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The problem of intrusion detection has new solutions, thanks to the widespread use of machine learning in the field of network security, but it still has a few issues at this time. Traditional machine learning techniques to intrusion detection rely on expert experience to choose features, and deep learning approaches have a low detection efficiency. In this paper, an intrusion detection model based on feature selection and improved one-dimensional convolutional neural network was proposed. This model first used the extreme gradient boosting decision tree (XGboost) algorithm to sort the preprocessed data, and then it used comparison to weed out 55 features with a higher contribution. Then, the extracted features were fed into the improved one-dimensional convolutional neural network (I1DCNN), and this network training was used to complete the final classification task. The feature selection and improved one-dimensional convolutional neural network (FS-I1DCNN) intrusion detection model not only solved the traditional machine learning method of relying on expert experience to extract features but also improved the detection efficiency of the model, reduced the training time while reducing the dimension, and increased the overall accuracy. In comparison to the I1DCNN model without feature extraction and the conventional one-dimensional convolutional neural network (1DCNN) model, the experimental results demonstrate that the FS-I1DCNN model's overall accuracy increases by 0.67% and 2.94%, respectively. Its accuracy, precision, recall, and F 1 -score were significantly better than those of the other intrusion detection models, including SVM and DBN. [ABSTRACT FROM AUTHOR]

With people's higher and higher spiritual requirements, fiber art is more common in people's lives. From ordinary fiber materials to handicrafts and daily necessities, there are shadows of fiber art. This paper aims to study the application of two-dimensional images in the visual and tactile dimensions of fiber art design. This paper proposes a three-dimensional simulation of two-dimensional images of art based on deep learning technology to achieve the purpose of convenient display and also analyzes the feature extraction in fiber art. In order to comprehensively compare the performance of the algorithms, this paper compares the performance of four categories: reconstruction speed, number of reconstructed point clouds, diversity of reconstructable categories, and reconstruction stability. The experimental results show that the number of point clouds obtained by the algorithm proposed in this paper far exceeds the other two reconstruction processes, and the average time is 34.4 min. This also shows that such algorithmic processes are more robust to clothing diversity. [ABSTRACT FROM AUTHOR]

Artificial intelligence and deep learning have attracted much attention from researchers in industry and academia. The volleyball movement standardization and recognition model involve the application of artificial intelligence and deep learning. In order to solve the problem that human action in volleyball video is continuous and effective spatial and temporal features need to be extracted from the video stream, the Inception module is decoupled and heterogeneous, replacing the original 5 × 5 convolutional structures with two 3 × 3 convolutional structures, as well as replacing the 3 × 3 convolutional structures with 1 × 3 and a 3 × 1 convolutional structure with internal parameter optimization to ensure the accuracy of recognition. The model uses the input motion video RGB map as the spatial input and the optical flow map as the temporal input, and the two are weighted 1 : 1 for feature fusion. Experiments are conducted on the volleyball action video and homemade dataset in UCF101, and the experimental data show that the accuracy of the DNet volleyball action standardization recognition model proposed in this paper is 94.12%, which proves that the method improves the recognition ability of the model while speeding up the training speed. The research presented in this paper provides important theoretical guidance for artificial intelligence and deep learning. [ABSTRACT FROM AUTHOR]

The traditional tennis serve training model has been deeply ingrained in the current training. For athletes, in order to master the essentials of technical movements proficiently, they must practice for a long time and repeatedly according to the guidance of coaches. Effectively correcting the image path of tennis serve can improve the level of tennis training and competition. When correcting the image path of the tennis serve, it is necessary to mark the corners of the error points according to the characteristics of rotating multidimensional characteristics of the serving action. The traditional method adopts the critical node control method to realize the extraction of limb features and complete the path correction of the serving image. The error points of the process are marked, which reduces the accuracy of the path correction. Based on the deep learning model, this paper proposes an optimization modeling method for tennis serve image path correction and builds a visual feature acquisition system for tennis serve action based on remote video monitoring. The processing method designs the edge segmentation algorithm for the collected visual images and, on this basis, marks the corner points of the error points of the serving action and realizes the optimal modeling of the path correction of the tennis serving image. In this paper, the DLT algorithm and MSDLT algorithm are compared first, and then, the DLT algorithm is compared with the algorithm in this paper. The results show that the success rate of this method is about 92%, while the success rate of DLT algorithm is only about 82%. This algorithm has obvious advantages. The method in this paper is used to correct the action shape of the tennis serving action, which has better real time and accuracy and superior performance and can accurately track the visual edge information feature points of the player during the serving process. [ABSTRACT FROM AUTHOR]

In order to further improve the ability of automatic feature extraction of deep learning technology and better solve the problem of system recognition ability declining when the number of training samples is reduced or the number of iterations is reduced, this paper proposes a fisher-based convolutional neural network algorithm, which automatically obtains the structural feature information of a face image with the help of a deep learning algorithm network and then reduces the number of weights with the help of a convolutional neural network to further reduce the complexity of the face recognition model. Through the system test, the verification data shows that the recognition error rate can be controlled at about 10% by extracting 10 of the 14 pictures of each type of face for training. When the training images are reduced, the recognition rate can be further improved. [ABSTRACT FROM AUTHOR]

This paper investigates the extraction of volleyball players' skeleton information and provides a deep learning-based solution for recognizing the players' actions. For this purpose, the convolutional neural network-based approach for recognizing volleyball players' actions is used. The Lie group skeleton has a large data dimension since it is used to represent the features retrieved from the model. The convolutional neural network is used for feature learning and classification in order to process high-dimensional data, minimize the complexity of the recognition process, and speed up the calculation. This paper uses the Lie group skeleton representation model to extract the geometric feature of the skeleton information in the feature extraction stage and the geometric transformation (rotation and translation) between different limbs to represent the volleyball players' movements in the feature representation stage. The approach is evaluated using the datasets Florence3D actions, MSR action pairs, and UTKinect action. The average recognition rate of our method is 93.00%, which is higher than that of the existing literature with high attention and reflects better accuracy and robustness. [ABSTRACT FROM AUTHOR]

