European Journal of Molecular & Clinical Medicine

The affective quality called Valence refers to the intrinsic goodness (positive valence) or badness (negative valence) of an event, object, or situation. For this purpose, a model for classification and characterization of emotions have been developed which is discussed in this paper. In this model, the images are smoothened using an Average Filter and are first identified through a Convolutional Neural Network which uses the ReLU activation function. Then, the valence is classified using a Support Vector Machine (SVC) classifier, which uses a Radial Basis Function (RBF) kernel. For this reason, the emotions are labeled according to their nature. The positive emotions are labeled 1 (inclusive of the neutral emotion) and the negative emotions are labeled as 0. The images from the FER 2013 dataset is used for Valence Recognition and is given via a RBF Kernel in a SVM, which classifies whether the emotion recognized is positive or negative. The haarcascade algorithm is implemented to detect the face. In this paper, the 7 human emotions (happiness, surprise, fear, anger, fear, disgust, sadness and neutral) have been identified and their valence recognized.

In this manuscript, a meshless technique based on radial basis function is used for solving the Sine-Gordan equation. Radial basis function combined with pseudospectral approach is used to solve the nonlinear Sine-Gordan equation. The given equation is transformed to a system of ODEs with the help of radial basis function then an ODE solver is used to solve the transformed ODEs. Numerical example is established to showcase the validness and efficiency of the method. Error norms are calculated to check the accuracy. The obtained numerical results are in good accord with the exact solution existed in literature.