Abstract Grammatical error correction (GEC) is an important element in language learning. In this article, based on deep learning, the application of the Transformer model in GEC was briefly introduced. Then, in order to improve the performance of the model on GEC, it was optimized by a generative adversarial network (GAN). Experiments were conducted on two data sets. It was found that the performance of the GAN-combined Transformer model was significantly improved compared to the Transformer model. The F 0.5 value of the optimized model was 53.87 on CoNIL-2014, which was 2.69 larger than the Transformer model; the generalized language evaluation understanding (GLEU) value of the optimized model was 61.77 on JFLEG, which was 8.81 larger than that of the Transformer model. The optimized model also had a favorable correction performance in an actual English essay. The experimental results verify the reliability of the GAN-combined Transformer model on automatic English GEC, suggesting that the model can be further promoted and applied in practice.

Abstract Gross domestic product (GDP) can well reflect the development of the economy, and predicting GDP can help better grasp the future economic trends. In this article, three different neural network models, the genetic algorithm – back-propagation neural network model, the particle swarm optimization (PSO) – Elman neural network (Elman NN) model, and the bat algorithm – long short-term memory model, were analyzed based on neural networks. The GDP data of Sichuan province from 1992 to 2020 were collected to compare the performance of the three models in predicting GDP. It was found that the mean absolute percentage error values of the three models were 0.0578, 0.0236, and 0.0654, respectively; the root-mean-square error values were 0.0287, 0.0166, and 0.0465, respectively; and the PSO-Elman NN model had the best performance in GDP prediction. The experimental results demonstrate that neural networks were reliable in predicting GDP and can be used for further applications in practice.

Abstract Distributed programming paradigms such as MapReduce and Spark have alleviated sequential bottleneck while mining of massive transaction databases. Of significant importance is mining High Utility Itemset (HUI) that incorporates the revenue of the items purchased in a transaction. Although a few algorithms to mine HUIs in the distributed environment exist, workload skew and data transfer overhead due to shuffling operations remain major issues. In the current study, Parallel Utility Computation (PUC) algorithm has been proposed with novel grouping and load balancing strategies for an efficient mining of HUIs in a distributed environment. To group the items, Transaction Weighted Utility (TWU) values as a degree of transaction similarity is employed. Subsequently, these groups are assigned to the nodes across the cluster by taking into account the mining load due to the items in the group. Experimental evaluation on real and synthetic datasets demonstrate that PUC with TWU grouping in conjunction with load balancing converges mining faster. Due to reduced data transfer, and load balancing-based assignment strategy, PUC outperforms different grouping strategies and random assignment of groups across the cluster. Also, PUC is shown to be faster than PHUI-Growth algorithm with a promising speedup.

Abstract In order to improve the integrity of the social network behavior feature extraction results for sports college students, this study proposes to be based on the clustering algorithm. This study analyzes the social network information dissemination mechanism in the field of college students’ sports, obtains the real-time social behavior data in the network environment combined with the analysis results, and processes the obtained social network behavior data from two aspects of data cleaning and de-duplication. Using clustering algorithm to determine the type of social network user behavior, setting the characteristics of social network behavior attributes, and finally through quantitative and standardized processing, get the results of college students’ sports field social network behavior characteristics extraction. The experimental results showed that the completeness of the method feature extraction results improved to 9.93%, and the average extraction time cost was 0.344 s, with high result integrity and obvious advantages in the extraction speed.

Abstract Segmentation of the medical image plays a significant role when it comes to diagnosis using computer aided system. This article focuses on the human corneal endothelium’s health, which is one of the filed research interests, especially in the human cornea. Various pathological environments fasten the extermination of the endothelial cells, which in turn decreases the cell density in an abnormal manner. Dead cells worsen the hexagonal design. The mutilated endothelial cells can no longer revive back and that gives room for neighbouring cells to migrate and expand so that they can fill in the space. The latter results in cell elongation that is unpredictable as well as increase in size and thinning. Cell density and shape are therefore considered major parameters when it comes to explaining the health condition attributed to corneal endothelium. In this study, medical feature extraction was obtained depending on the segmentation of the endothelial cell boundary, and the task of segmentation of such objects especially the thin, transparent, and unclear cell boundary is considered challenging due to the nature of the image capture during endothelium layer examination by ophthalmologists using confocal or specular microscopy. The resulting image suffers from various issues that affect the quality of the image. Low quality is due to non-uniformity of illumination and the presence of a lot of noise and artefacts resulting from high amounts of distortion, and most of these limitations are present because of the nature of the imaging modality. Usually, images contain certain kind of noise and also continuous shadow. Furthermore, the cells are separated by poor border, thereby leading to great difficulty in the segmentation of the images. The irregular shape of cell and also the contrast of such images seem to be low as they possess blurry boundaries with diverse objects existing in addition to the lack of homogeneity. The main aim of the study is to propose and develop a totally automatic, robust, and real-time model for the segmentation of endothelial cells of the human cornea obtained byin vivomicroscopy and computation of different clinical features of endothelial cells. To achieve the aim of this study a new scheme of image enhancement was proposed such as the Contrast-Limited Adaptive Histogram Equalisation (CLAHE) technique to enhance contrast. After that, a new image denoising technique called Wavelet Transform Filter and Butterworth Bandpass for Segmentation is used. Subsequently, brightness level correction is applied by using the moving average filter and the CLAHE to reduce the effects of the non-uniform image lighting produced as a result of the previous step. The main aim of this article is the segmentation of endothelial cells, which involves precise detection of the endothelial contours. So a new segmentation model was proposed such that the shape of the cells will be extracted, and the contours were highlighted. This stage is followed by clinical feature extraction and uses the features for diagnosis. In this stage, several relevant clinical features such as pleomorphism mean cell perimeter, mean cell density, mean cell area, and polymegathism are extracted. The role of these clinical features is crucial for the early detection of corneal pathologies as well as the evaluation of the health of the corneal endothelium layer. The findings of this study were promising.