The entire auditing process is complicated and tedious and requires a lot of human resources. Therefore, the intelligent development of auditing is the general trend. In order to improve the audit quality, this paper establishes an intelligent financial audit model that can predict the audit opinion of the consolidated financial statements. This paper proposes an audit opinion prediction model based on the fusion of deep belief neural network (DBN) and long-short term memory (LSTM). First, an indicator system is established for audit opinions, and multiple financial parameters are used to describe possible audit opinions. On this basis, a DBN network is designed to complete deep feature extraction and used for LSTM training. According to the prediction model obtained by training, the subsequent audit opinion can be scientifically predicted. In the experiment, the method in this paper is tested based on financial audit related data sets and compared with the prediction results of traditional multilayer perceptron (MLP), convolutional neural network (CNN), and LSTM models. The results verify the validity and reliability of the model in this paper. [ABSTRACT FROM AUTHOR]

In order to improve the recognition accuracy of action poses for athletes in martial arts competitions, it is considered that a single frame pose does not have the temporal features required for sequential actions. Based on deep learning, this paper proposes an image arm movement analysis technology in martial arts competitions. The motion features of the arm are extracted from the bone sequence. Taking human bone motion information as temporal dynamic information, combined with RGB spatial features and depth map, the spatiotemporal features of arm motion data are formed. In this paper, we set up a slow frame rate channel and a fast frame rate channel to detect sequential motion of images. The deep learning model takes 16 frames from each video as samples. The softmax classifier is used to get the classification result of which action category the human action in the video belongs to. The test results show that the accuracy and recall rate of the arm motion analysis technology based on deep learning in martial arts competitions are 95.477% and 92.948%, respectively, with good motion analysis performance. [ABSTRACT FROM AUTHOR]

Public English instruction focuses on language skill development while also enhancing students' business professional knowledge and all-around ability under the direction of public English teaching objectives. From a knowledge standpoint, public English instruction contains a lot of tacit knowledge, making it appropriate for case teaching. This paper analyses the knowledge-locking factors in traditional case teaching based on deep learning from the multimodality perspective and builds a mental model to compensate for the shortcomings of the traditional case teaching. In this paper, a multidimensional psychological model is built that overcomes the limitations of conventional approaches. It is based on the multimodal feature extraction and modal selection algorithm of deep learning and multidimensional psychology in public English teaching. The method suggested in this paper outperforms the benchmark model created for the three evaluation indices and also demonstrates the benefits of multimodality. Create a fully integrated multidimensional mental model of the teaching and activity situations so that students can learn with a passion, become more engaged in practising their English, and ultimately develop the language skills required for employment. [ABSTRACT FROM AUTHOR]

There are various problems in diagnosing and treating tumor diseases in significant hospitals. The content includes misjudgement and over-surgery issues. For example, the judgment of pulmonary nodules mainly relies on artificial experience, and most of the artificial experience is too radical. This paper is mainly based on the extensive medical data of significant hospitals, extracts the diagnosis and treatment data and digital images of similar cases from the extensive database, classifies them through the deep learning of the computer, and then proposes the control mechanism and the solution of the doctor's misjudgement and excessive medical treatment. This method mainly relies on the CT and MRI digital images of various types of tumor diseases accumulated in the history of major hospitals. Based on the preliminary judgment of each diagnosis and treatment and the results of surgical and pathological examinations, the accumulation of various types of digital images from the history is used. The features are analysed and extracted, the model is built, and finally, a predictive analysis system for this type of tumor is obtained, which can predict the benign and malignant cases of currently occurring cases and avoid the limitations and instability of artificial experience greatest extent. It is proved by experiments and combined with Spearman to remove redundancy. The redundancy removal method SVM_RFE is used for dimensionality reduction. The method can timely correct the misjudgement of the doctor's experience and effectively reduce the instability of the manual, which provides a solution for solving the contradiction between doctors and patients and improving the scientific of diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent. [ABSTRACT FROM AUTHOR]

In the foggy environment, the images collected outdoors are prone to problems, such as low contrast and loss of details. In order to solve this problem, this paper proposes an algorithm based on multiscale parallel-depth separable convolutional neural network (MSP-DSCNN) to remove fog from foggy images and improve image quality. The multiscale feature extraction module extracts texture feature details from fog images at different scales and extracts high-dimensional and low-dimensional features from fog images by using parallel depth and shallow channels. In order to further optimize the network model, a split convolution method is proposed, which can split the feature graph into two categories, one is the main feature and the other is the secondary feature. The key information is extracted from the main features with high complexity, and the compensation information is extracted from the minor features with low complexity. Experiments show that compared with other algorithms, the model constructed in this paper has obvious advantages in defogging effect, natural color of restored images, good detail retention, and dominant indicators. It effectively solves the problems of incomplete haze, color offset, and poor visibility of detail maintenance in the current image. [ABSTRACT FROM AUTHOR]

For the building energy consumption models with complex scale sensitivity, it is difficult to achieve ideal prediction effect with single-granularity prediction model. Therefore, this paper proposed a multigranularity MgHa-LSTM model based on convolutional recurrent neural network, including a multigranularity feature extraction module and a long-term dependency capture module. Multigranularity feature extraction included granularity segmentation, feedback mechanism, and parallel convolutional modules, which can capture short-term scale sensitivity dependencies. Long-term dependency capture consists of a hybrid attention mechanism and long-short term memory layers, which are able to capture long-term dependencies. For building energy consumption patterns with different scale sensitivity, MgHa-LSTM, MLP, CNN, LSTM, and MsC-LSTM models were constructed on the IHEPC building energy consumption dataset used in this paper for comparative experiments. The experimental results showed that on the IHEPC dataset, the MSE of the building energy consumption prediction model is 0.2821 based on the MgHa-LSTM model proposed in this paper, which is equivalent to 93.72% of the MsC-LSTM model with the smallest MSE among other deep learning prediction models. Compared with other deep learning prediction models, the prediction results of the MgHa-LSTM building energy consumption prediction model are more accurate. [ABSTRACT FROM AUTHOR]

