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Non-contact identification of cattle is the basis for cattle management, insurance claims, and traceability of milk sources and meat products. In order to lower the threshold of deep learning technology in practical application and improve the recognition accuracy, this paper uses the localization and classification paradigm, fills the image and proposes the LKA (Locate Key Area) module, which further improves the identification accuracy of cattle without using any labeling information. To avoid the impact of different collection devices, we use three brands of mobile phones and two cameras to collect data. Due to the fact that CNN features do not have scale invariance, the image is filled as a square, and the filling value is discussed. Finally, (0,0,0) is selected for filling. In order to reduce the labeling of new data, the face, torso, and body of the cattle are first detected, and then automatically trimmed to build a classification dataset. In order to further improve the accuracy of classification, LKA module is proposed. This module locates key areas based on the results of class activation map and uses shallow features for monitoring. Thereby completing fine-grained feature extraction without supervision information. Finally, the features of different parts are adjusted for fusion recognition. Experiments were conducted on the head, torso, and whole of cattle. Based on the ResNet50 network architecture, after filling the image into a square, the recognition accuracy has been improved by 0.30%, 0.21%, and 0.43%, respectively. After further adding LKA modules, the accuracy rates were improved by 0.59%, 0.59%, and 0.45%, respectively. In the fusion experiment, ResNet50_LKA and MobileNetV3_LKA achieves 100% accuracy. Experiments have realized dataset construction and fine-grained feature extraction without labeled information, and achieved high recognition accuracy, proving the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of a statistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy. [Copyright &y& Elsevier]

Wireless networks and mobile applications have grown very rapidly and have made a significant impact on computer systems. Especially, the usage of mobile phones and PDA is increased very rapidly. Added functions and values with these devices are thus greatly developed. If some regularity can be known from the user mobility behavior, then these functions and values can be further expanded and used intelligently. This paper thus attempts to discover personal mobility patterns for helping systems provide personalized service in a wireless network. The classification and the duration of each location area visited by a mobile user are used as important attributes in representing the results. A data mining algorithm has then been proposed, which is based on the AprioriAll algorithm, but different from it in several ways. Experiments are also made to show the effect of the proposed algorithm. [Copyright &y& Elsevier]