One of the major and fundamental issue is emotion recognition during the development of an interactive computer system [1-3]. Recognition of facial emotion/expression is essential, because nowadays it place its wide applications in various sectors like psychological distress and pain detection [4]. Some fields like psychology, sociology, and automatic expression recognition, therefore provided a considerable importance for this emotion recognition process to create a highly user affable software and user agents in these fields. This process of automatic facial expression recognition (FER) has exhibited its large implications in the human computer interaction (HCI) field [5]. Recently, the affective computing is considered as the most significant study field in HCI, which highly intends to improve the human-machine interaction by clearly recognizing the emotion Abstract Group-based emotion recognition (GER) is an interesting topic in both security and social area. In this paper, a GER with hybrid optimization based recurrent fuzzy neural network is proposed which is from video sequence. In our work, by utilizing the Neural Network the emotion recognition (ER) is performed from group of people. Initially, original video frames are taken as input and pre-process it from multi user video data. From this pre-processed image, the feature extraction is done by Multivariate Local Texture Pattern (MLTP), gray-level co-occurrence matrix (GLCM), and Local Energy based Shape Histogram (LESH). After extracting the features, certain features are selected using Modified Sea-lion optimization algorithm process. Finally, recurrent fuzzy neural network (RFNN) classifier based Social Ski-Driver (SSD) optimization algorithm is proposed for classification process, SSD is used for updating the weights in the RFNN. Python platform is utilized to implement this work and the performance of accuracy, sensitivity, specificity, recall and precision is evaluated with some existing techniques. The proposed method accuracy is 99.16%, recall is 99.33%, precision is 99%, sensitivity is 99.93% and specificity is 99% when compared with other deep learning techniques our proposed method attains good result.

Emotion recognition from human faces are recently considered as growing topic for the applications in HCI (human-computer interaction) field. Therefore, a new framework is introduced in this method for emotion recognition from video. Human faces may carry huge features which increase the complexity of recognizing the emotions from the give video. Therefore, to minimize such defect, the wrapper based feature selection technique is introduced which reduce the complexity of proposed recognition framework. Initially, the frames from the input video is preprocessed. Next, the features exhibited by each emotions are extracted with geometric and local binary pattern-based feature extraction methods. Then, the features that reduce the performance of recognition technique is avoided using a feature selection algorithm. It selects the features that provides effective result on recognition process. Finally, the selected features are provided to deep belief network (DBN) for emotion recognition. The weight parameter selection of DBN is improved using an efficient Harris Hawk optimization algorithm. The performance of presented architecture is evaluated using a three different datasets they are FAMED, CK+, and MMI. The overall rate shown by proposed architecture is found better than existing methods. Furthermore, the precision, recall, and specificity are also evaluated for six different emotions (angry, disgust, fear, happy, sad, and surprise) in this proposed method. This entire emotion recognition process is implemented in Python platform.

This note analyzes the unsupervised fuzzy neural network (FNNU) of Kwan and Cai and finds the following: the FNNU is a clustering net, not a classifier net, and the number of clusters the network settles to may be less or more than the actual number of pattern classes-sometimes it could even be equal to the number of training data points! The huge number of connections in the FNNU can be drastically reduced without degrading its performance. The algorithm does not have any learning capability for its parameters. Computational experience shows that usually the performance of an multilayer perceptron (MLP) is comparable to that of even a supervised version of FNN (trained by gradient descent algorithm) in terms of recognition scores, but an MLP has a much faster convergence than the supervised version of FNN.

The controllers developed so far for the on-grid and off-grid operation is based on frequency regulation at grid and have yield poor switching by inducing oscillation. Hence to solve this problem in this paper, the switching between the on-grid and off-grid are made by the Emperor penguin based Adaptive fuzzy neuro inference system (EP-ANFIS) controller, which works based on the energy supplied to the load. To serve the transient load condition the hybrid storage unit is modelled by social-ski driver algorithm (SSD) that will schedule the energy supplement between the grid and generator. The proposed controller model provides stable operation during the switching process that is without the transient oscillation the switching is smooth. The error in data selection for the ANFIS is reduced by the Emperor penguin optimization (EPO) as 0.1 for the test data. The proposed work is implemented in Matlab/Simulink platform. The results are compared with the existing works in terms of voltage, current, power, and THD. When examining for transient load condition the settling time for the controller to the steady state is 0.78 s which is comparatively low with PID, PI, ANFIS, and ANFIS-PSO controllers. The THD is reduced to 9.11% from the existing methods by maintain the fundamental frequency of 50 Hz.