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- 저자 : Hussan M. I. Thariq (검색결과 2 건)
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An Optimized User Behavior Prediction Model Using Genetic Algorithm On Mobile Web Structure
( M. I. Thariq Hussan ),( B. Kalaavathi ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.5
With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users` behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.
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Effective Anomaly Identification in Surveillance Videos Based on Adaptive Recurrent Neural Network
Arul U.,Arun V.,Rao T. Prabhakara,Baskaran R.,Kirubakaran S.,Hussan M. I. Thariq 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3
Surveillance systems completed in true environment are of a solid nature. As the environment is uncertain and variable, care gradually becomes confusing when moving away from a stable and controlled environment. Evidence to distinguish stressful abnormalities in video surveillance is a problematic issue due to leakage, video screaming, contradictions and motives. Hence, in this paper, adaptive recurrent neural network is developed for anomaly detection from the videos. The projected technique is a combination of recurrent neural network and crystal structure algorithm. In the anomality detection, the video should be changed into frames. After that, the images should be enhanced for improving image quality. Once, the image quality is enhanced, the image background should be eliminated for achieving object detection. In the proposed technique, the region of interest is utilized to attain the object detection step in the images. The detected object images are used to tracking the object in the images by using the proposed classifer. To enhance the object tracking system, the feature extraction is a required topic in the system. Maximally stable extremal regions is used to extract the required features from the images. Finally, the proposed classifer is utilized to achieve anomaly detection based on object movement in the input images. The projected strategy is implemented and evaluated by performance metrices. It is contrasted with conventional techniques such as convolutional neural network-particle swarm optimization (CNN-PSO) and CNN respectively.
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