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An open challenge in information distillation is the evaluation and optimization of the utility of ranked lists with respect to flexible user interactions over multiple sessions. Utility depends on both the relevance and novelty of documents, and the novelty in turn depends on the user interaction history. However, user behavior is non-deterministic. We(More)
In this paper, the main area of concentration was to optimize the rules generated by Association Rule Mining (apriori method), using Genetic Algorithms. In general the rule generated by Association Rule Mining technique do not consider the negative occurrences of attributes in them, but by using Genetic Algorithms (GAs) over these rules the system can(More)
This paper examines a new approach to information distillation over temporally ordered documents, and proposes a novel evaluation scheme for such a framework. It combines the strengths of and extends beyond conventional adaptive filtering, novelty detection and non-redundant passage ranking with respect to long-lasting information needs ("tasks" with(More)
(2S)-2-{[(3,5-Diflurophenyl)acetyl]amino}-N-[(3S)-1-methyl-2-oxo-5-phenyl-2,3-dihydro-1H-1,4-benzodiazepin-3-yl]propanamide (compound E) is a gamma-secretase inhibitor capable of reducing amyloid beta-peptide (1-40) and amyloid beta-peptide (1-42) levels. In this study we investigated the effect of in vivo administration of compound E on guinea-pig plasma,(More)
In this paper, the main area of concenhution wos to optimize the rules generated by Association Rule Mining (apriori metho4, using Genetic Algorithms. In general the rule generated by Association Rule Mining technique do not consider the negative occurrences of attributes in them, but by using Genetic Algorithms (GAS) over these rules the system can predict(More)