scholarly journals Análisis y Estimación de Precipitación para Modelado de Caudal del Río Juan Díaz en el Distrito de Panamá Utilizando Redes Neuronales

2018 ◽  
Vol 3 (1) ◽  
pp. 963
Author(s):  
Fernando X. Arias ◽  
Maytee Zambrano
Keyword(s):  
Neural Network ◽  
High Risk ◽  
Urban Development ◽  
Geographical Area ◽  
Potential Solution ◽  
Flow Measurements ◽  
Design And Implementation ◽  
Ground Station ◽  
High Precipitation ◽  
Del Rio

When high levels of urban development, and erratic patterns of high precipitation combine in a small geographical area, there is a significant increase in the risk of human and/or material losses due to flooding and related incidents. With the objective of providing a method for the estimation of precipitation patterns in an area with a high risk of flooding, the current document describes the design and implementation of a neural-network-based system as a potential solution. With the use of TRMM satellite data, and ground station flow measurements in the Juan Díaz river, two models are developed for the estimation of the behavior of these magnitudes: one for estimating precipitation levels based on time, and one that estimates the flow of the river as a function of precipitation.Keywords: modeling, estimation, precipitation, flow, river.

Design and implementation of a neural network building block

2002 ◽  
Author(s):  
R.J. Schalkoff ◽  
A.E. Turner ◽  
R. Singh ◽  
K.F. Poole ◽  
S. King ◽  
...  
Keyword(s):  
Neural Network ◽  
Building Block ◽  
Design And Implementation ◽  
Network Building

Identification of WHO II/III gliomas by 16 prognostic-related gene signatures using machine learning methods

2021 ◽  
Vol 28 ◽  
Author(s):  
YaMeng Wu ◽  
Yu Sa ◽  
Yu Guo ◽  
QiFeng Li ◽  
Ning Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Overall Survival ◽  
High Risk ◽  
Who Grade ◽  
Machine Learning Methods ◽  
Medium Risk ◽  
Grade Ii ◽  
Fully Connected ◽  
Genome Atlas

Background: It is found that the prognosis of gliomas of the same grade has large differences among World Health Organization(WHO) grade II and III in clinical observation. Therefore, a better understanding of the genetics and molecular mechanisms underlying WHO grade II and III gliomas is required, with the aim of developing a classification scheme at the molecular level rather than the conventional pathological morphology level. Method: We performed survival analysis combined with machine learning methods of Least Absolute Shrinkage and Selection Operator using expression datasets downloaded from the Chinese Glioma Genome Atlas as well as The Cancer Genome Atlas. Risk scores were calculated by the product of expression level of overall survival-related genes and their multivariate Cox proportional hazards regression coefficients. WHO grade II and III gliomas were categorized into the low-risk subgroup, medium-risk subgroup, and high-risk subgroup. We used the 16 prognostic-related genes as input features to build a classification model based on prognosis using a fully connected neural network. Gene function annotations were also performed. Results: The 16 genes (AKNAD1, C7orf13, CDK20, CHRFAM7A, CHRNA1, EFNB1, GAS1, HIST2H2BE, KCNK3, KLHL4, LRRK2, NXPH3, PIGZ, SAMD5, ERINC2, and SIX6) related to the glioma prognosis were screened. The 16 selected genes were associated with the development of gliomas and carcinogenesis. The accuracy of an external validation data set of the fully connected neural network model from the two cohorts reached 95.5%. Our method has good potential capability in classifying WHO grade II and III gliomas into low-risk, medium-risk, and high-risk subgroups. The subgroups showed significant (P<0.01) differences in overall survival. Conclusion: This resulted in the identification of 16 genes that were related to the prognosis of gliomas. Here we developed a computational method to discriminate WHO grade II and III gliomas into three subgroups with distinct prognoses. The gene expression-based method provides a reliable alternative to determine the prognosis of gliomas.

scholarly journals Identification Of Number Using Artificial Neural Network Backpropagation

2018 ◽  
Vol 215 ◽  
pp. 01011
Author(s):  
Sitti Amalia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Data ◽  
Simulation Test ◽  
Backpropagation Algorithm ◽  
Network Parameter ◽  
Artificial Intelligent ◽  
Design And Implementation ◽  
Network Training ◽  
Artificial Neural

This research proposed to design and implementation system of voice pattern recognition in the form of numbers with offline pronunciation. Artificial intelligent with backpropagation algorithm used on the simulation test. The test has been done to 100 voice files which got from 10 person voices for 10 different numbers. The words are consisting of number 0 to 9. The trial has been done with artificial neural network parameters such as tolerance value and the sum of a neuron. The best result is shown at tolerance value varied and a sum of the neuron is fixed. The percentage of this network training with optimal architecture and network parameter for each training data and new data are 82,2% and 53,3%. Therefore if tolerance value is fixed and a sum of neuron varied gave 82,2% for training data and 54,4% for new data

scholarly journals Design and implementation of an intelligent vision and sorting system

2009 ◽  
Author(s):  
◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Support Vector Machines ◽  
Robot Control ◽  
Support Vector ◽  
Design And Implementation ◽  
The Neural Network ◽  
Vector Machines ◽  
Control Computer ◽  
Sorting System

This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.

The Spatial Consequences of Bureaucratic Decisionmaking

1983 ◽  
Vol 15 (11) ◽  
pp. 1489-1500
Author(s):  
J R Bohland ◽  
J Gist
Keyword(s):  
Spatial Distribution ◽  
High Risk ◽  
Risk Aversion ◽  
Urban Development ◽  
Grant Program ◽  
Basic Goal ◽  
High Distress ◽  
Type Of Behavior

The research tests the proposition that risk aversion is a basic goal of bureaucratic decisionmaking within the Urban Development Action Grant Program (UDAGP) and that this type of behavior influences the spatial distribution of UDAGP grants. The results demonstrate that risk aversion is evident and that it tends to divert funds away from those cities with high distress. Political accommodation is shown to be evident in cases where bureaucrats are faced with high-risk projects. This accommodation influences the spatial distribution of UDAGP funds.

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