Glaucoma is an eye disease that leads to irreversible vision loss. Accurate Optic Disc (OD) and Optic Cup (OC) segmentation can effectively facilitate ophthalmologist in glaucoma diagnosis. Recently, a series of deep learning approaches attain promising performance in OD and OC segmentation but still face the challenge to precisely segment OC boundary with enhanced computational efficiency. To address this issue, we propose a novel network named Robust Multiscale Feature Extraction with Depthwise Separable Convolution (RMSDSC‐Net), which can better solve the challenging tradeoff between segmentation performance and network cost. The proposed RMSDSC‐Net is mainly composed of Multiscale Input (MSI), Depthwise Separable Convolution Unit (DSCU), Dilated Convolution Block (DCB), and External Residual Connection (ERC). First, the introduction of MSI can reduce the information loss due to the pooling layers used in the network for capturing rich feature representations. Next, to enhance segmentation performance and computational efficiency, this paper designs DSCU and DCB modules to avoid spatial information loss from minor details of the image and preserve more high‐level semantic features. Finally, this paper develops ERC established between the encoding layers and decoding layers to minimize the feature degradation problem. Hence, a high segmentation performance can be achieved using a shallow network. To evaluate the performance of the proposed network, extensive experiments have been enforced on two publicly available databases, DRISHTI‐GS and REFUGE. Our approach outperforms the state‐of‐the‐art approaches with the Dice Coefficient of (0.978, 0.919) and (0.965, 0.910) for OD and OC segmentation on DRISHTI‐GS and REFUGE databases, respectively. As a result, the proposed approach has a strong potential in analyzing fundus images for glaucoma diagnosis. [ABSTRACT FROM AUTHOR]

Many IP (intellectual property) will also be infringed, and it will become more and more serious, causing huge economic losses to people or groups who own IP. If punitive damages can be made for intentional but intentional infringement of IP, it will help to stop intentional infringement of IP, and it can also give due compensation to the obligee's loss. Based on this, this paper analyzes the introduction and implementation of punitive damages in IP law. In this paper, a relatively efficient text classification method is realized by using the method of deep learning and model design. Similar texts in the training set are grouped into a group, and each group is considered as a common text vector. Then, a threshold is set to deal with clusters higher than the threshold, and the training set is reformed. Through the experimental results, the feature extraction effect of this model is improved, the classification accuracy is constantly improved, and the final convergence rate of this model reaches 95%. It has been verified that the system can realize the automatic classification of basic Chinese patent texts. [ABSTRACT FROM AUTHOR]

Sea ice disasters are already one of the most serious marine disasters in the Bohai Sea region of our country, which have seriously affected the coastal economic development and residents' lives. Sea ice classification is an important part of sea ice detection. Hyperspectral imagery and multispectral imagery contain rich spectral information and spatial information and provide important data support for sea ice classification. At present, most sea ice classification methods mainly focus on shallow learning based on spectral features, and the good performance of the deep learning method in remote sensing image classification provides a new idea for sea ice classification. However, the level of deep learning is limited due to the influence of input size in sea ice image classification, and the deep features in the image cannot be fully mined, which affects the further improvement of sea ice classification accuracy. Therefore, this paper proposes an image classification method based on multilevel feature fusion using residual network. First, the PCA method is used to extract the first principal component of the original image, and the residual network is used to deepen the number of network layers. The FPN, PAN, and SPP modules increase the mining between layer and layer features and merge the features between different layers to further improve the accuracy of sea ice classification. In order to verify the effectiveness of the method in this paper, sea ice classification experiments were performed on the hyperspectral image of Bohai Bay in 2008 and the multispectral image of Bohai Bay in 2020. The experimental results show that compared with the algorithm with fewer layers of deep learning network, the method proposed in this paper utilizes the idea of residual network to deepen the number of network layers and carries out multilevel feature fusion through FPN, PAN, and SPP modules, which effectively solves the problem of insufficient deep feature extraction and obtains better classification performance. [ABSTRACT FROM AUTHOR]

The advancement in technology is taking place with an accelerating pace across the globe. With the increasing expansion and technological advancement, a vast volume of text data are generated everyday, in the form of social media platform, websites, company data, healthcare data, and news. Indeed, it is a difficult task to extract intriguing patterns from the text data, such as opinions, summaries, and facts, having varying length. Because of the problems of the length of text data and the difficulty of feature value extraction in news, this paper proposes a news text classification method based on the combination of deep learning (DL) algorithms. In order to classify the text data, the earlier approaches use a single word vector to express text information and only the information of the relationship between words were considered, but the relationship between words and categories was ignored which indeed is an important factor for the classification of news text. This paper follows the idea of a customized algorithm which is the combination of DL algorithms such as CNN, LSTM, and MLP and proposes a customized DCLSTM-MLP model for the classification of news text data. The proposed model is expressed in parallel with word vector and word dispersion. The relationship among words is represented by the word vector as an input of the CNN module, and the relationship between words and categories is represented by a discrete vector as an input of the MLP module in order to realize comprehensive learning of spatial feature information, time-series feature information, and relationship between words and categories of news text. To check the stability and performance of the proposed method, multiple experiments were performed. The experimental results showed that the proposed method solves the problems of text length, difficulty of feature extraction in the news text, and classification of news text in an effective way and attained better accuracy, recall rate, and comprehensive value as compared to the other models. [ABSTRACT FROM AUTHOR]

This paper combines the artificial intelligence (AI) and deep leaning technologies to classify the spread of national culture. First, we use the Python language to build a crawler technology in order to obtain the sample data of national culture from authoritative websites. Then, we use the natural language processing (NLP) expertise to analyze and preprocess the text of national diversity. In this way, we realize vectorization and the feature word extraction of the text related to national cultural resources based on the doc2vec technology. The vectorized texts of national cultural resources are clustered based on the K-means clustering technique. Moreover, the elbow rule process is implemented to determine the optimal number of clustering clusters. Finally, the text association relationship of the national cultural resources is obtained. Moreover, this paper adopts the unsupervised training method, which can reveal the semantic information contained in the text of national cultural resources. This can also help us during the identification process of the category connection amongst the texts of national cultural resources and provide us with methodological support for the gathering, storing, and other smart services of enormous national cultural resources. The outcomes demonstrate that the correctness rate of the suggested algorithm is greater than the accuracy of the linear regression and can reach up to 80%. [ABSTRACT FROM AUTHOR]

