FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology حسب Pieter B. Burger (4172578)

"…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …"

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

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"

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

"…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …"

SSA4LSMOP حسب Zongbin Qiao (19814199)

"…<p dir="ltr">SSA4LSMOP is a multi-objective optimization algorithm library, which aims to provide researchers and developers with efficient and easy-to-use multi-objective optimization solutions. …"

Flowchart scheme of the ML-based model. حسب Noshaba Qasmi (20405009)

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP حسب Xiaoyuan Wang (492534)

"…In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. …"

Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data حسب Fei Xue (24567)

"…Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. …"

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML حسب Ayush Garg (21090944)

"…The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. …"

Table_1_Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.docx حسب Liyin Zhang (6371999)

"…As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention.…"

Optimal 8-mer and 9-mer SARS-CoV-2 epitope identification. حسب Mariah Hassert (5746874)

"…As controls for strong Kb and Db binders respectively, Ova peptide and ZIKV E294 were input into the same algorithm. Optimal peptide epitopes are highlighted based on functional T cell data in combination with RMA-S stabilization assay data.…"

Face recognition accuracy of distinctive ways. حسب Yan Liu (25061)

Changes in loss curves of different models. حسب Yan Liu (25061)

RFB structure. حسب Yan Liu (25061)

Facial recognition flowchart in FaceNet network. حسب Yan Liu (25061)

Visualization results of face recognition. حسب Yan Liu (25061)

ROC curves of different models. حسب Yan Liu (25061)