Long English sentences in machine translation become the main factor affecting the quality of machine translation with its characteristics of more words and complex sentence structure. In this paper, we propose a translation method incorporating syntactic features in the field of natural language processing to address the problem of poor translation quality of English long sentences in machine translation. The method combines the linguistic template-based translation method and the conditional random field-based statistical translation method to slice and process long sentences by syntactic dimension and statistical dimension, and then improve the quality of machine translation. The experimental results show that the BLEU score of long sentences of English corpus improves about 2.1% and the NIST score improves about 1.1% after the training of the model proposed in this paper, which proves that the translation method of this paper improves the translation quality of long sentences of English.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Travel review text reviews objectively reflect travellers' real perceptions of tourist destinations and services, and are also one of the important ways of internet word-of-mouth communication. According to the study, travellers will obtain information about products and other travellers' reviews of tourism through various channels before making purchase decisions, and use them as the basis for whether to continue purchasing. With the help of the research method of big data, the text takes the travel virtual community and the online review text of tourism on the web platform as the research material. To address the problems of previous models, we propose an attention-based mechanism LSTM, called SA-BiLSTM, for travel evaluation sentiment analysis. We integrate the attention mechanism into the LSTM and use it to improve the representation capability of the LSTM. The attention mechanism ignores the distance between words, which effectively solves the gradient disappearance and gradient explosion problems encountered by LSTM, and the combination of LSTM and attention mechanism is equivalent to model fusion at the structural level, which enables the model to capture information in different directions in the text and enhances the robustness of the model. We validate the good results of our model relative to state-of-the-art models on numerous real datasets.

In order to improve the similarity between the starting pose and the actual structure and the signal to noise ratio (SNR) of the image, this paper proposed a new approach to correct the starting pose of the athlete based on deformation model and image repair. Firstly, dual Kinect sensors were used to collect the starting posture data of athletes and construct the three-dimensional image of the starting posture of athletes. Secondly, the method of attitude 3D image segmentation based on self-segmentation theory is used to obtain the attitude feature artefact region. Finally, after the artefact is repaired, the image attitude correction method based on B-spline deformation model completes the attitude correction. The test results show that the similarity between the image pose structure and the actual structure is as high as 0.98 and the minimum peak signal-to-noise ratio is 0.93 dB.

In order to improve the collection accuracy of multi-source information, reduce the false alarm rate of tunnel driving risk and improve the accuracy of environmental perception, a new tunnel environment awareness method based on multi-source information fusion is proposed in this paper. Firstly, R-Fans-16 line lidar and GP-KD6Q01FC monocular camera are selected as the information acquisition equipment. Secondly, according to the multi-source information fusion principle, the Markov distance between the observation value and the actual value is calculated to complete the multi-source data fusion. Finally, the fused information is input into the Adaboost classifier to complete the accurate classification perception of the autonomous driving tunnel environment. The experimental results show that, compared with the traditional environment awareness methods, the proposed method can accurately collect multi-source information, and can achieve high-precision perception of tunnel environment, with the highest perception accuracy of 97.7%.

In order to improve the similarity between the starting pose and the actual structure and the signal to noise ratio (SNR) of the image, this paper proposed a new approach to correct the starting pose of the athlete based on deformation model and image repair. Firstly, dual Kinect sensors were used to collect the starting posture data of athletes and construct the three-dimensional image of the starting posture of athletes. Secondly, the method of attitude 3D image segmentation based on self-segmentation theory is used to obtain the attitude feature artefact region. Finally, after the artefact is repaired, the image attitude correction method based on B-spline deformation model completes the attitude correction. The test results show that the similarity between the image pose structure and the actual structure is as high as 0.98 and the minimum peak signal-to-noise ratio is 0.93 dB.

The identification and characterisation of blood cells are needed for the diagnosis of blood-related diseases. To identify and classify different types of blood cells, image processing techniques are necessary. However, the medical images are generally corrupted with different types of noises. These noises affect the quality of images and causes impact in image processing. Denoising is a pre-processing method in image processing to acquire clear vision, eliminate noise, and enhance the quality of images. In this paper, a novel approach for medical image denoising based on discrete wavelet transform (DWT) and adaptive thresholding optimised with improved arithmetic optimisation algorithm and guided filter is proposed. Initially, the input image is denoised using DWT. Then, the denoised image is again processed with a guided filter for enhancing and improving the denoising performance. Moreover, from the experimental analysis, it is proven that the proposed approach provides better performance than the existing denoising methods.