In signal-level fusion, raw data inputs are taken from sensors and the output is reliable and accurate along with capitalization of a few noises. The authors [3] have proposed a hybrid model for fault detection.
In an advanced configuration view, users can define the specific dataset for the learning process, catering to various tasks, including image recognition, natural language processing, or predictive analytics. Users also have the flexibility to select the learning model, from traditional ML models to ...
Anomaly detection techniques depend upon various parameters like the nature of the dataset, type of the anomaly, labelling of the data, and output pattern of the data for different application domains for various research areas, including statistics, machine learning, data mining, and information theo...
(AAAI 2025 Oral) Pose as a Modality: A Psychology-Inspired Network for Personality Recognition with a New Multimodal Dataset [paper] (2025.03.17) (ICLR 2025) State Space Model Meets Transformer: A New Paradigm for 3D Object Detection [paper] (2025.03.18) MamBEV: Enabling State Space Models ...
et al. AI4MARS: a dataset for terrain-aware autonomous driving on Mars. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 1982–1991 (2021). Bayer, T. Planning for the un-plannable: redundancy, fault protection, contingency planning and anomaly response for the mars ...
FooBaR: Fault Fooling Backdoor Attack on Neural Network Training. [link] [code] Jakub Breier, Xiaolu Hou, Martín Ochoa and Jesus Solano. IEEE Transactions on Dependable and Secure Computing, 2022. DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection. [...
The Internet of Things (IoT) is a vast network of devices with sensors or actuators connected through wired or wireless networks. It has a transformative e
In this paper, we use the EfficientNetV2B0 model for bird species classification, applying transfer learning on a dataset of 525 bird species. We also employ the BiRefNet model to remove backgrounds from images in the training set. The generated background-removed images are mixed with the ...
Ufer and Ommer[39] adopted the pre-trained Alexnet on ImageNet dataset[40] to generate image pyramids and feature pyramids, and extracted the CNN features from the feature maps of the first four convolution layers according to the Shannon formula and non-maximum suppression algorithm. To obtain ...
The characteristic Algorithm tested can take the values Yes and No, describing whether the paper demonstrated an evaluation of the method, meaning that its performance was tested on a dataset. The ML task is divided into the elementary problem types that are encountered in machine learning, such ...