Variety identification of wheat using mass spectrometry with neural networks and the influence of mass spectra processing prior to neural network analysis

2002 ◽  
Vol 16 (12) ◽  
pp. 1232-1237 ◽  
Author(s):  
Helle Aagaard Sørensen ◽  
Maria Maddalena Sperotto ◽  
Marianne Petersen ◽  
Can Keşmir ◽  
Louise Radzikowski ◽  
...  
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Neural Networks ◽  
Network Analysis ◽  
Mass Spectra ◽  
Variety Identification ◽  
Neural Network Analysis ◽  
Spectra Processing

scholarly journals Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials

2021 ◽  
Author(s):  
Daniil A. Boiko ◽  
Evgeniy O. Pentsak ◽  
Vera A. Cherepanova ◽  
Evgeniy G. Gordeev ◽  
Valentine P. Ananikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Carbon Material ◽  
Deep Neural Network ◽  
Carbon Materials ◽  
Material Surface ◽  
Neural Network Analysis ◽  
Pd Nanoparticle ◽  
Sem Imaging

Defectiveness of carbon material surface is a key issue for many applications. Pd-nanoparticle SEM imaging was used to highlight “hidden” defects and analyzed by neural networks to solve order/disorder classification and defect segmentation tasks.

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.

scholarly journals Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

2019 ◽  
Vol 11 (5) ◽  
pp. 1449 ◽  
Author(s):  
Koffi Dumor ◽  
Li Yao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
International Trade ◽  
Network Analysis ◽  
Gravity Model ◽  
Accurate Estimation ◽  
Belt And Road Initiative ◽  
Neural Network Analysis ◽  
Belt And Road ◽  
The Belt And Road

The Belt and Road Initiative (BRI) under the auspices of the Chinese government was created as a regional integration and development model between China and her trade partners. Arguments have been raised as to whether this initiative will be beneficial to participating countries in the long run. We set to examine how to estimate this trade initiative by comparing the relative estimation powers of the traditional gravity model with the neural network analysis using detailed bilateral trade exports data from 1990 to 2017. The results show that neural networks are better than the gravity model approach in learning and clarifying international trade estimation. The neural networks with fixed country effects showed a more accurate estimation compared to a baseline model with country-year fixed effects, as in the OLS estimator and Poisson pseudo-maximum likelihood. On the other hand, the analysis indicated that more than 50% of the 6 participating East African countries in the BRI were able to attain their predicted targets. Kenya achieved an 80% (4 of 5) target. Drawing from the lessons of the BRI and the use of neural network model, it will serve as an important reference point by which other international trade interventions could be measured and compared.

scholarly journals Bankruptcy in Indian Private Sector Banks: A Neural Network Analysis

2020 ◽  
Vol 19 (3) ◽  
pp. 89-114
Author(s):  
Surbhi Dhama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Private Sector ◽  
Financial Institutions ◽  
Economic Condition ◽  
Banking Industry ◽  
Financial Data ◽  
Neural Network Analysis ◽  
Private Banks

This paper aims to predict the bankruptcy in Indian private banks using financial ratios such as ROA, GNPA, EPS, PAT, and GNP of the country. This paper also explains the importance of Ohlson’s number, Graham’s number and Zmijewski number as the major predictors of bankruptcy while developing a model using neural networks. For the prediction, the financial data for private sector banks of India such as HDFC, HDFC, ICICI, AXIS, YES bank, KOTAK MAHINDRA Bank, FEDERAL BANK, INDUSIND Bank, RBL and KARUR VYSYA for the last 10 years from 2010-2019 have been analysed. The model developed during the research will help the financial institutions and banks in India to understand the economic condition of the banking industry.

The Possibilities of Neural Network Analysis to Evaluate the Prognosis of Chronic Heart Failure in Elderly Patients

10.12737/9067 ◽  
2015 ◽  
Vol 22 (1) ◽  
pp. 6-11
Author(s):  
Медведев ◽  
N. Medvedev ◽  
Лобынцева ◽  
E. Lobyntseva
Keyword(s):  
Neural Network ◽  
Heart Failure ◽  
Neural Networks ◽  
Chronic Heart Failure ◽  
Network Analysis ◽  
Vascular System ◽  
Interstitial Fibrosis ◽  
Activity Level ◽  
Neural Network Analysis ◽  
Matrix Metalloproteinase 1

New diagnostic approaches to establish the severity of chronic heart failure as a widespread syndrome on a background of cardiovascular diseases should integrate the results of various studies of the pathogenesis and create a basis for risk assessment of its progression, estimation of the individual prognosis. To develop an algorithm of integrated assessment and prediction of functional disorders of the cardio-vascular system, a neural network analysis of echo- and Doppler-cardiography indicators, markers of subclinical inflammation, lipid disorders, oxidative stress, apoptosis, interstitial fibrosis in the myocardium, reflecting the severity of the major pathogenetic processes in the progression of heart failure in elderly hypertensive patients was carried out. The use of neural network analysis by means of neuro-imitator NeuroPro 0,25 on the basis of a consultation of neural networks has provided a highly accurate assessment of the risk of cardiovascular disorders. As results of the experiment were 15 neural networks of minimum structure with their simplification by reducing the number of input signals, allowed to accurately predict the functional class of heart failure. The highest factor importance of reducing serum levels of tissue inhibitor of matrix metalloproteinase-1 less than 500 pg/ml, the increasing end-diastolic dimensions of the left ventricle over 5 cm, the activity level of high-sensitivity C-reactive protein more than 5 mg/l in determining the prognosis of progression of chronic heart failure were identified

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