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The hydrological cycle in the natural environment plays a crucial role in influencing human societal progress and everyday life, particularly in the realm of agriculture. Precipitation is a vital component of the natural water cycle. In recent years, multiple approaches for estimating rainfall have been developed by researchers to achieve improved results. However, the precision of conventional rainfall estimation techniques remains inconsistent, particularly in instances of heavy rainfall, which can result in considerable errors. Scholars have turned their attention to deep learning techniques, which excel at processing raw data and autonomously identifying model parameters. In this study, we present and compare two deep learning frameworks for precipitation estimation based on BPNN and CNN, in contrast to traditional methods. We also use a real dataset to validate the effectiveness of the deep learning models, and the experimental outcomes indicate that the CNN-based precipitation estimation method outperforms several other models.

With the vigorous development of computer technology, its application in various fields is increasingly deepening, especially in the application of martial arts performance training models. This paper focuses on the practical application and far-reaching impact of computer image structure optimization in martial arts performance training models. Computer image structure optimization is a multidisciplinary technique that involves image processing, computer vision, and machine learning, aiming to optimize the visual effects and quality of images through algorithms and models. In the training model of martial arts performance, computer image structure optimization technology plays a crucial role. By utilizing advanced technologies such as color management, light and shadow processing, motion estimation and compensation, the clarity, smoothness, and coherence of martial arts performance images can be effectively improved, thereby enhancing the artistic effect and viewing value of martial arts performances. The study delved into the application effect of computer image structure optimization in martial arts performance training models. Through empirical research, we have found that optimized images can not only more accurately display the details and techniques of martial arts movements, but also create a more stunning performance atmosphere. Meanwhile, computer image structure optimization technology can also provide more accurate and efficient feedback mechanisms for martial arts performance training, helping athletes better adjust and improve their movements.

At present, progressive strength training in sports training is mainly based on free strength training equipment and combination equipment, and the load intensity depends on the events, seasons and individual conditions of athletes. High-quality training is the key to improving sports performance and training efficiency, and a better grasp of movement rhythm can effectively improve maximum strength. This paper studies the influence of progressive strength training based on deep learning (DL) on athletes’ upper limbs and circumference. Making full use of the existing 2D pose estimation is mature, and applying it to athletes’ 3D pose estimation can effectively improve the accuracy of athletes’ 3D pose estimation, the last layer outputs 3D attitude prediction. The results show that the circumference of left arm in group A, group B and common movement rhythm is also significantly different from those before training ($P=0.00$), with the increase in the values of 3.36%, 6.43% and 2.01%, respectively. It can be seen that after eight weeks of maximum strength of bench press, the three movement rhythm groups also have a significant impact on the arm circumference of the subjects. In this paper, the classification error of 3DPoseNet network is reduced by at least 6.38% on average, which shows the effectiveness of this method.

With the vigorous development of artificial intelligence technology, especially in the field of deep learning, English education and teaching models are facing unprecedented opportunities and challenges. In order to enable students to master English knowledge more efficiently and align with international standards, it is particularly important to study the optimization of English teaching models. This paper uses specific deep learning algorithms and techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to model and optimize English teaching modes, aiming to solve many problems in English teaching and improve the quality of English teaching. Through deep learning algorithms, we can analyze students’ learning behavior, habits and grades, thereby providing them with personalized learning resources and teaching strategies. Deep learning technologies such as CNN and RNN are used to recognize keywords and phrases in text, as well as to process sequence data such as speech and text, helping teachers better understand students’ learning needs and interests, thereby adjusting teaching content and methods. In addition, the adaptive nature of deep learning algorithms allows automatic adjustment of teaching content and difficulty according to the actual situation of students, providing them with learning resources and support that better meet their learning needs. This study not only applies deep learning algorithms to optimize English teaching modes but also delves into how these algorithms affect the learning outcomes of students and the teaching efficiency of teachers. Through empirical research and case analysis, we hope to provide new ideas and methods for the future development of English education.

