scholarly journals A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Weili Zeng ◽  
Juan Li ◽  
Zhibin Quan ◽  
Xiaobo Lu
Keyword(s):  
Neural Network ◽  
The United States ◽  
Convolution Kernel ◽  
Training Set ◽  
Neural Network Approach ◽  
Delay Propagation ◽  
Learning Framework ◽  
Spherical Distance ◽  
Model Training ◽  
Delay Prediction

Due to the strong propagation causality of delays between airports, this paper proposes a delay prediction model based on a deep graph neural network to study delay prediction from the perspective of an airport network. We regard airports as nodes of a graph network and use a directed graph network to construct airports’ relationship. For adjacent airports, weights of edges are measured by the spherical distance between them, while the number of flight pairs between them is utilized for airports connected by flights. On this basis, a diffusion convolution kernel is constructed to capture characteristics of delay propagation between airports, and it is further integrated into the sequence-to-sequence LSTM neural network to establish a deep learning framework for delay prediction. We name this model as deep graph-embedded LSTM (DGLSTM). To verify the model’s effectiveness and superiority, we utilize the historical delay data of 325 airports in the United States from 2015 to 2018 as the model training set and test set. The experimental results suggest that the proposed method is superior to the existing mainstream methods in terms of accuracy and robustness.

scholarly journals Explaining COVID-19 Outbreaks with Reactive SEIRD Models

2021 ◽  
Author(s):  
Kunal Menda ◽  
Lucas Laird ◽  
Mykel J. Kochenderfer ◽  
Rajmonda S. Caceres
Keyword(s):  
Neural Network ◽  
United States ◽  
Infection Rate ◽  
Expectation Maximization ◽  
The United States ◽  
County Level ◽  
Training Set ◽  
Single Model ◽  
Partial Observability ◽  
Level Data

AbstractCOVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. In this work, we seek to explain the diversity in epidemic progressions by considering an extension to the compartmental SEIRD model. The model we propose uses a neural network to predict the infection rate as a function of time and of the prevalence of the disease. We provide a methodology for fitting this model to available county-level data describing aggregate cases and deaths. Our method uses Expectation-Maximization in order to overcome the challenge of partial observability—that the system’s state is only partially reflected in available data. We fit a single model to data from multiple counties in the United States exhibiting different behavior. By simulating the model, we show that it is capable of exhibiting both single peak and multi-peak behavior, reproducing behavior observed in counties both in and out of the training set. We also numerically compare the error of simulations from our model with a standard SEIRD model, showing that the proposed extensions are necessary to be able to explain the spread of COVID-19.

scholarly journals Research of Neural Network Approach of Objects Detection in the Images

2020 ◽  
pp. 15-21
Author(s):  
R. N. Kvetny ◽  
R. V. Masliy ◽  
A. M. Kyrylenko ◽  
V. V. Shcherba
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
High Performance ◽  
Learning Rate ◽  
Validation Dataset ◽  
Neural Network Approach ◽  
Validation Data ◽  
Compromise Value ◽  
Model Training

The article is devoted to the study of object detection in ima­ges using neural networks. The structure of convolutional neural networks used for image processing is considered. The formation of the convolutional layer (Fig. 1), the sub-sampling layer (Fig. 2) and the fully connected layer (Fig. 3) are described in detail. An overview of popular high-performance convolutional neural network architectures used to detect R-FCN, Yolo, Faster R-CNN, SSD, DetectNet objects has been made. The basic stages of image processing by the DetectNet neural network, which is designed to detect objects in images, are discussed. NVIDIA DIGITS was used to create and train models, and several DetectNet models were trained using this environment. The parameters of experiments (Table 1) and the compari­son of the quality of the trained models (Table 2) are presented. As training and validation data, we used an image of the KITTI database, which was created to improve self-driving systems that do not go without built-in devices, one of which could be the Jetson TX2. KITTI’s images feature several object classes, including cars and pedestrians. Model training and testing was performed using a Jetson TX2 supercomputer. Five models were trained that differed in the Base learning rate parameter. The results obtained make it possible to find a compromise value for the Base learning rate para­meter to quickly obtain a model with a high mAP value. The qua­lity of the best model obtained on the KITTI validation dataset is mAP = 57.8%.

scholarly journals Explaining COVID-19 outbreaks with reactive SEIRD models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kunal Menda ◽  
Lucas Laird ◽  
Mykel J. Kochenderfer ◽  
Rajmonda S. Caceres
Keyword(s):  
Neural Network ◽  
United States ◽  
Expectation Maximization ◽  
Simulated Data ◽  
The United States ◽  
County Level ◽  
Training Set ◽  
Single Model ◽  
Partial Observability ◽  
Level Data

AbstractCOVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our lack of understanding of how the pandemic has evolved leads to increasing errors in our ability to predict the spread of the disease. This work seeks to explain this diversity in epidemic progressions by considering an extension to the compartmental SEIRD model. The model we propose uses a neural network to predict the infection rate as a function of both time and the disease’s prevalence. We provide a methodology for fitting this model to available county-level data describing aggregate cases and deaths. Our method uses Expectation-Maximization to overcome the challenge of partial observability, due to the fact that the system’s state is only partially reflected in available data. We fit a single model to data from multiple counties in the United States exhibiting different behavior. By simulating the model, we show that it can exhibit both single peak and multi-peak behavior, reproducing behavior observed in counties both in and out of the training set. We then compare the error of simulations from our model with a standard SEIRD model, and show that ours substantially reduces errors. We also use simulated data to compare our methodology for handling partial observability with a standard approach, showing that ours is significantly better at estimating the values of unobserved quantities.

