Multimodal reference functions. حسب Ruiyu Zhan (21602031)

"…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. …"

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

"…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. حسب Ruiyu Zhan (21602031)

"…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. …"

Acurracy comparison of CCA and FBCCA algorithms. حسب Hossein Hamidi Shishavan (19688384)

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

"…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)

"…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)

"…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. …"

MWOA-BiLSTM machine fault detection process. حسب Yi-Qiang Xia (20161326)

الموضوعات:

Run times of two algorithms. حسب Gourab Saha (8987405)

الموضوعات:

Comparison of classification accuracy across different machine learning models using the objective function metric. حسب Amal H. Alharbi (21755906)

The Pseudo-Code of the IRBMO Algorithm. حسب Chenyi Zhu (9383370)

الموضوعات:

Sensor value scenario for fuzzy logic algorithm. حسب Gourab Saha (8987405)

الموضوعات:

Sensor value scenario for average value algorithm. حسب Gourab Saha (8987405)

الموضوعات:

Trainable parameters for different algorithms. حسب Anwar Hossain Efat (19942283)

Mean and standard deviation of RBF classifier performance across different training algorithms with cross-validation. حسب Mohammad Hossein Hadizadeh Isfahani (22634016)

Mean and standard deviation of MLP classifier performance across different training algorithms with cross-validation. حسب Mohammad Hossein Hadizadeh Isfahani (22634016)

Table 7 displays the results of evaluations performed on fixed-dimension multimodal benchmark functions. حسب Ruiyu Zhan (21602031)

Water consumption due to average value and fuzzy logic algorithm. حسب Gourab Saha (8987405)

الموضوعات:

Crop yield prediction accuracy using different ML algorithms. حسب Gourab Saha (8987405)

الموضوعات:

Rosenbrock function losses for . حسب Shikun Chen (14625352)

الموضوعات: