Classifiers for modeling of mineral potential

[Extract] Classification and allocation of land-use is a major policy objective in most countries. Such an undertaking, however, in the face of competing demands from different stakeholders, requires reliable information on resources potential. This type of information enables policy decision-makers to estimate socio-economic benefits from different possible land-use types and then to allocate most suitable land-use. The potential for several types of resources occurring on the earth's surface (e.g., forest, soil, etc.) is generally easier to determine than those occurring in the subsurface (e.g., mineral deposits, etc.). In many situations, therefore, information on potential for subsurface occurring resources is not among the inputs to land-use decision-making [85]. Consequently, many potentially mineralized lands are alienated usually to, say, further exploration and exploitation of mineral deposits. Areas with mineral potential are characterized by geological features associated genetically and spatially with the type of mineral deposits sought. The term 'mineral deposits' means .accumulations or concentrations of one or more useful naturally occurring substances, which are otherwise usually distributed sparsely in the earth's crust. The term 'mineralization' refers to collective geological processes that result in formation of mineral deposits. The term 'mineral potential' describes the probability or favorability for occurrence of mineral deposits or mineralization. The geological features characteristic of mineralized land, which are called recognition criteria, are spatial objects indicative of or produced by individual geological processes that acted together to form mineral deposits. Recognition criteria are sometimes directly observable; more often, their presence is inferred from one or more geographically referenced (or spatial) datasets, which are processed and analyzed appropriately to enhance, extract, and represent the recognition criteria as spatial evidence or predictor maps. Mineral potential mapping then involves integration of predictor maps in order to classify areas of unique combinations of spatial predictor patterns, called unique conditions [51] as either barren or mineralized with respect to the mineral deposit-type sought

An enhanced performance model for metamorphic computer virus classification and detectioN

Metamorphic computer virus employs various code mutation techniques to change its code to become new generations. These generations have similar behavior and functionality and yet, they could not be detected by most commercial antivirus because their solutions depend on a signature database and make use of string signature-based detection methods. However, the antivirus detection engine can be avoided by metamorphism techniques. The purpose of this study is to develop a performance model based on computer virus classification and detection. The model would also be able to examine portable executable files that would classify and detect metamorphic computer viruses. A Hidden Markov Model implemented on portable executable files was employed to classify and detect the metamorphic viruses. This proposed model that produce common virus statistical patterns was evaluated by comparing the results with previous related works and famous commercial antiviruses. This was done by investigating the metamorphic computer viruses and their features, and the existing classifications and detection methods. Specifically, this model was applied on binary format of portable executable files and it was able to classify if the files belonged to a virus family. Besides that, the performance of the model, practically implemented and tested, was also evaluated based on detection rate and overall accuracy. The findings indicated that the proposed model is able to classify and detect the metamorphic virus variants in portable executable file format with a high average of 99.7% detection rate. The implementation of the model is proven useful and applicable for antivirus programs

Air Force Institute of Technology Research Report 2009

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics