Artificial Neural Network Model for Predicting the Viscosity of Crosslinked Polyacrylamide and Polyethylenimine Polymer Gel for Oilfield Water Control by S M Safayet Aziz (20832236)

“…The model was trained using the Levenberg-Marquardt algorithm. The hidden layer uses the tangent sigmoid (Tansig) activation function, and the output layer employs a linear (Purelin) activation function. …”

k-means based load estimation of domestic smart meter measurements by Meng Cheng (19655023)

“…The developed algorithm applies cluster centres - of previously clustered load profiles - and distance functions to estimate missing and future measurements. …”

Parameter estimates of mixed generalized Gaussian distribution for modelling the increments of electroencephalogram data by Zoe Salinger (19090613)

“…For this purpose, a new strictly stationary strong mixing diffusion model with marginal multimodal (three-peak) distribution (MixGGDiff) and exponentially decaying autocorrelation function for modeling of increments of EEG data was proposed. …”

Supplementary materials for PhD thesis 'Characterisation Of The Blazhko Effect In RR Lyrae Stars Using SuperWASP Data' by Paul Greer (22619328)

“…Blazhko periods were calculated for 18 out of 20 highly modulated objects by phase-folding the amplitude modulation induced upper envelope function of their light curves.<br><br>983 Blazhko objects have been identified using coincident low frequency peaks and sidebands, or equidistant sidepeaks, in frequency spectra created using the CLEAN algorithm. …”

Data Sheet 1_Artificial clicks (Porpoise ALert) affect acoustic monitoring of harbour porpoises and their echolocation behaviour.pdf by Joseph G. Schnitzler (12084395)

“…The effectiveness of management measures depends on the availability of reliable monitoring data, which is essential given the urgent need to improve the conservation of harbour porpoises, which are declining in the western Baltic Sea within the waters of Schleswig-Holstein. Finally, the study design was maximised to provide further information on PAL functionality and effectiveness as warning devices. …”

Data envelopment analysis clustering: an optimization based machine learning by Qingxian An (6837521)

“…Through an iterative heuristic algorithm, we optimize the clustering process by minimizing the distance between the cross-evaluated weights and the common weights within a cluster. …”

Relational network prepared by the authoring team. by Lesedi Mamodise Modise (20826978)

“…Additionally, team members are interconnected with color-coded bands that denote functional clusters within the group. <i>Figure representing relationships among team members found at:</i> <a href="https://www.teamcard.io/team/demo" target="_blank">https://www.teamcard.io/team/demo</a>. …”

Table 1_Phosphate transporter gene families in rye (Secale cereale L.) – genome-wide identification, characterization and sequence diversity assessment via DArTreseq.xlsx by David Chan-Rodriguez (4865152)

“…Based on DArTreseq genotyping of 94 diverse rye accessions we identified 820 polymorphic sites within rye ScPht, including 12 variants identified by the SIFT algorithm as having a potentially deleterious effect, of which three are scored as high confidence. …”

Data Sheet 1_Phosphate transporter gene families in rye (Secale cereale L.) – genome-wide identification, characterization and sequence diversity assessment via DArTreseq.docx by David Chan-Rodriguez (4865152)

“…Based on DArTreseq genotyping of 94 diverse rye accessions we identified 820 polymorphic sites within rye ScPht, including 12 variants identified by the SIFT algorithm as having a potentially deleterious effect, of which three are scored as high confidence. …”

Data Sheet 1_Feature selection and aggregation for antibiotic resistance GWAS in Mycobacterium tuberculosis: a comparative study.pdf by Kirill Reshetnikov (21562853)

“…In this study, we evaluated several such methods, namely, logistic regression with different regularization penalty functions, a recently introduced algorithm for solving the best-subset selection problem (ABESS) and “Hungry, Hungry SNPos” (HHS) a heuristic algorithm specifically developed to identify resistance-associated genetic variants in the presence of resistance co-occurrence. …”

PCNVBrowser by Bioinfo lab. (16615338)

“…</p><p dir="ltr">In total, the resource includes <b>41,964 CNV files</b>, organized at multiple levels of integration:</p><ul><li><b>Individual-level integration</b>: CNV calls from four algorithms combined per individual sample.</li><li><b>Tool-specific population-level integration</b>: CNVs aggregated across individuals within each population for each detection tool.…”

Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes by Juliette Legrand (21771740)

“…The application compares different predictive algorithms and test their capacity at forecasting river discharges from other river stations. …”

Data Sheet 1_Prefrontal meta-control incorporating mental simulation enhances the adaptivity of reinforcement learning agents in dynamic environments.pdf by JiHun Kim (20945054)

“…In addition, hippocampal function, particularly mental simulation capacity, proves essential in this adaptive process. …”

Image 3_Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma.jpeg by Beichen Cai (16842684)

“…Single-cell RNA sequencing (scRNA-seq) was leveraged to investigate the cellular heterogeneity within the SKCM tumor microenvironment, and macrophage populations were categorized using the Monocle2 algorithm. …”

Image 5_Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma.jpeg by Beichen Cai (16842684)

“…Single-cell RNA sequencing (scRNA-seq) was leveraged to investigate the cellular heterogeneity within the SKCM tumor microenvironment, and macrophage populations were categorized using the Monocle2 algorithm. …”

Image 8_Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma.jpeg by Beichen Cai (16842684)

“…Single-cell RNA sequencing (scRNA-seq) was leveraged to investigate the cellular heterogeneity within the SKCM tumor microenvironment, and macrophage populations were categorized using the Monocle2 algorithm. …”

Table 1_Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma.docx by Beichen Cai (16842684)

“…Single-cell RNA sequencing (scRNA-seq) was leveraged to investigate the cellular heterogeneity within the SKCM tumor microenvironment, and macrophage populations were categorized using the Monocle2 algorithm. …”

Image 2_Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma.jpeg by Beichen Cai (16842684)

“…Single-cell RNA sequencing (scRNA-seq) was leveraged to investigate the cellular heterogeneity within the SKCM tumor microenvironment, and macrophage populations were categorized using the Monocle2 algorithm. …”

Image 6_Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma.jpeg by Beichen Cai (16842684)

“…Single-cell RNA sequencing (scRNA-seq) was leveraged to investigate the cellular heterogeneity within the SKCM tumor microenvironment, and macrophage populations were categorized using the Monocle2 algorithm. …”

Image 7_Identification and validation of a KRAS-macrophage-associated gene signature as prognostic biomarkers and potential therapeutic targets in melanoma.jpeg by Beichen Cai (16842684)

“…Single-cell RNA sequencing (scRNA-seq) was leveraged to investigate the cellular heterogeneity within the SKCM tumor microenvironment, and macrophage populations were categorized using the Monocle2 algorithm. …”