In the layout of urban planning, the rational design of green land includes the optimal choice of landscape and vegetation, which needs to solve the conflict of local/global optimal solution and understand the rationality of urban green layout with multi-objective natural language. According to the demand for multi-objective optimal solutions in urban green land planning, we study the target deep learning algorithm based on space vector mutation operation. In this paper, we propose to search for a local optimal solution based on Diversity Indicator Double and Niched Local Search Evolutionary Algorithm diversity fitness and filter the information of objective space and decision space through the niche method. It intends to avoid the imbalance between the diversity of goal space and decision space caused by the wrong distance information. By improving genetic diversity, we take the complex changes of gene vector reaction in generating offspring as the research focus. Based on the improvement of multi-objective non-dominated genetic variation, we search for the optimal transfer function relationship in the primary stage of variation to achieve the goal. For high-dimensional planning with constraints, we first select the coupling model to reduce the dimension, and then combine the spatial reconstruction data clustering to complete the clustering. Priority is given to the optimal selection of the output of the non-dominated solution set obtained in each stage to improve the algorithm’s performance. Experiments show that the proposed algorithm, combined with the use of different mutation strategies, outperforms the current mainstream algorithms in solving multi-objective problems of natural language understanding, especially in solving complex relational problems, which is 10.2% and 15.6% higher than other algorithms. The frequency of parameter operation can be updated in real-time, which strengthens the convergence effect of the algorithm. It is proved that the research content of this paper has better application potential in multi-objective spatial decision-making. The research content of this paper can not only achieve intelligent layout optimization for urban green space system but also carry out reasoning for large-scale spatial layout. The effect is higher than the current mainstream algorithm and plays a role in promoting science and technology for future urban construction and long-term planning.

In today’s data-driven era, the accuracy and forward-looking prediction of university financial data are of vital significance for the rational allocation of educational resources and strategic planning. However, the financial data of colleges and universities come from various sources, have complex structure, and are scattered in different database systems, which brings great challenges to data integration and prediction. In order to predict university financial data more accurately, a research on university financial data integration forecast based on deep learning is put forward. Firstly, the Ontology method is used to integrate university financial data. Through data preprocessing, a shared vocabulary is constructed, and the semantic information of college finance is expressed by means of formal ontology, and the ontology attributes of key concepts are extracted. In addition, MC algorithm is used for security processing of integrated data to ensure the security of data in distributed database, and redundant processing of integrated data to generate unified XML format integrated data. Next, with the help of deep belief network in deep learning, feature extraction and dimensionality reduction are carried out on the integrated university financial data. Then, the university financial data prediction model based on these characteristics is constructed, and the university financial data integrated prediction based on deep learning is realized. The experimental results show that the proposed method not only achieves remarkable results in data integration, but also performs well in prediction. This research not only provides a new solution for university financial data forecasting, but also has important significance in theory and practice. In theory, it enriches the theoretical framework of deep learning and data integration. In practice, by improving the accuracy of financial data forecast, the proposed method can help universities to better allocate resources and make strategic planning, and promote the sustainable development of education.

With the rapid development of computer vision technology, rendering and stylization of landscape drawings have become the focus of attention in the field of design. This paper aims to study the rendering stylization method of landscape drawings based on computer vision technology and discuss how to apply advanced vision algorithms to the automatic stylization of landscape design drawings. This paper reviews the application status of computer vision technology in the field of landscape design and analyzes the challenges and opportunities faced by the current technology. This paper proposes a new landscape drawing stylization framework, which combines deep learning, image processing and pattern recognition techniques to achieve efficient rendering and stylization of landscape drawings. By learning and training on a large amount of landscape drawing data, the model is able to recognize and simulate different rendering styles, thus providing designers with a variety of design solutions. In addition, the paper explores the importance of user interaction in the stylization process and proposes a user-oriented stylization approach that allows designers to customize and adjust rendering effects through simple interactions. Finally, the effectiveness and practicability of the proposed method are verified through a series of experiments. The experimental results show that the proposed method can significantly improve the quality and efficiency of landscape drawing rendering, and bring new technical innovations to the field of landscape design.

In the view of art form change, there is a perspective problem in the detail part of multi-frame film and television special effects (TSEs) images in the special effects rendering module, which leads to the greater ambiguity of multi-frame film and TSEs images. In order to advance the talent of identifying the detail structures and characteristics of multi-frame film and TSEs images, a visual communication technology of multi-frame film and TSEs images in the view of art form change grounded on deep learning is suggested. The transmission relation model of detail characteristics of multi-frame film and TSEs images in the field of artistic form change is constructed, and the attenuation treatment of detail action special effects of multi-frame film and TSEs images in the field of artistic form change is carried out with the assistance of deep learning approaches. The detailed information and color of multi-frame film and TSEs images are obtained by using prior map knowledge instead of rendering basic color. Over the technique of filtering and preserving, the multi-frame film and TSEs images are directed to screen the detailed feature quantity of the input film and TSEs images in the field of artistic form change. In addition, the Sobel operator is practiced to perceive the scrawny edge information of multi-frame film and TSEs images, and block fitting interpolation and depth filtering analysis are used in the edge area to realize the visual communication and color feature parameter identification and amplification of multi-frame film and TSEs images in the field of artistic form change, so as to improve the fuzzy enhancement of multi-frame film and TSEs images. The test results and results indicate that the average Peak signal-to-noise ratio (PSNR) of the proposed method is about 37.7166$$dB, which is 49.08% and 52.08% higher than the PSNRs of the Harris method and SUFR method, respectively. It has been confirmed that this scheme can successfully solve the edge blurring and distortion problems of multi-frame movies and image images in the field of art form change.

