Experimental results based on benchmark and real-world data show that, compared to their competitors, our feature weighting approaches show higher classification accuracy, yet at the same time maintain the simplicity and lower execution time of the final models.
All three (biogeographic, taxonomic and latitudinal) approaches showed high differentiation between the Southern Indian (F. c. kelaarti, S. Deccan, latitudinal range: 10 19.9o N) and the Upper Gangetic Plains/North East (F. c. affinis, Upper Gangetic Plains/North East, latitudinal range: 20 28.9o) populations, suggesting isolation by distance.
A spearman rank correlation test of the allocations made using the two approaches shows high correlation, a ρ of 0.96 significant at the 1% significance level.
The proposed approach showed high sensitivity from both visual and absorbance measurements.
The proposed approach shows high potential to account the dissipated energy in the fracture process zone in the ductile adhesives.
From the experimental results, it can be concluded that the proposed approach shows high adaptability and efficiency in e-learning recommendation.
Under the optimal experimental conditions, this approach shows high selectivity even toward single-base mismatch and improves sensitivity greatly for target DNA with a linear range of 2.5 fM–1 pM, achieving a detection limit as low as 0.8 fM.
The approach shows high potential for use in downscaling and other climate change impact assessment studies, especially for the planning and design of hydrologic systems that are sensitive to the characterisation of persistence in the hydrologic time series.
The fuzzy-based control approach shows high flexibility due to the lack of limitation on part of control signals to the mathematical absolute values.
On this basis, our approach shows high specificity (range 0.50 0.93) but moderate sensitivity (range 0.33 0.80 depending on miRNA and filtering criteria) (Table 6).
