Multiple criteria programming models for VIP E-Mail behavior analysis
Web Intelligence and Agent Systems
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The ability to identify neuronal damage resulting from HIV-1-associated dementia (HAD) is crucial for designingspecific therapies for the treatment of HAD. This paperproposes a two-class model of multiple criteria linearprogramming (MCLP) to classify the HAD neural dendriticand synaptic damages. The damages are measured by anumber of quantitative variables such as the change ofneuritis, arbors, branch nodes, and cell bodies. Givencertain classes, including brain derived neurotrophic factor(BDNF) treatment, non-treatment, glutamate treatment, andgp120 (HIV-1 envelop protein) from laboratory cellobservations, we use the two-class MCLP model to learn thedata patterns between two classes so that we can discoverthe knowledge about the HAD neural dendritic and synapticdamages under different treatments. This knowledge can beapplied to design and study specific therapies for theprevention or reversal of the neuronal demise associatedwith HAD. In the paper, we first describe the technicalbackground of the two-class models that includes concepts,modeling and computer algorithms. Then, we conduct aseries of learning experimental tests on the data oflaboratory cell observations. We also illustrate somesignificance and implications of learning results in the HADresearch.