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This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of SVM and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor α. Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient. In the early stages of optimization, its speed is low to avoid falling into local optimization. In the later stages of optimization, the speed is higher, and finding the optimal solution is easier, balancing the two different global and local optimization capabilities to complete efficient convergence. The dynamic weight update strategy was adopted to perform position updates based on adaptive dynamic weights. First, the dataset of Case Western Reserve University was used for simulation, and the results showed that the diagnosis accuracy of IGWO-SVM was 98.75%. Then, the IGWO-SVM model was trained and tested using data obtained from the full-life-cycle test platform of mechanical transmission bearings independently researched and developed by Nanjing Agricultural University. The fault diagnosis accuracy and convergence value of the adaptation curve were compared with those of PSO-SVM (particle swarm optimization) and GWO-SVM diagnosis models. Results showed that the IGWO-SVM model had the highest rolling bearing fault diagnosis accuracy and the best diagnosis convergence.

Artificial intelligence has penetrated every aspect of people’s lives with the rapid development of science and technology. In basketball, how to effectively utilize artificial intelligence technology to improve the effectiveness of training has become a problem worth studying. A Mediapipe-based 3D human posture domain adaptive detection method is proposed in the study by combining statistics and human limb proportion simulation. The modified SVM classification algorithm is used to compare and classify the newly acquired key point data information of the system with the standard basketball action key point data information preset by the system to judge whether the basketball action is up to the standard or not, so as to design the basketball teaching aid system based on artificial intelligence. Based on this basis, after testing the recognition performance of the system, its practical application effect is explored. The total basketball scores improved after the experiment (86.26±2.228 for the experimental group and 81.34±9.672 for the control group), but there was no significant difference (P>0.05). The control group was slower than the experimental group by 0.64 seconds and 0.77 seconds overall in the cross-step breakout time with the ball and the same-side step breakout time with the ball, respectively, indicating that the experimental group was more proficient in basketball technology. The system has been tested in various ways to meet the multiple needs of the users, meet the expected standards, and be able to be applied in real-world scenarios.

The overall recognition rate will reduce due to the increase of emotional confusion in multiple speech emotion recognition. To solve the problem, we propose a speech emotion recognition method based on the decision tree support vector machine (SVM) model with Fisher feature selection. At the stage of feature selection, Fisher criterion is used to filter out the feature parameters of higher distinguish ability. At the emotion classification stage, an algorithm is proposed to determine the structure of decision tree. The decision tree SVM can realize the two-step classification of the first rough classification and the fine classification. Thus the redundant parameters are eliminated and the performance of emotion recognition is improved. In this method, the decision tree SVM framework is firstly established by calculating the confusion degree of emotion, and then the features with higher distinguish ability are selected for each SVM of the decision tree according to Fisher criterion. Finally, speech emotion recognition is realized based on this model. The decision tree SVM with Fisher feature selection on CASIA Chinese emotion speech corpus and Berlin speech corpus are constructed to validate the effectiveness of our framework. The experimental results show that the average emotion recognition rate based on the proposed method is 9% higher than traditional SVM classification method on CASIA, and 8.26% higher on Berlin speech corpus. It is verified that the proposed method can effectively reduce the emotional confusion and improve the emotion recognition rate.

With the development and application of machine learning, significant advances have been made in landslide susceptibility mapping. However, due to challenges in actual field landslide investigations, current landslide susceptibility mapping is usually characterized by insufficient landslide samples (positive samples) and low reliability of non-landslide samples (negative samples). Considering Lianghe County in Yunnan Province, China, as an example, this paper aims to research the effectiveness of three oversampling models in generating positive samples for landslides: Conditional Tabular Generative Adversarial Networks (CTGAN), Generative Adversarial Networks (GAN), and the traditional Synthetic Minority Oversampling Technique (SMOTE) algorithms. Additionally, three machine learning methods, including 1D Convolutional Neural Network-Long Short-Term Memory Neural Network (CNN-LSTM), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT) classifiers, are used for landslide susceptibility assessment. We also devise a non-landslide data (negative samples) screening method utilizing a self-trained support vector machine within a semi-supervised framework. The results show that by training on the dataset after negative sample screening, the AUC values for the 1D-CNN-LSTM, RF, and GBDT models have shown significant improvement, increasing from (0.778, 0.869, 0.849) to (0.837, 0.936, 0.877). Compared with the original training set, the prediction accuracy of the three machine learning models is improved after training on the augmented data by CTGAN, GAN, and SMOTE models. The RF model, augmented with 200 positive samples generated by CTGAN, achieves the highest prediction accuracy in the study (AUC = 0.962). The 1D CNN-LSTM model achieves its highest prediction accuracy (AUC = 0.953) when augmented with 200 positive samples from GAN. Similarly, the GBDT model reaches its highest prediction accuracy (AUC = 0.928) when augmented with 200 positive samples created by SMOTE. In addition, the spatial distribution of data indicates that the data generated by the generative adversarial model exhibits higher diversity, which can be used for landslide susceptibility assessment.

Traditional music is a cultural treasure emerging from the long history of mankind, and the study of traditional music has important artistic and humanistic values. In this paper, the SVM algorithm under incremental learning is used to construct a traditional music style pattern recognition model using the extracted traditional music style feature parameters. The database is constructed by using traditional music compositions containing five music styles, and the data are pre-emphasized and pre-processed by adding windows and frames. After extracting the time-domain feature parameters and MFCC feature parameters of the database songs, the recognition model constructed in this paper is used for traditional music style pattern recognition. In traditional music style recognition, the accuracy of this paper’s model for five traditional music styles is around 90%, and the accuracy of traditional music recognition for opera style is as high as 95.11%. Overall, the model constructed in this paper is able to effectively recognize the styles of traditional music through the extracted traditional music style feature parameters.

