Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

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Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

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Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy by Ayla Arslan (17943365)

“…</p> <p>The study identifies specific variants (L111R, R114C, D123N, G150S, and L243P) in the coding region of the GABRD gene, which are predicted as deleterious by multiple algorithms. …”

Procedure of the DCT-based EIT algorithm. by Rongqing Chen (249906)

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Comparison of details of different algorithms. by Hao Wu (65943)

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Structure of the LMPEC algorithm model. by Hao Wu (65943)

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Multimodal reference functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Algorithm for generating virtual patients. by Adrianne L. Jenner (11133854)

“…<b>2)</b> The model evaluated is then simulated on this parameter set to obtain <i>y</i>(<i>t</i>, <i>p</i>). <b>3)</b> A simulated annealing algorithm is then used to determine a parameter set that optimises the objective function <i>J</i>(<i>p</i>) (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1009753#ppat.1009753.e059" target="_blank">Eq 17</a>). …”

The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Single-peaked reference functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Predictive Mixing for Density Functional Theory (and Other Fixed-Point Problems) by L. D. Marks (1949689)

“…Density functional theory calculations use a significant fraction of current supercomputing time. …”

Sub-network model structure in the LMPEC algorithm. by Hao Wu (65943)

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Reconstructed images using <i>ℓ</i>₂ and <i>ℓ</i>₂-smth algorithms for the first study case. by Mona Selim (12969227)

Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Fixed-dimensional multimodal reference functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

“…For the early-stage diabetes dataset, LGWO-BP achieved an accuracy of 0.92, a recall of 0.93, a precision of 0.88, an F1-score of 0.91, and an AUC of 0.95. Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Function <i>χ</i>_<i>opt</i> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

Standard benchmark 28 test functions. by Qianhang Du (13410314)

Training test results of different algorithms on natural scene exposure dataset. by Hao Wu (65943)

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