Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Generally,conventional methods for anomaly detection rely on clustering,proximity,or classification.With themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient.This research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured data.Every node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph structures.Due to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and healthcare.Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies.The Graph-based deep learning networks are designed to predict unknown objects and outliers.In our case,they detect unusual objects in the form of malicious nodes.The edges between nodes represent a relationship of nodes among each other.In case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an anomaly.The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome.Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance videos.Performing autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places.The suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniques.

Social Distancing and Isolation Management Using Machine-to-Machine Technologies to Prevent Pandemics

Social distancing and self-isolation management are crucial preventive measures that can save millions of lives during challenging pandemics of diseases such as the Spanish u,swine u,and coronavirus disease 2019(COVID-19).This study describes the comprehensive and effective implementation of the Industrial Internet of Things and machine-to-machine technologies for social distancing and smart self-isolation management.These technologies can help prevent outbreaks of any disease that can disperse widely and develop into a pandemic.Initially,a smart wristband is proposed that incorporates Bluetooth beacon technology to facilitate the tracing and tracking of Bluetooth Low Energy beacon packets for smart contact tracing.Second,the connectivity of the device with Android or iOS applications using long-term evolution technology is realized to achieve mobility.Finally,mathematical formulations are proposed to measure the distance between coordinates in order to detect geo-fencing violations.These formulations are specically designed for the virtual circular and polygonal boundaries used to restrict suspected or infected persons from trespassing in predetermined areas,e.g.,at home,in a hospital,or in an isolation ward.The proposed framework outperforms existing solutions,since it is implemented on a wider scale,provides a range of functionalities,and is cost-effective.

Symmetry solutions of a nonlinear elastic wave equation with third-order anharmonic corrections

Lie symmetry method is applied to analyze a nonlinear elastic wave equation for longitudinal deformations with third-order anharmonic corrections to the elastic energy. Symmetry algebra is found and reductions to second-order ordinary differential equations （ODEs） are obtained through invariance under different symmetries. The reduced ODEs are further analyzed to obtain several exact solutions in an explicit form. It was observed in the literature that anharmonic corrections generally lead to solutions with time-dependent singularities in finite times singularities, we also obtain solutions which Along with solutions with time-dependent do not exhibit time-dependent singularities.