Greedy Man Optimization Algorithm (GMOA) by Hamed Nozari (20601020)

“…<p dir="ltr">The Greedy Man Optimization Algorithm (GMOA), developed by Hamed Nozari, is a novel bio-inspired metaheuristic designed to solve complex single and multi-objective optimization problems. …”

Effectiveness of RWOA and other SOTA algorithms. by Junhao Wei (6816803)

“…<div><p>Whale Optimization Algorithm (WOA) is a biologically inspired metaheuristic algorithm with a simple structure and ease of implementation. …”

A robust privacy-preserving online monitoring algorithm for disease surveillance by Miaomiao Yu (519470)

“…Further, we introduce an adaptive estimation method and a weighting function to reduce the sensitivity loss in the monitoring and robustness loss in the private monitoring algorithm. …”

Pathway activity and single-cell level functional states of CEDGs in cancer. by Yuxi Gu (11386081)

Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Table 7_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Table 3_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Table 2_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Table 1_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Table 4_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Table 5_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”

Envelope of the colored noise signal <i>ϵ</i>_(<i>k</i>) from recording <i>2–81731-A-10.wav</i> as a function of sample number <i>k</i>. by Martin Dodek (20623832)

Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

Scatter plots of PATH⁺ and our implementation of TNet-BP’s predictions on a held-out, test subset of PDBBind v2020 refined set for one run (90:10 train:test split ratio,... by Yuxi Long (11024307)

Strong negative correlation between the precision of algorithms and the evolutionary distance from the training data. by Sven B. Gould (12237287)

Main parameters of braking system. by Honglei Pang (22693724)

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EMB and SBW system structure. by Honglei Pang (22693724)

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Raw data. by Honglei Pang (22693724)

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Code program. by Honglei Pang (22693724)

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The HIL simulation data flowchart. by Honglei Pang (22693724)

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