In order to meet the challenges of increasingly diverse clothing styles and consumer demands in the market, traditional fabric design methods have become difficult to meet the needs of rapid iteration and innovation due to their time-consuming, costly, and subjective preferences of designers. In view of this, we propose an innovative fabric design optimization simulation strategy aimed at breaking through these bottlenecks through technological means. This strategy cleverly integrates advanced technologies of variational autoencoder (VAE) and generative adversarial network (GAN). First, VAE is used to capture and learn the complex distribution characteristics of existing clothing fabric designs, which include key information such as fabric texture, color matching, and structural details. Subsequently, GAN uses the hidden vectors obtained from VAE as input to generate brand new fabric design samples. During the training phase, GAN continuously iterates and optimizes, engaging in intense “adversarial” interactions between its generator and discriminator. The generator is dedicated to creating new samples that are as close to real fabric designs as possible, while the discriminator is responsible for identifying the authenticity of these samples. This process is implemented through backpropagation (BP) loss function, ensuring that the generated fabric design can visually simulate real fabrics. Experimental verification shows that this method can not only effectively generate high-quality and realistic clothing fabric designs, but also greatly shorten the design cycle and reduce costs.

Tourism route planning is affected by the factors such as tourism destination and tourists’ preference, which leads to poor automatic matching of routes. A mathematical model of tourism route planning based on deep learning is proposed. Under the condition of total route constraints, according to the historical tourism preference information and prior information of tourists, a big data model for the distribution of personalized characteristics of tourists is established, spatial constraints and time constraints parameters are input, and the feature matching of geographic location information and tourist interest parameter information is carried out by using the deep learning method, and the statistical feature quantity of the parameters of the tourism route planning model is extracted. Under the control of the deep learning and geographic information data set, Carry out multi-constraint and multi-objective hierarchical analysis of tourists and tourism destinations, and realize the optimization design of tourism route planning algorithm. The simulation results show that the accuracy of this method is high and the deviation is small. It improves the satisfaction level of tourists and helps users complete a series of visual analysis tasks such as route mining, route planning and destination analysis.

It is difficult for traditional generation methods to accurately match dance movements and dance music in the automatic generation of dance. This paper introduces the technologies related to deep learning (DL) and proposes a system for automatic dance generation based on DL. The dance generation algorithm is the system’s linchpin. The first step is to extract dance and audio characteristics. Identifying the skeletal data of the dance movement is crucial to the extraction process. This paper employs an enhanced 3D convolutional neural network to determine the dance movement skeleton sequence. In the second step, a generative model capable of generating dance moves that precisely match the dance music is designed. The experimental results demonstrate that the dance movement recognition method proposed in this paper is highly accurate, that the dance generation method is very close to the actual dance movement, that the music matching rate is more accurate, and that the dance generation effect is favorable.

Using deep learning methods, this study provides insights into the significant impact of corporate executive behavior on firm performance, particularly through the lens of vocal emotions. Considering that emotions play a crucial role in leadership effectiveness as well as corporate success, this paper employs a Long Short-Term Memory (LSTM) network to meticulously categorize the emotions in executive speeches into positive, neutral, and negative categories. The initial stage requires rigorous pre-processing of the speech signal, including collection, denoising and feature extraction using Mel Frequency Cepstrum Coefficients (MFCC). Subsequently, LSTM models are trained on these preprocessed data for sentiment classification. This study further innovates by combining sentiment analysis with Key Performance Indicators (KPIs) to scrutinize the correlation between executives’ emotional expressions and company performance. Through statistical analysis and machine learning techniques, we assess the significance of this correlation and present evidence that highlights the predictive power of executives’ emotional expressions on firm performance metrics. Our findings not only contribute to an understanding of the nuanced ways in which leadership behavior impacts firm performance, but also open avenues for enhancing executive training and performance assessment methods. This paper demonstrates the classification accuracy of our model and its effectiveness in correlating executive emotions with firm performance, providing valuable insights into the interplay between leadership emotional intelligence and firm success.

