scholarly journals Graph Convolutional Networks by Architecture Search for PolSAR Image Classification

2021 ◽  
Vol 13 (7) ◽  
pp. 1404
Author(s):  
Hongying Liu ◽  
Derong Xu ◽  
Tianwen Zhu ◽  
Fanhua Shang ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spatial Relations ◽  
Classification Problem ◽  
Search Method ◽  
Sample Distribution ◽  
Convolutional Networks ◽  
Training Samples ◽  
Graph Neural Networks

Classification of polarimetric synthetic aperture radar (PolSAR) images has achieved good results due to the excellent fitting ability of neural networks with a large number of training samples. However, the performance of most convolutional neural networks (CNNs) degrades dramatically when only a few labeled training samples are available. As one well-known class of semi-supervised learning methods, graph convolutional networks (GCNs) have gained much attention recently to address the classification problem with only a few labeled samples. As the number of layers grows in the network, the parameters dramatically increase. It is challenging to determine an optimal architecture manually. In this paper, we propose a neural architecture search method based GCN (ASGCN) for the classification of PolSAR images. We construct a novel graph whose nodes combines both the physical features and spatial relations between pixels or samples to represent the image. Then we build a new searching space whose components are empirically selected from some graph neural networks for architecture search and develop the differentiable architecture search method to construction our ASGCN. Moreover, to address the training of large-scale images, we present a new weighted mini-batch algorithm to reduce the computing memory consumption and ensure the balance of sample distribution, and also analyze and compare with other similar training strategies. Experiments on several real-world PolSAR datasets show that our method has improved the overall accuracy as much as 3.76% than state-of-the-art methods.

scholarly journals SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

2021 ◽  
Vol 40 (3) ◽  
pp. 1-13
Author(s):  
Lumin Yang ◽  
Jiajie Zhuang ◽  
Hongbo Fu ◽  
Xiangzhi Wei ◽  
Kun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Large Scale ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Structure Information ◽  
Graph Neural Networks ◽  
Node Labels ◽  
Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

scholarly journals Resource Allocation in Large-Scale Wireless Control Systems with Graph Neural Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 2634-2641
Author(s):  
Vinicius Lima ◽  
Mark Eisen ◽  
Konstatinos Gatsis ◽  
Alejandro Ribeiro
Keyword(s):  
Neural Networks ◽  
Resource Allocation ◽  
Control Systems ◽  
Large Scale ◽  
Wireless Control ◽  
Graph Neural Networks

Graph-based knowledge tracing: Modeling student proficiency using graph neural networks

2021 ◽  
pp. 1-16
Author(s):  
Hiromi Nakagawa ◽  
Yusuke Iwasawa ◽  
Yutaka Matsuo
Keyword(s):  
Neural Networks ◽  
Student Performance ◽  
Classification Problem ◽  
Computer Assisted ◽  
Graph Structure ◽  
Inductive Bias ◽  
Additional Information ◽  
Knowledge Tracing ◽  
Open Datasets ◽  
Graph Neural Networks

Recent advancements in computer-assisted learning systems have caused an increase in the research in knowledge tracing, wherein student performance is predicted over time. Student coursework can potentially be structured as a graph. Incorporating this graph-structured nature into a knowledge tracing model as a relational inductive bias can improve its performance; however, previous methods, such as deep knowledge tracing, did not consider such a latent graph structure. Inspired by the recent successes of graph neural networks (GNNs), we herein propose a GNN-based knowledge tracing method, i.e., graph-based knowledge tracing. Casting the knowledge structure as a graph enabled us to reformulate the knowledge tracing task as a time-series node-level classification problem in the GNN. As the knowledge graph structure is not explicitly provided in most cases, we propose various implementations of the graph structure. Empirical validations on two open datasets indicated that our method could potentially improve the prediction of student performance and demonstrated more interpretable predictions compared to those of the previous methods, without the requirement of any additional information.

scholarly journals Support Vector Machines in Big Data Classification: A Systematic Literature Review

2021 ◽  
Author(s):  
Mohammad Hassan Almaspoor ◽  
Ali Safaei ◽  
Afshin Salajegheh ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Support Vector ◽  
Research Areas ◽  
Large Scale Data ◽  
Training Samples ◽  
Big Data Classification ◽  
Scale Data

