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Over the past few decades, composite materials are extensively used in various industries due to their high specific stiffness, strength, weight sensitivity and cost-effectiveness.
The meta-neuron utilizes the global information stored in the network (synaptic weights) and the local information present in the input spike pattern to determine a weight sensitivity modulation factor for a given synapse.
Also, according to weight sensitivity analysis input of electrical fields with 63% relative importance percentage has a grater impression on the response variable of velocity of sound in liquid water.
In this paper, we extend our research significantly, to find an optimal configuration of the wire mesh collimator specifically for semi-compressed breast tumor detection, by looking into four major factors: weight, sensitivity, spatial resolution and tumor contrast.
Based on the weight sensitivity modulation factor and the postsynaptic potential of a neuron, the meta-neuron based learning rule updates the synaptic weights in the network to produce precise shifts in the spike times of the postsynaptic neurons.
All elements in the AHP PCM were then picked to check the weight sensitivity.
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Subsequently, an analysis on the damage weights sensitivity is undertaken.
The high level of the factor weights sensitivity requires intensive concentrations for all factor weight-rating processes.
By weighting sensitivity and specificity equally this cut-off point is assumed to represent the lowest overall misclassification.
On the other hand, if the primary objective is to distinguish between "importantly improved" and "stable" children a cut-off value of 22.2 will be the most appropriate as this represents the optimum cut-off when weighting sensitivity and specificity equally.
Lending equal weight to sensitivity and specificity, 35.5 was the score at which sensitivity and 1-specificity were maximized, which we rounded up to 36.
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