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In two experiments, the performance of patients with frontal lobe lesions was examined on implicit and explicit tests of conceptual memory for organized lists of words.
In the greenhouse experiments, the performance of picking and cutting was investigated.
In the diagnosis experiments, the performance achieved was approximately 81% of correct classification.
Through numerous experiments, the performance of the proposed learning algorithms is evaluated.
In conducted segmentation experiments, the performance results of SAMS approach are compared with the ones of BackMS method.
In the experiments, the performance of MAGA-Net is validated on both well-known real-world benchmark networks and large-scale synthetic LFR networks with 5000 nodes.
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In these experiments, the performances of eight fusion techniques are compared on all four datasets.
In the experiments, the performances of these algorithms are investigated in terms of visual quality and quantitative measures.
By simulation and experiment, the performance of dual-stage is evaluated and compared.
FInure 5 displays a theck estimated with the projective particle firstr and thexperiment parthele filter.
In this experiment the performance scalability of our approach was evaluated.
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