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Journal articles on the topic 'XGBooster'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

YILDIRIM, Ahmet, and Ali GÜNEŞ. "Predicting Stock Prices Using Machine Learning." International Journal of Engineering Research and Applications 14, no. 7 (2024): 81–88. http://dx.doi.org/10.9790/9622-14078188.

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Predicting the closing prices of stock instruments, which have become one of the prominent investment tools in our era, has become an important topic. Investors tend to use their investments to gain profit with minimal risk. Accordingly, predicting the closing prices of stocks based on future closing values is crucial. Two different methods are used for these decisions: fundamental analysis and technical analysis. With the rapidly developing software and hardware technology, the use of statistical methods in technical analysis is rapidly increasing. In this study, the closing values of APPLE,

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Bechoo Lal, Prof. Th Basanta, and Dr. Mutum Vidyarani Devi. "Exploring the NSL-KDD Dataset: A Comprehensive Analysis about Intrusion Detection System (IDS)." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 715–22. https://doi.org/10.32628/ijsrst251222614.

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In this research article the researcher emphasized the Network threats and hazards are evolving at a high-speed rate in recent years. Many mechanisms (such as firewalls, anti-virus, anti-malware, and spam filters) are being used as security tools to protect networks. An intrusion detection system (IDS) is also an effective and powerful network security system to detect unauthorized and abnormal network traffic flow. This article presents a review of the research trends in network-based intrusion detection systems (NIDS), their approaches, and the most common datasets used to evaluate IDS Model

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Zeng, Fanchao, Qing Gao, Lifeng Wu, et al. "Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model." Atmosphere 16, no. 4 (2025): 419. https://doi.org/10.3390/atmos16040419.

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Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), (2) a feature-optimized XGBoost variant incorporating Pearson correlation analysis (XGBoost2), and (3) an enhanced CPSO-XGBoost model integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection and parameter t

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Riando, Dhafin, and Afiyati Afiyati. "Implementasi Algoritma XGBoost untuk Memprediksi Harga Jual Cabai Rawit di DKI Jakarta." Eduvest - Journal of Universal Studies 4, no. 9 (2024): 7877–89. http://dx.doi.org/10.59188/eduvest.v4i9.3784.

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This research focuses on applying the XGBoost algorithm to analyze and predict cayenne pepper prices. The main aim is to exploit XGBoost's exceptional capability to manage large datasets and discern intricate patterns for precise price forecasting. The dataset comprises historical cayenne pepper price data, along with pertinent economic and climatic factors. The XGBoost model was developed and validated on this dataset, with its performance assessed using metrics. The results indicated a high level of accuracy, achieving an R² score of 99% on the training set and 92% on the test set, reflectin

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Srinivasan, Bhavana. "Comparative Analysis of LightGBM and XGBoost for Predictive Risk Assessment in Blockchain Transactions within the Metaverse." Journal of Current Research in Blockchain 2, no. 1 (2025): 1–12. https://doi.org/10.47738/jcrb.v2i1.23.

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The growing integration of blockchain technology within the metaverse has created an urgent need for effective systems to assess and mitigate transaction risks. This study investigates the use of machine learning models, specifically LightGBM and XGBoost, for predictive risk analysis in blockchain transactions. A dataset comprising 50,000 blockchain transactions, with 75% categorized as low-risk and 25% as high-risk, was used to evaluate the performance of these models across key metrics. LightGBM emerged as the superior model, achieving an accuracy of 91.2%, surpassing XGBoost's 89.5%. Additi

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Tran, Thanh-Ngoc, and Quoc-Dai Nguyen. "Research on the Influence of Genetic Algorithm Parameters on XGBoost in Load Forecasting." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18849–54. https://doi.org/10.48084/etasr.8863.

