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Showing 3 results for Shokri
Dr. Mehrdad Fadaei Pellehshahi, Dr. Sohrab Kordrostami, Dr. Amir Hosein Refahi Sheikhani, Dr. Marzieh Faridi Masouleh, Dr. Soheil Shokri,
Volume 11, Issue 2 (2-2020)
Abstract
In this study, an alternative method is proposed based on recursive deep learning with limited steps and prepossessing, in which the data is divided into A unit classes in order to change a long short term memory and solve the existing challenges. The goal is to obtain predictive results that are closer to real world in COVID-19 patients. To achieve this goal, four existing challenges including the heterogeneous data, the imbalanced data distribution in predicted classes, the low allocation rate of data to a class and the existence of many features in a process have been resolved. The proposed method is simulated using the real data of COVID-19 patients hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020, which has led to recovery or death. The obtained results are compared against three valid advanced methods, and are showed that the amount of memory resources usage and CPU usage time are slightly increased compared to similar methods and the accuracy is increased by an average of 12%.
Dr. Mehrdad Fadaei Pellehshahi, Prof. Sohrab Kordrostami, Dr. Amir Hossein Refahi Sheikhani, Dr. Marzieh Faridi Masouleh, Dr Soheil Shokri,
Volume 13, Issue 2 (12-2022)
Abstract
In this paper, a new method is presented using a combination of deep learning method, specifically recursive neural network, and Markov chain. The aim is to obtain more realistic results with lower cost in predicting COVID-19 patients. For this purpose, the BestFirst algorithm is used for the search section, and the Cfssubseteval algorithm is implemented for evaluating the features in the data preprocessing section. The proposed method is simulated using the real data of COVID-19 patients who were hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020. The obtained results were compared with three valid advanced methods. The results showed that the proposed method significantly reduces the amount of memory resource usage and CPU usage time compared to similar methods, and at the same time, the accuracy also increases significantly.
Yasaman Zibaei Vishghaei, Sohrab Kordrostami, Alireza Amirteimoori, Soheil Shokri,
Volume 15, Issue 1 (7-2024)
Abstract
The traditional inverse data envelopment analysis (IDEA) models assess specific performance metrics in relation to changes in others, without taking into consideration the existence of random and undesirable outputs. This study presents a novel inverse DEA model with random and undesirable outputs, enabling the estimation of some random performance measures for changes of other random measures. The proposed chance-constrained inverse DEA model integrates both managerial and natural disposability constraints. By using the introduced approach, the estimation of natural disposable random inputs is presented for changes in random desirable outputs. Also, undesirable outputs are assessed for the perturbation of managerial disposable random inputs while the stochastic efficiency is maintained. The models are solved as linear problems, with a numerical example provided to illustrate their application. The findings indicate that this approach is effective for evaluating efficiency and performance metrics in scenarios involving random and undesirable outputs.
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