As consumption upgrades, China’s cultural and creative industry is experiencing rapid growth. However, user empathy remains lacking in some products. This paper explores a design method for these products based on empathy theory. It employs the TF-IDF algorithm to extract semantic features from product texts and uses the SVM algorithm for classification. Post-classification, the LDA theme model analyzes sentiment, integrating both visual and semantic models to enhance product design. An analysis of cultural and creative products using data analysis software reveals that approximately 79.5% of user comments exhibit positive sentiment, with an average sentiment score of 2.9218 and a peak score of 39.1363. This suggests strong positive emotional responses from nearly 80% of users. The proposed method effectively enhances user-product interaction and empathy.

In order to effectively extract the multi-dimensional data of teaching quality evaluation and accurately evaluate the quality of network distance teaching quality in universities, an optimal classification algorithm for network distance teaching quality evaluation is proposed. From the perspectives of teaching attitude, teaching skill, teaching skill, teaching content, teaching method and means, the quality evaluation indices of network distance teaching is designed. Combined with the evaluation indices, the multi-dimensional data mining method based on OLAP technology is used to mine the required multi-dimensional data of teaching quality evaluation in the distance teaching data warehouse of universities, the required multi-dimensional data is input into SVM algorithm to solve the optimal classification hyperplane of multi-dimensional data and implement the optimal classification of multi-dimensional data. The quality of distance teaching is evaluated by improving the salp group algorithm and setting the penalty factor and kernel function of SVM algorithm. The experimental results show that the classification accuracy of this method for multidimensional data is over 90%, and the evaluation accuracy is as high as 99%, it can extract multi-dimensional teaching quality evaluation from the network distance teaching data, which has good classification effect and can improve the accuracy of network distance teaching quality evaluation.

In track and field sports, footwork can greatly affect the effect and performance of sports. Accurate footwork can effectively improve the performance of professional athletes, and for ordinary trainers, it can reduce the probability of training injuries. To solve the problem that traditional footwork is inaccurate and not well accepted by people, this paper has used an image processing method based on support vector machine (SVM) algorithm to identify and track the footwork. In this paper, a 13-s video image was extracted frame by frame from the athletes’ videos in Olympic sports competitions, and the athletes’ footwork was used as a benchmark to track their motion trajectories, extracting the corresponding feature points and categorizing them. 10 school athletes, 6 males and 4 females, were selected to track their movement pace and trajectory with a camera. The behaviors were standardized according to the extracted features, and the behaviors before and after standardization were compared. The results showed that the SVM algorithm had the most stable classification accuracy, higher recognition accuracy and better performance compared with other classification algorithms. Image processing of standardized track and field movements was effective in improving athletes’ performance, with all 10 athletes tested improving their performance between 0.4 and 0.6. The SVM algorithm-based image processing method is more acceptable after validation of its effectiveness, and the method can be extended more easily.

Our country’s market economy is composed of enterprises. However, due to their inherent credit deficiencies and high risks of management, it is very difficult for them to obtain financing support. Based on this, this article studies Error Back Propagation (BP) to establish (SMEs). Based on the relevant concepts of the supply chain management budget model, it explores the main factors influencing the financial impact of SMEs and the benefits of the supply chain budget in solving problems expenditure of SMEs, support vector machine is mainly based on solving the main credit risks of small and medium-sized enterprises, such as poor information transparency, low credit and various risk unknown factors. BP neural network is an algorithm that takes into account the components of supply chain financial financing. This article first gives a simple background and theoretical introduction to the under the current supply chain finance model, and then proposes to use SVM and BP neural network algorithms to build and the model has been trained and tested. After collecting relevant references, we will establish authoritative risk assessment rules in accordance with this article according to these standards. These experts are mainly people with many years of experience in the financial industry, and they also have a certain influence in the industry. The risk assessment established for this can be the analysis of factors such as risk indicators and government intervention in this experiment.

The exact cause of psoriasis is still unclear and there is no treatment available for its permanent reemission. The available biologics for disease treatment, are stated to be associated with a high cost of treatment, a significantly increased risk of serious infections, and have also been reported to show major contradictions in patients with tuberculosis and cardiovascular disorders. Therefore, drug repurposing could be an appealing strategy to find novel treatments for psoriasis, saving time, cost and with viable chance of success. The goal of the present study was to identify the FDA approved drugs which can be proposed as potential anti-psoriasis drugs. The known drug target interactions of 19 autoimmune diseases, 4 cardiovascular risk factors, and each of infectious, lung, and mood disorders were retrieved using various public databases, i.e., DrugBank, PharmGKB, clinicaltrial.gov database, TTD, CTD, and the Unified Medical Language System NDF-RT. The drug target interaction of prioritised drugs, obtained using molecular function GO mappings from the QuickGO database through NBI score was analysed using a molecular docking approach. Further, one-SVM algorithm prediction was done to validate the docking outcome and molecular dynamics simulation of top drug-target molecule was performed to propose potential anti-psoriasis drugs. The study identified Pioglitazone, Trimipramine and Dimetindene as top three contender amongst many other drugs as a new indication against psoriasis.