Objective: To develop a real-time application for human behavior classification using 2- Dimensional Convolution Neural Network, VGG16 and ResNet50. Methods: This study provides novel system which considers sitting, standing walking as normal behaviors. It consists of three major steps: dataset collection, training, testing. In this work real time images are used. there 2271 trained 539 testing...

For autonomous driving, moving objects like vehicles and pedestrians are of critical importance as they primarily influence the maneuvering and braking of the car. Typically, they are detected by motion segmentation of dense optical flow augmented by a CNN based object detector for capturing semantics. In this paper, our aim is to jointly model motion and appearance cues in a single convolution...

In this study, we intend to diagnose Choroidal Neovascularization in retinal Optical Coherence Tomography (OCT) images using Deep Learning Architectures. OCT can be used differentiate between a healthy eye and an with CNV disease. the Retinal Pigment Epithelial layer experiences changes various properties which related assistance of Images. This paper proposes technique for finding OCTA picture...

Abstract: A CNN (Convolutional Neural Network) is a class of deep neural networks that are most used for analyzing visual imagery. This the widely learning algorithm image and video recognition, classification, segmentation, medical analysis natural language processing, many more. But, training Network from scratch requires more time lot data, performance model affected. ‘Transfer learning’ bes...

Emotion being a subjective thing, leveraging knowledge and science behind labeled data and extracting the components that constitute it, has been a challenging problem in the industry for many years. With the evolution of deep learning in computer vision, emotion recognition has become a widely-tackled research problem. In this work, we propose two independent methods for this very task. The fi...

Many forms of air pollution increase as science and technology rapidly advance. In particular, fine dust harms the human body, causing or worsening heart lung-related diseases. this study, level in Seoul after 8 h is predicted to prevent health damage We construct a dataset by combining two modalities (i.e., numerical image data) for accurate prediction. addition, we propose multimodal deep lea...

Deep learning has given way to a new era of machine learning, apart from computer vision. Convolutional neural networks have been implemented in image classification, segmentation and object detection. Despite recent advancements, we are still in the very early stages and have yet to settle on best practices for network architecture in terms of deep design, small in size and a short training ti...

Abstract In the study of using images to display car paint defects, current need is use deep Convolutional Neural Networks (CNN) identify and classify different types so as give full play application image processing in field automatic defect detection. Using collected images, defects dataset established. The preprocessing process original data three classification models based on CNN are visua...

<p>Lung cancer is a common type of that causes death if not detected early enough. Doctors use computed tomography (CT) images to diagnose lung cancer. The accuracy the diagnosis relies highly on doctor's expertise. Recently, clinical decision support systems based deep learning valuable recommendations doctors in their diagnoses. In this paper, we present several models detect non-small ...

a sigmoid function is necessary for creation a chaotic neural network (cnn). in this paper, a new function for cnn is proposed that it can increase the speed of convergence. in the proposed method, we use a novel signal for controlling chaos. both the theory analysis and computer simulation results show that the performance of cnn can be improved remarkably by using our method. by means of this...