Human Stress Recognition by Correlating Vision and EEG Data

Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.

A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

Crop protection is a great obstacle to food safety,with crop diseases being one of the most serious issues.Plant diseases diminish the quality of crop yield.To detect disease spots on grape leaves,deep learning technology might be employed.On the other hand,the precision and efficiency of identification remain issues.The quantity of images of ill leaves taken from plants is often uneven.With an uneven collection and few images,spotting disease is hard.The plant leaves dataset needs to be expanded to detect illness accurately.A novel hybrid technique employing segmentation,augmentation,and a capsule neural network(CapsNet)is used in this paper to tackle these challenges.The proposed method involves three phases.First,a graph-based technique extracts leaf area from a plant image.The second step expands the dataset using an Efficient Generative Adversarial Network E-GAN.Third,a CapsNet identifies the illness and stage.The proposed work has experimented on real-time grape leaf images which are captured using an SD1000 camera and PlantVillage grape leaf datasets.The proposed method achieves an effective classification of accuracy for disease type and disease stages detection compared to other existing models.