eXtreme gradient boosting algorithm with machine learning: A review

ZA Ali, ZH Abduljabbar, HA Tahir, AB Sallow… - Academic Journal of …, 2023‏ - cir.nii.ac.jp
< jats: p> The primary task of machine learning is to extract valuable information from the
data that is generated every day, process it to learn from it, and take useful actions. Original …

[HTML][HTML] Ai in thyroid cancer diagnosis: Techniques, trends, and future directions

Y Habchi, Y Himeur, H Kheddar, A Boukabou, S Atalla… - Systems, 2023‏ - mdpi.com
Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years,
offering advanced tools and methodologies that promise to revolutionize patient outcomes …

Forecasting gold price with the XGBoost algorithm and SHAP interaction values

SB Jabeur, S Mefteh-Wali, JL Viviani - Annals of Operations Research, 2024‏ - Springer
Financial institutions, investors, mining companies and related firms need an effective
accurate forecasting model to examine gold price fluctuations in order to make correct …

Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process

KK Yun, SW Yoon, D Won - Expert Systems with Applications, 2021‏ - Elsevier
The stock market has performed one of the most important functions in a laissez-faire
economic system by gathering people, companies, and flows of money for several centuries …

A survey on multi-objective hyperparameter optimization algorithms for machine learning

A Morales-Hernández, I Van Nieuwenhuyse… - Artificial Intelligence …, 2023‏ - Springer
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
performance of Machine Learning (ML) algorithms. Several methods have been developed …

Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost)

T Kavzoglu, A Teke - Bulletin of Engineering Geology and the Environment, 2022‏ - Springer
Abstract Machine learning algorithms have progressively become a part of landslide
susceptibility map** practices owing to their robustness in dealing with complicated and …

[HTML][HTML] A survey on wearable technology: History, state-of-the-art and current challenges

A Ometov, V Shubina, L Klus, J Skibińska, S Saafi… - Computer Networks, 2021‏ - Elsevier
Technology is continually undergoing a constituent development caused by the appearance
of billions new interconnected “things” and their entrenchment in our daily lives. One of the …

Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC)

MN Uddin, J Ye, B Deng, L Li, K Yu - Journal of Building Engineering, 2023‏ - Elsevier
This study aims to provide an effective and accurate machine learning approach to predict
the compressive strength (CS) and flexural strength (FS) of 3D printed fiber reinforced …

The comparison of LightGBM and XGBoost coupling factor analysis and prediagnosis of acute liver failure

D Zhang, Y Gong - Ieee Access, 2020‏ - ieeexplore.ieee.org
This paper focuses on the comparison of dimensionality reduction effect between LightGBM
and XGBoost-FA. With respect to XGBoost, LightGBM can be built in the effect of …

Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models

SK Bhagat, T Tiyasha, SM Awadh, TM Tung… - Environmental …, 2021‏ - Elsevier
Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in
two Bays (ie, Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) …