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In this research, we propose the deep learning based approach for identification of individual cattle based on their primary muzzle point (nose pattern) image pattern characteristics to addressing the problem of missed or swapped animals and false insurance claims.
Unlike rule based approaches of identification and classification, machine learning approaches have emerged much superior for the process of classification.
Ahmad et al. [73] presented an ACO based approach for expert identification and query routing in social networks.
The paper proposes a new data-flow based approach for the identification of backbones in infinite clusters on 2-D percolation site lattices of dimension L × L. The infinite cluster is identified first, then a multi step algorithm is applied for the reduction of the infinite cluster to its backbone.
Others have advocated the genetic distance threshold based approach for species identification.
In threshold based approach for species identification, the major concerns are the variation in the rate to fix a threshold and that a single locus threshold can be confounded by introgression or selection [38], [39].
We used a two-step based approach for the identification of orthologous proteins across all 18 Pseudomonas genomes.
A neural network based approach of identification for Preisach-type hysteresis is proposed.
The ongoing expansion in neuropeptide diversity lies in the unbiased and global mass-spectrometry-based approaches for identification and quantitation of peptides.
To better understand the roles of TEs in organization, structure and evolution of the silkworm genome, we used a combination of de novo, structure-based and homology-based approaches for identification of the silkworm TEs and identified 1308 silkworm TE families.
This indicates filtering based approaches are suboptimal for the identification of rare Mendelian disorders, when compared to more sophisticated rare-variant association tests.
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