Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of ...Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.展开更多
The aim of this work is to investigate the general functional properties of the intracellular governing gene/epigene networks. A body of mathematics used in automata and graphs theories is adequate for revealing the g...The aim of this work is to investigate the general functional properties of the intracellular governing gene/epigene networks. A body of mathematics used in automata and graphs theories is adequate for revealing the general dynamic properties of governing gene and epigene networks and provides a methodic basis for efficient analytical algorithms. The obtained results permit to reveal the properties of the characteristic function （transitions and outputs） of the cellular automata as models for the intracellular governing gene/epigene networks.展开更多
文摘Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.
文摘The aim of this work is to investigate the general functional properties of the intracellular governing gene/epigene networks. A body of mathematics used in automata and graphs theories is adequate for revealing the general dynamic properties of governing gene and epigene networks and provides a methodic basis for efficient analytical algorithms. The obtained results permit to reveal the properties of the characteristic function （transitions and outputs） of the cellular automata as models for the intracellular governing gene/epigene networks.