Abstract The traditional recognition method of whitewash behavior of accounting statements needs to analyze a large number of special data samples. The learning rate of the algorithm is low, resulting in low recognition accuracy. To solve the aforementioned problems, this article proposes a method to identify the whitewash behavior of university accounting statements based on the FCM clustering algorithm. This article analyzes the motivation of university accounting statement whitewashing behavior, studies the common means of statement whitewashing, and establishes a fuzzy set for the identification of university accounting statement whitewashing behavior. By calculating the fuzzy partition coefficient, the membership matrix of whitewash behavior recognition is established, and the whitewash behavior is classified through the iteration of the FCM algorithm. The comparative experimental results show that the recognition method has good recognition performance, low recognition error rate, and recognition accuracy of 82%.

Abstract Combining images with music is a music visualization to deepen the knowledge and understanding of music information. This study briefly introduced the concept of music visualization and used a convolutional neural network and long short-term memory to pair music and images for music visualization. Then, an emotion classification loss function was added to the loss function to make full use of the emotional information in music and images. Finally, simulation experiments were performed. The results showed that the improved deep learning-based music visualization algorithm had the highest matching accuracy when the weight of the emotion classification loss function was 0.2; compared with the traditional keyword matching method and the nonimproved deep learning music visualization algorithm, the improved algorithm matched more suitable images.

Abstract In order to realize the sustainable development of urban overall space, aiming at the increasingly serious environmental problems in the process of contemporary rapid urbanization, based on the relationship between urban and rural space and environmental capacity, a suitability evaluation method of urban and rural spatial planning based on artificial intelligence is proposed. This paper constructs the theoretical system of sustainable development evaluation of urban and rural spatial resources and uses artificial intelligence technology to reasonably select evaluation factors and standardize the evaluation indicators so as to achieve the research goal of accurately evaluating the suitability of urban and rural spatial planning. It analyzes the influencing factors of urban–rural spatial standard regional division, develops the design of the spatial planning suitability analysis system, and establishes the urban-rural spatial suitability evaluation. The scope of sustainability evaluation was extended to the level of urban and rural spatial planning and design, and the preliminary work was done to solve the problems of urban spatial structure and decision-making. The experimental results show that the planning method has high environmental adaptability and rationality.

Abstract Using optical add–drop multiplexer/remover multiplexer (OADM), it is possible to add or remove wavelengths and change or route them through the various nodes and networks. At this moment, key problems in add–drop multiplexer (ADM) are the bandwidth, modulation format, and reuse wavelength. In this article, the Optisystem software simulation is used as a platform to design, test, and verify the method applied to the current work; the OADM is proposed based on the metro network to get distribution between nodes over a transmission link; OADM analysis was presented with four channels (193.1, 193.2, 193.3, and 193.4 THz) at total bandwidth of 1.6 Tb/s, none-return-to-zero (NRZ), and return to zero coding types. Experiment one shows that the average output power is −17.997 dBm, the average drop power is −17.997 dBm, and the average add power is −18.338 dBm, the average gain is −0.0429 dB, the average noise figure is 0 dB, the average power input signal is 10.679 dBm, the average of power output signal is 10.633 dBm, and the average output optical signal-to-noise ratio (OSNR) is 0 dB, However, the second experiment shows that the average output power is −24.238 dBm, the average drop power is −24.288 dBm, and the average add power is −24.753 dBm, the average gain is −0.0417 dB, the average noise figure is 0 dB, average power input signal is 7.691 dBm, average of power output signal is 7.677 dBm, and the average output OSNR 0 dB. The system supports four input channels, four add channels, four output channels, and four drop channels. The results are acceptable after three spans of Solitons fiber with 600 km length, 200 km for each span. Nonetheless, it is believed that it is well justified to adopt these schemes in the current optical network with a low cost for overall expenditure.

Abstract High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm accuracy is tested using the decision tree (DT), support vector machine (SVM), andK-nearest neighbor (KNN) classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy (Δ) is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy.