Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

“…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Application of ABC SMC to the fitting of the binary flip with cusp model. by Elena Camacho-Aguilar (10906327)

Bayesian sequential design for sensitivity experiments with hybrid responses by Yuxia Liu (1779592)

“…To deal with the problem of complex computation involved in searching for optimal designs, fast algorithms are presented using two strategies to approximate the optimal criterion, denoted as SI-optimal design and Bayesian D-optimal design, respectively. …”

The overview of the proposed method. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

“…<p>Five main steps, including reading, preprocessing, feature selection, classification, and association rule mining were applied to the mRNA expression data. 1) Required data was collected from the TCGA repository and got organized during the reading step. 2) The pre-processing step includes two sub-steps, nested cross-validation and data normalization. 3) The feature-selection step contains two parts: the filter method based on a t-test and the wrapper method based on binary Non-Dominated Sorting Genetic Algorithm II (NSGAII) for mRNA data, in which candidate mRNAs with more relevance to early-stage and late-stage Papillary Thyroid Cancer (PTC) were selected. 4) Multi-classifier models were utilized to evaluate the discrimination power of the selected mRNAs. 5) The Association Rule Mining method was employed to discover the possible hidden relationship between the selected mRNAs and early and late stages of PTC firstly, and the complex relationship among the selected mRNAs secondly.…”

Probability graphical model of scMSI. by Yuqian Liu (1413763)

Illustration of the workflow combining BayesFlow with missing data encoding. by Zijian Wang (481291)

Segmentation, trace denoising and spike extraction framework. by Changjia Cai (10647521)

Table_5_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.DOCX by Dongying Zheng (8522289)

Table_1_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.XLSX by Dongying Zheng (8522289)

Image_1_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.TIF by Dongying Zheng (8522289)

Table_2_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.DOCX by Dongying Zheng (8522289)

Image_2_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.tif by Dongying Zheng (8522289)

Table_4_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.DOCX by Dongying Zheng (8522289)

Table_6_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.DOCX by Dongying Zheng (8522289)

Table_7_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.DOCX by Dongying Zheng (8522289)

Table_3_Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study.XLSX by Dongying Zheng (8522289)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…This process generated a ground-truth binary semantic segmentation mask and determined the bounding box coordinates (XYWH) for each cell. …”

Advanced constraint programming formulations for additive manufacturing machine scheduling problems by Eray Cakici (19241348)

“…The proposed CP model significantly benefits from the introduction of interval variables to replace binary assignment variables, and pre-definitions to narrow the search space, resulting in increased search performance. …”