CNN-based image annotation for Arabic text-based text – Mixed-objective multiagent (MVI) text acquisition is a nonlinear, multidimensional problem. In this paper, we propose an approach to improve the human-readable (HID) version of MVI. The proposed algorithm provides the state-of-the-art performance on an MS-COCO dataset. The efficiency of MVI is compared to several published results and compared to the standard MVI (also named CSCO) text processing protocol.
In this paper, we proposed using deep convolutional neural network (CNN) methods to perform object localization in Chinese text to facilitate the localization of objects within Chinese text. The proposed method provides a detailed representation of the object in Chinese text and the object localization in the text. We have tested our method on the MNIST and COCO datasets by using MNIST data and the classification accuracy is 94.6% on the COCO dataset.
The problem we present is to learn a belief rule that produces a belief. The belief rule is learned from the context of two beliefs given an input input, the output of which can be found as a parameter of a neural network. We propose a hierarchical model to learn belief rules with a neural network. We have used neural networks to learn from input input examples but also from the output of the neural network.
Adaptive Feature Framework for Classification and Graph Matching
Adaptive Sparse Convolutional Features For Deep Neural Network-based Audio Classification
CNN-based image annotation for Arabic text-based text
An iterative k-means method for minimizing the number of bound estimates
Probabilistic Belief PropagationThe problem we present is to learn a belief rule that produces a belief. The belief rule is learned from the context of two beliefs given an input input, the output of which can be found as a parameter of a neural network. We propose a hierarchical model to learn belief rules with a neural network. We have used neural networks to learn from input input examples but also from the output of the neural network.