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The gangue recognition technology is one of the key technologies in the intelligent construction of mines. To address the problem of low accuracy of the gangue recognition model caused by low illumination and high dust environment at the working face and the difficulty of recognizing small target gangue, a coal gangue image recognition model based on CSPNet-YOLOv7 target detection algorithm is proposed. Cross Stage Partial Network (CSPNet) is used to improve the backbone feature extraction network of YOLOv7 model, optimize the gradient information to reduce the network parameters, while Recursive Feature Pyramid (RFP) and Switchable Auto Convolution (SAC) to replace the simple up and down sampling and normal convolution modules in the neck feature extraction network, and to enhance the generalization ability of the network by using three migration training for feature learning of different widths and depths. The experimental results show that the CSPNet-YOLOv7 model has an average accuracy mean of 97.53%, an accuracy rate of 92.24%, a recall rate of 97.91%, an F1 score of 0.95, a model parametric number of 30.85×106, a floating point operation count of 42.15×109, and a frame rate of 24.37 f/s transmitted per second, Compared to the YOLOv7 model, the average mean accuracy is improved by 7.46%, and the number of parameters and floating point operations are reduced by 17.23% and 60.41%, respectively, compared to the FasterRCNN-Resnet50, YOLOv3, YOLOv4, MobileNet V2 -YOLOv4, YOLOv4-VGG, YOLOv5s models. The CSPNet-YOLOv7 model has the highest average accuracy mean for coal gangue identification, while the number of parameters and floating point operations is small, which has a good balance between identification accuracy and speed. Finally, the CSPNet-YOLOv7 model is validated through downhole field tests, providing an effective technical means for accurate coal gangue identification.

Abstract As infrastructure-as-a-service clouds quickly grow, an increasing number of businesses and people are moving their application development to the cloud. The purpose of the research is to solve the problem of identifying memory anomalies in cloud virtual machines and improve the accuracy of the model in detecting abnormal situations. This paper presents a model for detecting virtual machine anomalies in IaaS cloud platform. The model considers the unique properties of monitoring metrics as time-series data and proposes an approach based on four important virtual machine monitoring metrics. The study also develops an adaptive anomaly detection system based on deep Q-network algorithms and migration learning principles for the variety of VM monitoring data in the cloud. The testing findings reveal that utilizing a Zoom layer with a 2-kernel size can increase detection accuracy to 96.7%. This demonstrates that a portion of the experimental data can extract the temporal features using the Zoom layer and different kernel sizes. The research model for anomaly detection had a classification accuracy of 99.8%. The deep Q-network model’s final anomaly detection accuracy varies from 96.7 to 98.6%. The outcomes of the research improved the system’s security and dependability, showed the worth of the overall framework design, and significantly decreased the number of resources needed for system operation and maintenance.

In aquaculture, quantifying the real-time feeding behaviour of fish is essential for making feeding decisions. However, most existing methods for assessing fish appetite are inefficient and subjective. To address these issues, this study proposes an improved tilapia feeding level classification model with ResNet34. First, we introduce the attention module CBAM into the ResNet34 model to adjust the attention of the model according to the importance of different channel features and enhance the effective extraction of important features. We then used migration learning to transfer the knowledge learned from the source data (ImageNet dataset) to the tilapia ingestion image dataset, which allowed us to train the tilapia ingestion behaviour classification model faster while retaining the pre-trained model. Experimental results showed that the improved ResNet34 model in this study achieved an accuracy of 99.72%, an improvement of 7.84 percentage points over the original model. In addition, the model outperformed models such as MobileNetV2, AlexNet, VGG11, ShuffleNet_v2_x0_5 and ResNet18 in terms of accuracy, precision, recall and F1 scores.These results suggest that the proposed method can accurately identify feeding behavior of fish and provide a scientific basis for determining feeding amounts.

Current single-model methods for fine-grained image classification suffer from insufficient generalisation ability, while multi-model fusion methods suffer from weight curing. The study suggests and experimentally tests a dynamic weight multi-model fusion strategy for transfer learning-based fine-grained picture classification. The results of the experiment showed that the suggested fusion model enhanced recognition accuracy by 1.33%, 1.19%, and 0.83% compared to the single model on the medical dataset and 3.25%, 1.34%, and 7.28% on the agronomy dataset, respectively. Furthermore, when compared to the comparison method, the models under the proposed method of the study improved recognition accuracy by 0.18%, 0.61%, 0.43%, and 0.43% on the medical dataset, and the experimental time consumed was 3.25 minutes less than that of the sum-of-maximum-probabilities method; however, the fusion models of the proposed method of the study had higher recognition accuracy than that of the comparison met Overall, the proposed dynamic weight multi-model fusion method for fine-grained image classification using migration learning has better performance and generalisation ability, which can improve performance while reducing time cost, and has higher application value for the actual fine-grained image classification task.

The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018–2022, achieving high migration accuracy (R2 = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine.

The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale co-occurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%.

In this paper, the self-attention layer of a graph convolutional neural network is first constructed to output the important information in the network structure. The migration learning network model is established, and the sample data are preprocessed and trained sequentially. The final processing results are used as the initial data for abnormal power consumption detection. Introduce Bayes’ theorem to optimize the hyperparameters of the model. The optimized model is applied in the abnormal power consumption detection system to identify abnormal power consumption events and provide specific processing solutions. Through the detection of the system, it was found that the voltage of the test user dropped from a 100V cliff to about 20V in late November, which was determined by the system to be a power consumption abnormality, and, therefore, an operation and maintenance order was issued. The site survey revealed that the data was in line with the system detection. Calculating the power consumption information of another user, the phase voltage of this user stays around 85-100V, far below 150V, so the undercounting of power is verified for the user, and the amount of power that should be recovered is 201.22kW.

The current music recommendation is less efficient and cannot meet the needs of most people. This paper combines deep learning and migration learning to research music recommendations. It mainly utilizes the hybrid recommendation method of label recommendation and noise reduction autoencoder to achieve accurate music recommendations after extracting the features of music and the user’s preference features. In addition, in order to explore the recommendation effect of the model in this paper, the deviation between the measured score and the actual score of this model is compared and analyzed as well as the similarity difference of music with different features is explored in terms of HR, Recall, and NDCG index performance. The results show that the number of songs with a deviation of 2 or less in the predicted scores of this recommender system all account for 88% of the total number of songs, which is better than other models. In terms of HR, Recall, and NDCG, this paper’s model has better recommendation performance than other models, and the similarity of music recommendation between the same album and different albums is more than 0.65. This study is of great significance for eliminating the information barriers to enhance and explore the value of music data information.

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomatoes and the plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rates and poor generalizations of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network (CNN) has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and a K-means clustering method was used to adjust more appropriate anchor sizes than manual setting, to improve detection accuracy. The test results showed that the mean average precision (mAP) was significantly improved compared with the traditional Faster R-CNN model. The training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of a precise targeting pesticide application system and an automatic picking device.