In order to stabilize the operation of innovation and entrepreneurship education and improve the graduate employment ability of sports training professionals,this paper,based on the literature,work experience and actu...In order to stabilize the operation of innovation and entrepreneurship education and improve the graduate employment ability of sports training professionals,this paper,based on the literature,work experience and actual environment background,promotes the development and employment of sports training professionals in sports colleges and universities.展开更多
Network intrusion detection systems need to be updated due to the rise in cyber threats. In order to improve detection accuracy, this research presents a strong strategy that makes use of a stacked ensemble method, wh...Network intrusion detection systems need to be updated due to the rise in cyber threats. In order to improve detection accuracy, this research presents a strong strategy that makes use of a stacked ensemble method, which combines the advantages of several machine learning models. The ensemble is made up of various base models, such as Decision Trees, K-Nearest Neighbors (KNN), Multi-Layer Perceptrons (MLP), and Naive Bayes, each of which offers a distinct perspective on the properties of the data. The research adheres to a methodical workflow that begins with thorough data preprocessing to guarantee the accuracy and applicability of the data. In order to extract useful attributes from network traffic data—which are essential for efficient model training—feature engineering is used. The ensemble approach combines these models by training a Logistic Regression model meta-learner on base model predictions. In addition to increasing prediction accuracy, this tiered approach helps get around the drawbacks that come with using individual models. High accuracy, precision, and recall are shown in the model’s evaluation of a network intrusion dataset, indicating the model’s efficacy in identifying malicious activity. Cross-validation is used to make sure the models are reliable and well-generalized to new, untested data. In addition to advancing cybersecurity, the research establishes a foundation for the implementation of flexible and scalable intrusion detection systems. This hybrid, stacked ensemble model has a lot of potential for improving cyberattack prevention, lowering the likelihood of cyberattacks, and offering a scalable solution that can be adjusted to meet new threats and technological advancements.展开更多
The enrollment expansion of higher vocational education is not only a strategic measure to alleviate the structural contradiction of employment,but also an important support for economic transformation and upgrading.H...The enrollment expansion of higher vocational education is not only a strategic measure to alleviate the structural contradiction of employment,but also an important support for economic transformation and upgrading.How to deal with the expansion of enrollment and ensure the quality of training is an important task of higher vocational colleges at this stage.Based on the development requirements of the new era,it is of great practical significance to build a perfect quality assurance system of talent training.展开更多
文摘In order to stabilize the operation of innovation and entrepreneurship education and improve the graduate employment ability of sports training professionals,this paper,based on the literature,work experience and actual environment background,promotes the development and employment of sports training professionals in sports colleges and universities.
文摘Network intrusion detection systems need to be updated due to the rise in cyber threats. In order to improve detection accuracy, this research presents a strong strategy that makes use of a stacked ensemble method, which combines the advantages of several machine learning models. The ensemble is made up of various base models, such as Decision Trees, K-Nearest Neighbors (KNN), Multi-Layer Perceptrons (MLP), and Naive Bayes, each of which offers a distinct perspective on the properties of the data. The research adheres to a methodical workflow that begins with thorough data preprocessing to guarantee the accuracy and applicability of the data. In order to extract useful attributes from network traffic data—which are essential for efficient model training—feature engineering is used. The ensemble approach combines these models by training a Logistic Regression model meta-learner on base model predictions. In addition to increasing prediction accuracy, this tiered approach helps get around the drawbacks that come with using individual models. High accuracy, precision, and recall are shown in the model’s evaluation of a network intrusion dataset, indicating the model’s efficacy in identifying malicious activity. Cross-validation is used to make sure the models are reliable and well-generalized to new, untested data. In addition to advancing cybersecurity, the research establishes a foundation for the implementation of flexible and scalable intrusion detection systems. This hybrid, stacked ensemble model has a lot of potential for improving cyberattack prevention, lowering the likelihood of cyberattacks, and offering a scalable solution that can be adjusted to meet new threats and technological advancements.
文摘The enrollment expansion of higher vocational education is not only a strategic measure to alleviate the structural contradiction of employment,but also an important support for economic transformation and upgrading.How to deal with the expansion of enrollment and ensure the quality of training is an important task of higher vocational colleges at this stage.Based on the development requirements of the new era,it is of great practical significance to build a perfect quality assurance system of talent training.
