With the rapid development of text categorization technology, there are still some problems, such as low classification efficiency, low accuracy and incomplete extraction of text features, in the case of large amount of data and too many categorized attributes. In this paper, a hybrid model of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long-term and Short-term Memory Neural Network) combined with Attention (Attention Mechanism) is used to classify and process long text data. CNN extracts feature information from text, then uses BiLSTM to extract context semantics information, combines Attention to distribute weight of text information, and enters softmax classifier to classify. The experimental results show that the feature extraction of this model is more comprehensive, and the classification effect has been improved to a certain extent.
In order to solve the problem of difficult target matching and low matching efficiency in binocular measurement, this paper proposes a real-time target feature matching algorithm based on Binocular Stereo Vision-absolute window error minimization (CAEW, Calculate the Absolute Error Window ) to improve the speed and accuracy of measurements. Firstly, the calibration of the camera is solved by using Zhang's calibration method, and the Bouguet algorithm is used for Binocular Stereo Vision of the final calibration data. Then, the AdaBoost iterative algorithm is used to train the target detector for target recognition. The CAEW algorithm is compared with the commonly used SURF (Speeded-Up Robust Feature) algorithm. The evaluation data of experimental results showed that the CAEW algorithm can achieve an evaluation of more than 90%. It is significantly improved compared with the SURF algorithm and meet the needs of binocular real-time target matching.
Pets healthcare data would be stored in scattered manner due to the changes in the pet owner, service agency or other reasons, which result in a large number of repeated examinations in healthcare service process and even medical negligence. Therefore, we proposed a blockchain-based pet healthcare data sharing approach so that the pet's healthcare history information can be presented in a complete manner when needed and prevent data from being attacked intensively, moreover, data integrity and accountability in healthcare service processes can be fulfilled. The ciphertext-policy attribute-based encryption(CP-ABE) is used to safeguard the privacy of the pet owner and the confidentiality of the healthcare data of the pets stored in different healthcare service parties. The smart contract is applied to ensure the data interaction between users and the normal acquisition of data after the change of the pet owner. In addition, we provide security analysis and performance evaluation.
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