مطالب

Organizations face many challenges in the current unpredictable and changing business in which superiority in competition is one of their main goals. In this trend, the field of production has moved towards a paradigm called "Agile Production". If an organization wants to be agile, it must shorten the workflow of the process and provide the conditions for saving time. If a hospital is agile, it can respond when faced with internal and external changes and compete with other hospitals and provide appropriate and fast services to patients. Today, hospitals are currently trying to continuously evaluate their performance due to the increasing advancement of technology. A method in evaluating the performance of the organization is the Balanced Score Card (BSC) approach. In this, indicators measure the desired goals in the organization in four aspects of Internal process, Growth and Learning, Financial and Customer. In this approach, important aspects of sustainable development are ignored , therefore, the new method of Sustainable Balanced Score Card (SBSC) is used to evaluate the performance, which takes into account the environmental index. This study aimed to determine the factors that can be important for evaluating agility in the hospital and then classify them based on the perspectives of SBSC. In the next step, using a questionnaire and statistical hypothesis test, the effectiveness of these factors was confirmed. Then, the importance of each perspective in the field of agility was determined by hierarchical analysis, and this model was presented using the scoring method. Accordingly, the "Economic" index had the highest priority and the "Environmental" index had the lowest priority. Due to the high importance of the Economic index, suggestions were made, the most important of which is the use of long-term loans for the promotion and development of hospitals, effective cooperation, and the use of various insurance services of insurance organizations, considering inventory and tools. , Equipment, essential devices in the warehouse for greater efficiency. Regarding the innovation of this research, we can refer to the evaluation of agility from the perspective of the scorecard by considering the criterion of stability in the hospital.

Accurate stock market prediction can assist in an efficient portfolio and risk management. However, accurately predicting stock price trends still is an elusive goal, not only because the stock market is affected by policies, market environment, and market sentiment, but also because stock price data is inherently complex, noisy, and nonlinear. Recently, the rapid development of deep learning can make the classifiers more robust, which can be used to solve nonlinear problems. This study proposes a hybrid framework using Long Short-Term Memory, Autoencoder, and Deep Neural Networks (LSTM-AE-DNNs). Specifically, LSTM-AE is responsible for extracting relevant features, and in order to predict price movement, the features are fed into two deep learning models based on a recurrent neural network (RNN) and multilayer perceptron (MLP). The dataset used for this is Dow Jones daily stock for 2008-2018, which was used in this article. Besides, to further assess the prediction performance of the proposed model, original stock features are fed to the single RNN and MLP models. The results showed that the proposed model gives the more accurate and best results compared to another. In particular, LSTM-AE+RNN shows a better performance than the LSTM-AE+MLP. In addition, hybrid models show better performance compared to a single DNN fed with the all-stock features directly.