Aiming at the needs of network public opinion analysis and crisis public opinion early warning in colleges and universities, the semantic sentiment analysis method is studied in this paper. Most of the public opinion information comes from short text comment information, and its text is separated from the written language, the structure becomes simpler, and it lacks normativity, which brings certain difficulties to the extraction of text features. Traditional sentiment analysis methods often rely on emotional dictionaries and feature extraction, and with the continuous change of Internet culture, a technical help is needed to make even the dictionary updated. Based on the analysis and study of attention mechanism and deep learning related technologies, an LSTM model is proposed to mine the deep semantic characteristics of text, which can accurately determine its emotional tendency. The main tasks are as follows: according to the CNN and LSTM text processing, CNN can better extract the local features of the text, and LSTM can retain the text history information and effectively extract the global features of the sequence. The CBOW model is optimized to pay more attention to the feature vectors that affect the classification results during the calculation process. Finally, the improved model in this paper compares the accuracy, recall rate, loss rate, and F1 value of the traditional model to indicate the performance evaluation index of the model. [ABSTRACT FROM AUTHOR]

In order to meet the requirement of designing the educational curriculum and extract the relevant data toward students, we in this paper utilize the deep learning architecture to study the personalized understanding and educational feature extraction algorithm of the high school and college students. Aiming at the insufficiency of deep collaborative filtering recommendation algorithm that can only discover the paths that have attracted students' attention, the requirements of intelligent course are proposed by us. First, the word vector permutation discourse poke model is extracted to produce audible vectors of message data, a well-designed learning algorithm is proposed subsequently to extract and fuse multiple heterogeneous educational data. Then, the SVD algorithm program is used to reduce the range of feature vectors. And thus, the model is completely functional and we identify unified channels and users. The method of combining hand-extracted low-level features with high-level human gesture features (image visual gate features and image deep features) automatically extracted by our adopted deep network is used to identify students' various emotions. The conclusive product is a flat semantic description that effectively detects copying and objection. Experiments are performed on a generic dataset, and we use the old-fashioned manual birth method and two well-known data sets VGG16 and fine-tune AlexNet. [ABSTRACT FROM AUTHOR]

Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter. This is achieved by processing the radio-frequency signals. Feature extraction and classifier selection are key factors that affect SEI performance. This paper proposes a deep convolutional neural network model based on multisignal feature fusion to identify the emitters. As part of the implementation model, the methods of singular spectrum analysis (SSA), variational mode decomposition (VMD), and intrinsic time-scale decomposition (ITD) are used to extract various signal features of emitter signals. Finally, a multichannel deep learning model is adopted to fuse each signal feature automatically and identify different signal emitters. Experimental results show that the proposed method completely considers the complementarity and independence of varying signal features and excavates hidden deep feature information. Hence, the process is considered reliable and effective. [ABSTRACT FROM AUTHOR]

Natural language processing is an important branch of deep learning. In particular, the classification of short texts is one of the main tasks of computer linguistics, because it ensures the security of information. Therefore, this paper reviews the text classification methods for the first time, aiming at comparing the modern methods to solve the text classification problems, determine the trend in this direction, and select the best algorithm to be applied to research and business tasks. In this paper, with the help of the emerging algorithm in the field of deep learning, a web domain knowledge text classification method based on feature words is developed with the support vector machine (SVM) as the basic classification algorithm. Two datasets representing basic research disciplines and emerging research fields are selected to verify the proposed research framework. Experiments show that the performance of this method is improved by 3% compared with the benchmark method. [ABSTRACT FROM AUTHOR]

Strengthening the construction of intellectual property rights of SM-TE (small and medium-sized scientific and technological enterprises) in China is an important measure to speed up the development of SM-TE, and improve their scientific and technological innovation ability and market competitiveness. In this paper, a patent recommendation algorithm based on deep semantic similarity is proposed to solve the problem of low calculation accuracy of similarity matrix among users in sparse interaction matrix. The algorithm trains the patent corpus, and obtains the Doc2vec DL (Deep Learning) model, and then constructs the semantic similarity matrix among patents through the DL model. On this basis, to further improve the modeling ability of semantic expression and feature extraction, this paper optimizes CNN (Convolutional Neural Network) model, using a variety of pretrained word vector models, multi-layer classifiers, etc., to improve the model accuracy and generate feature vectors of different dimensions. The results show that the accuracy, recall rate and F1 value of the proposed algorithm are better than those of the traditional recommendation algorithm, which are 22.41%, 20.86% and 21.51% respectively. The experiment shows that this paper can guide Chinese enterprises to establish and improve the risk warning system of independent intellectual property rights, thus reducing the losses of enterprises. [ABSTRACT FROM AUTHOR]

Not only were traditional artificial neural networks and machine learning difficult to meet the processing needs of massive images in feature extraction and model training but also they had low efficiency and low classification accuracy when they were applied to image classification. Therefore, this paper proposed a deep learning model of image classification, which aimed to provide foundation and support for image classification and recognition of large datasets. Firstly, based on the analysis of the basic theory of neural network, this paper expounded the different types of convolution neural network and the basic process of its application in image classification. Secondly, based on the existing convolution neural network model, the noise reduction and parameter adjustment were carried out in the feature extraction process, and an image classification depth learning model was proposed based on the improved convolution neural network structure. Finally, the structure of the deep learning model was optimized to improve the classification efficiency and accuracy of the model. In order to verify the effectiveness of the deep learning model proposed in this paper in image classification, the relationship between the accuracy of several common network models in image classification and the number of iterations was compared through experiments. The results showed that the model proposed in this paper was better than other models in classification accuracy. At the same time, the classification accuracy of the deep learning model before and after optimization was compared and analyzed by using the training set and test set. The results showed that the accuracy of image classification had been greatly improved after the model proposed in this paper had been optimized to a certain extent. [ABSTRACT FROM AUTHOR]

