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At present, detection and recognition of animals remain a complicated task and no distinctive technique can solve all situations strongly and effectively. Animal detection in images is challenging because of the complexity of wild environments. Including several different things: The large differences in shapes and color appearances of variations of objects which belong to the same class, and Lighting/Illumination Conditions. This paper presents an effective and efficient animal detection system for Birds detection in images. MobileNet SSD model is a Single Shot Multibox Detection (SSD) suggested in this work, which is capable of handling different shapes and view angles of the objects. This approach allows extracting features of image regions and using deep learning techniques for classifying regions into two categories: (bird and non-animal). The experimental results demonstrated the efficiency of classification and detection of single and multi-animals even in difficult environmental conditions. The accuracy of detection and classification is up to 98.8%. This methodology is more accurate than other similar approaches. This paper exhibits a method for training a convolutional neural network (CNN) based on object detection classifiers. [ABSTRACT FROM AUTHOR]

One of the major fields of study in the analysis and mining of social networks is the study of social network patterns and development. Link prediction has attracted extensive attention from the scientist in the network science. Despite incredible endeavors to create modern prediction algorithms that can give way better prediction precision in social network topologies that are extricated from complex datasets approximately clients, their exercises, and connections. But most of the recent approaches have not been broadly evaluated. This paper focuses on the missing link prediction between nodes in a systematic way by formalizing and developing approaches about future interactions that can be extracted from network topology for analyzing the proximity measures of nodes, links, and their attributes in a network. The main assumption of this paper is that the missing links must have opposite features concerning the original links. It turns out that feature engineering via measured the node or edge attributes and network structures based provided the best characterization in this sense. Some of network topology-based prediction methods are Common Neighbors, Sørensen Index, Hub Depressed Index, Adamic Index, Salton's cosine formula, spectral clustering, etc. Experimental results using AUC and ROC proved that based on Network topology and previous information may be more or less difficult to accurately predicted future connections so, there is no single clear winner among similarity metrics. [ABSTRACT FROM AUTHOR]