The phenomena of urbanization is multi-faceted and impacts numerous societal domains such as infrastructure, environment, and quality of life. Assessing urbanization’s quality is crucial for long-term planning and sustainable growth. Nevertheless, traditional methods employed in assessing the quality of urbanization are usually inaccurate and inefficient due to their dependency on manual approaches and simplistic models. This paper eliminates the drawbacks associated with the current methods by introducing a unique procedure referred to as Urban Growth Forecast using Deep Learning (UGF-DL). The main problem made in these papers is lack of precise and timely evaluation for urbanization quality. UGF-DL applies approaches to deep learning, with a focus on convolutional neural networks (CNNs) to predict correctly the patterns of city growth unlike other papers that do not make substantial improvement upon prediction. To train CNN models for predicting future urban expansion, UGF-DL uses large-scale datasets like satellite imagery, socio-economic indicators, and historical urbanization data among others. UGF-DL incorporates intelligent algorithms to capture intricate spatial–temporal relationships within cities this allows for more precise forecasting than is possible with more traditional methods. Among the many advantages of the proposed method are real-time monitoring of dynamics in urbanization; identification of prospective areas for infrastructure development; and assessment of effects caused by urban policies among others. Moreover, UGF-DL helps policymakers and city planners make proactive decisions that lead to sustainable cities with more resilience. Assessment criteria have been used to evaluate the effectiveness of UGF-DL: these include; accuracy prediction analysis, range distribution study, ground truth comparison, etc., which have been highlighted in this paper.

With the rapid evolution of digital technology, graphic design has become increasingly pivotal across various domains. While traditional image enhancement methods have addressed issues in texture boundaries and information retrieval, they often neglect challenges posed by noise in graphic design, leading to uneven enhancements. Therefore, this study proposes a multi-scale detail enhancement method to improve the visual perception quality of graphic design images. Nonlinear transformation is applied to the image to obtain a preliminary enhanced image. Subsequently, both the preliminary enhanced image and the low brightness image are simultaneously fed into a multi-scale feature extraction block for feature extraction. In order to improve the ability of online learning of semantic features, a U-shaped feature enhancement module is introduced in each scale feature extraction branch, which increases the feature extraction of contextual information. Finally, the enhanced image is obtained by integrating multi-scale feature information. The experimental results show that this method is relatively superior in terms of visual effects and metrics, and significantly improves color restoration, texture preservation, and detail enhancement, providing a promising direction for image enhancement in graphic design.

This paper proposes a deep learning-based method for path planning of college students’ morality and faith instruction. Using a questionnaire and sampling inspection, the teaching effect index test and automatic monitoring of morality and faith teaching data are conducted. The feasible teaching strategy index of college students’ morality and faith teaching is based on students’ deep learning needs and expectations for ideological and political teaching in colleges and universities. The actual effect and teaching effect of teaching reform and innovation serve as the basic monitoring coefficient. Using deep learning and dynamic optimization detection methods, a feature clustering model for data collection and deep and surface learning of morality and faith instruction for college students is developed. A deep learning model for teaching morality and faith to college students is developed using a significant feature analysis method. Implement multidimensional spatial path optimization and data fitting. Perform quantitative regression analysis of college students’ critical, creative, and higher-order thinking in the index data of morality and faith teaching strategies. Detect and extract index data on morality and faith teaching strategies for college students. The test results show that this method improves the practicability and originality of morality and faith instruction for college students by optimizing the course planning and index data of feasible teaching strategies.

Teaching reform is usually a unified adjustment based on the current teaching objectives, lacking pertinence. In order to improve the teaching quality and students’ learning quality of sports dance course in colleges and universities, according to the teaching quality evaluation data, this paper proposes the teaching method of sports dance course in colleges and universities based on in-depth learning. According to the sports dance action image after the visual error correction, the OpenPose human posture estimation algorithm is used to implement the dancer’s motion posture estimation, obtain the sequence data of the dancer’s skeleton joint points, and use the depth convolution neural network optimized by particle swarm optimization algorithm to build the sports dance action recognition model, with the obtained sequence data of the dancer’s skeleton joint points as the effective input. The result of sports dance movement recognition is obtained through training, which is used as the basis for students to correct their dance movements and improve their professional skills. According to the teaching process organized by teachers and the goal of cultivating students’ professional skills, a sports dance teaching evaluation index system is constructed, and the deep convolution neural network optimized by particle swarm optimization algorithm is used to build a sports dance teaching evaluation model. With the quantified data of the secondary indicators in the teaching evaluation index system as the model input, the sports dance teaching evaluation results are obtained through training. The findings will serve as a dependable foundation for educators to enhance the quality of instruction and assist students in enhancing their proficiency in sports dance. The experimental outcomes demonstrate that this approach is adept at identifying sports dance movements effectively and precisely evaluating sports dance classes, thereby establishing a solid footing for substantially boosting the instructional impact of sports dance courses in higher education institutions.

