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

“…<div><p>Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. …”

code&data by Liyuan Liu (18298084)

“…The study identified outdoor sports facilities in Shanghai using advanced deep-learning techniques on remote sensing data. It then developed a greedy heuristic algorithm based on the Gaussian Two-Step Floating Catchment Area method and Gini coefficient analysis for evaluating and optimizing facility accessibility and fairness. …”

Comparison of different algorithms. by Jiandong Qiu (20389944)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Data and code. by Hao Cao (2248066)

“…The results demonstrate that the VMD-BILSTM-AEAM algorithm achieves a mean True Positive Rate (TPR) of 0.919 with a 95% confidence interval of 0.915 to 0.924, a mean False Positive Rate (FPR) of 0.090 with a 95% confidence interval of 0.087 to 0.092, and a mean Area Under the Curve (AUC) of 0.919 with a 95% confidence interval of 0.915 to 0.923. …”

Comparison of federated learning algorithms. by Duy-Dong Le (22057362)

Data and code resources. by Sam Hall-McMaster (10343795)

“…These findings point to a possible neural implementation of an adaptive algorithm for generalization across tasks.…”

Prediction of pharmaceuticals occurrence based on sales data and Machine learning algorithms. by Júlia Bijos (14489543)

“…</p><p dir="ltr"><b>Antibioticos/Carbamazepina</b>: contains the main codes of the prediction models to classify the occurrence concentrations of some antibiotics and Carbamazepine, by tree boosting algorithms.…”

TreeMap 2016 Stand Size Code Algorithm (Image Service) by U.S. Forest Service (17476914)

“…format=iso19139 "> ISO-19139 metadata</a></li><li> <a href="https://data-usfs.hub.arcgis.com/datasets/usfs::treemap-2016-stand-size-code-algorithm-image-service "> ArcGIS Hub Dataset</a></li><li> <a href="https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_ForestEcology/TreeMap_2016_StandSizeCode_Algorithm/ImageServer "> ArcGIS GeoService</a></li></ul><div> For complete information, please visit <a href="https://data.gov">https://data.gov</a>.…”

Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

Table 7_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

Table 3_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

Table 2_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

Table 1_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

Table 4_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

Table 5_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…Three genes such as Zm00001eb176680, Zm00001eb176940, and Zm00001eb179190 expressed as bZIP transcription factor 68, glycine-rich cell wall structural protein 2, and aldehyde dehydrogenase 11 (ALDH11), respectively were commonly predicted as top-most candidates between abiotic stress and combined stresses and were identified from a weighted gene co-expression network as the hub genes in the brown module. …”

G R code algorithm. by R. Sakthivel (2589547)

“…The algorithm was developed and coded in Verilog and simulated using Modelsim. …”

HaPy-Bug - Bug localization learning to rank algorithm results by Piotr Przymus (14564009)

Source Code and Dataset by Swathi Ganesan (14394924)

Code and data. by Shangyang Jin (21772466)