scholarly journals Understanding the impact of precision quantization on the accuracy and energy of neural networks

2017 ◽  
Author(s):  
Soheil Hashemi ◽  
Nicholas Anthony ◽  
Hokchhay Tann ◽  
R. Iris Bahar ◽  
Sherief Reda
Keyword(s):  
Neural Networks ◽  
The Impact

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

scholarly journals IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 676
Author(s):  
Andrej Zgank
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Markov Models ◽  
Audio Signal ◽  
Audio Signals ◽  
Mel Frequency Cepstral Coefficients ◽  
Animal Activity ◽  
The Impact ◽  
Acoustic Classification ◽  
Swarm Activity

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.

scholarly journals Quantitative Dynamics Effects of Belt and Road Economies Trade Using Structural Gravity and Neural Networks

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110326
Author(s):  
Koffi Dumor ◽  
Li Yao ◽  
Jean-Paul Ainam ◽  
Edem Koffi Amouzou ◽  
Williams Ayivi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Bilateral Trade ◽  
Belt And Road Initiative ◽  
Neural Network Analysis ◽  
Analysis Techniques ◽  
Belt And Road ◽  
The Impact ◽  
Trade Partners

Recent research suggests that China’s Belt and Road Initiative (BRI) would improve the bilateral trade between China and its partners. This article uses detailed bilateral export data from 1990 to 2017 to investigate the impact of China’s BRI on its trade partners using neural network analysis techniques and structural gravity model estimations. Our main findings suggest that the BRI countries would raise exports by a modest 5.053%. This indicates that export and network upgrades should be considered from economic and policy perspectives. The results also show that neural networks is more robust compared with structural gravity framework.

Are Chinese Cities Oversized?

2019 ◽  
Vol 43 (6) ◽  
pp. 632-654
Author(s):  
Daidai Shen ◽  
Jean-Claude Thill ◽  
Jiuwen Sun
Keyword(s):  
Neural Networks ◽  
Equilibrium Model ◽  
Urban Population ◽  
Urban System ◽  
Agglomeration Economies ◽  
City Size ◽  
Chinese City ◽  
Intermediate Solution ◽  
Optimal City Size ◽  
The Impact

In this article, the socioeconomic determinants on urban population in China are empirically investigated with a theoretical equilibrium model for city size. While much of the research on urban size focuses on the impact of agglomeration economies based on “optimal city size” theory, this model is eschewed in our research due to its theoretical paradox in the real world, and we turn instead toward an intermediate solution proposed by Camagni, Capello, and Caragliu. This equilibrium model is estimated on a sample of 111 prefectural cities in China with multiple regression and artificial neural networks. Empirical results have shown that the model explains the variance in the data very well, and all the determinants have significant impacts on Chinese city sizes. Although sample cities have reached their equilibrium sizes as a whole, there is substantially unbalanced distribution of population within the urban system, with a strong contingent of cities that are either squarely too large or too small.

The impact of ageing, fasting and high-fat diet on central and peripheral glucose tolerance and glucose-sensing neural networks in the arcuate nucleus

2017 ◽  
Vol 29 (10) ◽  
pp. e12528 ◽  
Author(s):  
M. van den Top ◽  
F.-Y. Zhao ◽  
R. Viriyapong ◽  
N. J. Michael ◽  
A. C. Munder ◽  
...  
Keyword(s):  
Neural Networks ◽  
Glucose Tolerance ◽  
High Fat Diet ◽  
Arcuate Nucleus ◽  
Glucose Sensing ◽  
High Fat ◽  
The Impact

scholarly journals Impact of Low Resolution on Image Recognition with Deep Neural Networks: An Experimental Study

2018 ◽  
Vol 28 (4) ◽  
pp. 735-744 ◽  
Author(s):  
Michał Koziarski ◽  
Bogusław Cyganek
Keyword(s):  
Neural Networks ◽  
Image Recognition ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Dynamic Range ◽  
Super Resolution ◽  
Image Resolution ◽  
Quality Data ◽  
Low Resolution ◽  
The Impact

