Developing a Fuzzy Expert System to Examine Hazard Analysis in The United States Army

<p>The research conducted in this thesis is an attempt to determine if there are any biographical similarities between second lieutenants in the US Army who view risk in a similar manner and if so, which are the most significant. A study was conducted using in-group surveys of 72 second lieutenants receiving training in Infantry Basic Officer Leader Course (IBOLC). The participants were provided with two written surveys each presenting a scenario based on military activities. They were asked to evaluate the scenarios and fill out Risk Management (RM) worksheets based on the US Army's doctrinal process for conducing RM. The RM worksheets were then evaluated based on the hazards they identified for each hazard category. A fuzzy expert system was developed to evaluate the lieutenants' performance in assessing the different hazards. The output results were then evaluated using the two-step cluster process and one way ANOVA test in SPSSS. The cluster results showed Platoon and Major as the two most significant predictors for cluster formation on the Foot March Scenario. Of the two only Platoon demonstrated statistical significance on the ANOVA. Prior Service and Platoon were the two most significant predictors for cluster formation on the Maintenance scenario but neither showed statistical significance.</p>

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

TSYS School of Computer Science

Year dc:date.available

2015

Author dc:creator

• Karels, Charles J.

Contributors dc:contributor

• Rania Hodhod
• Angkul Kongmunvattana
• Shamim Khan

• Risk Management
• Second Lieutenant
• Infantry Basic Officer Leader Course
• IBOLC
• US Army
• Computer Engineering
• Numerical Analysis and Scientific Computing

Language dc:language

English

Repository record dc:identifier

https://csuepress.columbusstate.edu/theses_dissertations/171

OAI identifier oai:identifier

oai:csuepress.columbusstate.edu:theses_dissertations-1171