Scientific Papers

Comparing Statistical and Machine Learning Classifiers: Alternatives for Predictive Modeling in Human Factors Research The results for all scientific studies can be difficult to analyze due to False Negative and False Positive Data that are creating fuzzy results due to undesired noises created by false data. Most scientific studies where using Linear Regression Modeling to analyze data collected, but such method are quite inaccurate for PREDICTIVE MODELING FOR HUMAN FACTORS RESEARCH. Due to the robustness and the high-performance of the results produced by our method for Human Performance Prediction, this article was selected in 2001 for publishing as a milestone paper by the prestigious Journal of The Human Factors and Ergonomics Society (HFES), an interdisciplinary nonprofit professional organization covering the fields of human factors and ergonomics. It was founded in 1957 and is an organization of professionals devoted to the science of human factors and ergonomics. The society’s mission is to promote the discovery and exchange of knowledge concerning the characteristics of human beings that are applicable to the design of systems and devices of all kinds. Over Twenty years later, this Genetic Modeling for Human Factors Research is still leading in the field of Self Learning Automated Systems for scientific research; and that explains in part the primary successes of MindfulChoice℠ and Mind Focused Coaching℠ innovative method in Cognition and Behavioral Psychology …