Search

The complex and changing environment in the field brings a more significant detection challenge to camera surveillance. This paper proposes target detection in camera surveillance images using the improved YOLOv5s algorithm for target tracking in surveillance images. Firstly, to address the weak feature extraction ability of YOLOv5s for small-scale and overlapping targets, the feature extraction performance of YOLOv5s is improved by combining the attention mechanism and substitution loss function in the deep learning network to improve the feature extraction performance of YOLOv5s for target detection in field camera surveillance images. Then, to test the improved algorithm’s performance, the improved algorithm in this paper is compared with SSD, Faster R-CNN, and YOLOv5s detection methods, and performance comparison experiments are done on the basis of the dataset. The results show that the average detection accuracy (MAP) of the algorithm in this paper is improved by 19%, 14.5% and 6.3% than SSD, Faster R-CNN, and YOLOv5s, respectively, and the average speed of detection is 324 FPS, the accuracy is improved, the detection is faster, and for the YOLOv5 m with DA and PT has a higher AP than the other models in this paper. This study enhances the scalability of YOLOv5s algorithm in complex environments, which is crucial for advancing image target detection.

Under the background of population aging, the rational use of resources for senior care services has more difficulty, leading to increasingly prominent conflicts between supply and demand. This paper uses the Gini coefficient, cohort effect, and other methods to build a matching supply and demand model for innovative senior care services based on the multi-source data of senior care services. The probabilistic linguistic term set is used for evaluation and a calculation method is proposed to match satisfaction. The model’s feasibility is demonstrated by analyzing arithmetic examples. Five types of senior care demand groups can be obtained through clustering analysis, with three kinds of intelligent senior care, namely, competent institutions, smart communities, and smart homes, in which the Gini coefficient value of community senior care facilities in X city is 0.4203, and there is an imbalance between supply and demand. The method proposed in this paper identifies five types of elderly groups, which can be matched with different innovative senior care services for elderly groups with distinct characteristics.

Science and technology and new industries are constantly changing, providing a more precise direction for innovation and entrepreneurship education in colleges and universities. To reform the cultivation system of college students’ innovation and entrepreneurship ability, this paper proposes the cluster core academic competence model (KAI) to analyze the learning process and the degree of knowledge absorption of college students engaged in bi-innovation education. The Group Learning Generation Network (GLGN) is constructed, and the GCN is used to update the GLGN, which can clearly show the practice and degree of difficulty of knowledge implementation, and focus on cultivating college students’ innovation and entrepreneurship ability. After the experimental research in this paper, the national innovation and entrepreneurship policy and innovation and entrepreneurship courses significantly correlate with the cultivation of innovation and entrepreneurship ability of college students. The Pearson correlation coefficient is 0.233 and 0.183 respectively, and the significance P-value is less than 0.01. From the aspect of the results of the innovation and entrepreneurship ability cultivation, the score of the college students’ participation in innovation and entrepreneurship competitions has risen to 0.098 after the reform, which shows that the reformed training system pays more attention to the education of innovation and entrepreneurship practice of college students.

An important research topic is how teachers explore emotional intelligence in international Chinese language teaching and utilize emotional factors to cultivate students’ motivation to learn. Based on the theoretical model of emotional intelligence, this paper constructs a system of emotional intelligence factors for international Chinese teachers by combining cross-cultural communication theory. In terms of obtaining the relevant factors affecting teachers’ emotional intelligence, this paper introduces structural equation modeling to measure the pertinent aspects. It constructs the PLSSEM emotional intelligence model by estimating the model’s parameters through partial least squares. In terms of analyzing the level of emotional intelligence of international Chinese teachers, this paper chooses a total of 200 international Chinese teachers from various Chinese universities as the research sample. It uses SPSS software to analyze the data in terms of the parameter estimation of the emotional intelligence model, the test evaluation, and the differences in the level of emotional intelligence in multiple dimensions. The results show that the path coefficients of each latent variable of the PLS-SEM emotional intelligence model exceeded 0.3, and the value-added fitness index of the model exceeded 0.95. The emotional intelligence level scores of the 200 teachers averaged 4.557, and there were significant differences in their gender, teaching age, and education level within the 1% range. The emotional intelligence factor system of international Chinese teachers can clarify the specific reasons affecting their development. It can help teachers better develop emotional intelligence and help improve the quality of international Chinese education and teaching.

As the “smart sports” continue to heat up, some colleges and universities have launched the first practice in sports in terms of information technology and extensive data management. In this paper, we first constructed an innovative sports public service platform using information technology, created an intelligent management mode of sports in colleges and universities, and realized the synergistic governance of services and management of sports. Then, based on entropy weight-TOPSIS method, it constructed an evaluation model for the quality of intelligent sports management. It solved the implementation effect of college innovative sports from informationization management matching degree, safety management feedback and informationization sports teaching feedback. The results show that the average value of the feedback scores for college sports safety management is above 35 points. Students’ learning engagement and sports interest before and after the introduction of sports teaching management in colleges and universities showed significant differences at the 1% level. The research presented in this paper is conducive to deepening and expanding theoretical research on topics such as computerized sports management and campus sports, and promoting modernization and development of sports teaching and management.

