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The oral English test in domestic universities requires teachers to modify a large number of candidates' oral recordings. This is the work of using time repeatedly. Using the CALL system to realize the automation of conversation recording can reduce the burden of teachers' work. Therefore, it is of great practical significance to develop an automatic and accurate scoring system for oral English. With the development of artificial intelligence, deep learning technology has been gradually applied in various fields. Similarly, in the application of oral scoring, deep learning technology makes the implementation of such a system possible. Based on the deep learning technology, this paper proposes an automatic scoring algorithm for spoken language and implements a detailed design and evaluation system. The system consists of two modules. The pronunciation standard of spoken pronunciation and the content of spoken pronunciation are scored, and the sum of these two scores is the final score. Finally, this paper uses 650 oral English recordings from a college English test to train the artificial neural network. Experimental results show that if the training data set is small, the BP network model can obtain better comprehensive evaluation performance. [ABSTRACT FROM AUTHOR]

A scoring approach of business English oral training based on a deep neural network is put forward. Based on speech recognition technology, automatic correction technology, deep learning technology, machine learning technology, and the scoring data of business English oral training generated by the general computerized examination platform system, this paper uses the audio data of students' oral expression questions collected by the existing general computerized examination platform, and studies artificial intelligence technology, deep learning technology, and machine learning technology to train the intelligent scoring system, it realizes the intelligent scoring of oral expression questions, which plays a positive role in language training guidance and correction. Finally, the accuracy of the intelligent scoring system is verified by the correlation between the score of the intelligent scoring system and the manual score of teachers. The higher the correlation with manual scoring, the higher the scoring accuracy of the intelligent scoring system. [ABSTRACT FROM AUTHOR]

Wastewater is created by pharma firms and has become a huge worry for the ecosystem. There is a significant amount of toxins that are being dropped continuously from numerous pharmaceutical companies that causes serious damages to the environment and public health because of its comprising high organics as well as inorganic loadings and thus requirements appropriate treatment before final disposal to the ecosystem. Goal of this approach is to treat the wastewater treatment model with industrial data. Algorithms of the artificial neural network (ANN) were employed progressively to predict parameters for wastewater plants. This provision assists users to take remedial measures and function the process by the standards. It is proven as beneficial technology because of its complicated mechanism, dynamic and inconsistent changes in aspects, to overcome some of the limitations of common mathematical models for the wastewater treatment plant. The target is to achieve better prediction accuracy in wastewater treatment model. In this paper, ANN approaches are relevant to the prediction of input and effluent chemical oxygen demand (COD) for effluent treatment procedures. Artificial neural networks (ANNs) offer accurate technique modeling for complex systems using an artificial intelligence technique. Three distinct types of back-propagation ANN were devised to avoid the concentration of wastewater treatment facilities in the concentration of COD, suspended particles, and mixed liquid solids in an epidermal water treatment tank (MLSS). To anticipate COD levels in influential and effluent areas, two ANN-based techniques have been presented. The proper structure for the neural network models was identified via a variety of training and model testing methods. An efficient and robust forecasting tool has been created for the ANN model. [ABSTRACT FROM AUTHOR]

Computer music creation boasts broad application prospects. It generally relies on artificial intelligence (AI) and machine learning (ML) to generate the music score that matches the original mono-symbol score model or memorize/recognize the rhythms and beats of the music. However, there are very few music melody synthesis models based on artificial neural networks (ANNs). Some ANN-based models cannot adapt to the transposition invariance of original rhythm training set. To overcome the defect, this paper tries to develop an automatic synthesis technology of music teaching melodies based on recurrent neural network (RNN). Firstly, a strategy was proposed to extract the acoustic features from music melody. Next, the sequence-sequence model was adopted to synthetize general music melodies. After that, an RNN was established to synthetize music melody with singing melody, such as to find the suitable singing segments for the music melody in teaching scenario. The RNN can synthetize music melody with a short delay solely based on static acoustic features, eliminating the need for dynamic features. The proposed model was proved valid through experiments. [ABSTRACT FROM AUTHOR]

Load training is an important part of the daily training of aerobics athletes. Therefore, the research on the characteristics of aerobics movement of athletes in load training has been paid extensive attention by teaching units. Deep learning algorithm in artificial intelligence algorithm is used to study the characteristics of calisthenics teaching and training, which is a beneficial exploration to improve the scientific of calisthenics training programs. The basic principles of the neural network algorithm and implementation process are described. After the basic structure and characteristics of the network are analyzed, a comprehensive solution to optimize and improve the regularized deep belief network algorithm is proposed. The results show that compared with other learning algorithms, the feature classification error rate of the optimized regularized deep belief network algorithm is 6% lower than that of other algorithms. Although the training speed of the model decreases, the convergence period of the deep neural network algorithm can better achieve the extraction accuracy of abstract features of training data when the deep learning period increases. Being less affected by input parameters, the algorithm will have better stability and be more conducive to extracting the features favorable to classification. In the field of deep learning, the advantages of the deep neural networks can effectively classify the training characteristics of aerobics athletes and provide scientific decision-making basis for subsequent teaching and training. [ABSTRACT FROM AUTHOR]

With the rapid advancement of information technology, artificial intelligence and machine learning have become the central technology tools for information sharing. To speed up the efficiency of information resource transmission of national government departments and improve the informatization level of government social management and public service systems, the persona system is designed using an artificial neural network, and a social service and management resource pool system is developed. The behavior data randomly generated by users in daily life is collected and cleaned, and training samples are extracted for training an artificial neural network. Next, the demographic attribute tags and interest tags are modelled, and the social service and management resource pool system is built and tested. Results show that for the population attribute label construction, the index value using the app name is mapped to 0 or 1, and the sample sampling ratio is set to 1.0. The proposed model achieved the overall accuracies of 85.2%, 74.5%, and 99.0% for the prediction of constructed age, academic qualifications, and interest label, respectively. The constructed system greatly deepens the visualization of the characteristics of social governance elements. The system can enhance the level of resource sharing by government departments and provide the foundation for spatial decision-making in smart social governance. [ABSTRACT FROM AUTHOR]