IFC (Industry Foundation Classes) is a standard format for information exchange developed by Building SMART, dedicated to the collaborative work of various software in architectural design, construction and operation and maintenance. With IFC standard for various BIM (Building Information Modeling), the software provides a unified data structure and file exchange format for data exchange. However, lacking formal rigidity, data exchange is often arbitrary and prone to errors, omissions, and misrepresentations. This study applies the machine learning technique LightGBM to examine BIM elements and IFC The accuracy of the mapping between classes is extracted through feature engineering, and the BIM model element detection model is constructed. By using the BIM model training set for training, the results show that our model is more than 97.8 % accurate. And compared to the popular machine learning models, our model has higher performance.
Nowadays, Internet is an indispensable part of most people's life, but malicious programs always make people defensive. Malicious programs not only seriously affect people's daily experience, but also most likely threaten users' property security, or even directly threaten the normal operation of society. In this paper, we extract feature words by N-Gram model, further improve the classification performance by using TF-IDF algorithm, and train a high-performance malicious program classification and identification model by using LightGBM algorithm. Through experimental analysis, the accuracy rate of the model is over 96%. Compared with conventional classification models, the model has superior performance.
While numerous security products, including data leakage prevention, have been added to corporate cybersecurity strategies, securing confidential data and assets remains a major challenge for businesses and organizations. According to a survey by a research institute in the United States, most of the most costly cybercrime cases are caused by theft by insiders, followed by DDoS and Web-based attacks. In this paper, the LightGBM algorithm is used, and the feature extraction uses the bag of words and the IF-IDF model to construct a malicious operation behavior detection model. By training with the classic SEA training set, the results show that our model is more than 97% accurate. And compared to the popular classification models, our model has higher performance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.