Quality grading of jujubes using composite convolutional neural networks in combination with RGB color space segmentation and deep convolutional generative adversarial networks

2020 ◽  
Author(s):  
Zhongyuan Guo ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Jianping Ju ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Color Space ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Quality Grading ◽  
Rgb Color Space

scholarly journals Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Holly Burrows ◽  
Javad Zarrin ◽  
Lakshmi Babu-Saheer ◽  
Mahdi Maktab-Dar-Oghaz
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
User Interface ◽  
Convolutional Neural Networks ◽  
Mental Health Problems ◽  
Well Being ◽  
Working Environment ◽  
Generative Adversarial Networks ◽  
Deep Convolutional Neural Networks ◽  
Adversarial Networks

It is becoming increasingly apparent that a significant amount of the population suffers from mental health problems, such as stress, depression, and anxiety. These issues are a result of a vast range of factors, such as genetic conditions, social circumstances, and lifestyle influences. A key cause, or contributor, for many people is their work; poor mental state can be exacerbated by jobs and a person’s working environment. Additionally, as the information age continues to burgeon, people are increasingly sedentary in their working lives, spending more of their days seated, and less time moving around. It is a well-known fact that a decrease in physical activity is detrimental to mental well-being. Therefore, the need for innovative research and development to combat negativity early is required. Implementing solutions using Artificial Intelligence has great potential in this field of research. This work proposes a solution to this problem domain, utilising two concepts of Artificial Intelligence, namely, Convolutional Neural Networks and Generative Adversarial Networks. A CNN is trained to accurately predict when an individual is experiencing negative emotions, achieving a top accuracy of 80.38% with a loss of 0.42. A GAN is trained to synthesise images from an input domain that can be attributed to evoking position emotions. A Graphical User Interface is created to display the generated media to users in order to boost mood and reduce feelings of stress. The work demonstrates the capability for using Deep Learning to identify stress and negative mood, and the strategies that can be implemented to reduce them.

scholarly journals Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer’s Disease Image Data Classification

2021 ◽  
Vol 13 ◽  
Author(s):  
Robert Logan ◽  
Brian G. Williams ◽  
Maria Ferreira da Silva ◽  
Akash Indani ◽  
Nicolas Schcolnicov ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Ensemble Learning ◽  
Convolutional Neural Networks ◽  
Unmet Need ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Interdisciplinary Approaches

Recent advancements in deep learning (DL) have made possible new methodologies for analyzing massive datasets with intriguing implications in healthcare. Convolutional neural networks (CNN), which have proven to be successful supervised algorithms for classifying imaging data, are of particular interest in the neuroscience community for their utility in the classification of Alzheimer’s disease (AD). AD is the leading cause of dementia in the aging population. There remains a critical unmet need for early detection of AD pathogenesis based on non-invasive neuroimaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). In this comprehensive review, we explore potential interdisciplinary approaches for early detection and provide insight into recent advances on AD classification using 3D CNN architectures for multi-modal PET/MRI data. We also consider the application of generative adversarial networks (GANs) to overcome pitfalls associated with limited data. Finally, we discuss increasing the robustness of CNNs by combining them with ensemble learning (EL).

scholarly journals Blind First-Order Perspective Distortion Correction Using Parallel Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4898
Author(s):  
Neil Patrick Del Gallego ◽  
Joel Ilao ◽  
Macario Cordel
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Input Image ◽  
Distortion Correction ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Perspective Distortion ◽  
Corrected Image ◽  
Perspective Distortions

In this work, we present a network architecture with parallel convolutional neural networks (CNN) for removing perspective distortion in images. While other works generate corrected images through the use of generative adversarial networks or encoder-decoder networks, we propose a method wherein three CNNs are trained in parallel, to predict a certain element pair in the 3×3 transformation matrix, M^. The corrected image is produced by transforming the distorted input image using M^−1. The networks are trained from our generated distorted image dataset using KITTI images. Experimental results show promise in this approach, as our method is capable of correcting perspective distortions on images and outperforms other state-of-the-art methods. Our method also recovers the intended scale and proportion of the image, which is not observed in other works.

scholarly journals Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 35592-35605 ◽  
Author(s):  
Abdelrahman M. Shaker ◽  
Manal Tantawi ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Generative Adversarial Networks ◽  
Adversarial Networks
Sign in / Sign up

Export Citation Format

Share Document