Research

Machine learning is the foundation of my research where the tools and techniques are explored. In 1959, Arthur Samuel defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed”. That has been the basic of machine learning and through current technologies the learning component has become more complex with big data environment.My recent studies focuses on deep learning and genetic algorithms as well as statistical machine learning algorithms.Deep learning is a fast-growing area in machine learning research that shows promising breakthroughs in speech, text and image recognition.It’s based on endowing a neural network with many hidden layers, enabling a computer to learn tasks, organize information and find patterns on its own.
In knowledge engineering, my research focuses on the engineering of intelligence embedded in the application software that solves big,complex and knowledge-intensive tasks. The methods used are mainly derived from machine learning algorithms such as deep learning method and adaptive genetic algorithms.
My research in gamification focuses on game design technology to find aesthetic values in a game. The information progress is defined by game refinement theory. The implementation with big data analytic is explored in business especially regarding experience, behavior and personality of the customers towards a particular product. The methods from machine learning are used here as well.
In business informatic, my research focuses on the studies of representation, processing, and communication of information in a business environment .The central notion is  to understand how big data , through computational intelligence, can transform business into a  profitable entity.