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Electric load forecasting is crucial in a power system comprising electricity generation, transmission, distribution, and retail. Due to its high accuracy, the ensemble learning method XGBoost has been widely applied in load forecasting. XGBoost's performance depends on its hyperparameters and the Genetic Algorithm (GA) is a commonly used algorithm in determining the optimal hyperparameters for this model. In this study, we propose a flowchart algorithm to investigate the impact of GA parameters on the accuracy of XGBoost models over the hyperparameter grid for load forecasting. The maximum lo

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Yang, Hao, Jiaxi Li, Siru Liu, Xiaoling Yang, and Jialin Liu. "Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation." JMIR Medical Informatics 10, no. 6 (2022): e36958. http://dx.doi.org/10.2196/36958.

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Background Hypoglycemia is a common adverse event in the treatment of diabetes. To efficiently cope with hypoglycemia, effective hypoglycemia prediction models need to be developed. Objective The aim of this study was to develop and validate machine learning models to predict the risk of hypoglycemia in adult patients with type 2 diabetes. Methods We used the electronic health records of all adult patients with type 2 diabetes admitted to West China Hospital between November 2019 and December 2021. The prediction model was developed based on XGBoost and natural language processing. F1 score, a

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Makarand, Bhosale Sakshi. "Machine Learning Enabled Inventory Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35109.

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Effective inventory management is a critical aspect of business operations, ensuring optimal stock levels, minimizing costs, and meeting customer demand. Accurate inventory prediction plays a pivotal role in achieving these objectives. This project explores the application of the XGBoost algorithm, a powerful machine learning technique, for inventory prediction. XGBoost's ability to handle complex nonlinear relationships and its robust performance make it a promising approach for this task. The project aims to develop an inventory prediction system using the XGBoost algorithm, leveraging histo

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Makarand, Bhosale Sakshi. "Machine Learning Enabled Inventory Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30009.

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Effective inventory management is a critical aspect of business operations, ensuring optimal stock levels, minimizing costs, and meeting customer demand. Accurate inventory prediction plays a pivotal role in achieving these objectives. This project explores the application of the XGBoost algorithm, a powerful machine learning technique, for inventory prediction. XGBoost's ability to handle complex nonlinear relationships and its robust performance make it a promising approach for this task. The project aims to develop an inventory prediction system using the XGBoost algorithm, leveraging histo

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Gu, Kai, Jianqi Wang, Hong Qian, and Xiaoyan Su. "Study on Intelligent Diagnosis of Rotor Fault Causes with the PSO-XGBoost Algorithm." Mathematical Problems in Engineering 2021 (April 26, 2021): 1–17. http://dx.doi.org/10.1155/2021/9963146.

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On basis of fault categories detection, the diagnosis of rotor fault causes is proposed, which has great contributions to the field of intelligent operation and maintenance. To improve the diagnostic accuracy and practical efficiency, a hybrid model based on the particle swarm optimization-extreme gradient boosting algorithm, namely, PSO-XGBoost is designed. XGBoost is used as a classifier to diagnose rotor fault causes, having good performance due to the second-order Taylor expansion and the explicit regularization term. PSO is used to automatically optimize the process of adjusting the XGBoo

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Harriz, Muhammad Alfathan, Nurhaliza Vania Akbariani, Harlis Setiyowati, and Handri Santoso. "Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction." Jambura Journal of Informatics 5, no. 1 (2023): 1–6. http://dx.doi.org/10.37905/jji.v5i1.18814.

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This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost algorithm to forecast the capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from the Jakarta Open Data website for the period 2020-2021 as a training medium, we achieved a mean absolute percentage error of 69. However, after the model was fine-tuned, the MAPE was significantly reduced by 28.99% to 49.97. The XGBoost algorithm was found to be effective in detecting patterns and trends in the data, which can be used to improve routes and plan future studies by providing valuabl

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Chen, Rongwen, MingFu Zheng, Fulin Li, et al. "Thrust force requirement prediction using HOA‐XGBoost for TBM tunneling in squeezing ground." ce/papers 8, no. 2 (2025): 1439–47. https://doi.org/10.1002/cepa.3230.