Ceramic powder characterization by multilayer perceptron (MLP) data compression and classification

1984 ◽  
Vol 5 (3) ◽  
pp. 225-239
Author(s):  
Bonifazi G. ◽  
Burrascano P.
Keyword(s):  
Neural Network ◽  
Data Compression ◽  
Ceramic Powder ◽  
Morphological Characteristics ◽  
Image Data ◽  
Training Set ◽  
Neural Network Approach ◽  
Image Data Compression ◽  
Testing Patterns ◽  
Compressed Data

A neural network approach for pattern classification has been explored in the present paper as part of the recent resurgence of interest in this area. Our research has focused on how a multilayer feedforward structure performs in the particular problem of particle characterization. The proposed procedure, after suitable data preprocessing, consists of two distinct phases: in the former, a feedforward neural network is used to obtain an image data compression. In the latter, a neural classifier is trained on the compressed data. All the tests have been conducted on a sample constituted by two different typologies of ceramic particles, each characterized by a different microstructure. The sample image of different particles acquired and directly digitalized by scanning electron microscopy has been processed in order to achieve the best conditions to obtain the boundary profile of each particle. The boundary is thus assumed to be representative of the morphological characteristics of the ceramic products. Using the neural approach, a classification accuracy as high as 100% on a training set of 80 sub-images was achieved. These networks correctly classified up to 96.9% of 64 testing patterns not contained in the training set.

Spatial Diversity in Local Government Revenue Effort under Decentralization: A Neural-Network Approach

10.1068/c16r ◽  
2005 ◽  
Vol 23 (5) ◽  
pp. 657-677 ◽  
Author(s):  
Mildred E Warner ◽  
James E Pratt
Keyword(s):  
Neural Network ◽  
State Policy ◽  
Local Governments ◽  
Classification Tree ◽  
The United States ◽  
Spatial Diversity ◽  
Federal Aid ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Impact

Decentralization reflects a global trend to increase the responsiveness of state and local governments to economic forces, but it raises the challenge of how to secure redistributive goals. Theoretically, as the equalizing impact of federal aid declines under devolution, we expect subnational state-level government policy to become more important, and geographic diversity in local governments' efforts to raise revenue to increase. In this paper we explore the impact of state fiscal centralization and intergovernmental aid on local revenue effort with the aid of Census of Governments data for county areas from 1987 for the Mid-Atlantic and East North Central region of the United States, with particular attention paid to rural counties. The 1987 period was chosen because it is the first year in which state policy trends diverged from federal decentralization trends and both state aid and state centralization increased while federal aid to localities continued to decline. Using a neural-network approach, we explore the spatially differentiated impact of state policy and find complementary responses in effort among some localities and substitution responses among others. Classification-tree analysis of this diversity suggests that decentralization and the competitive government it promotes are likely to exacerbate inequality among local governments.

scholarly journals Toward the use of neural networks for influenza prediction at multiple spatial resolutions

2021 ◽  
Vol 7 (25) ◽  
pp. eabb1237
Author(s):  
Emily L. Aiken ◽  
Andre T. Nguyen ◽  
Cecile Viboud ◽  
Mauricio Santillana
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Real Time ◽  
The United States ◽  
Network Approach ◽  
Internet Search ◽  
Learning Methods ◽  
Neural Network Approach ◽  
Machine Learning Methods ◽  
Search Data

Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care–based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal “data gap,” but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.

scholarly journals Phonovisual Biases in Language: is the Lexicon Tied to the Visual World?

2021 ◽  
Author(s):  
Andrea Gregor de Varda ◽  
Carlo Strapparava
Keyword(s):  
Neural Network ◽  
Empirical Support ◽  
Concrete Word ◽  
Visual Features ◽  
Language Family ◽  
Further Training ◽  
Training Set ◽  
Learning Framework ◽  
Phonetic Representation ◽  
Processing Network

The present paper addresses the study of cross-linguistic and cross-modal iconicity within a deep learning framework. An LSTM-based Recurrent Neural Network is trained to associate the phonetic representation of a concrete word, encoded as a sequence of feature vectors, to the visual representation of its referent, expressed as an HCNN-transformed image. The processing network is then tested, without further training, in a language that does not appear in the training set and belongs to a different language family. The performance of the model is evaluated through a comparison with a randomized baseline; we show that such an imaginative network is capable of extracting language-independent generalizations in the mapping from linguistic sounds to visual features, providing empirical support for the hypothesis of a universal sound-symbolic substrate underlying all languages.

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