Optimization in science and engineering
Ali Sheykhani; Farshad Hosseinzadeh Lotfi; Arash Maghsoudi
Abstract
Worldwide, the rate of preterm births is increasing, so there will be significant health, development and economic problems. Premature birth is one of the leading causes of death and a significant cause for the loss of human potential among survivors around the world. Complications of preterm birth are ...
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Worldwide, the rate of preterm births is increasing, so there will be significant health, development and economic problems. Premature birth is one of the leading causes of death and a significant cause for the loss of human potential among survivors around the world. Complications of preterm birth are the single largest direct cause of neonatal death. Current methods for early detection of such labor are insufficient. One promising technique, recognized in monitoring uterine activity, is the use of advanced device learning algorithms and electrohistrography (EHG) induction. In this article, a learning machine is designed to diagnose different types of deliveries. Using deep learning algorithms, electrohistrographic signals have been used to detect preterm birth. The results were obtained using a data set that included 262 cases for women who had a preterm delivery and 38 cases for women who had a preterm delivery. Using the "cross" technique, 4 types of data sets were implemented in two ways, with training and without training. The results obtained in this study showed that the error on this set of data was one percent.
Data mining and related topics
Saman Haratizadeh; Fatemeh Rezaee
Abstract
Purpose: Selection of the best stocks for the portfolio as well as allocating the optimal amount of capital per stock in the portfolio are serious challenges in investing in the stock market. The use of machine learning capacities in the process of optimal capital allocation among portfolio assets has ...
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Purpose: Selection of the best stocks for the portfolio as well as allocating the optimal amount of capital per stock in the portfolio are serious challenges in investing in the stock market. The use of machine learning capacities in the process of optimal capital allocation among portfolio assets has received less attention and usually, the same weight is assigned to portfolio stocks or traditional risk assessment methods are used to divide capital between portfolio stocks. The common disadvantage of these methods is that they all use simple and inflexible mechanisms to estimate the performance of a set. The purpose of this paper is to show for the first time, that machine learning can be used to create a more effective mechanism for estimating performance, which leads to a more efficient allocation of capital to portfolio stocks.Methodology: Our proposed framework, uses two predictive models based on machine learning. In the first step, stocks historical information is used in a return forecasting model, then based on the predicted returns, the appropriate stocks of the portfolio are selected. In the second step, a separate forecasting model predicts portfolio returns by taking into account both the forecasted returns in the first model and the expected risk of the stocks. At the end based on the predicted return of the numerous random portfolios, the appropriate weight for each asset is selected.Findings: Comparing the returns of adjusted portfolios with this model and adjusted portfolios with other portfolio optimization methods shows the superiority of the proposed model.Originality/Value: In this paper, by using machine learning models, the process of selecting the appropriate stock of the portfolio and allocating capital among the candidate stocks is done optimally.