Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. حسب Alexis M. Thornton (11068403)

Function graph and algorithm iterative graph. حسب WanRu Zhao (18980374)

الموضوعات:

Relearning under noisy feedback signal using recursive-least-squares algorithm and local learning algorithm [47]. حسب Barbara Feulner (10104552)

"…<p>(A-B) Relearning performance, measured as mean squared error (MSE), as a function of the amplitude of the noise in the feedback signal using recursive-least-squares (RLS) algorithm (A) and an alternative implementation with a local learning algorithm (Eprop) (B). …"

Test results of multimodal benchmark functions. حسب 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. حسب 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. حسب 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. …"

Attack and defense behaviors with the sensory–motor algorithm between multiple predators and prey. حسب Hiroyuki Ichijo (13891153)

Speed response and torque response results when parameter <i>H</i>₀ changes. حسب Haoran Cui (7235234)

الموضوعات:

Performance of the Algorithm 1. حسب Bogumił Kamiński (7827794)

Ping-Pong percentage versus number of time samples. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"

Handover percentage versus number of time samples. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"

Handover percentage versus number of users. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"

MADM methods’ general flowchart. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"

Saaty’s scale of importance. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"

Load distribution results. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"

Power consumption results. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"

Handover percentage results. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"

System model. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"

Candidate networks attribute values. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"

Load distribution results. حسب Nada Ahmed Ezz-Eldien (17396553)

الموضوعات: "…gravitational search algorithm…"