The normalized acoustic resistance (blue lines) and reactance (red dotted lines) of the optimization case in Fig 7. by Xiaogang Li (299205)

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Cross section of the metagrating and the distribution of effective homogeneous layers. by Xiaogang Li (299205)

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The result for the risk assessment on three datasets using different optimal methods. by Jiaqing Huang (5018492)

High School Impact on College Level Research by Christopher Li (8222820)

“…The third project focused on using genetic algorithms (GA) to retrieve human skin optical properties from visual reflectance spectroscopy (VRS) data, proving more accurate and computationally faster than manual fitting.…”

Upper and lower bounds of bootstrap CIs for Shannon entropy and Rényi entropy at different confidence levels. by Qin Gong (118801)

Cut-based matheuristic algorithms to detect tacit collusion in deregulated electricity markets by Murat Elhüseyni (20744561)

“…In order to detect such collusions, we consider a non-linear bi-level problem formulation. The original bi-level formulation is reformulated using two alternative mixed integer linear programming formulations utilizing the linear programming property of the lower level and various linearization methods. …”

Identification of CRG based on WGCNA algorithm. by Lin Feng (84785)

<i>I</i>-<i>U</i> characteristics using all investigated methods in Table 1: RTC-F solar cell. by Ziad M. Ali (12516532)

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<i>I</i>-<i>U</i> characteristics using all investigated methods in Table 2: PWP201 solar module. by Ziad M. Ali (12516532)

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Model’s measure methods. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Summary of BirdNET performance (F1 score) for each parameter, averaged across all combinations of the other parameters and aggregation levels. Values represent the range and mean ±... by Andrew J. Fairbairn (22231970)

Classification accuracy of feature set 2 and feature set 3 in each algorithm. by Quan Shao (20148209)

Comparison of Δ, ℵ, and Θ model for classical and FF model at level 0.90. by Nausheen Razi (20529049)

Three-level model structure. by Zhanni Huang (20577481)

“…Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data. …”

Proposing sequence genetic distances with a locally greedy algorithm. by Hannah Waddel (21520023)

Risk Groups Immune Infiltration Analysis by ssGSEA Algorithm. by Yongling Wang (13261656)

The result for the risk assessment on A, B, C bank using different features. by Jiaqing Huang (5018492)

Comparison of experimental results of information entropy using different methods. by Cheng-jie Chen (22272090)

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Significantly enriched Gene ontology (GO) terms in select modules detected using the DGCA algorithm. by Fang Liu (13271)

Machine learning algorithms using the inflammatory prognostic index for contrast-induced nephropathy in NSTEMI patients by Faysal Şaylık (15296054)

“…Naive Bayes and k-nearest neighbors were the best ML algorithms for the prediction of CIN patients.</p> <p><b>Conclusion:</b> IPI might be used as an easily obtainable marker for CIN prediction using ML algorithms.…”