Behavior human analysis is always a significant aspect in societal communication. The human behavior analysis is developed based on few factors like human activity and action recognition. Human action recognition is an significant feature in different safety fields. The assessment of the action recognition algorithm depends on the appropriate extraction and the learning data. In the human action recognition, classification plays the major role so in order to this effectively Gated Recurrent Neural Network is used with an increased computation level. Feature extraction is one of the essential factor in human action recognition it will influence the performance and computation time of the algorithm. This paper presented an approach for human action recognition based on new mixture deep learning model. The proposed method is evaluated on the different data sets like UCF Sports, KTH and UCF101. On UCF Sports data set the proposed method has given an average of 96.8%.

Clustering is an important technique in data mining.Clustering a large data set is difficult and time consuming. An approach called data labeling has been suggested for clustering large databases using sampling technique to improve efficiency of clustering. A sampled data is selected randomly for initial clustering and data points which are not sampled and unclustered are given cluster label or an outlier based on various data labeling techniques. Data labeling is an easy task in numerical domain because it is performed based on distance between a cluster and an unlabeled data point. However, in categorical domain since the distance is not defined properly between data points and between data point with cluster, then data labeling is a difficult task for categorical data. In this paper, we have proposed a method for data labeling using Relative Rough Entropy for clustering categorical data. The concept of entropy, introduced by Shannon with particular reference to information theory is a powerful mechanism for the measurement of uncertainty information. In this method, data labeling is performed by integrating entropy with rough sets. In this paper, the cluster purity is also used for outlier detection. The experimental results show that the efficiency and clustering quality of this algorithm are better than the previous algorithms.

The video surveillance technology is used to find crime events in public places and capture live public events. Hence, detecting the criminalist before the crime actions is the most needed event to catch the criminalist. However, the presence of noise content in the trained video has raised the difficulties in crime specification by maximizing the complexity range of the data. To overcome this issue, this research has designed a novel lion-based deep belief neural paradigm (LbDBNP) to identify criminals by their activities and handling tools. Initially, three types of datasets were trained to the system then the training flaws were eliminated in the preprocessing layer. Hereafter, the cleaned data is imported to the classification module to detect the crime events present in the video. Subsequently, the designed model is implemented using the Python framework in Windows 10 platform. To evaluate the efficiency of the designed model, the attack is launched in the proposed model after those metrics are calculated. In addition, the robustness of the designed system is verified by three datasets, such as UCSDped1, UCSDped2, and avenue crime. Also, the key parameters of the designed model have been evaluated and compared with other existing schemes to verify the proposed model's robustness by achieving the finest outcomes.

The proportionate increase in the size of the data with increase in space implies that clustering a very large data set becomes difficult and is a time consuming process. Sampling is one important technique to scale down the size of dataset and to improve the efficiency of clustering. After sampling, allocating unlabeled data point into proper cluster is difficult in the categorical domain and in real situations data changes over time. However, clustering this type of data not only decreases the quality of clusters and also disregards the expectation of users, who usually require recent clustering results. In both the cases mentioned above, one is of allocating unlabeled data point into proper clusters after the sampling and the other is of finding clustering results when data changes over time which is difficult in the categorical domain. In this paper, using node importance technique, a rough set based method proposed to label unlabeled data point and to find the next clustering result based on the previous clustering result.

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