After the birth of deep learning, artificial intelligence has entered a vigorous period of rapid development. In this process of rising and growing, we have made one achievement after another. When deep learning is applied to fruit target detection, due to the complex recognition background, large similarity between models, serious texture interference, and partial occlusion of fruits, the fruit target detection rate based on traditional methods is low. In order to solve these problems, a BCo-YOLOv5 network model is proposed to recognize and detect fruit targets in orchards. We use YOLOv5s as the basic model for feature image extraction and target detection. This paper introduces BCAM (bidirectional cross attention mechanism) into the network and adds BCAM between the backbone network and the neck network of the YOLOv5s basic model. BCAM uses weight multiplication strategy and maximum weight strategy to build a deeper position feature relationship, which can better assist the network in detecting fruit targets in fruit images. After training and testing the network, the map BCo-YOLOv5 network model reaches 97.70%. In order to verify the detection ability of the BCo-YOLOv5 network to citrus, apple, grape, and other fruit targets, we conducted a large number of experiments BCo-YOLOv5 network. The experimental results of the BCo-YOLOv5 network show that this method can effectively detect citrus, apple, and grape targets in fruit images, and the fruit target detection method based on BCo-YOLOv5 network is better than most orchard fruit detection methods. [ABSTRACT FROM AUTHOR]

Deep learning has recently received extensive attention in the field of rolling-bearing fault diagnosis owing to its powerful feature expression capability. With the help of deep learning, we can fully extract the deep features hidden in the data, significantly improving the accuracy and efficiency of fault diagnosis. Despite this progress, deep learning still faces two outstanding problems. (1) Each layer uses the same convolution kernel to extract features, making it difficult to adaptively select convolution kernels based on the features of the input image, which limits the network's adaptability to different input features and leads to weak feature extraction. (2) Large number of parameters and long training time. To solve the above problems, this paper proposes an integrated deep neural network that combines an improved selective kernel network (SKNet) with an enhanced Inception-ResNet-v2, named SIR-CNN. First, based on the SKNet, a new three-branch SKNet was designed. Second, the new SKNet is embedded into a depthwise separable convolution network such that the model can adaptively select convolution kernels of different sizes during training. Furthermore, the convolution structure in the Inception-ResNet-v2 network was replaced by the improved depthwise separable convolution network to achieve effective feature extraction. Finally, the time-frequency maps of the raw vibration signals are obtained through short-time Fourier transform (STFT) and then sent to the proposed SIR-CNN network for experiments. The experimental results show that the proposed SIR-CNN achieves superior performance compared to other methods. [ABSTRACT FROM AUTHOR]

Epilepsy, a neurological disease associated with seizures, affects the normal behavior of human beings. The unpredictability of epileptic seizures has caused great obstacles to the treatment of the disease. The automatic seizure detection method based on electroencephalogram (EEG) can assist experts in predicting seizures to improve treatment efficiency. Epileptic seizure detection cannot be achieved accurately using the single-view characteristics of the signals. Moreover, manual feature extraction is a time-consuming task. To design a high-performance seizure identification method, automatic learning of multi-view features becomes an indispensable part for seizure detection. Therefore, the paper proposes a multi-input deep feature learning networks (MDFLN) model, which comprehensively considers the features from the time domain and the time–frequency (TF) domain for EEG signals. The MDFLN model automatically extracts the feature information of the signals through deep learning networks. Then, the bidirectional long short-term memory (BLSTM) network is used to distinguish seizure and nonseizure events. Furthermore, the effectiveness of the proposed network structure is verified in two public datasets. The experimental results demonstrate that the classification accuracy of the proposed method based on multi-view features is at least 2.2% higher than the single-view features. The MDFLN achieves better performance on CHB-MIT and Bonn datasets with accuracy of 98.09% and 98.4%, respectively. The fine-tuned model with the validation set also improves the classification performance. Compare with the state-of-the-art seizure detection methods, the multi-input deep learning network has superior competence with high sensitivity on the CHB-MIT dataset. The proposed automatic seizure detection method can reduce time consumption and effectively assist experts in the clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Monitoring the wheat growth process by machine vision with artificial feature extraction suffers from poor objectivity and low efficiency. To solve this problem, this paper introduces deep learning into monitoring the wheat growth process. The convolutional neural network (CNN) has been widely used in image classification as a common algorithm in deep learning. The application of deep feature extraction networks could automatically recognize and extract image features. However, the large number of parameters and computational overhead brought by regular deep CNNs make it difficult to apply these models to embedded devices with limited storage space and computing power. Therefore, the knowledge distillation method has been proposed for the feature extraction network of the target detection network to improve the performance of shallow feature extraction networks and ensure recognition accuracy while reducing the computational burden and model size. This paper took ResNet50 and MobileNet as two different teacher networks to guide the student model MobileNet for training. The experimental results showed that when ResNet50 was used as teacher model, MobileNet had the best recognition effect. The average recognition accuracy of the student model MobileNet was 97.3%, and the model size was compressed to only 19.71 MB, which is 88.9% smaller than that of ResNet50. Knowledge distillation improves the accuracy of the obtained model and reduces the number of network model parameters. Furthermore, it reduces the model running time and the cost of model deployment greatly. The application of the knowledge distillation method could provide further technical support for intelligent wheat production in the field. [ABSTRACT FROM AUTHOR]

Aiming to achieve efficient and accurate network rumor detection, this paper proposes a rumor detection method based on deep learning network. First, this method uses API interface and web crawler to construct a large data set of information samples on the microblog platform. Then, this method processes and analyzes the large data set through multiple embedded layers to provide a complete and reliable analysis data set for the detection model. The rumor detection model is composed of bidirectional long- and short-term memory (Bi-LSTM) network and convolutional neural network (CNN), which can realize deep and efficient feature extraction for the analysis data set and ensure the optimal performance of rumor detection method. The simulation results show that the precision and accuracy of the proposed method are 0.9771 and 0.9726, respectively, which are better than the comparison algorithms. The proposed method has effective network rumor screening and identification performance. [ABSTRACT FROM AUTHOR]

With the development of information collection technology, the data that people need to deal with are also increasing, which brings problems such as isomerization of data types, poor data quality, and fast data generation speed. At present, as an important method of data fusion technology, data fusion method based on deep learning has become an effective way of data fusion under the background of big data, which has important research significance. There is a problem with heterogeneous data types between time series data and text data, and it is difficult to fuse them effectively by traditional data fusion methods. In order to make full use of the information contained in text data and improve the accuracy of time series prediction, this paper proposes a data fusion model based on FC-SAE. In this model, GloVe and CNN are used to extract the features of text data, FC neural network is used to extract the potential features of time series data, and then, the SEA model is used to fuse the data, which fully discovers the relationship between data and greatly improves the prediction accuracy. [ABSTRACT FROM AUTHOR]