Abstract Due to the advances made in recent years, methods based on deep neural networks have been able to achieve a state-of-the-art performance in various computer vision problems. In some tasks, such as image recognition, neural-based approaches have even been able to surpass human performance. However, the benchmarks on which neural networks achieve these impressive results usually consist of fairly high quality data. On the other hand, in practical applications we are often faced with images of low quality, affected by factors such as low resolution, presence of noise or a small dynamic range. It is unclear how resilient deep neural networks are to the presence of such factors. In this paper we experimentally evaluate the impact of low resolution on the classification accuracy of several notable neural architectures of recent years. Furthermore, we examine the possibility of improving neural networks’ performance in the task of low resolution image recognition by applying super-resolution prior to classification. The results of our experiments indicate that contemporary neural architectures remain significantly affected by low image resolution. By applying super-resolution prior to classification we were able to alleviate this issue to a large extent as long as the resolution of the images did not decrease too severely. However, in the case of very low resolution images the classification accuracy remained considerably affected.

scholarly journals Machine Learning Methods Applied for Modeling the Process of Obtaining Bricks Using Silicon-Based Materials

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7232
Author(s):  
Costel Anton ◽  
Silvia Curteanu ◽  
Cătălin Lisa ◽  
Florin Leon
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Raw Materials ◽  
Optimization Procedure ◽  
Exhaust Emission ◽  
Energy Potential ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Emission Changes ◽  
The Impact

Most of the time, industrial brick manufacture facilities are designed and commissioned for a particular type of manufacture mix and a particular type of burning process. Productivity and product quality maintenance and improvement is a challenge for process engineers. Our paper aims at using machine learning methods to evaluate the impact of adding new auxiliary materials on the amount of exhaust emissions. Experimental determinations made in similar conditions enabled us to build a database containing information about 121 brick batches. Various models (artificial neural networks and regression algorithms) were designed to make predictions about exhaust emission changes when auxiliary materials are introduced into the manufacture mix. The best models were feed-forward neural networks with two hidden layers, having MSE < 0.01 and r2 > 0.82 and, as regression model, kNN with error < 0.6. Also, an optimization procedure, including the best models, was developed in order to determine the optimal values for the parameters that assure the minimum quantities for the gas emission. The Pareto front obtained in the multi-objective optimization conducted with grid search method allows the user the chose the most convenient values for the dry product mass, clay, ash and organic raw materials which minimize gas emissions with energy potential.

scholarly journals Atmospheric Pollutant Flow and Precipitation: Modeling Effects on the Vegetation Ecosystem

2018 ◽  
Vol 7 (3) ◽  
pp. 157-161
Author(s):  
Allag Fateh ◽  
Saddek Bouharati ◽  
Lamri Tedjar ◽  
Mohamed Fenni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Wide Distribution ◽  
Climatic Conditions ◽  
Mathematical Methods ◽  
Atmospheric Pollutant ◽  
Artificial Neural ◽  
Precipitation Modeling ◽  
The Impact

Because of their fixed life and wide distribution, plants are the first victims of air pollution. The atmosphere is considered polluted when the increase of the rate of certain components causes harmful effects on the different constituents of the ecosystems. The study of the flow of air near a polluting source (cement plant in our case), allows to predict its impact on the surrounding plant ecosystem. Different factors are to be considered. The chemical composition of the air, the climatic conditions, and the impacted plant species are complex parameters to be analyzed using conventional mathematical methods. In this study, we propose a system based on artificial neural networks. Since artificial neural networks have the capacity to treat different complex parameters, their application in this domain is adequate. The proposed system makes it possible to match the input and output spaces. The variables that constitute the input space are the chemical composition, the concentration of the latter in the rainwater, their duration of deposition on the leaves and stems, the climatic conditions characterizing the environment, as well as the species of plant studied. The output variable expresses the rate of degradation of this species under the effect of pollution. Learning the system makes it possible to establish the transfer function and thus predict the impact of pollutants on the vegetation.

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