The Machine Learning Embedded Method of Parameters Determination in the Constitutive Models and Potential Applications for Hydrogels

2021 ◽  
Vol 13 (01) ◽  
pp. 2150001 ◽  
Author(s):  
Shoujing Zheng ◽  
Zishun Liu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Chemical Potential ◽  
Constitutive Models ◽  
Experimental Application ◽  
Potential Applications ◽  
The Neural Networks ◽  
Hydrogel Swelling ◽  
Embedded Method ◽  
Fitting Parameters

We propose a machine learning embedded method of parameters determination in the constitutional models of hydrogels. It is found that the developed logistic regression-like algorithm for hydrogel swelling allows us to determine the fitting parameters based on known swelling ratio and chemical potential. We also put forward the neural networks-like algorithm, which, by its own property, can converge faster as the layer deepens. We then develop neural networks-like algorithm for hydrogel under uniaxial load for experimental application purpose. Finally, we propose several machine learning embedded potential applications for hydrogels, which would provide directions for machine learning-based hydrogel research.

scholarly journals Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 823
Author(s):  
Ting Peng ◽  
Xiefei Zhi ◽  
Yan Ji ◽  
Luying Ji ◽  
Ye Tian
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Air Temperature ◽  
Post Processing ◽  
Learning Methods ◽  
Processing Methods ◽  
Machine Learning Methods ◽  
Extended Range ◽  
The Neural Networks ◽  
Maximum Air Temperature

The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.

Machine Learning Techniques for Estimating Amount of Coolant Required in Shipping of Temperature Sensitive Products

2020 ◽  
Vol 10 (10) ◽  
pp. 67-70
Author(s):  
Divya Choudhary ◽  
Siripong Malasri
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Threshold Temperature ◽  
Support Vector ◽  
Temperature Sensitive ◽  
Learning Techniques ◽  
The Neural Networks

This paper implements and compares machine learning algorithms to predict the amount of coolant required during transportation of temperature sensitive products. The machine learning models use trip duration, product threshold temperature and ambient temperature as the independent variables to predict the weight of gel packs need to keep the temperature of the product below its threshold temperature value. The weight of the gel packs can be translated to number of gel packs required. Regression using Neural Networks, Support Vector Regression, Gradient Boosted Regression and Elastic Net Regression are compared. The Neural Networks based model performs the best in terms of its mean absolute error value and r-squared values. A Neural Network model is then deployed on as webservice to score allowing for client application to make rest calls to estimate gel pack weights

A honeycomb model for tortuosity of flow path in the leaf venation network

2014 ◽  
Vol 25 (06) ◽  
pp. 1450015 ◽  
Author(s):  
Han Liu ◽  
Ming-Qing Zou ◽  
Da-Lun Wang ◽  
Shan-Shan Yang ◽  
Ming-Chao Liang
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Porous Media ◽  
Thermal Diffusion ◽  
Empirical Constant ◽  
Leaf Venation ◽  
Biotechnology Research ◽  
Potential Applications ◽  
The Neural Networks ◽  
Important Significance

A honeycomb model is designed according to the leaf veins, which is expressed as a function of porosity and tortuosity, and there is no empirical constant in this model. We mainly applied it to the leaf venation network, and the prediction in our model are compared with that from available correlations obtained by matching the numerical results, both of which are consistent with each other. Our model and relations may have important significance and potential applications in leaf venation and porous media. They also have a certain guiding significance to fluid heat transfer and thermal diffusion, as well as biotechnology research, e.g. veins and the neural networks of human.

scholarly journals Toward accurate platform-aware performance modeling for deep neural networks

2021 ◽  
Vol 21 (1) ◽  
pp. 50-61
Author(s):  
Chuan-Chi Wang ◽  
Ying-Chiao Liao ◽  
Ming-Chang Kao ◽  
Wen-Yew Liang ◽  
Shih-Hao Hung
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Network Performance ◽  
Percentage Error ◽  
Training Time ◽  
Fine Grain ◽  
The Neural Network ◽  
Machine Learning Model ◽  
The Neural Networks ◽  
System Developer

