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Found 10 papers, 1 papers with code
Predictive linguistic cues for fake news: a societal artificial intelligence problem
Media news are making a large part of public opinion and, therefore, must not be fake.
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising
Despite substantial advances in network architecture performance, the susceptibility of adversarial attacks makes deep learning challenging to implement in safety-critical applications.
Transfer learning for cancer diagnosis in histopathological images
Transfer learning allows us to exploit knowledge gained from one task to assist in solving another but relevant task.
Collaborative adversary nodes learning on the logs of IoT devices in an IoT network
Our results show that the predicting performance of the AdLIoTLog model trained by our method degrades by 3-4% in the presence of attack in comparison to the scenario when the network is not under attack.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Network Traffic Analysis based IoT Device Identification
DFP identifies a device by using implicit identifiers, such as network traffic (or packets), radio signal, which a device used for its communication over the network.
Neural Machine Translation model for University Email Application
The low BLEU score of Google Translation in comparison to our model indicates that the application based regional models are better.
Transfer Learning using CNN for Handwritten Devanagari Character Recognition
This paper presents an analysis of pre-trained models to recognize handwritten Devanagari alphabets using transfer learning for Deep Convolution Neural Network (DCNN).
Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases
Pathologists find tedious to examine the status of the sentinel lymph node on a large number of pathological scans.
IoT Device Fingerprint using Deep Learning
IAT is the time interval between the two consecutive packets received.
