Table 11_Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.docx by Hui Sheng (371448)

“…Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. …”

Table 9_Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.xlsx by Hui Sheng (371448)

“…Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. …”

Table 4_Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.xlsx by Hui Sheng (371448)

“…Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. …”

Table 2_Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.xlsx by Hui Sheng (371448)

“…Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. …”

Table 3_Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.xlsx by Hui Sheng (371448)

“…Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. …”

Table 7_Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.xlsx by Hui Sheng (371448)

“…Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. …”

Table 6_Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.xlsx by Hui Sheng (371448)

“…Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. …”

Table 5_Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.xlsx by Hui Sheng (371448)

“…Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. …”

Image 2_Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.jpeg by Hui Sheng (371448)

“…Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. …”

High-Dimensional Variable Clustering based on Maxima of a Weakly Dependent Random Process by Alexis Boulin (20659921)

“…Our work provides some theoretical insights into the consistency of our algorithm, demonstrating that under certain conditions it can effectively identify clusters in the data with a computational complexity that is polynomial in the dimension. …”

SURGE - Spatial User Recommendations using Geographical metrics and sEmantics by Ramon Hermoso (20135370)

“…The query and every element in the set of data consists of physical coordinates and a set of keywords. …”

In this paper, we use the term AI in its broadest sense. by Aidan Crilly (21743791)

“…<p>It thus covers a wide-ranging set of algorithms, including machine-learning and deep-learning techniques as subcategories, as illustrated here. …”

Video 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.mp4 by Matthew Yedutenko (5142461)

“…Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. …”

Data Sheet 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.pdf by Matthew Yedutenko (5142461)

“…Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. …”

supporting data for PHD thesis entitled " Arousal Regulation and Neurofeedback Treatment for ADHD Children" by Yuliang Wang (9151616)

“…Analyses use standardized mean differences (Hedges g) under random-effects models, stratified by comparator type (medicine, active, sham, passive) and, where applicable, contrasted across protocol families (customised algorithm, SCP, SMR, TBR).</p><p dir="ltr">The supporting dataset contains the <b>raw arm-level descriptive statistics</b> required to compute effect sizes: per study, outcome, and timepoint it lists group means, standard deviations, and sample sizes for neurofeedback and control arms, along with rater, comparator category, protocol type, and outcome direction coding (so higher values consistently reflect the intended construct). …”

Data Sheet 1_Immunological biomarkers and gene signatures predictive of radiotherapy resistance in non-small cell lung cancer.zip by Jie Lv (235936)

“…Using advanced machine learning techniques like SVM-RFE, LASSO regression, and random forest algorithms, four pivotal genes—TGFBI, FAS, PTK6, and FA2H—were identified. …”

Supporting files for thesis "Deep-learning-based Morphological Modelling: Case Study in Soft Robot Control, Shape Sensing and Deformation" by Yingqi Li (9151304)

“…Considering 2D images are more accessible and common compared to 3D topology data, this thesis also investigates soft tissue modelling via medical images. …”

Echo Peak by Rocco De Marco (14146593)

“…</p><p dir="ltr">For classification, the algorithm iteratively processes the audio in overlapping time windows. …”

Table 1_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…Additionally, the least absolute shrinkage and selection operator (LASSO) regression algorithm in machine learning was used to screen transcription factors such as bHLH, WRKY, and MYB that influenced the expression of TaAkPHY genes. …”

Table 12_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…Additionally, the least absolute shrinkage and selection operator (LASSO) regression algorithm in machine learning was used to screen transcription factors such as bHLH, WRKY, and MYB that influenced the expression of TaAkPHY genes. …”