self-driving car industry to guide the vehicle to its destination. Deep Learning and Machine Learning are also used in corrosion science and engineering. They are used to choose the inhibitor molecules from a large pool of available molecules.