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Found 8 papers, 0 papers with code
Evolution of Neural Architectures for Financial Forecasting: A Note on Data Incompatibility during Crisis Periods
This note focuses on the optimization of neural architectures for stock index movement forecasting following a major market disruption or crisis.
Coevolution of Neural Architectures and Features for Stock Market Forecasting: A Multi-objective Decision Perspective
In a multi objective setting, a portfolio manager's highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement.
A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting
This study proposes a new framework to evolve efficacious yet parsimonious neural architectures for the movement prediction of stock market indices using technical indicators as inputs.
An Efficient Framework for Automated Screening of Clinically Significant Macular Edema
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.
Multi-objective Evolutionary Approach to Grey-Box Identification of Buck Converter
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.
Multi-Objective Evolutionary Framework for Non-linear System Identification: A Comprehensive Investigation
The present study proposes a multi-objective framework for structure selection of nonlinear systems which are represented by polynomial NARX models.
Efficient Feature Selection of Power Quality Events using Two Dimensional (2D) Particle Swarms
A novel two-dimensional (2D) learning framework has been proposed to address the feature selection problem in Power Quality (PQ) events.
A Two-Dimensional (2-D) Learning Framework for Particle Swarm based Feature Selection
This paper proposes a new generalized two dimensional learning approach for particle swarm based feature selection.
