Aims: Pains caused by the invasive actions such as intramuscular injection lead to the physical and mental tensions in the children. Therefore, such pains should be given relief. One of the main priorities in the nursing is to notice methods that reduce pains due to the invasive actions in the children. The aim of this study was to investigate the effects of injection displayed on a doll on the pain intensity due to the intramuscular injection in the preschool children. Materials & Methods:In the randomized controlled clinical trial, 62 kids aged between 4 and 6 years with pharyngitis were studied in the clinic of the health network of Khalil-abad Township in 2015. The intramuscular injection of penicillin 6.3.3 was administrated for the kids. The subjects, selected by simple lottery, were divided into two groups including experimental and control groups (n=31 per group). Data was collected using a demographic characteristic collecting form and Oucher standard pain assessment tool. In experimental group, the kid watching, one intramuscular injection was displayed on a doll by a nurse; then, the kid underwent an intramuscular injection. In control group, the routine injection method was done. Data was analyzed by SPSS 19 software using Mann-Whitney, independent T, and Chisquare tests. Findings: Mean pain intensity after injection in experimental group (3.22±0.90) was significantly lower than control group (4.19±0.83; p<0.001). Conclusion: The injection displayed on a doll before the intramuscular injection might lead to pain reduction in the preschool kids.

Abstract— Stocks are investments that have dynamic movements. Stock price changes move every day even hourly. With very fast changes, stock prices require predictions to be able to determine stock market projections. Predictions are used to reduce risk when making transactions. In this study, predictions of stock price trends were made using the Recurrent Neural Network (RNN). The approach taken is to perform a time series analysis using the RNN variance, namely Long Short Term Memory (LSTM). Hyperparameter construction in the LSTM model testing simulation can estimate stock prices with maximum percentage accuracy. The results showed that the prediction model produced a loss function of 0.0012 and a training time of 73 m/step. The evaluation was carried out with the RMSE which resulted in a score of 17.13325. Predictions are obtained after doing machine learning using 1239 data. The RMSE and LSTM models are calculated by changing the number of epochs, the variation between the predicted stock price and the current stock price. Computations are carried out using a stock market dataset that includes open, high, low, close, adj prices, closes, and volumes. The main objective of this study is to determine the extent to which the LSTM algorithm anticipates stock market prices with better accuracy. Code can be seen at iranihoeronis/RNN-LSTM (github.com) Keywords— Stock Prediction, Time Series, Recurrent Neural Network (RNN), Long Short Term Memory (LSTM).