Prediction of Recurrence in Pyogenic Vertebral Osteomyelitis: Application of Artificial Neural Network Using Time-Series Data of C-Reactive Protein

2020 ◽  
Author(s):  
Jihye Kim ◽  
Jae Sung Yee ◽  
Seok Woo Kim ◽  
Jae-Keun Oh ◽  
Tae-Hwan Kim
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Vertebral Osteomyelitis ◽  
Series Data ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Pyogenic Vertebral Osteomyelitis ◽  
Artificial Neural

scholarly journals Feature extraction for time-series data: An artificial neural network evolutionary training model for the management of mountainous watersheds

2009 ◽  
Vol 73 (1-3) ◽  
pp. 49-59 ◽  
Author(s):  
Thomas J. Glezakos ◽  
Theodore A. Tsiligiridis ◽  
Lazaros S. Iliadis ◽  
Constantine P. Yialouris ◽  
Fotis P. Maris ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Time Series Data ◽  
Training Model ◽  
Series Data ◽  
Artificial Neural ◽  
Mountainous Watersheds

scholarly journals Aplikasi Artificial Neural Network (ANN) dalamPeramalan Time Series Data: StudiKasus Lama SinarMatahari

2018 ◽  
pp. 207
Author(s):  
Sulistyarini Sulistyarini
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Series Data ◽  
Marriage Ceremony ◽  
Library Research ◽  
Wedding Ceremony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper discusses wedding ceremony in Central Lombok village of Plambik, which is potential to be a cultural attraction that supports the development of tourism. Marriage ceremony in Plambik has a number of stages, which are not necessarily similar to those customly practiced by other groups of Sasak people. in order to hold a wedding ceremony. This paper aimed to explore merariq tradition which is uniquely held by Sasak community in Plambik.  Data of this research were collected through library research and interviews with Plambik natives. The data were then analyzed by comparing the documentary notes with the actual practices of merariq by Plambik villagers. The finding indicated unique features of merariq stages in Plambik.

Artificial neural network forecasting of nonlinear Markov processes

2007 ◽  
Vol 85 (3) ◽  
pp. 279-294 ◽  
Author(s):  
Sean W Fleming
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
River Flow ◽  
Time Series Data ◽  
Series Data ◽  
Underlying Structure ◽  
Good Prediction ◽  
Data Set ◽  
Artificial Neural

I assessed the performance characteristics of the feed-forward artificial neural network (ANN) as a first-order nonlinear Markov modelling technique. The ability to recover the underlying structure of five synthetic random time series was first tested. The method was then applied to an observed geophysical time series, and the results were compared against external empirical constraints and a simple representation of the underlying physics. The Monte Carlo experiments suggested that the ANN–Markov technique: (i) yields good prediction skill; (ii) in general, accurately retrieves the form of the iterative mapping, even for extremely noisy data; (iii) accomplishes the foregoing without any need to consider or adjust for the distributional characteristics of the data or driving noise; and (iv) accurately estimates the distribution of the strictly stochastic signal component. Application to a historical river-flow record again showed good forecast skill. Moreover, the robustness, flexibility, and simplicity of the method permitted easy identification of the fundamental nonlinear physical dynamics of this environmental system directly from the time series data, perhaps belying the common perception of ANNs as a strictly black-box prediction technique. The ANN–Markov technique may thus serve as a valuable data-driven tool for guiding the development of both process-based and parameteric statistical models. The lack of specific distributional assumptions and requirements notwithstanding, it was also found that manual distributional transformations may permit the method to be tuned to particular applications by emphasizing or de-emphasizing certain features of the data. Drawbacks to the method include substantial data-set length requirements, a general limitation of ANNs, as well as an inconsistent but potentially troubling tendency to partially imprint the form of the ANN activation function upon the estimated recursion relationship. PACS Nos.: 02.50.Ga, 05.10.–a, 05.45.Tp, 07.05.Mh, 02.50.Ey, 92.40.Fb

Forecasting Time Series Data Using Artificial Neural Network

2021 ◽  
pp. 113-130
Author(s):  
Sarbjit Singh ◽  
Kulwinder Singh Parmar ◽  
Harpreet Kaur ◽  
Jatinder Kaur
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Series Data ◽  
Artificial Neural

scholarly journals Artificial neural network forecasting performance with missing value imputations

2020 ◽  
Vol 9 (1) ◽  
pp. 33
Author(s):  
Nur Haizum Abd Rahman ◽  
Muhammad Hisyam Lee
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Missing Values ◽  
Time Series Data ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Series Data ◽  
Missing Value ◽  
Artificial Neural

<span lang="EN-US">This paper presents time series forecasting method in order to achieve high accuracy performance. In this study, the modern time series approach with the presence of missing values problem is developed. The artificial neural networks (ANNs) is used to forecast the future values with the missing value imputations methods used known as average, normal ratio and also the modified method. The results are validated by using mean absolute error (MAE) and root mean square error (RMSE). The result shown that by considering the right method in missing values problems can improved artificial neural network forecast accuracy. It is proven in both MAE and RMSE measurements as forecast improved from 8.75 to 4.56 and from 10.57 to 5.85 respectively. Thus, this study suggests by understanding the problem in time series data can produce accurate forecast and the correct decision making can be produced.</span>

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