In the burgeoning landscape of higher education, the effective evaluation of online English teaching outcomes has become increasingly pivotal. This paper focuses on the benefits of employing long short-term memory (LSTM) networks for such evaluations, capitalizing on their strength in processing time-series data and identifying long-term learning patterns. We detail a structured approach using LSTM for assessing the quality of online English education, encompassing data collection, preprocessing, model training, and analysis. The innovative aspect of our methodology lies in its application of LSTM to interpret complex student interaction data, providing a more nuanced understanding of educational effectiveness. Experimental results affirm the LSTM model’s capability to offer accurate and meaningful insights into teaching effectiveness, marking a significant advancement in the application of deep learning techniques in educational assessment.

Aiming at the problems of low positioning accuracy, poor map readability and weak robustness when the mobile robot implements the SLAM technology due to the existence of dynamic objects in the mobile robot working environment, an SLAM technology algorithm for mobile robot dynamic environment positioning based on semantic information is proposed. Firstly, the front end of the ORB-SLAM2 framework will be used, combined with the YOLO v4 target detection algorithm, to extract the ORB features of the input image. Meanwhile, the YOLO v4 target detection algorithm is used to obtain the dynamic and static areas of the object containing semantic information in the scene image, to obtain the preliminary semantic dynamic and static areas, and to perform semantic segmentation in the image; Then, the dynamic object region is selected by using the image polar equation, and the ORB feature points distributed on the dynamic object are eliminated. Finally, the processed feature points and adjacent key frames are used for inter-frame matching to estimate the camera pose to build a static environment map. The experiment used the open TUM dataset to compare the proposed algorithm with the traditional ORB-SLAM2 test results show that the proposed algorithm improves the pose estimation accuracy by 75% compared with the ORB-SLAM2 in the dynamic environment, and the map construction effect is significantly enhanced. The experimental results show that this algorithm can eliminate the influence of dynamic objects on SLAM technology, improve the positioning accuracy of the system, and expand the application field of the system.

Economic market forecasting involves predicting future market conditions based on various data. By analyzing the market trends, organizations can make informed decisions, and strategize for potential economic shifts. Accurate forecasting is crucial for minimizing risks and maximizing opportunities. In this research, we aim to develop an innovative future economic forecasting model through a deep learning (DL) algorithm. For this, we propose a novel Drosophila Food Search-drivenGate Adjusted Long Short-Term Memory$($DFS-GA-LSTM$)$ for analyzing the market trends and providing extensive forecasting results of future economics. We obtained a dataset comprising historical financial market data and economic indicators from various media sources. It includes stock prices and economic growth metrics to train and evaluate the proposed model. Data normalization is utilized to pre-process the gathered raw information. Kernel principle component analysis (kernel-PCA) is utilized to remove the crucial features from the processed data. In our proposed model, the DFS optimization algorithm iteratively fine-tunes the GA-LSTM architecture to enhance forecasting accuracy. The established model is implemented using Python software. During the outcome analysis phase, we evaluate our model’s performance across several parameters like RMS (0.19), RMSE (0.009), MAE (0.00011), MSE 0.003, and MAPE (26.7%). In addition, we conducted comparative analyses using diverse existing methodologies. The result reveals the superiority and efficiency of the suggested future economic forecasting model.

Insect recognition plays a crucial role in agricultural production and maintaining ecological balance. However, the vast variety and differing forms of insects make traditional image recognition methods, which rely on manual identification, low in accuracy and efficiency. This study aims to explore the application of deep learning technology in insect image recognition by proposing a deep learning-based method to enhance recognition accuracy and efficiency through automatic extraction of image features using a deep learning model. We utilized a combination of web-collected insect images and existing datasets to provide comprehensive training data, and developed an advanced deep learning model named Advanced Overlap Patch Relevance Extraction and Classification (AOPREC). The model’s design includes overlapping patch operations to minimize redundancy in background information, enhancing the model’s ability to manage image details and local features. Additionally, we replaced the multilayer perceptron (MLP) in the vision transformer (ViT) with a more efficient classifier that combines convolutional neural networks (CNN) and long short-term memory (LSTM) structures, significantly improving classification performance. Furthermore, Gaussian error linear unit (GELU) was employed as the activation function to optimize training efficiency and generalization, ensuring consistent performance across various images. Experimental results demonstrate that the AOPREC model excels in insect image classification and object detection tasks, significantly improving recognition accuracy.