In this paper, we provide a fine-grain machine learning-based method, PerfNetV2, which improves the accuracy of our previous work for modeling the neural network performance on a variety of GPU accelerators. Given an application, the proposed method can be used to predict the inference time and training time of the convolutional neural networks used in the application, which enables the system developer to optimize the performance by choosing the neural networks and/or incorporating the hardware accelerators to deliver satisfactory results in time. Furthermore, the proposed method is capable of predicting the performance of an unseen or non-existing device, e.g. a new GPU which has a higher operating frequency with less processor cores, but more memory capacity. This allows a system developer to quickly search the hardware design space and/or fine-tune the system configuration. Compared to the previous works, PerfNetV2 delivers more accurate results by modeling detailed host-accelerator interactions in executing the full neural networks and improving the architecture of the machine learning model used in the predictor. Our case studies show that PerfNetV2 yields a mean absolute percentage error within 13.1% on LeNet, AlexNet, and VGG16 on NVIDIA GTX-1080Ti, while the error rate on a previous work published in ICBD 2018 could be as large as 200%.

scholarly journals Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12073
Author(s):  
Indira Mikkili ◽  
Abraham Peele Karlapudi ◽  
T. C. Venkateswarulu ◽  
Vidya Prabhakar Kodali ◽  
Deepika Sri Singh Macamdas ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Imaging Techniques ◽  
Learning Tools ◽  
Rt Pcr ◽  
Drug Molecules ◽  
Potential Applications ◽  
The Neural Networks ◽  
Polymerase Chain ◽  
The Internet Of Things

The coronavirus disease (COVID-19) pandemic has caused havoc worldwide. The tests currently used to diagnose COVID-19 are based on real time reverse transcription polymerase chain reaction (RT-PCR), computed tomography medical imaging techniques and immunoassays. It takes 2 days to obtain results from the RT-PCR test and also shortage of test kits creating a requirement for alternate and rapid methods to accurately diagnose COVID-19. Application of artificial intelligence technologies such as the Internet of Things, machine learning tools and big data analysis to COVID-19 diagnosis could yield rapid and accurate results. The neural networks and machine learning tools can also be used to develop potential drug molecules. Pharmaceutical companies face challenges linked to the costs of drug molecules, research and development efforts, reduced efficiency of drugs, safety concerns and the conduct of clinical trials. In this review, relevant features of artificial intelligence and their potential applications in COVID-19 diagnosis and drug development are highlighted.

scholarly journals Modelling and Analyzing the Employees’ Engagement in Workplace using Machine learning Tools

2021 ◽  
Vol 9 (6) ◽  
pp. 243-249
Author(s):  
Mahassine BEKKARI ◽  
EL FALLAHI Abdellah
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Human Capital ◽  
New Economy ◽  
Gradient Boosting ◽  
Learning Tools ◽  
Response Variable ◽  
Complex Relation ◽  
Gradient Boosting Machine ◽  
The Neural Networks

In a new economy where immaterial capital is crucial, companies are increasingly aware of the necessity to efficiently manage human capital by optimizing its engagement in the workplace. The accession of the human capital through its engagement is an efficient leverage that leads to a real improvement of the companies’ performance. Despite the staple attention towards human resource management, and the efforts undertaken to satisfy and motivate the personnel, the issue of engagement still persists. The main objective of this paper is to study and model the relation between eight predictors and a response variable given by the employees’ engagement. We have used different models to figure out the relation between the predictors and the dependent variable after carrying out a survey of several employees from different companies. The techniques used in this paper are linear regression, ordinal logistic regression, Gradient Boosting Machine learning and neural networks. The data used in this study is the results of a questionnaire completed by 60 individuals. The results obtained show that the neural networks perform slightly the rest of models considering the training and validation error of modelling and also highlight the complex relation linking the predictors and the predicted.

scholarly journals Predicting atmospheric optical properties for radiative transfer computations using neural networks

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200095
Author(s):  
Menno A. Veerman ◽  
Robert Pincus ◽  
Robin Stoffer ◽  
Caspar M. van Leeuwen ◽  
Damian Podareanu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Optical Properties ◽  
Radiative Transfer ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Climate Modelling ◽  
Atmospheric Conditions ◽  
Promising Tool ◽  
The Neural Networks

