![]() In higher layers, we adapt multi-linear relationship networks to GCN by exploring the dimension transformation and freezing part of the covariance structure. In lower layers, we propose grouped GCN to combine the graph connectivity from different modalities for a more complete spatial feature extraction. In this work, we propose two interaction techniques for handling features in lower layers and higher layers, respectively. Leveraging the advantage of multi-modal machine learning, we propose to develop modality interaction mechanisms for this problem in order to reduce the generalization error by reinforcing the learning of multi-modal coordinated representations. To incorporate multiple relationships into a spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks. Each relationship represents a kind of spatial dependency, such as region-wise distance or functional similarity. Graph convolution network-based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing. ![]() In order to show the validity of the proposed hybrid data mining technique, an online data set containing network of a suspicious group has been utilized and main leaders of network has been identified. The research includes interpretation of data from different networks using hybrid data mining technique. In this research, a data mining-based framework has been proposed for surveillance. The solution lies in a system that allows automated surveillance through classification and other data mining techniques that can be used for extraction of useful information out of these inputs. ![]() In addition to that, employing an adequate number of people for the job is not always feasible either. But, even in presence of high definition (HD) security cameras and manpower to monitor the live feed 24/7, room for missing important information due to human error exists. ![]() ![]() In the current times where human safety is threatened by man-made and natural calamities, surveillance systems have gained immense importance. ![]()
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