Kurtograms of different signals. by Hang Zhao (143592)

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

“…In this study, we utilized the extreme gradient boosting (XGBoost) algorithm to facilitate the discovery and characterization of ABX₃ compounds based on vast data sets generated by DFT calculations. …”

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

“…In this study, we utilized the extreme gradient boosting (XGBoost) algorithm to facilitate the discovery and characterization of ABX₃ compounds based on vast data sets generated by DFT calculations. …”

Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach by Soundous Touati (20282599)

“…In this study, we utilized the extreme gradient boosting (XGBoost) algorithm to facilitate the discovery and characterization of ABX₃ compounds based on vast data sets generated by DFT calculations. …”

Dendrogram of the stock prices. by Muhammad Hilal Alkhudaydi (21560690)

“…Investors will always look for a portfolio that can handle the required amount of risk while still producing the desired level of expected returns. This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

Descriptive statistics on stock prices. by Muhammad Hilal Alkhudaydi (21560690)

“…Investors will always look for a portfolio that can handle the required amount of risk while still producing the desired level of expected returns. This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

Correlation heatmap of the principal components. by Muhammad Hilal Alkhudaydi (21560690)

“…Investors will always look for a portfolio that can handle the required amount of risk while still producing the desired level of expected returns. This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

(a). Comparison of Translation Accuracy. by Na Wang (193263)

Effect of segmentation errors on phenotype assignments. by Matthias Bruhns (22250913)

Design of stiffened panels for stress and buckling via topology optimization: data by Sheng Chu (19655605)

“…An effective topology optimization parameterization is presented using multiple level set functions. Plate elements are employed to model the stiffened panels. …”

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

CIAHS-Data.xls by Yingchang Li (22195585)

“…At the national scale, CIAHS sites were represented as point elements using geographic coordinates, and a spatial distribution map was generated (Fig. 1).…”

Supporting data for Histone crotonylation is a novel epigenetic regulation and a therapeutic vulnerability for liver cancer treatment by Qidong Li (9069932)

16S rRNA sequencing raw data from a thermophilic trickle bed reactor for biogas upgrading by Getachew Birhanu Abera (20402390)

“…The temperature of the room was continuously monitored using a digital thermometer attached to near the TBRs ((EL-WiFi-TP Wireless Temperature Data Loggers, Ohio, U.SA.).…”

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. …”

Table 8_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 7_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. …”

Image 4_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif 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 5_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. …”