The radiative transfer equations are well known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parametrization (RRTMGP). To minimize computa- tional costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimized BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within 0.5 W m −2 compared to RRTMGP. Our neural network-based gas optics parametrization is up to four times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations while retaining high accuracy. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

scholarly journals On the Application of Machine Learning Techniques to Regression Problems in Sea Level Studies

2019 ◽  
Vol 36 (9) ◽  
pp. 1889-1902
Author(s):  
Magnus Hieronymus ◽  
Jenny Hieronymus ◽  
Fredrik Hieronymus
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Sea Level ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Coastal Protection ◽  
High Temporal Resolution ◽  
Learning Techniques ◽  
The Neural Networks ◽  
Regression Problems

Long sea level records with high temporal resolution are of paramount importance for future coastal protection and adaptation plans. Here we discuss the application of machine learning techniques to some regression problems commonly encountered when analyzing such time series. The performance of artificial neural networks is compared with that of multiple linear regression models on sea level data from the Swedish coast. The neural networks are found to be superior when local sea level forcing is used together with remote sea level forcing and meteorological forcing, whereas the linear models and the neural networks show similar performance when local sea level forcing is excluded. The overall performance of the machine learning algorithms is good, often surpassing that of the much more computationally costly numerical ocean models used at our institute.

scholarly journals Review and comparative analysis of machine learning libraries for machine learning

2019 ◽  
Vol 27 (4) ◽  
pp. 305-315
Author(s):  
Migran N. Gevorkyan ◽  
Anastasia V. Demidova ◽  
Tatiana S. Demidova ◽  
Anton A. Sobolev
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Comparative Analysis ◽  
Programming Languages ◽  
Multilayer Perceptron ◽  
Tabular Form ◽  
Training Time ◽  
Current State ◽  
Different Types ◽  
The Neural Networks

The article is an overview. We carry out the comparison of actual machine learning libraries that can be used the neural networks development. The first part of the article gives a brief description of TensorFlow, PyTorch, Theano, Keras, SciKit Learn libraries, SciPy library stack. An overview of the scope of these libraries and the main technical characteristics, such as performance, supported programming languages, the current state of development is given. In the second part of the article, a comparison of five libraries is carried out on the example of a multilayer perceptron, which is applied to the problem of handwritten digits recognizing. This problem is well known and well suited for testing different types of neural networks. The study time is compared depending on the number of epochs and the accuracy of the classifier. The results of the comparison are presented in the form of graphs of training time and accuracy depending on the number of epochs and in tabular form.

scholarly journals Solar Energetic Particle Event occurrence prediction using Solar Flare Soft X-ray measurements and Machine Learning

2021 ◽  
Author(s):  
Sigiava Aminalragia-Giamini ◽  
Savvas Raptis ◽  
Anastasios Anastasiadis ◽  
Antonis Tsigkanos ◽  
Ingmar Sandberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Solar Flares ◽  
Energetic Particle ◽  
Real Life ◽  
Solar Energetic Particle ◽  
Solar Energetic Particle Event ◽  
X Rays ◽  
The Neural Networks ◽  
Event Occurrence

The prediction of the occurrence of Solar Energetic Particle (SEP) events has been investigated over many years and multiple works have presented significant advances in this problem. The accurate and timely prediction of SEPs is of interest to the scientific community as well as mission designers, operators, and industrial partners due to the threat SEPs pose to satellites, spacecrafts and crewed missions. In this work we present a methodology for the prediction of SEPs from the soft X-rays of solar flares associated with SEPs that were measured in 1 AU. We use an expansive dataset covering 25 years of solar activity, 1988-2013, which includes thousands of flares and more than two hundred identified and catalogued SEPs. Neural networks are employed as the predictors in the model providing probabilities for the occurrence or not of an SEP which are converted to yes/no predictions. The neural networks are designed using current and state-of the-art tools integrating recent advances in the machine learning field. The results of the methodology are extensively evaluated and validated using all the available data and it is shown that we achieve very good levels of accuracy with correct SEP occurrence prediction higher than 85% and correct no-SEP predictions higher than 92%. Finally we discuss further work towards potential improvements and the applicability of our model in real life conditions.

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