For many years in the society, farmers rely on experts to diagnose and detect chicken diseases. As a result, lose domesticated birds due late diagnoses or lack of reliable experts. With available tools from artificial intelligence machine learning based computer vision image analysis, most common diseases affecting can be identified easily images droppings. In this study, we propose deep soluti...

We propose a deep neural network for the segmentation of the prostate in MRI images. The segmentation is performed using a residual fully convolutional neural network. Automatic shape learning is allowed using a Compositional Pattern-Producing Network. Moreover, a multi-pass architecture is designed to foster self-consistent segmentation. The model is trained and tested on the dataset of the ch...

Recent breakthroughs in the development of multi-layer convolutional neural networks have led to stateof-the-art improvements in the accuracy of non-trivial recognition tasks such as large-category image classification and automatic speech recognition [1]. These many-layered neural networks are large, complex, and require substantial computing resources to train and evaluate [2]. Unfortunately,...

Recently, deep learning algorithm becomes a great solution for various field. Convolutional Neural Network (CNN), a kind of neural network, is known as suitable method for image processing. This paper proposes a realtime object recognition system with CNN. Since many images are needed for deep learning, this system contains image web crawler that collects images automatically. This paper will s...

motivated by the Convolutional Neural Networks about digit recognition and ImageNet deep neural network by Krizhevsky et al. [1], I did this project on Guqin notation recognition, which classified reduced characters with positioned 1-10 (一 -十) in handwritten Chinese characters and translated to other music recording scores. I built a four-layer convolutional neural network using adjusted CaffeN...

Going deep is essential for deep learning. However it is not easy, there are many ways of going deep but most of them are ineffective. In this work, we propose two novel constrains in the design of deep structure to guarantee the performance gain when going deep. Firstly, for each convolutional layer, its capacity of learning more complex patterns should be guaranteed; Secondly, the receptive f...

In this paper, we describe a deep learning framework for analyzing the customer feedback as part of our participation in the shared task on Customer Feedback Analysis at the 8th International Joint Conference on Natural Language Processing (IJCNLP 2017). A Convolutional Neural Network (CNN) based deep neural network model was employed for the customer feedback task. The proposed system was eval...

Deep convolutional neural networks (CNNs) achieve remarkable performance on image classification tasks. Recent studies, however, have demonstrated that generalization abilities are more important than the depth of neural networks for improving performance on image classification tasks. Herein, a new neural network called SwGridNet is proposed. A SwGridNet includes many convolutional processing ...