Researchers are interested in Facial Emotion Recognition (FER) because it could be useful in many ways and has promising applications. The main task of FER is to identify and recognize the original facial expressions of users from digital inputs. Feature extraction and emotion recognition make up the majority of the traditional FER. Deep Neural Networks, specifically Convolutional Neural Network (CNN), are popular and highly used in FER due to their inherent image feature extraction process. This work presents a novel method dubbed as EfficientNet-XGBoost that is based on Transfer Learning (TL) technique. EfficientNet-XGBoost is basically a cascading of the EfficientNet and the XGBoost techniques along with certain enhancements by experimentation that reflects the novelty of the work. To ensure faster learning of the network and to overcome the vanishing gradient problem, our model incorporates fully connected layers of global average pooling, dropout and dense. EfficientNet is fine-tuned by replacing the upper dense layer(s) and cascading the XGBoost classifier making it suitable for FER. Feature map visualization is carried out that reveals the reduction in the size of feature vectors. The proposed method is well-validated on benchmark datasets such as CK+, KDEF, JAFFE, and FER2013. To overcome the issue of data imbalance, in some of the datasets namely CK+ and FER2013, we augmented data artificially through geometric transformation techniques. The proposed method is implemented individually on these datasets and corresponding results are recorded for performance analysis. The performance is computed with the help of several metrics like precision, recall and F1 measure. Comparative analysis with competent schemes are carried out on the same sample data sets separately. Irrespective of the nature of the datasets, the proposed scheme outperforms the rest with overall rates of accuracy being 100%, 98% and 98% for the first three datasets respectively. However, for the FER2013 datasets, efficiency is less promisingly observed in support of the proposed work.

In ongoing year, the expanding number of wearable sensors on human can fill for some, needs like crisis care, medicinal services remote observing, individual diversion and correspondence and so forth. The WBAN empowers the clinical applications to be created utilizing electronic gadgets and sensors. The WBAN is made by wearing little sensors on the human body. A significant issue is the means by which to protect security and protection of patient's clinical social insurance data over remote correspondence. The vitality utilization and information security are as yet significant difficulties in medicinal services applications. In this paper we proposed an algorithm to secure patients data from eavesdropping attack with help of RSA secret key encryption technique. The target of this paper is to propose a remote sensor organize framework in which both pulse and internal heat level of various patients can screen on PC simultaneously utilizing RF arrange. The proposed model framework incorporates two sensor hubs and beneficiary hub (base station). The sensor hubs can transmit information to collector utilizing remote nRF handset module. The nRF handset module is utilized to move the information from microcontroller to PC and a graphical UI (GUI) is created to show the deliberate information and spare to database. Numerous advances have demonstrated their productivity in supporting WBANs applications, for example, remote observing, biofeedback and helped living by reacting to their particular nature of administration (QoS) necessities. Because of various accessible innovations, choosing the fitting innovation for a clinical application is being a difficult undertaking. In this paper, the distinctive clinical applications are introduced. The most widely recognized advances utilized in WBANs are featured. At long last, a coordinating between every application and the relating reasonable innovation is considered.