As discussed in Section 7.2.4, both expert-based and deep learning (DL) approaches have respective strengths and shortcomings in realistic trajectory modeling, and the combination of both methods has the penitential for improved performance. Thus, in collaboration with Hao Cheng, we conduct a ...
4.3. Baseline methods 4.4. Parameter settings 5. Experimental results 5.1. Single-model fusion experiments 5.2. Ensemble fusion experiments 5.3. Limitations of the proposed method 5.4. Ablation experiments 6. Conclusions 思维导图: 论文信息 论文题目:Texture and artifact decomposition for improving...
Generally, deep learning approaches take an entire input image and an associated trimap to infer the alpha matte using convolutional neural networks. Such methods however may fail when dealing with high-resolution input images in sizes of 5000×5000 pixels or higher due to hardware limitations. The...
datasetscontains the data augmentation methods and the Pytorch datasets for 1D and 2D signals. modelscontains the models used in this project. utilscontains the functions for realization of the training procedure. Usage download datasets use the train.py to test MLP, CNN, and MLP models ...
To avoid the explicit computation of the high-dimensional nonlinear term, namely N(Vqˆ(μ,t),μ,t), one might still rely on hyper-reduction methods, which may entail a large reduced mesh and remain expensive; • for the computation of the residual as in (6), we still have to ...
presents a secure system tailored for IoMT devices to combat DDoS cyberattacks targeting patient medical data, employing the average convolution layer (CNN-ACL), which exhibits outstanding performance relative to other machine learning methods based on the KDDCUP99 and CICIDS2017 datasets. Meanwhile [...
Compared to traditional methods, DL employs deep neural networks (DNNs) as classifiers and offers several advantages: The utilization of vast amounts of communication data can considerably enhance modulation recognition accuracy. Automated feature extraction circumvents the constraints of professional ...
CNN_ECG.py Conv1D_ECG.py Dense_ECG.py LICENSE MIT-BH.zip README.md RNN_ECG.py training2017.zip README MIT license DeepECG ECG classification programs based on ML/DL methods. There are two datasets: training2017.zipfile contains one electrode voltage measurements taken as the difference betwe...
They still face the drawbacks of existing static and dynamic malware detection methods, i.e., they cannot effectively detect malware only running in memory. Moreover, a memory dump can be reshaped into an RGB image, but the size of a memory dump file is the same as the virtual machine’...
Experimental results on the NWPU-RESISC45, AID, UC-Merced, and WHU-RS datasets demonstrate that the proposed approach yields significantly higher classification performance in comparison with existing state-of-the-art deep-learning-based methods. The source codes are available to the research community...