This paper justifies this approach using computer simulations to validate the multi-ion-irradiation Laboratory.
We investigate the performance of our approach using computer simulations and explore the sample sizes and data structures required to provide high power for detection of effects and accurate estimation of the relative risks conferred.
Promising results have also been seen with a novel training approach using computer simulated standardized patients, although these lack nonverbal communication and emotional cues, which are so essential in the real world.
Many water management problems have applied a systems approach using computer models to simulate physical processes (Jeppsson and Hellstrom 2002; Lund et al. 2006) and institutional factors, such as the capacity for interagency and organizational collaboration (Cassady et al. 2008; Temel 2005).
This chapter shows how a different approach, using computers to model real world problems, can allow a person to widely explore the design space and very quickly acquire a deeper knowledge, which has traditionally been the domain of the experienced engineer.
Others have suggested that a more structured approach using computer-patient interviewing [ 35] is the way forward.
The results of this survey were intended to provide valuable information regarding the extent to which English mental health trusts can effectively deliver and support the guided self-help approach using computer-based packages in order to increase access to psychological therapies, increase service efficiency and reduce patient waiting times.
In the following, we first describe the new method and then assess the absolute accuracy of the new approach by using computer simulations.
This paper proposes using computer vision approaches based on local feature representation, feature learning, and classification for sex prediction from human cranial data obtained from CT scans.
We therefore summarize the contributions of the paper as follows: This paper proposes using computer vision approaches based on local feature representation, feature learning, and classification for sex prediction from human cranial data obtained from CT scans.
