Journal ArticleDOI
On Improving the accuracy with Auto-Encoder on Conjunctivitis
TLDR
The results show that the proposed AE-based model can not only improve the classification accuracy but also be beneficial to solve the problem of False Positive Rate.About:
This article is published in Applied Soft Computing.The article was published on 2019-08-01. It has received 39 citations till now. The article focuses on the topics: Autoencoder & Encoder.read more
Citations
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Journal ArticleDOI
Deep Learning Models for Real-time Human Activity Recognition with Smartphones
TL;DR: A smartphone inertial accelerometer-based architecture for HAR is designed and a real-time human activity classification method based on a convolutional neural network (CNN) is proposed, which uses a CNN for local feature extraction on the UCI and Pamap2 datasets.
Journal ArticleDOI
Faster R-CNN for multi-class fruit detection using a robotic vision system
Shaohua Wan,Sotirios K. Goudos +1 more
TL;DR: This work is a pioneer to create a multi-labeled and knowledge-based outdoor orchard image library using 4000 images in the real world and improvement of the convolutional and pooling layers is achieved to have a more accurate and faster detection.
Journal ArticleDOI
Cognitive computing and wireless communications on the edge for healthcare service robots
TL;DR: This paper reviews several state-of-the-art technologies, such as the human–robot interface, environment and user status perceiving, navigation, robust communication and artificial intelligence, of a mobile healthcare robot and discusses in details the customized demands over offloading the computation and communication tasks.
Journal ArticleDOI
Link prediction in paper citation network to construct paper correlation graph
TL;DR: A link prediction approach that combines time, keywords, and authors’ information and optimizes the existing paper citation network is proposed and a number of experiments demonstrate the feasibility and achieve good performance.
Journal ArticleDOI
Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion
Alishba Adeel,Muhammad Attique Khan,Muhammad Sharif,Faisal Azam,Jamal Hussain Shah,Tariq Umer,Shaohua Wan +6 more
TL;DR: An automated system for segmentation and recognition of grape leaf diseases is proposed which acquired an average segmentation accuracy rate of 90% and classification accuracy is above 92% which is superior in contrast of existing techniques.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.