Discriminative correlation filters (DCF) are efficient in visual tracking and have advanced the field significantly. However, the symmetry of correlation (

Regression testing involves validating a software system after modification to ensure that the previous bugs have been fixed and no new error has been raised. Finding faults early and increasing the fault detection rate are the main objectives of regression testing. A common technique involves re-executing the whole test suite, which is time consuming. Test case prioritization aims to schedule the test cases in an order that could achieve the regression testing goals early in the testing phase. Recently, machine learning techniques have been extensively used in regression testing to make it more effective and efficient. In this paper, we propose and investigate whether an Artificial Neural Network (ANN)-based approach can improve the version specific test case prioritization approach. The proposed approach utilizes the combination of test cases complexity information and software modification information with an ANN, for early detection of critical faults. Three new factors have been proposed, based on which an ANN is trained and finally it can automatically assign priorities to new test cases. The proposed approach is empirically evaluated with two software applications. Effectiveness metrics, such as fault detection rate, accuracy, precision, and recall are examined. The results suggest that the proposed approach is both effective and feasible.