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Showing 3 results for Learning
Mr. Mirmohammad Musavi, Dr. Reza Tavakkoli-Moghaddam, Ms. Farnaz Rayat,
Volume 8, Issue 1 (4-2017)
Abstract
We present a bi-objective model for a green truck scheduling and routing problem at a cross-docking system. This model determines three key decisions at the cross dock: (1) defining a sequence and schedule of inbound trucks at the receiving door, (2) specifying a sequence and a schedule of outbound trucks at the shipping door, and (3) determining the routes of the outbound truck while serving customers. The first objective function is related to responsiveness of the network that minimizes time window violations and the second objective function minimizes total fuel consumption of trucks in order to consider the environmental factor of the network. Also, a learning effect is considered in loading and unloading process times. To solve the bi-objective model, an archived multi-objective simulated annealing (AMOSA) is used and modified. Finally, a number of test problems are solved and the efficiency of the proposed AMOSA is compared with the e-constraint method.
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 Mohammad Alizadehjamal, Dr Seyed Jalal Langari ,
Volume 12, Issue 2 (11-2021)
Abstract
The purpose of the present study was to determine the effect of education using mathematical games on learning and retention of third grade elementary students. This research in terms of purpose was conducted as an applied research. Also in terms of implementation and data collection method, the quasi-experimental method and pre-test-post-test design with a control group was used. The statistical population of the present study included all 6,500 female third grade elementary school students in District 1 of Mashhad- Iran. The sampling method in this study was in convenience form that included 60 students and were selected through convenience sampling method, thus two classes with 30 female students for each classroom were selected among the elementary girls' schools in District 1 of Mashhad- Iran. In order to collect data, two researcher-made tests of learning and retention were used, the validity of which was confirmed by experts and its reliability was calculated based on Cronbach's alpha equal to 0.81 and 0.83, respectively. Multivariate analysis of covariance (MANCOVA) was used in order to test the hypotheses inferential analysis. The results of data analysis showed that math games are effective on students math both learning and retention (P <0.01). Therefore, it can be concluded that education using math games is effective and has increased students' learning and retention.
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