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

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

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

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

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

Optimization Performance Comparison of Metaheuristic Algorithms. by Xie Qiu (13243770)

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

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

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

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

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

Comparative performance evaluation of 3Mont with other feature selection algorithms over 10 iterations. by Miray Unlu Yazici (14792680)

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

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IRBMO vs. meta-heuristic algorithms boxplot. by Chenyi Zhu (9383370)

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IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

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WGCNA and machine learning algorithms. by Shushu Wang (6870191)

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

Multimodal reference functions. by 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. by 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. by 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. …”