Bipan Tudu

TL;DR: The performance of the proposed technique is verified to estimate black tea quality using two kernel classifiers, namely support vector machine and recently proposed vector valued regularized kernel function approximation method, which confirms the effectiveness of the propose technique of tea quality estimation using ET signals.

TL;DR: Three types of feature selection methods namely, t-statistics, Fisher's criterion and minimum redundancy maximum relevance (MRMR) technique are used to select the most informative features in an electronic nose system with black tea samples to achieve improved classification performance.

TL;DR: Three different regression models such as artificial neural network, vector-valued regularized kernel function approximation, and support vector regression are used to evaluate the performance of the proposed method for prediction of TF and TR using ET signals.

TL;DR: A novel method to model the responses of electronic tongue (ET) sensors using autoregressive (AR) and AR moving average techniques is presented and provides better or similar performance compared with some other methods proposed in the literature for ET signal classification.

TL;DR: This research work shows the efficient prediction of black tea quality using machine learning algorithm with e-nose and investigates the potential of three different types of multi-class support vector machine (SVM) to build taster-specific computational models.