The visual system has a strong information processing ability, and visual attention tracking has various applications in various scenes. This paper mainly focuses on the sports scene of ice and snow far mobilization. It constructs a visual attention system model based on eye tracking. It first establishes an eye tracking system framework using deep learning, and improves the gaze estimation by optimizing the feature extraction network. The visual attention system model was constructed using particle filtering based on motion feature cognition. In the eye-tracking visual attention system model experiments, the Accuracy of the improved eye-tracking system in this paper can be significantly improved to 1.13°, and the error of the visual attention system can be kept within 10°. Furthermore, the four ice and snow sports scene types have an average accuracy of 85.47%, and the constructed model performs well. This study offers a guide for effectively combining eye tracking technology and visual attention.

In the face of increasingly fierce competition for talents, strengthening the cultivation of high-potential talent echelon and improving the core competitiveness of enterprises have become the most serious, urgent and realistic issues. The competency iceberg model and the talent echelon construction process are combined in this paper, and a talent competency model and a hierarchical thesaurus are established. For the influencing factors and comprehensive evaluation of talent competency elements in enterprise talent ladder construction, this paper introduces the entropy weight and TOPSIS methods for assessment. It explores the relevant factors of talent competency through multiple linear regression model. The research example is Enterprise Z, and appropriate research data is gathered through questionnaires to examine the development of enterprise talent echelon construction based on multidimensional data analysis technology. The study found that among the elements of talent competency, the weight of knowledge and skills reaches 0.3718, and the highest relative closeness of the ten middle managers is 0.798. The enterprise’s talent competency will significantly increase by 0.772 percentage points when the job evaluation process rises by 1 percentage point. The enterprise talent ladder construction needs to improve the corresponding job assessment process, establish a sustainable talent reserve system, and smooth career development channels to effectively enhance the enterprise talent ladder construction capacity.

In clinical medicine, due to some accidents will inevitably produce the situation of missing data, this study for its with missing and truncated data, the use of mathematical statistics methods for inference supplement. After classifying the types of incomplete data, the article utilizes the great likelihood and empirical likelihood to form a linear statistical model to infer such data. It verifies it through simulation experiments and example analysis. In the simulation experiment, for the case of the same missing probability, as the number of samples increases from 150 to 300, the bias, variance, and mean square error of this paper’s algorithm in parameter β1 are reduced to 0.0122, 0.1435, and 0.1441, respectively. In the actual statistical inference analysis of cardiac disease and heart transplantation, the standard error of this paper’s method reduces by 0.0576 compared with that of CAA, and the inference The results are by the reality. In clinical medicine, this study proposes a practical statistical extrapolation method and a realization path for objective interpretation when incomplete data is present.

The intelligent use of digital teaching resources and the deep penetration of information technology can help promote the change and development of English teaching and improve the sharing, interactivity, interestingness and effectiveness of English translation teaching resources. This paper develops an intelligent classroom teaching model for English translation that combines scaffolding and multimodal discourse teaching using the U Campus teaching platform. To create an intelligent learning environment for English translation, this paper uses distributed crawler technology to crawl English teaching resources and Minhash algorithm to de-weight and optimize the data, constructs an English translation corpus, and introduces a lightweight Transformer network to establish a machine translation model. This paper quantitatively analyzes the teaching effect of the English translation smart classroom teaching model using data from University C students as the research object. The results of the study show that the students in class A1 scored 95.02 points in topic comprehension, the translation theory and skills improved by 8.46 points compared with the students in class A2, and there was a significant difference between the students’ cross-cultural communication skills at the 1% level. By integrating the information technology platform with English translation teaching, students’ translation skills can be enhanced, they can learn more English resources, and their intercultural knowledge and competence can be improved.

The rapid development of modern science and technology promotes technological innovation in education, and this paper constructs the higher education innovation model of “Artificial Intelligence+Education” to realize the goal of responding to the construction of education informatization in higher education. The intelligent education platform is created using the theoretical foundation of three-layer education to manage data integratedly. The intelligent mode includes face recognition and voice recognition, which are realized using LBP feature extraction and neural network algorithms. Their effects are verified. The model is better than the traditional education model in all dimensions of the impact of student training. The P is less than 0.05, which is a significant difference, and the effect of education management is improved by 0.02~0.09. This paper’s “Artificial Intelligence + Education” innovation model has certain advantages in higher education management and student training, and provides a practical path to the construction of informationization and intelligent development of education. It provides a pragmatic path for constructing information and the thoughtful development of education.