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Intelligent video surveillance systems have been used recently for automatic monitoring of human interactions. Although they play a significant role in reducing security concerns, there are many challenges for distinguishing between normal and abnormal behaviors such as crowded environments and camera viewpoint. In this paper, we propose a novel deep violence detection framework based on the specific features derived from handcrafted methods. These features are related to appearance, speed of movement, and representative image and fed to a convolutional neural network (CNN) as spatial, temporal, and spatiotemporal streams. The spatial stream trained the network with each frame in the video to learn environment patterns. The temporal stream contained three consecutive frames to learn motion patterns of violent behavior with a modified differential magnitude of optical flow. Moreover, in spatio-temporal stream, we introduced a discriminative feature with a novel differential motion energy image to represent violent actions more interpretable. This approach covers different aspects of violent behavior by fusing the results of these streams. The proposed CNN network is trained with violence-labeled and normal-labeled frames of 3 Hockey, Movie, and ViF datasets which comprised both crowded and uncrowded situations. The experimental results showed that the proposed deep violence detection approach outperformed state-of-the-art works in terms of accuracy and processing time. [ABSTRACT FROM AUTHOR]

The experimental purpose of X-ray diffraction is to analyze crystalline material structure at the atomic and molecular levels. Such experiments are known as X-ray crystallography. Traditionally, human experts do it with some domain knowledge. X-ray crystallography is useful in numerous domains, e.g., physics, chemistry, and biology. It is tough to own manual physics of diffraction patterns to see a crystal structure with a colossal data set. A convolutional neural network (CNN) is a deep neural network that maps an input image into a high-dimensional space. CNN produces an affordable function for image classification. This paper uses an extension of the convolutional neural network to predict crystal structure from diffraction patterns. We propose a machine-enabled method to predict crystallographic size and space group from a limited number of XRD patterns for small films. We overcome the problem of scarce data within the development of building materials by combining the learning model of moderately monitored equipment, a physics information-enhancing strategy using data generated from the Inorganic Crystal Structure Database, and test data. We compare our approach with a large variety of typical addition as modern machine learning-based classification techniques for crystal structure prediction. Results show that our proposed system outperforms all the baselines by a significant margin for the crystal structure prediction task. Results also show the impact of the number of layers in the all-convolutional neural network architecture for crystal structure prediction. [ABSTRACT FROM AUTHOR]