Flash animation, as a kind of digital learning resource, is an important media for delivering information content, and more importantly, it is an important online learning resource with text, graphics, images, audio, video, interaction, dynamic effects, etc. Flash animation, with its powerful multimedia interaction and presentation capabilities, is widely used in distance education, high-quality course websites, Q&A platforms, etc. With the continuous development of deep learning, the 3D shape feature extraction method combined with deep learning has become a hot research topic. In this paper, we combine deep learning with traditional 3D shape feature extraction methods, so that we can not only break the bottleneck of nondeep learning methods but also improve the accuracy of 3D shape data classification and retrieval tasks, especially in the case of non-rigid 3D shapes. The scheme in this paper not only does not require a large number of training samples but also its feature extraction for flash animation is accurate. Experiments show that the success rate of accurate feature extraction of this paper's scheme is higher than that of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The typical pretrained model's feature extraction capabilities are insufficient for medical named entity identification, and it is challenging to express word polysemy, resulting in a low recognition accuracy for electronic medical records. In order to solve this problem, this paper proposes a new model that combines the BERT pretraining model and the BilSTM-CRF model. First, word embedding with semantic information is obtained by pretraining the corpus input to the BERT model. Then, the BiLSTM module is utilized to extract further features from the encoded outputs of BERT in order to account for context information and improve the accuracy of semantic coding. Then, CRF is used to modify the results of BiLSTM to screen out the annotation sequence with the largest score. Finally, extensive experimental results show that the performance of the proposed model is effectively improved compared with other models. [ABSTRACT FROM AUTHOR]

At present, the traditional phase unwrapping algorithm is difficult to balance the accuracy and unwrapping efficiency. The traditional phase unwrapping algorithm is difficult to balance the accuracy and efficiency in the phase unwrapping experiments of simulated and measured topographic interferograms. In this paper, the phase unwrapping technology will be studied under the framework of deep learning theory according to the development trend of InSAR intelligence. A phase unwrapping algorithm based on deep GAN is proposed. The model structure includes dense convolution layer blocks and series structure, so that the network can achieve a better balance between feature extraction and detail preservation of interferogram. This is helpful to improve the phase unwrapping accuracy and training efficiency of the network. The experimental results show that the algorithm has a good expansion effect on the interferogram with high signal-to-noise ratio. The synthesis algorithm makes full use of the advantages of the branch cutting method and the finite element method. The phase reliable region and unreliable region are determined, and the transmission of phase error from the unreliable region to reliable region is effectively avoided. The accuracy of the phase unwrapping results in the reliable region is ensured, and the overall phase unwrapping convergence accuracy is greatly improved. [ABSTRACT FROM AUTHOR]

Retinal vessel segmentation is essential for the detection and diagnosis of eye diseases. However, it is difficult to accurately identify the vessel boundary due to the large variations of scale in the retinal vessels and the low contrast between the vessel and the background. Deep learning has a good effect on retinal vessel segmentation since it can capture representative and distinguishing features for retinal vessels. An improved U-Net algorithm for retinal vessel segmentation is proposed in this paper. To better identify vessel boundaries, the traditional convolutional operation CNN is replaced by a global convolutional network and boundary refinement in the coding part. To better divide the blood vessel and background, the improved position attention module and channel attention module are introduced in the jumping connection part. Multiscale input and multiscale dense feature pyramid cascade modules are used to better obtain feature information. In the decoding part, convolutional long and short memory networks and deep dilated convolution are used to extract features. In public datasets, DRIVE and CHASE_DB1, the accuracy reached 96.99% and 97.51%. The average performance of the proposed algorithm is better than that of existing algorithms. [ABSTRACT FROM AUTHOR]

In order to realize students' in-depth understanding of music teaching content, it is necessary to reasonably allocate teaching materials according to the teaching content. Therefore, this paper puts forward the application of the concept of the Iot network and deep learning in music teaching. After the teaching resources are vectorized, the distinguishing local registration method of the DLA model is used to extract their features. Based on the dimension of the teaching content, the features of the teaching content are output in the DLA model, the music teaching resources are allocated according to the minimum matching error criterion, the hyperbolic tangent function is taken as the activation function, and the feature error is filtered by maximum aggregation. The experimental results show that the design method can, according to the music teaching content, have more than 80% accuracy in the shallow distribution and deep distribution of teaching materials, and play a positive role in promoting students' in-depth learning. [ABSTRACT FROM AUTHOR]

Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy, and weak adaptive ability. This method separates image feature extraction and classification into two steps for classification operation. The deep learning model has a powerful learning ability, which integrates the feature extraction and classification process into a whole to complete the image classification test, which can effectively improve the image classification accuracy. However, this method has the following problems in the application process: first, it is impossible to effectively approximate the complex functions in the deep learning model. Second, the deep learning model comes with a low classifier with low accuracy. So, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of well multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping to complete the complex function approximation in the deep learning model. And a sparse representation classification method based on the optimized kernel function is proposed to replace the classifier in the deep learning model, thereby improving the image classification effect. Therefore, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. The experimental results show that the proposed method not only has a higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effect, thus further improving image classification accuracy. [ABSTRACT FROM AUTHOR]

The recommendation system is an active, personalized, and real-time technology platform proposed in the 1990s to solve the problem of information overload. The recommendation system can constantly adjust the recommendation results according to the real-time behaviors of users. In other words, if the user's interest changes, the recommendation system can present different information to the user. Therefore, the recommendation system is the best way to solve the problem of information overload in entrepreneurial projects. Based on the ConvMF algorithm, this paper proposes an entrepreneurial project recommendation algorithm based on a deep neural network and matrix decomposition. A deep neural network was established for the extraction of the hidden features of entrepreneurial projects, and a convolution neural network was used to process the text description information of entrepreneurial projects. One-hot coding was used to process the regional characteristics and financing round characteristics of entrepreneurial projects, and word embedding was used to process the label features of entrepreneurial projects. The implicit features of users are extracted from the user's rating matrix using matrix decomposition technology. Finally, recommendations are made according to the implicit characteristics of users and the items learned. [ABSTRACT FROM AUTHOR]

