Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

A neural network model for preeclampsia prediction based on risk factors

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Hamid Mokhtari Torshizi ◽  
Masoumeh Abaspour ◽  
Ali Ameri ◽  
Atefeh Ebrahimi ◽  
Masoumeh Mirzamoradi
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Network Model ◽  
Neural Network Model

Insole System-Based Neural Network Model to Evaluate Force Risk in Cube Method: Application to Pepper Farming Tasks

2020 ◽  
Vol 10 (6) ◽  
pp. 1444-1451
Author(s):  
Hyunwoo Jung ◽  
Ahnryul Choi ◽  
Jose Moon ◽  
Seung Heon Chae ◽  
Kyungsuk Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Risk Factor ◽  
Musculoskeletal Disorders ◽  
Network Model ◽  
Neural Network Model ◽  
Observational Methods ◽  
Adult Males ◽  
Agricultural Field ◽  
Risk Levels

Most agricultural workers are exposed to musculoskeletal disorders due to the characteristics of agricultural work performed manually. As observational methods to prevent musculoskeletal disorders, a cube method has been proposed that considers the risk factors of posture, time and force workload simultaneously. However, force workload could evaluate using the weight of an object or qualitative measurement to prevent interfering with a worker’s occupation. The purpose of this study is to propose a novel method for evaluating quantitatively the risk factor of force in agricultural field using insole system and artificial neural network model. Agricultural simulated experiments were performed on ten healthy adult males and six observers were recruited to evaluate the risk factors of force for the experiments. The model was constructed using the signals measured in the insole system and the consensus among observers about evaluation results. To verify the performance of the model, the performance measurement was calculated using 10-fold cross-validation. The results of the proposed method are compared with those of the observers to verify reproducibility and usefulness. The model showed more than 97% prediction accuracy in all risk levels, and the proposed method showed 1.59%, 0.99 and 0.98 in the coefficient of variation, proportion agreement index, Cohen’s kappa coefficient, and high reproducibility and usefulness when compared with the observers’ evaluation. The method of quantitatively evaluating the risk factor of force proposed in this study is possible to be applied to various agricultural works using observational methods.

scholarly journals Research on diabetes prediction method based on electronic medical record data analysis

2020 ◽  
Vol 185 ◽  
pp. 03001
Author(s):  
Chen Hui ◽  
Wang Mingyuan ◽  
Tang Dingjun ◽  
Zhang Longwei ◽  
Guo Ziyan ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Risk Factors ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Network Model ◽  
Neural Network Model ◽  
Medical Record Data ◽  
Electronic Medical Record Data ◽  
Record Data

The continuous progress of computer science and technology has accelerated the pace of informatization construction of the medical system. Medical technology has developed rapidly in various research directions, and the construction of medical IT systems has been continuously improved. The popular application of electronic medical records has produced massive medical data in the medical process. At the same time, in medical behavior, more and more rely on data to make relevant judgments. The coverage of medical equipment is becoming more and more extensive, and the accuracy of data is constantly improving, and the clinical diagnosis is gradually shifting from qualitative judgment to quantitative analysis. Based on the analysis of electronic medical record data, this article studies and analyzes the risk factors leading to diabetes. By analyzing the characteristic variables, the risk factors significantly related to diabetes are obtained as the input variables of the BP neural network model. For complex problems, machine learning algorithms have higher accuracy and stronger generalization capabilities. Based on the BP artificial neural network model, this paper builds and builds a machine learning simulation to predict diabetes.

scholarly journals Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points

2005 ◽  
Vol 1 (4) ◽  
pp. 505-509 ◽  
Author(s):  
K.K. Aggarwal ◽  
Yogesh Singh ◽  
Pravin Chandra ◽  
Manimala Puri
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bayesian Regularization ◽  
Function Points

scholarly journals Application of BP Neural Network Model Optimized by Particle Swarm Algorithm in Predicting the Risk of Hypertension

2021 ◽  
Author(s):  
Yan Yan ◽  
Rong Chen ◽  
Jian Xu ◽  
Jialu Huang ◽  
Ling Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Prediction Performance ◽  
Swarm Optimization ◽  
Bp Neural Network Model

Abstract The BP neural network was optimized by particle swarm optimization algorithm (PSO), and the PSO-BP neural network model was constructed. The prediction effect of the model was evaluated comprehensively by comparing it with BP neural network model and Logistic regression model. Based on PSO-BP model, the mean impact value algorithm (MIV) was used to screen the risk factors of hypertension, and the disease risk prediction model was established. In the evaluation of fitting effect, the root mean square error and determination coefficient of PSO-BP neural network are 0.09 and 0.29, respectively. In the prediction performance comparison, the accuracy, sensitivity, specificity and area under the ROC curve of PSO-BP neural network were 85.38%, 43.90%, 96.66% and 0.86, respectively. The results show that the BP neural network optimized by particle swarm optimization has the best fitting effect and prediction performance. The MIV algorithm can screen out the risk factors related to hypertension, and then construct the disease prediction model, which can provide a new idea for the analysis of hypertension.

Intelligent Modeling of Risk Factors Contributing to Runway Incursion

2014 ◽  
Vol 1 (2) ◽  
pp. 23-34
Author(s):  
Antonia Replogle ◽  
John E. Bischoff ◽  
Christopher J. Willy ◽  
Robert A. Roncace
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Network Model ◽  
Neural Network Model ◽  
Safety Data ◽  
Federal Aviation Administration ◽  
Weather Data ◽  
Runway Safety ◽  
Runway Incursions ◽  
Runway Incursion

The purpose of this paper is to validate the use of an intelligent neural network model to identify the risk factors contributing to runway incursions. The study utilized multi-dataset fusion and a neural network model to identify risk factors. Historical runway safety data, weather data, and data on the physical characteristics of airports were obtained from multiple publicly available government websites. The results of the analysis showed that a neural network model was able to determine the factors most strongly associated with runway incursions, without the need for subjective weighting by safety experts used in most previous runway incursion studies. The Federal Aviation Administration could use a cyber-physical system, which combines human and computer processes, to analyze the runway incursion factors identified in the present study to determine which aspects of runway safety could be improved to reduce future incursions and save lives.

scholarly journals Encoding Longer-Term Contextual Information with Predictive Coding and Ego-Motion

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Junpei Zhong ◽  
Angelo Cangelosi ◽  
Tetsuya Ogata ◽  
Xinzheng Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Contextual Information ◽  
Predictive Coding ◽  
Sensory Information ◽  
Hierarchical Architecture ◽  
Motor Action ◽  
Neural Activities ◽  
Different Levels

Studies suggest that, within the hierarchical architecture, the topological higher level possibly represents the scenarios of the current sensory events with slower changing activities. They attempt to predict the neural activities on the lower level by relaying the predicted information after the scenario of the sensorimotor event has been determined. On the other hand, the incoming sensory information corrects such prediction of the events on the higher level by the fast-changing novel or surprising signal. From this point, we propose a predictive hierarchical artificial neural network model that examines this hypothesis on neurorobotic platforms. It integrates the perception and action in the predictive coding framework. Moreover, in this neural network model, there are different temporal scales of predictions existing on different levels of the hierarchical predictive coding architecture, which defines the temporal memories in recording the events occurring. Also, both the fast- and the slow-changing neural activities are modulated by the motor action. Therefore, the slow-changing neurons can be regarded as the representation of the recent scenario which the sensorimotor system has encountered. The neurorobotic experiments based on the architecture were also conducted.

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