The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i. e., exudate segmentation and imbalanced datasets.
In essence, the proposed approach casts grey-box identification problem into a multi-objective framework to balance bias-variance dilemma of model building while explicitly integrating a priori knowledge into the structure selection process.
The present study proposes a multi-objective framework for structure selection of nonlinear systems which are represented by polynomial NARX models.
A novel two-dimensional (2D) learning framework has been proposed to address the feature selection problem in Power Quality (PQ) events.
This paper proposes a new generalized two dimensional learning approach for particle swarm based feature selection.