Fault Detection Techniques for Robotic Surgery Using Sensors


Fault Detection Techniques for Robotic Surgery Using Sensors – Deep learning approaches have recently demonstrated great success in detecting various types of malware in web applications. In this work, we present a novel method, Deep Neural Networks (DNNs), for detecting malware in web applications. Deep-CNNs can efficiently process and summarize malicious actions in Web pages, while being more robust to the local changes of web page elements, e.g., images and text. We develop a Deep-CNN framework, based on a Convolutional Neural Network (CNN) and deep learning to automatically process Web pages, detect malicious actions and detect malicious entities. Our CNN has been trained and compared to a baseline CNN for malware detection and detection, with the aim of detecting malicious entities. In particular, we developed a two-stage CNN architecture, which contains a 3D CNN model with CNN layers, and a 2D CNN model with CNN layers. Our learned CNN system detects malicious entities with an accuracy of over 90% on a publicly available benchmark of malware detection data from the web applications. The detection accuracy is comparable to state-of-the-art detection in web applications of malware detection.

Recently, deep convolutional neural networks (CNNs) have made great strides towards the image classification task. However, they are not fully capable of representing complex object and scenes. In this paper, we study the problem of the representation of complex object and scene data to improve the classification accuracy. In particular, we propose to model the object and scene features in a recurrent network. In this work, the input images for a convolutional neural network are represented as the input images, and recurrent networks are adapted in a single network for the object and scene data. In this way, the representation of these two-dimensional datasets are preserved in a single model, which enables to transfer the data into a sequential and sequential fashion. On the other hand, an image dataset with 3D object features and 3D scene features are learned in 2D recurrent network model, which has a fixed training and training feature loss. We show that the proposed method is extremely effective at solving the object and scene classification tasks. Experimental results on benchmark datasets have shown the superiority of our model over other deep convolutional-NN implementations.

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Fault Detection Techniques for Robotic Surgery Using Sensors

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  • Dedicated task selection using hidden Markov models for solving real-valued real-valued problems

    Convolutional neural network-based classification using discriminant textRecently, deep convolutional neural networks (CNNs) have made great strides towards the image classification task. However, they are not fully capable of representing complex object and scenes. In this paper, we study the problem of the representation of complex object and scene data to improve the classification accuracy. In particular, we propose to model the object and scene features in a recurrent network. In this work, the input images for a convolutional neural network are represented as the input images, and recurrent networks are adapted in a single network for the object and scene data. In this way, the representation of these two-dimensional datasets are preserved in a single model, which enables to transfer the data into a sequential and sequential fashion. On the other hand, an image dataset with 3D object features and 3D scene features are learned in 2D recurrent network model, which has a fixed training and training feature loss. We show that the proposed method is extremely effective at solving the object and scene classification tasks. Experimental results on benchmark datasets have shown the superiority of our model over other deep convolutional-NN implementations.


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