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The first part of the model is developed to classify tool wear by using fuzzy logic.
A new object-oriented approach is developed to classify glaciovolcanic landforms (Procedure A) and their landform elements boundaries (Procedure B).
Then, a combination of rule-based scheme with Probabilistic Actively Learned Support Vector Machines is developed to classify the features calculated from the related tracked facial points.
After reviewing the main characteristics of CCIs, a conceptual framework is developed to classify motives to trademark and motives not to trademark for firms in these industries.
A clustering method is developed to classify the vertices according to their surface types, and a connected region generation approach is developed for final segmentation.
An analytical model is developed to classify the impulsive response of sandwich beams based on the relative time-scales of core compression and the bending/stretching response of the sandwich beam.
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An MLP (Multilayer Perceptron) Artificial Neural Network Model has been developed to classify the signature signals.
The base taxonomy was developed to classify relationships between pairs of sites.
Then, a multinomial logit model was developed to classify various network patterns using the six metrics.
Probabilistic neural network (PNN) models are developed to classify the vehicle type and its distance.
Different indices have been developed to classify rivers and assess land use and climate change impacts on rivers.
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