scholarly journals Neural network approach to data-driven estimation of chemotactic sensitivity in the Keller-Segel model

2021 ◽  
Vol 18 (6) ◽  
pp. 8524-8534
Author(s):  
Sunwoo Hwang ◽  
◽  
Seongwon Lee ◽  
Hyung Ju Hwang ◽  
Keyword(s):  
High Accuracy ◽  
Data Driven ◽  
Neural Net ◽  
Chemical Signal ◽  
Network Approach ◽  
Constant Multiple ◽  
Neural Network Approach ◽  
Signal Region ◽  
The Difference ◽  
The Mathematical Model

<abstract><p>We consider the mathematical model of chemotaxis introduced by Patlak, Keller, and Segel. Aggregation and progression waves are present everywhere in the population dynamics of chemotactic cells. Aggregation originates from the chemotaxis of mobile cells, where cells are attracted to migrate to higher concentrations of the chemical signal region produced by themselves. The neural net can be used to find the approximate solution of the PDE. We proved that the error, the difference between the actual value and the predicted value, is bound to a constant multiple of the loss we are learning. Also, the Neural Net approximation can be easily applied to the inverse problem. It was confirmed that even when the coefficient of the PDE equation was unknown, prediction with high accuracy was achieved.</p></abstract>

scholarly journals Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data

2021 ◽  
Vol 13 (17) ◽  
pp. 3537
Author(s):  
Jean-Marie Vient ◽  
Frederic Jourdin ◽  
Ronan Fablet ◽  
Baptiste Mengual ◽  
Ludivine Lafosse ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
State Of The Art ◽  
Satellite Observations ◽  
Data Driven ◽  
Network Approach ◽  
Satellite Measurements ◽  
Neural Network Approach ◽  
Reconstruction Performance

Due to complex natural and anthropogenic interconnected forcings, the dynamics of suspended sediments within the ocean water column remains difficult to understand and monitor. Numerical models still lack capabilities to account for the variabilities depicted by in situ and satellite-derived datasets. Besides, the irregular space-time sampling associated with satellite sensors make crucial the development of efficient interpolation methods. Optimal Interpolation (OI) remains the state-of-the-art approach for most operational products. Due to the large increase of both in situ and satellite measurements more and more available information is coming from in situ and satellite measurements, as well as from simulation models. The emergence of data-driven schemes as possibly relevant alternatives with increased capabilities to recover finer-scale processes. In this study, we investigate and benchmark three state-of-the-art data-driven schemes, namely an EOF-based technique, an analog data assimilation scheme, and a neural network approach, with an OI scheme. We rely on an Observing System Simulation Experiment based on high-resolution numerical simulations and simulated satellite observations using real satellite sampling patterns. The neural network approach, which relies on variational data assimilation formulation for the interpolation problem, clearly outperforms both the OI and the other data-driven schemes, both in terms of reconstruction performance and of a greater ability to recover high-frequency events. We further discuss how these results could transfer to real data, as well as to other problems beyond interpolation issues, especially short-term forecasting problems from partial satellite observations.

scholarly journals Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

2019 ◽  
Vol 18 ◽  
pp. 117693511985757
Author(s):  
Mohammed M Abdelsamea ◽  
Marghny H Mohamed ◽  
Mohamed Bamatraf
Keyword(s):  
Feature Space ◽  
High Accuracy ◽  
High Dimensional ◽  
Automated Classification ◽  
Network Approach ◽  
Neural Network Approach ◽  
Mammographic Images ◽  
Learning Technique ◽  
Updating Procedure

We propose a novel neural network approach for the classification of abnormal mammographic images into benign or malignant based on their texture representations. The proposed framework has the capability of mapping high dimensional feature space into a lower-dimension, in a supervised way. The main contribution of the proposed classifier is to introduce a new neuron structure for map representation and adopt a supervised learning technique for feature classification. This is achieved by making the weight updating procedure dependent on the class reliability of the neuron. We showed high accuracy (95.2%) for our proposed approach in the classification of abnormal real mammographic images when compared to other related methods.

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