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Showing 3 results for Fadaei
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. Mahdi Homayounfar , Dr. Mehdi Fadaei , Mr. Hamed Gheibdoust , Dr. Hamidreza Rezaee Kelidbari,
Volume 13, Issue 1 (6-2022)
Abstract
Abstract: Recently, Multi-objective optimization by ratio analysis (MOORA) as a new and efficient Multiple-criteria decision-making (MCDM) method was applied in different areas for ranking alternatives and choosing the best ones. MOORA method evaluates the studied options by using positive and negative criteria. In this paper, a literature review is presented to study the MOORA methodology and its applications. So, all published papers in Science Direct journals are investigated and categorized from different perspectives (application area, journal of publication, year of publication, authors’ nationality, and type of data in form of fuzzy /crisp). The papers covered several filed: material selection, energy, welding process, and surface roughness, automotive and wire, fuel selection, logistics and transportation, heat transfer, optimization, and other topics. It is hoped that the study is useful for researchers and also a useful reference for practitioners and academics to improve their future research. The highest amount of using the MOORA method with the number of 15 articles is related to material selection, which shows the importance of using the MOORA method for material selection. And the lowest amount of using the MOORA method with the number of two articles is related to fuel selection. The present study was able to provide a framework for future research by reviewing the MOORA method. The results show that the MOORA method is one of the most efficient methods for evaluating options in different fields, which can be used in different areas.
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.
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