Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes.The use of deep learning(DL)technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification.The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention.Human action recognition(HAR)is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level.The advancements of the DL models help to accomplish improved recognition performance.In this view,this paper presents a smart deep-based human behavior classification(SDL-HBC)model for real-time video surveillance.The proposed SDL-HBC model majorly aims to employ an adaptive median filtering(AMF)based pre-processing to reduce the noise content.Also,the capsule network(CapsNet)model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam optimizer.Finally,the differential evolution(DE)with stacked autoencoder(SAE)model is applied for the classification of human activities in the intelligent video surveillance system.The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset.The experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.

Hybrid Metaheuristics Based License Plate Character Recognition in Smart City

Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)approaches.In this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city environments.The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them.For effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline network.In addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 model.Finally,Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs.The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods.

Evolutionary Intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

Diabetic Retinopathy(DR)has become a widespread illness among diabetics across the globe.Retinal fundus images are generally used by physicians to detect and classify the stages of DR.Since manual examination of DR images is a time-consuming process with the risks of biased results,automated tools using Artificial Intelligence(AI)to diagnose the disease have become essential.In this view,the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification(ODL-FDRDC)technique.The intention of the proposed ODLFDRDC technique is to identify DR and categorize its different grades using retinal fundus images.In addition,ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions.Moreover,the fusion of two DL models namely,CapsNet and MobileNet is used for feature extraction.Further,the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm(COA).Gated Recurrent Unit(GRU)is also utilized to identify DR.The experimental results of the analysis,accomplished by ODL-FDRDC technique against benchmark DR dataset,established the supremacy of the technique over existing methodologies under different measures.