Schematic diagram of the cut-and-mark operation. by Xiang Tian (4369285)

“…Next, a CP model (IPMMPO-CP) applicable to multi-scenario HFS problems is proposed. Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. …”

Layout of hybrid flow shop scheduling. by Xiang Tian (4369285)

“…Next, a CP model (IPMMPO-CP) applicable to multi-scenario HFS problems is proposed. Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. …”

Probe combines and as a 2-aggregation. by Xiang Tian (4369285)

“…Next, a CP model (IPMMPO-CP) applicable to multi-scenario HFS problems is proposed. Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. …”

Scheduling Gantt chart for instance j10c10c6. by Xiang Tian (4369285)

“…Next, a CP model (IPMMPO-CP) applicable to multi-scenario HFS problems is proposed. Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. …”

Scheduling Gantt chart for instance j30c5e10. by Xiang Tian (4369285)

“…Next, a CP model (IPMMPO-CP) applicable to multi-scenario HFS problems is proposed. Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. …”

Box plot comparison on instance j30c5e10. by Xiang Tian (4369285)

“…Next, a CP model (IPMMPO-CP) applicable to multi-scenario HFS problems is proposed. Finally, based on a large number of instances and real cases, IPMMPO-CP is compared with 9 representative algorithms and 2 latest CP models. …”

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by 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. …”

Diversity and specificity of lipid patterns in basal soil food web resources by Jakob Kühn (7288466)

“…In marine environments, multivariate optimization models (Quantitative Fatty Acid Signature Analysis) and Bayesian approaches (source-tracking algorithm) were established to predict the proportion of predator diets using lipids as tracers. …”

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

“…Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction.…”

Supporting data for "clinical-oriented surgical planning based on finite element method and automate-generated surgical templates assisting the spinal surgery" by Tianchi Wu (11062323)

“…Remaining algorithm needed was reimplemented from open-source libraries.…”

Data Sheet 1_A novel method for power transformer fault diagnosis considering imbalanced data samples.docx by Jun Chen (4238)

“…Introduction<p>Machine learning-based power transformer fault diagnosis methods often grapple with the challenge of imbalanced fault case distributions across different categories, potentially degrading diagnostic accuracy. …”

DataSheet1_Towards Computational Modeling of Human Goal Recognition.pdf by Shify Treger (11973125)

“…Moreover, the proposed algorithm marries rationality-based and plan-library based methods seamlessly.…”

DataSheet1_Towards Computational Modeling of Human Goal Recognition.pdf by Shify Treger (11973125)

“…Moreover, the proposed algorithm marries rationality-based and plan-library based methods seamlessly.…”

Collaborative Research: SI2-SSI: ELSI-Infrastructure for Scalable Electronic Structure Theory by Volker Blum (3683170)

“…The ELectronic Structure Infrastructure (ELSI) project provides an open-source software interface to facilitate the implementation and optimal use of high-performance solver libraries covering cubic scaling eigensolvers, linear scaling density-matrix-based algorithms, and other reduced scaling methods in between. …”

Collaborative Research: SI2-SSI: ELSI - Infrastructure for Scalable Electronic Structure Theory by Volker Blum (3683170)

“…The ELectronic Structure Infrastructure (ELSI) project provides an open-source software interface to facilitate the implementation and optimal use of high-performance solver libraries covering cubic scaling eigensolvers, linear scaling density-matrix-based algorithms, and other reduced scaling methods in between. …”

LinearSolve.jl: because A\b is not good enough by Christopher Rackauckas (9197216)

“…For dense matrices, LU-factorization, QR-factorization, and SVD-factorization approaches are all possible ways to solve this, each making an engineering trade-off between performance and accuracy. While with Julia's Base you can use lu(A)\b, qr(A)\b, and svd(A)\b, this idea does not scale to all of the cases that can arise. …”