Total mean square error (MSE) of training and testing data in the best implementation of the ANN-BTA network for estimating the mortality rate of COVID-19 patients in the ranges of... by Mouhamad Bodaghie (14838511)

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A neural network designed to estimate the mortality rate of those infected with COVID-19 virus in the period of February 19, 2020 to May 18, 2020. by Mouhamad Bodaghie (14838511)

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COVID-19 growth rate in the period of February 19, 2020 to May 18, 2020 in Iran. by Mouhamad Bodaghie (14838511)

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The MSE value of ANN-BTA network after interval limiting [1,1.2] and obtaining the lowest error rate in estimating mortality rate. by Mouhamad Bodaghie (14838511)

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The results of the sum of the squared error in 20 implementation of ANN-BTA network in three ranges of [0.1, 0.4], [0.4, 0.7], [0.7, 1]. by Mouhamad Bodaghie (14838511)

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Fig 12 - by Mouhamad Bodaghie (14838511)

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Fig 7 - by Mouhamad Bodaghie (14838511)

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The MSE value of ANN-PSO network after interval limiting [1,1.2] and obtaining the lowest error rate in estimating mortality rate. by Mouhamad Bodaghie (14838511)

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The MSE value of ANN-PSO network after interval limiting [1,1.2] and obtaining the lowest error rate in estimating basic reproduction numberand. by Mouhamad Bodaghie (14838511)

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S1 Data - by Mouhamad Bodaghie (14838511)

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Fig 5 - by Mouhamad Bodaghie (14838511)

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Fig 3 - by Mouhamad Bodaghie (14838511)

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An epidemiological learning model. by Mouhamad Bodaghie (14838511)

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Fig 4 - by Mouhamad Bodaghie (14838511)

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Total mean square error (MSE) of training and testing data in the best implementation of the ANN-PSO network for estimating the mortality rate of COVID-19 patients in the ranges of... by Mouhamad Bodaghie (14838511)

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Total mean square error (MSE) of training and experimental data in the best implementation of ANN-BTA network for estimating basic reproduction number related to COVID-19 patients... by Mouhamad Bodaghie (14838511)

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The MSE value of ANN-BTA network after interval limiting [1,1.2] and obtaining the lowest error rate in estimating basic reproduction numberand. by Mouhamad Bodaghie (14838511)

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Fig 8 - by Mouhamad Bodaghie (14838511)

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Network propagation identifies ageing-associated genes as well as genes distinguishing prostate tumors of different grades. by Konstantina Charmpi (9901053)

May 1st −7st Metro Line 2 OD Statistics Table. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”