Long Term Predictions of NO2 Average Values via Deep Learning

2021 ◽  
pp. 595-610
Author(s):  
Pierfrancesco Bellini ◽  
Stefano Bilotta ◽  
Daniele Cenni ◽  
Enrico Collini ◽  
Paolo Nesi ◽  
...  
Keyword(s):  
Deep Learning

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Spoken content retrieval and understanding using deep learning

Impact ◽  
2021 ◽  
Vol 2021 (1) ◽  
pp. 9-11
Author(s):  
Lin-shan Lee
Keyword(s):  
Deep Learning ◽  
Scientific Community ◽  
Online Courses ◽  
The Internet ◽  
Human Voice ◽  
Content Retrieval ◽  
Youtube Videos ◽  
Access Information ◽  
Teaching Machines

Spoken content refers to all content over the Internet which includes human voice, essentially those in multimedia, such As YouTube videos and online courses. Today such content is retrieved via Google primarily based on human-generated text labels, because Google can only retrieve text over the Internet. The goal of this project is to produce technologies to retrieve accurately and efficiently such spoken content directly based on the included audio sounds instead of text labels, because machines today can listen to human voice just as they can read the text. The long term goal is to create a spoken version of Google, which may revolutionize the ways in which humans access information and improve their knowledge. Professor Lin-shan Lee at National Taiwan University is leading this project. He has been a distinguished leader in the global scientific community for the area of teaching machines to speak and listen to human voice for many years.

P1218Deep learning survival analysis enhances the value of hybrid PET/CT for long-term cardiovascular event prediction

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
L E Juarez-Orozco ◽  
J W Benjamins ◽  
T Maaniitty ◽  
A Saraste ◽  
P Van Der Harst ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Survival Analysis ◽  
Deep Learning ◽  
Ct Angiography ◽  
Time To Event ◽  
Clinical Variables ◽  
Pet Ct ◽  
Early Revascularization

Abstract Background Deep Learning (DL) is revolutionizing cardiovascular medicine through complex data-pattern recognition. In spite of its success in the diagnosis of coronary artery disease (CAD), DL implementation for prognostic evaluation of cardiovascular events is still limited. Traditional survival models (e.g.Cox) notably incorporate the effect of time-to-event but are unable to exploit complex non-liner dependencies between large numbers of predictors. On the other hand, DL hasn't systematically incorporated time-to-event for prognostic evaluations. Long-term registries of hybrid PET/CT imaging represent a suitable substrate for DL-based survival analysis due the large amount of time-dependent structured variables that they convey. Therefore, we sought to evaluate the feasibility and performance of DL Survival Analysis in predicting the occurrence of myocardial infarction (MI) and death in a long-term registry of cardiac hybrid PET/CT. Methods Data from our PET/CT registry of symptomatic patients with intermediate CAD risk who underwent sequential CT angiography and 15O-water PET for suspected ischemia, was analyzed. The sample has been followed for a 6-year average for MI or death. Ten clinical variables were extracted from electronic records including cardiovascular risk factors, dyspnea and early revascularization. CT angiography images were evaluated segmentally for: presence of plaque, % of luminal stenosis and calcification (58 variables). Absolute stress PET myocardial perfusion data was evaluated globally and regionally across vascular territories (4 variables). Cox-Nnet (a deep survival neural network) was implemented in a 5-fold cross-validated 80:20 split for training and testing. Resulting DL-hazard ratios were operationalized and compared to the observed events developed during follow-up. The performance of Cox-Nnet evaluating structured CT, PET/CT, and PET/CT+clinical variables was compared to expert interpretation (operationalized as: normal coronaries, non-obstructive CAD, obstructive CAD) and to Calcium Score (CaSc), through the concordance (c)-index. Results There were 426 men and 525 women with a mean age of 61±9 years-old. Twenty-four MI and 49 deaths occurred during follow-up (1 month–9.6 years), while 11.5% patients underwent early revascularization. Cox-Nnet evaluation of PET/CT data (c-index=0.75) outperformed categorical expert interpretation (c-index=0.54) and CaSc (c-index=0.65), while hybrid PET/CT and PET/CT+clinical (c-index=0.75) variables demonstrated incremental performance overall independent from early revascularization. Conclusion Deep Learning Survival Analysis is feasible in the evaluation of cardiovascular prognostic data. It might enhance the value of cardiac hybrid PET/CT imaging data for predicting the long-term development of myocardial infarction and death. Further research into the implementation of Deep Learning for prognostic analyses in CAD is warranted.

scholarly journals Deep Learning to Assess Long-term Mortality From Chest Radiographs

2019 ◽  
Vol 2 (7) ◽  
pp. e197416 ◽  
Author(s):  
Michael T. Lu ◽  
Alexander Ivanov ◽  
Thomas Mayrhofer ◽  
Ahmed Hosny ◽  
Hugo J. W. L. Aerts ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chest Radiographs ◽  
Long Term Mortality

Deep learning for ethoacoustical mapping: Application to a single Cachalot long term recording on joint observatories in Vancouver Island

2018 ◽  
Vol 144 (3) ◽  
pp. 1776-1777
Author(s):  
Herve Glotin ◽  
Paul Spong ◽  
Helena Symonds ◽  
Vincent Roger ◽  
Randall Balestriero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Vancouver Island

scholarly journals Deep Learning-Based Long-Term Power Allocation Scheme for NOMA Downlink System in S-IoT

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 86288-86296 ◽  
Author(s):  
Yunyu Sun ◽  
Ye Wang ◽  
Jian Jiao ◽  
Shaohua Wu ◽  
Qinyu Zhang
Keyword(s):  
Deep Learning ◽  
Power Allocation ◽  
Allocation Scheme ◽  
Downlink System

scholarly journals Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3021 ◽  
Author(s):  
Wonju Seo ◽  
Namho Kim ◽  
Sehyeon Kim ◽  
Chanhee Lee ◽  
Sung-Min Park
Keyword(s):  
Deep Learning ◽  
Mental Stress ◽  
Detection Algorithm ◽  
Workplace Stress ◽  
Physiological Signals ◽  
Work Related ◽  
Average Accuracy ◽  
Conventional Machine ◽  
End To End

Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network’s neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress.

scholarly journals A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1691 ◽  
Author(s):  
Da Li ◽  
Yingke Lei ◽  
Haichuan Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Feed Forward Neural Network ◽  
Research Attention ◽  
Outdoor Environments ◽  
Dispersion Problem ◽  
Fingerprint Database ◽  
The Internet Of Things

In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positioning approach is proposed to solve the problem of outdoor positioning. Considering the outstanding performance of deep learning in image classification, LTE signal measurements are converted into location grayscale images to form a fingerprint database. In order to deal with the instability of LTE signals, prevent the gradient dispersion problem, and increase the robustness of the proposed deep neural network (DNN), the following methods are adopted: First, cross-entropy is used as the loss function of the DNN. Second, the learning rate of the proposed DNN is dynamically adjusted. Third, this paper adopted several data enhancement techniques. To find the best positioning fingerprint and method, three types of fingerprint and five positioning models are compared. Finally, by using a deep residual network (Resnet) and transfer learning, a hierarchical structure training method is proposed. The proposed Resnet is used to train with the united fingerprint image database to obtain a positioning model called a coarse localizer. By using the prior knowledge of the pretrained Resnet, feed-forward neural network (FFNN)-based transfer learning is used to train with the united fingerprint database to obtain a better positioning model, called a fine localizer. The experimental results convincingly show that the proposed DNN can automatically learn the location features of LTE signals and achieve satisfactory positioning accuracy in outdoor environments.

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