Performance of machine learning algorithms. by Wenxue Liu (6877754)

Raw Data for "Development of Decision Support Systems Based on Fuzzy and Binary Logic for the FOREX Foreign Exchange Market" by Serhii Hetsko (22413559)

Machine Learning-Assisted Accelerated Research of Energy Storage Properties of BaTiO₃–BiMeO₃ Ceramics by Jian Liu (33711)

“…After that, multiple machine learning algorithm models were built to train and predict <i>W</i>_rec and η. …”

Data Sheet 1_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

Data Sheet 2_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl... by Ao Zhang (372387)

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

“…The algorithm of deep deterministic policy gradient (DDPG) along with domain randomization and offline retraining facilitates fast initialization and stable path following, even under varying tip load, demonstrating its advantages over Jacobian model-based and supervised-learning-based control methods. …”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

Data Sheet 1_Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.pdf by Claire R. van Genugten (20626733)

“…For future development, it is recommended that developers utilize complex analytical techniques that can handle real-or near-time data such as machine learning, passive monitoring, and conduct further research into empirical-based decision rules and points for optimization in terms of enhanced effectiveness and user-engagement.…”

Table 1_Reducing misdiagnosis in AI-driven medical diagnostics: a multidimensional framework for technical, ethical, and policy solutions.docx by Yue Li (102191)

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

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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)

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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 8_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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 7_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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. …”

Image 4_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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 5_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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 2_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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. …”

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

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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. …”

Image 7_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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 3_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx by Zhu Yang (756364)

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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. …”

Image 6_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…Co-expression network analysis suggested that TaAkPHY genes may specifically regulate downstream genes associated with stress responses, chloroplast development, and circadian rhythms. 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. …”