Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity.

Emotion recognition is defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communication, and many more. This paper proposes a new facial emotional recognition model using a convolutional neural network. Our proposed model, "ConvNet", detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. The features extracted by the Local Binary Pattern (LBP), region based Oriented FAST and rotated BRIEF (ORB) and Convolutional Neural network (CNN) from facial expressions images were fused to develop the classification model through training by our proposed CNN model (ConvNet). Our method can converge quickly and achieves good performance which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this study focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases at first, and then apply the generalization techniques to the JAFFE and CK+ datasets respectively in the testing stage to evaluate the performance of the model. In the generalization approach on the JAFFE dataset, we get a 92.05% accuracy, while on the CK+ dataset, we acquire a 98.13% accuracy which achieve the best performance among existing methods. We also test the system's success by identifying facial expressions in real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. However, when compared to other validation methods, the suggested technique was more accurate. ConvNet also achieved validation accuracy of 91.01% for the FER2013 dataset. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN .

Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection.

Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Convolutional Neural Networks (CNNs)-CheXNet and VGG-19 are trained and used to extract features from given X-ray images. These features are then ensembled for classification. To overcome data irregularity problem, Random Under Sampler (RUS), Random Over Sampler (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied on the ensembled feature vector. The ensembled feature vector is then classified using several Machine Learning (ML) classification techniques (Random Forest, Adaptive Boosting, K-Nearest Neighbors). Among these methods, Random Forest got better performance metrics than others on the available standard dataset. Comparison with existing methods shows that the proposed method attains improved classification accuracy, AUC values and outperforms all other models providing 98.93% accurate prediction. The model also exhibits potential generalization capacity when tested on different dataset. Outcomes of this study can be great to use for pneumonia diagnosis from chest X-ray images.