Machine Intelligence and Learning have been successfully applied to predict the outcomes of various problems in environmental science and other related areas. The capability of this approach is based on deep computation and computer algorithms. The computational speed and accuracy of results are another set of critical concerns which implies that a machine requires high energy consumption and large memory size. This impacts the environment in terms of temperature and toxic waste. Therefore, new algorithms and mathematical concepts must be developed to reduce the high energy consumption and computational resources such as memory. A few important algorithms for machine learning and other non-machine-learning classical algorithms will be discussed in a simplified version for non-computer scientist audiences.
Chidchanok Lursinsap received a B.Eng. (honor) in Computer Engineering from Chulalongkorn University, Thailand, M.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, USA. He was an associate professor at Center for Advanced Computer Studies, University of Louisiana, USA. Presently, he is a professor of computer science at Department of Mathematics and Computer Science, Chulalongkorn University. His research is in the areas of machine intelligence and neural computing. He has been involved in the DaSET conference for a few years as International Advisory Committee.