For the past several years, semantic segmentation method based on deep learning, especially Unet, have achieved tremendous success in medical image processing. The U‐shaped topology of Unet can well solve image segmentation tasks. However, due to the limitation of traditional convolution operations, Unet cannot realize global semantic information interaction. To address this problem, this paper proposes RT‐Unet, which combines the advantages of Transformer and Residual network for accurate medical segmentation. In RT‐Unet, the Residual block is taken as the image feature extraction layer to alleviate the problem of gradient degradation and obtain more effective features. Meanwhile, Skip‐Transformer is proposed, which takes Multi‐head Self‐Attention as the main algorithm framework, instead of the original Skip‐Connection layer in Unet to avoid the influence of shallow features on the network's performance. Besides, we add attention module at the decoder to reduce semantic differences. According to the experiments on MoNuSeg data set and ISBI_2018cell data set, RT‐Unet achieves better segmentation performance than other deep learning‐based algorithms. In addition, a series of further ablation experiments were conducted on Residual network and Skip‐Transformer, which verified the effectiveness and efficiency of the proposed methods in this paper. [ABSTRACT FROM AUTHOR]

Over the years, a number of semisupervised deep‐learning algorithms have been proposed for time‐series classification (TSC). In semisupervised deep learning, from the point of view of representation hierarchy, semantic information extracted from lower levels is the basis of that extracted from higher levels. The authors wonder if high‐level semantic information extracted is also helpful for capturing low‐level semantic information. This paper studies this problem and proposes a robust semisupervised model with self‐distillation (SD) that simplifies existing semisupervised learning (SSL) techniques for TSC, called SelfMatch. SelfMatch hybridizes supervised learning, unsupervised learning, and SD. In unsupervised learning, SelfMatch applies pseudolabeling to feature extraction on labeled data. A weakly augmented sequence is used as a target to guide the prediction of a Timecut‐augmented version of the same sequence. SD promotes the knowledge flow from higher to lower levels, guiding the extraction of low‐level semantic information. This paper designs a feature extractor for TSC, called ResNet–LSTMaN, responsible for feature and relation extraction. The experimental results show that SelfMatch achieves excellent SSL performance on 35 widely adopted UCR2018 data sets, compared with a number of state‐of‐the‐art semisupervised and supervised algorithms. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, the feature extraction method of multichannel acoustic emission (AE) signal provides a solid research foundation for digital and intelligent fault diagnosis of the hoisting system. More specifically, AE signal collected from the hoisting system is generally characterized by nonlinear and non‐stationary, thus making the traditional intelligent fault diagnosis methods cannot accurately extract the inherent fault features. To alleviate this problem and improve the accuracy of multichannel fault diagnosis, a new fault diagnosis method for hoisting system based on differential search algorithm‐variational mode decomposition (DSA‐VMD) and improved deep convolutional neural network (IDCNN) is proposed in this paper. Specifically, the proposed DSA‐VMD and IDCNN method is divided into two main components: (i) the inside parameters (K, a) of VMD is optimized to effectively extract the multichannel AE fault feature via DSA‐VMD and (ii) the extracted multichannel fault components are fed into the designed IDCNN algorithm to accomplish fault identification automatically. Experimental results from the hoisting system demonstrate the effectiveness of the proposed approach. Additionally, the superiority of the proposed approach has also been verified in extracting fault information and fault identification compared to the other multichannel fault diagnosis methods. [ABSTRACT FROM AUTHOR]

Medical image recognition plays an essential role in the forecasting and early identification of serious diseases in the field of identification. Medical pictures are essential to a patient's health record since they may be used to control, manage, and treat illnesses. On the other hand, image categorization is a difficult problem in diagnostics. This paper provides an enhanced classifier based on the outstanding Feature Selection oriented Clinical Classifier using the Deep Learning (DL) model, which incorporates preprocessing, extraction of features, and classifying. The paper aims to develop an optimum feature extraction model for successful medical imaging categorization. The proposed methodology is based on feature extraction with the pretrained EfficientNetB0 model. The optimum features enhanced the classifier performance and raised the precision, recall, F1 score, accuracy, and detection of medical pictures to improve the effectiveness of the DL classifier. The paper aims to develop an optimum feature extraction model for successful medical imaging categorization. The optimum features enhanced the classifier performance and raised the result parameters for detecting medical pictures to improve the effectiveness of the DL classifier. Experiment findings reveal that our presented approach outperforms and achieves 98% accuracy. [ABSTRACT FROM AUTHOR]

The database of the intelligent science management system includes the exchange of data between the basic data of the school and the various business systems, with centralised storage and centralised management, constituting a unified data centre. The completed intelligent science management system will become a reliable source of data to integrate the system to be developed with existing systems, making the data of the whole system unified, thus providing reliable data for information enquiry and decision analysis within the university, and laying a good foundation for the construction of the digital campus. The NRM_DeepNet model is an application model of the deep learning method of the nonlinear random matrix in the intelligent research management system, mainly in data processing and analysis, using deep learning technology for data classification processing, and then for research data modal feature extraction, the process of modal extraction applies the nonlinear random matrix for feature weighting. In the process of modal extraction, a nonlinear random matrix is applied to weight the features, and finally, different research data features of different research projects can be classified as intelligent and regularised. In this paper, we combine the practice of scientific research management, introduce the nonlinear random matrix and deep learning technology into the intelligent scientific research management system, and update its data and system management so as to realize intelligent and humanised scientific research management, thus reducing the labour intensity and improving the efficiency of scientific research work. [ABSTRACT FROM AUTHOR]

