A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data

Yang, Zhen; Talha, Muhammad

doi:10.1155/2021/1725490

Cited by 33 publications

(21 citation statements)

References 19 publications

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“…There were no significant variations in the frequency of postoperative problems and recurrence rates among the three groups in this research, suggesting that all three treatments offer a high assurance of safety. A study [ 23 ] reported the incidence of postoperative pancreatitis in patients with biliary tract stones between 1.8 and 7.2%, and one case of pancreatitis in the ERCP group in this study occurred, which was considered to be due to papilledema obstructing the drainage of pancreatic fluid, or it may be due to excessive amount and speed of contrast injection into the pancreatic duct. A study [ 24 ] found that indwelling ENBD drainage significantly reduced the risk of biliary tract infection and postoperative acute pancreatitis.…”

Section: Discussionmentioning

confidence: 66%

Differences in Outcome and Comparison of Stress and Immune Status in Patients with Recurrent Common Bile Duct Stones after Biliary Tract Surgery Choosing Three Procedures (ERCP, OCBDE, and LCBDE) for Treatment

Tan¹,

Zhang²

2022

Computational and Mathematical Methods in Medicine

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Objective. To compare the effect of choosing ERCP, OCBDE, and LCBDE for the treatment of patients with recurrent common bile duct stones after biliary tract surgery. Method. 115 patients with recurrent common bile duct stones after biliary surgery in our hospital were retrospectively analyzed and divided into three groups according to the procedure, 36 patients in the ERCP group, 38 patients in the OCBDE group, and 41 patients in the LCBDE group, and compared the efficacy, stress status, and immune status of the three groups. Result. The stone removal rates were 91.67%, 97.37%, and 97.56% in the ERCP, OCBDE, and LCBDE groups, respectively ( P > 0.05 ). There were statistical differences between the ERCP, OCBDE, and LCBDE groups in terms of operative time, postoperative recovery time of exhaustion, recovery time of defecation, recovery time of feeding, and hospitalization time ( P < 0.05 ). The postoperative complication rates were 8.33%, 10.53%, and 7.32% in the ERCP, OCBDE, and LCBDE groups, respectively ( P > 0.05 ). The recurrence rates within 1 year after surgery were 2.78%, 7.89%, and 2.44% in the ERCP, OCBDE, and LCBDE groups, respectively ( P > 0.05 ). Conclusion. ERCP has short operative time, short hospital stay, and rapid postoperative recovery. LCBDE has mild trauma, and OCBDE has a wide range of application. Each of the three procedures has its own advantages and shortcomings, and the most appropriate procedure should be selected on the basis of comprehensive evaluation.

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Section: Discussionmentioning

confidence: 66%

Differences in Outcome and Comparison of Stress and Immune Status in Patients with Recurrent Common Bile Duct Stones after Biliary Tract Surgery Choosing Three Procedures (ERCP, OCBDE, and LCBDE) for Treatment

Tan¹,

Zhang²

2022

Computational and Mathematical Methods in Medicine

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“…The accuracy and convergence rate of the three classification algorithms varies depending on the size of the number of samples picked in each loop iteration, as shown in the figure. The more examples there are, the more likely the selection engine will choose more useful samples for expert labeling, contributing more to the correct classification model and increasing the classification model's accuracy [ 29 ]. The comparison of classification accuracy and time cost of classification methods is shown in Table 5 .…”

Section: Experimental Simulation and Analysismentioning

confidence: 99%

Research on Music Style Classification Based on Deep Learning

Wang

Sohail

2022

Computational and Mathematical Methods in Medicine

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Music style is one of the important labels for music classification, and the current music style classification methods extract features such as rhythm and timbre of music and use classifiers to achieve classification. The classification accuracy is not only affected by the classifier but also limited by the effect of music feature extraction, which leads to poor classification accuracy and stability. In response to the abovementioned defects, a deep-learning-based music style classification method will be studied. The music signal is framed using filters and Hamming windows, and the MFCC coefficient features of music are extracted by discrete Fourier transform. A convolutional recurrent neural network structure combining CNN and RNN is designed and trained to determine the parameters to achieve music style classification. Analysis of the simulation experimental data shows that the classification accuracy of the studied classification method is at least 93.3%, and the classification time overhead is significantly reduced, the classification results are stable, and the results are reliable.

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“…ere are ten different sorts of music genres included in these 1000 files, each having 100 samples. MPCC is utilised to extract the distinctive characteristics of a music signal in this experiment [33,34]. e frame length is 512, and the number of frames is 2133. e sampling frequency is 48000 Hz, the sample bits are 16, the frame length is 512, and the number of frames is 2133 [33,34].…”

Section: Experimental Environment and Data Setmentioning

confidence: 99%

“…MPCC is utilised to extract the distinctive characteristics of a music signal in this experiment [33,34]. e frame length is 512, and the number of frames is 2133. e sampling frequency is 48000 Hz, the sample bits are 16, the frame length is 512, and the number of frames is 2133 [33,34]. In the stage of extracting Mel frequency cepstrum coefficient, 12 dimensional Mel filter is used, and its frequency index is shown in Table 2.…”

Section: Experimental Environment and Data Setmentioning

confidence: 99%

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Pan

2022

Journal of Mathematics

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Aiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed by framing, pre-emphasis, and windowing, and the characteristic parameters of the music signal are extracted by Mel frequency cepstrum coefficient analysis. The restricted Boltzmann machine is trained layer by layer to obtain the connection weights between layers of the depth belief network model. According to the output classification, the connection weights in the model are fine-tuned by using the error back-propagation algorithm. Based on the deep belief network model after fine-tuning training, the structure of the music genre classification network model is designed. Combined with the classification algorithm of sparse representation, for the training samples of sparse representation music genre, the sparse solution is obtained by using the minimum norm, the sparse representation of test vector is calculated, the category of training samples is judged, and the automatic classification of music genre is realized. The experimental results show that the music genre automatic classification effect of the proposed method is better, the classification accuracy rate is higher, and the classification time can be effectively shortened.

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A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data

Cited by 33 publications

References 19 publications

Differences in Outcome and Comparison of Stress and Immune Status in Patients with Recurrent Common Bile Duct Stones after Biliary Tract Surgery Choosing Three Procedures (ERCP, OCBDE, and LCBDE) for Treatment

Differences in Outcome and Comparison of Stress and Immune Status in Patients with Recurrent Common Bile Duct Stones after Biliary Tract Surgery Choosing Three Procedures (ERCP, OCBDE, and LCBDE) for Treatment

Research on Music Style Classification Based on Deep Learning

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

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