Abstract Classification is one of the most important and widely used issues in machine learning, the purpose of which is to create a rule for grouping data to sets of pre-existing categories is based on a set of training sets. Employed successfully in many scientific and engineering areas, the Support Vector Machine (SVM) is among the most promising methods of classification in machine learning. With the advent of big data, many of the machine learning methods have been challenged by big data characteristics. The standard SVM has been proposed for batch learning in which all data are available at the same time. The SVM has a high time complexity, i.e., increasing the number of training samples will intensify the need for computational resources and memory. Hence, many attempts have been made at SVM compatibility with online learning conditions and use of large-scale data. This paper focuses on the analysis, identification, and classification of existing methods for SVM compatibility with online conditions and large-scale data. These methods might be employed to classify big data and propose research areas for future studies. Considering its advantages, the SVM can be among the first options for compatibility with big data and classification of big data. For this purpose, appropriate techniques should be developed for data preprocessing in order to covert data into an appropriate form for learning. The existing frameworks should also be employed for parallel and distributed processes so that SVMs can be made scalable and properly online to be able to handle big data.

scholarly journals Kriging Convolutional Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 3187-3194
Author(s):  
Gabriel Appleby ◽  
Linfeng Liu ◽  
Li-Ping Liu
Keyword(s):  
Neural Networks ◽  
Spatial Interpolation ◽  
Recent Progress ◽  
Kriging Method ◽  
Convolutional Networks ◽  
Spatial Locality ◽  
Estimation Problems ◽  
Specific Configuration ◽  
Graph Neural Networks ◽  
Direct Use

Spatial interpolation is a class of estimation problems where locations with known values are used to estimate values at other locations, with an emphasis on harnessing spatial locality and trends. Traditional kriging methods have strong Gaussian assumptions, and as a result, often fail to capture complexities within the data. Inspired by the recent progress of graph neural networks, we introduce Kriging Convolutional Networks (KCN), a method of combining advantages of Graph Neural Networks (GNN) and kriging. Compared to standard GNNs, KCNs make direct use of neighboring observations when generating predictions. KCNs also contain the kriging method as a specific configuration. Empirically, we show that this model outperforms GNNs and kriging in several applications.

scholarly journals Domain Adversarial Neural Networks for Large-Scale Land Cover Classification

2019 ◽  
Vol 11 (10) ◽  
pp. 1153 ◽  
Author(s):  
Mesay Belete Bejiga ◽  
Farid Melgani ◽  
Pietro Beraldini
Keyword(s):  
Neural Networks ◽  
Land Cover ◽  
Transfer Learning ◽  
Large Scale ◽  
Domain Adaptation ◽  
Land Cover Classification ◽  
Target Domain ◽  
Training Samples ◽  
Latent Space ◽  
Large Scale Land

Learning classification models require sufficiently labeled training samples, however, collecting labeled samples for every new problem is time-consuming and costly. An alternative approach is to transfer knowledge from one problem to another, which is called transfer learning. Domain adaptation (DA) is a type of transfer learning that aims to find a new latent space where the domain discrepancy between the source and the target domain is negligible. In this work, we propose an unsupervised DA technique called domain adversarial neural networks (DANNs), composed of a feature extractor, a class predictor, and domain classifier blocks, for large-scale land cover classification. Contrary to the traditional methods that perform representation and classifier learning in separate stages, DANNs combine them into a single stage, thereby learning a new representation of the input data that is both domain-invariant and discriminative. Once trained, the classifier of a DANN can be used to predict both source and target domain labels. Additionally, we also modify the domain classifier of a DANN to evaluate its suitability for multi-target domain adaptation problems. Experimental results obtained for both single and multiple target DA problems show that the proposed method provides a performance gain of up to 40%.

Application of Probabilistic Neural Network in Pattern Classification

2013 ◽  
Vol 441 ◽  
pp. 738-741 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Classification ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Networks ◽  
Training Samples ◽  
The One ◽  
Speed Accuracy

The network model of probabilistic neural network and its method of pattern classification and discrimination are first introduced in this paper. Then probabilistic neural network and three usually used back propagation neural networks are established through MATLAB7.0. The pattern classification of dots on a two-dimensional plane is taken as an example. Probabilistic neural network and improved back propagation neural networks are used to classify these dots respectively. Their classification results are compared with each other. The simulation results show that compared with back propagation neural networks, probabilistic neural network has simpler learning rules, faster training speed and it needs fewer training samples; the pattern classification method based on probabilistic neural network is very effective, and it is superior to the one based on back propagation neural networks in classifying speed, accuracy as well as generalization ability.

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