The heterogeneous phantom. حسب Masashi Yagi (3186909)

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Fig 11 - حسب Masashi Yagi (3186909)

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Treatment plan for the biological study. حسب Masashi Yagi (3186909)

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Fig 9 - حسب Masashi Yagi (3186909)

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Fig 6 - حسب Masashi Yagi (3186909)

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Fig 10 - حسب Masashi Yagi (3186909)

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Fig 12 - حسب Masashi Yagi (3186909)

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Fig 5 - حسب Masashi Yagi (3186909)

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Fig 13 - حسب Masashi Yagi (3186909)

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The variation of the DVHs of dose distributions in the patient case reassessed for 28 different perturbations of beam ranges and positions in the CTV (orange) and OAR (light blue)... حسب Masashi Yagi (3186909)

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The <i>D</i>_<i>98</i> in the CTV and the for the nominal scenario in the patient case. حسب Masashi Yagi (3186909)

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Fig 14 - حسب Masashi Yagi (3186909)

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Inspection of Line Defects in Transition Metal Dichalcogenides Using a Microscopic Hyperspectral Imaging Technique حسب Xiangmin Hu (1612399)

Data_Sheet_1_A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.PDF حسب Seema Khadirnaikar (9227318)

"…Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs–based classification, and identified a group of patients with high stem cell–like characteristic in the 10 most common cancer types and CCLE cell lines. …"

Data_Sheet_2_A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.xlsx حسب Seema Khadirnaikar (9227318)

"…Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs–based classification, and identified a group of patients with high stem cell–like characteristic in the 10 most common cancer types and CCLE cell lines. …"

Model convergence to steady state. حسب Dmitrii Smirnov (8822324)

Tissue effects. حسب Dmitrii Smirnov (8822324)

Pseudo Code of RBMO. حسب Chenyi Zhu (9383370)

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"

P-value on CEC-2017(Dim = 30). حسب Chenyi Zhu (9383370)

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"

Memory storage behavior. حسب Chenyi Zhu (9383370)

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"