Wireless communication signals are often affected by noise and interference in the channel during transmission, which makes it difficult for the receiver to analyze. The signal enhancement technology can suppress the noise and interference in the signal, so as to improve the communication quality. It is one of the main research directions of signal processing. Classical enhancement methods separate the signals through separable transform domain. Artificial construction of the corresponding separable transform domain requires prior information of noise and interference, but they have the characteristics of randomness. Further, these methods usually use high-level features and rely on statistics, so they can only deal with specific noise conditions. At present, deep learning is increasingly applied in the field of wireless communications due to its powerful feature extraction ability for large sample sets. In this paper, a communication signal enhancement model based on generative adversarial network (GAN) is proposed. Compared with classical methods, the signal is operated directly and the model is trained end-to-end. It can adapt to different noise conditions and avoid the above problems. An independent and invisible test set is used to evaluate several comparative methods. The experimental results confirm the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis (CAD) has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network (CNN) and long short-term memory (LSTM) with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1-score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily. [ABSTRACT FROM AUTHOR]

It is a challenge for the current music teaching system to carry out teaching according to the difference of score difficulty and realize automatic grading. Therefore, identifying the difficulty of music score according to pitch is the key to individualize music teaching resources. This paper summarizes and analyzes the problem of pitch feature extraction in music teaching. In the pitch extraction, the audio signal is divided into frames, and the feature matching of high-pitched content in music teaching resources is realized by smoothing the pitch sequence. In addition, the pitch feature extraction algorithm in MIDI music score files is proposed, and the pitch feature matching model is constructed. Finally, a music tutoring system based on pitch feature matching is designed, including a music score learning tool, overall structure of system, and interaction between teachers and students. Tutoring strategies include three main functions: learning suggestions of knowledge points, skills in practice and training, and learning path adjustment. This study is helpful to further improve the music teaching model and realize intelligent and personalized music learning. [ABSTRACT FROM AUTHOR]

In order to improve recognition accuracy of clothing style and fully exploit the advantages of deep learning in extracting deep semantic features from global to local features of clothing images, this paper utilizes the target detection technology and deep residual network (ResNet) to extract comprehensive clothing features, which aims at focusing on clothing itself in the process of feature extraction procedure. Based on that, we propose a multideep feature fusion algorithm for clothing image style recognition. First, we use the improved target detection model to extract the global area, main part, and part areas of clothing, which constitute the image, so as to weaken the influence of the background and other interference factors. Then, the three parts were inputted, respectively, to improve ResNet for feature extraction, which has been trained beforehand. The ResNet model is improved by optimizing the convolution layer in the residual block and adjusting the order of the batch-normalized layer and the activation layer. Finally, the multicategory fusion features were obtained by combining the overall features of the clothing image from the global area, the main part, to the part areas. The experimental results show that the proposed algorithm eliminates the influence of interference factors, makes the recognition process focus on clothing itself, greatly improves the accuracy of the clothing style recognition, and is better than the traditional deep residual network-based methods. [ABSTRACT FROM AUTHOR]

In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. Using the advanced idea of deep learning-based end-to-end classification and the prominent feature extraction advantages of a fully convolution neural network, this paper proposes a first-arrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of first-arrival picking. In this paper, we first introduced the methodology of the UNet++-based picking method. And then, the performance of the proposed method is verified by the experiments with finite-difference forward modeling simulated signals and actual microseismic records under different signal-to-noise ratios, and finally, comparative experiments are performed using the U-Net-based first-arrival picking algorithm and the Short-Term Average to Long-Term Average (STA/LTA) algorithm. The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms. [ABSTRACT FROM AUTHOR]

In recent years, many deep learning algorithms based on seismic signals have been proposed to solve the moving target recognition problem in unattended ground sensor systems. Despite the excellent performance of these deep networks, most of them can only be deployed on cloud-based devices and cannot be deployed on low-power hardware devices due to the large network size. Second, since seismic signals are affected by the terrain, employing only seismic signals as reconnaissance means for unattended ground sensors cannot achieve multiterrain-type adaptability. In response, this paper proposes an MFC-TinyNet method facing a multiterrain. The method adds depthwise separable convolutional layers to the network, which effectively reduces the size of the network while keeping the target recognition accuracy constant, and solves the problem that the model is difficult to deploy on low-power hardware. It also uses the Mel-frequency spectrum feature extraction method to fuse sound and seismic signals to improve the accuracy of the model's moving target recognition on a multiterrain. Experiments demonstrate that the method can combine the two advantages of the small network model and multiterrain applicability. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), commonly known as drones, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. The utilization of UAVs in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area. This paper makes significant contributions by analyzing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. In this research, the effectiveness of nine powerful deep neural network models is measured for the detection of plant diseases using diverse methodologies. These deep neural networks are adapted to the immediate situation using transfer learning and deep extraction of features approaches. The presented study takes into account the used pretrained deep learning model for extracting features and fine-tuning. The deep feature extraction characteristics are subsequently categorized using support vector machines (SVMs) and extreme learning machines (ELMs). For measuring performance, the precision, sensitivities, specific, and F1-score are all evaluated. Deep feature extraction and SVM/ELM classification generated better outcomes than transfer learning, according to the analysis result. Furthermore, the analysis of the various methodologies tries to assess their effectiveness and costs. The different approaches, for example, confront difficulties such as investigating the region in the shortest possible time feasible, while eliminating the same region being searched by more drones, detecting parasites, and stopping their spread by applying the appropriate number of pesticides. Simulation models are a significant aid to researchers in conducting to evaluate these technologies and creating specific tactics and coordinating procedures capable of effectively supporting farms and achieving the aim. The main objective of this paper is to compare the search techniques of two distinct methods of parasitic to identify performance. [ABSTRACT FROM AUTHOR]

Aiming at the high miss detection and false detection rate of traditional SSD (single shot multibox detector) target detection algorithm in target detection, this paper proposes a lightweight detection algorithm for deep learning target detection model in order to improve the detection accuracy. Firstly, the real-time and efficient target detection backbone network VoVNet is used to replace the feature extraction network VGG16. The residual structure is integrated to solve the problem of VoVNet network degradation to improve network performance. Secondly, self-attention mechanism is introduced to capture multiscale local and global information to obtain richer image semantic features. According to the characteristics of the target sample size, the anchor frame is designed by using a priori information. In the network training, the anchor frame by selection enhancement based on IoU optimization is used to fully train the target information and strengthen the reading ability of the network to small-scale targets. Experiments on the public data set AI-TOD to show that the target detection lightweight model of deep learning has stronger detection ability and higher average detection accuracy than other algorithms, which proves the applicability and effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]