Dignant
发表于 2025-3-27 00:32:32
Mis.Config: Finding Misreferred Configuration Bugs in Web Application Using Thin Slicing,ences, we propose a bug-finding tool called Mis.Config using static analysis. We used control flow graphs (CFGs) and thin slicing to realize this purpose. In our experiment, we applied our tool to real-world software to investigate whether Mis.Config can find misreferenced configurations.
outskirts
发表于 2025-3-27 04:29:14
Hybrid Radius Particle Swarm Optimization Applications, mutation, forward backward propagation, and k-means combined with the Radius Particle Swarm Optimization (RPSO) to solve these problems. The efficiency of the proposed method is tested against the existing methods. The results show that the HRPSO gives better optimum results.
诙谐
发表于 2025-3-27 05:46:31
Classification of Cell Nuclei Using CNN Features,number of cell nuclei diagnosed as normal and the number of cell nuclei diagnosed as cancer is clear in the benign and metastasis, while the difference in malignant is vague.Moreover, it is confirmed that N/C ratio of malignancy and metastasis was slightly higher than that of benign.
漂亮
发表于 2025-3-27 09:35:06
http://reply.papertrans.cn/24/2344/234354/234354_34.png
Pruritus
发表于 2025-3-27 13:47:20
http://reply.papertrans.cn/24/2344/234354/234354_35.png
得体
发表于 2025-3-27 18:06:44
Studies in Computational Intelligencehttp://image.papertrans.cn/c/image/234354.jpg
符合国情
发表于 2025-3-28 01:16:35
http://reply.papertrans.cn/24/2344/234354/234354_37.png
他很灵活
发表于 2025-3-28 03:07:39
978-3-030-25215-1Springer Nature Switzerland AG 2020
发生
发表于 2025-3-28 07:22:29
https://doi.org/10.1007/978-1-4302-2354-2used intra-project data.However, it has limitations in prediction efficiency for new projects and projects without adequate training data. Studies of prediction have been carried out on cross-project defect prediction models (CPDP), i.e. models that are trained using other projects historical data.
新鲜
发表于 2025-3-28 12:21:52
The Essential Guide to Flash CS4ly basic method of machine learning. In this study, we used analog electronic circuits using alternative current to realize the neural network learning model. These circuits are composed by a rectifier circuit, Voltage-Frequency converter, amplifier, subtract circuit, additional circuit and inverter