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AbstractTunnel Boring Machine (TBM) has been widely used in deep and long tunnels due to its highly efficient advantage. However, TBM can be subject to adverse geology, especially the soft and weak ground, so the TBM jamming, which seriously affects the construction schedule, could happen. Thrust force requirement evaluation avoiding TBM jamming is therefore important for TBM tunnel construction at the early stage or machine designing phase. In this research, some intelligent machine learning models are constructed to conveniently assess the enough thrust force based on the thousands of numeri

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Lee, Jong-Hyun, and In-Soo Lee. "Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost." Batteries 9, no. 11 (2023): 544. http://dx.doi.org/10.3390/batteries9110544.

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Lithium batteries have recently attracted significant attention as highly promising energy storage devices within the secondary battery industry. However, it is important to note that they may pose safety risks, including the potential for explosions during use. Therefore, achieving stable and safe utilization of these batteries necessitates accurate state-of-charge (SOC) estimation. In this study, we propose a hybrid model combining temporal convolutional network (TCN) and eXtreme gradient boosting (XGBoost) to investigate the nonlinear and evolving characteristics of batteries. The primary g

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Sovia, Nabila Ayunda, Ni Wayan Surya Wardhani, Eni Sumarminingsih, and Elvo Ramadhan Shofa. "Enhancing Image Classification of Cabbage Plant Diseases Using a Hybrid Model Convolutional Neural Network and XGBoost." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 1 (2025): 278–89. https://doi.org/10.18860/cauchy.v10i1.30866.

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Classifying imbalanced datasets presents significant challenges, often leading to biased model performance, particularly in multiclass classification. This study addresses these issues by integrating Convolutional Neural Networks (CNN) and XGBoost, leveraging CNN’s exceptional feature extraction capabilities and XGBoost's robust handling of imbalanced data. The Hybrid CNN-XGBoost model was applied to classify cabbage plants affected by pests and diseases, which are categorized into five classes, with a significant imbalance between healthy and affected plants. The dataset, characterized by sev

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Nabilah Selayanti, Dwi Amalia Putri, Trimono Trimono, and Mohammad Idhom. "PREDIKSI HARGA PENUTUPAN SAHAM BBRI DENGAN MODEL HYBRID LSTM-XGBOOST." Informatika: Jurnal Teknik Informatika dan Multimedia 5, no. 1 (2025): 52–64. https://doi.org/10.51903/informatika.v5i1.1011.

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The ease of investing in the digital era has driven Generation Z to dominate stock market participation, particularly in blue-chip stocks such as PT Bank Rakyat Indonesia Tbk (BBRI). However, stock price fluctuations influenced by macroeconomic factors, regulations, and global market sentiment make it difficult for investors to make accurate decisions. Decisions based on insufficient information pose a significant risk of loss, especially for novice investors. This study proposes a hybrid LSTM-XGBoost approach for predicting BBRI stock prices, combining the strengths of LSTM in capturing nonli

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Zeng, Shuang, Chang Liu, Heng Zhang, Baoqun Zhang, and Yutong Zhao. "Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model." Energies 18, no. 2 (2025): 227. https://doi.org/10.3390/en18020227.

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To tackle the challenges of limited accuracy and poor generalization in short-term load forecasting under complex nonlinear conditions, this study introduces a Prophet–BO–XGBoost-based forecasting framework. This approach employs the XGBoost model to interpret the nonlinear relationships between features and loads and integrates the Prophet model for label prediction from a time-series viewpoint. Given that hyperparameters substantially impact XGBoost’s performance, this study leverages Bayesian optimization (BO) to refine these parameters. Using a Gaussian process-based surrogate model and an

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Zhao, Tianwen, Guoqing Chen, Sujitta Suraphee, Tossapol Phoophiwfa, and Piyapatr Busababodhin. "A hybrid TCN-XGBoost model for agricultural product market price forecasting." PLOS One 20, no. 5 (2025): e0322496. https://doi.org/10.1371/journal.pone.0322496.

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Price volatility in agricultural markets is influenced by seasonality, supply-demand fluctuations, policy changes, and climate. These factors significantly impact agricultural production and the broader macroeconomy. Traditional time series models, limited by linear assumptions, often fail to capture the nonlinear nature of price fluctuations. To address this limitation, we propose an integrated forecasting model that combines TCN and XGBoost to improve the accuracy of agricultural price volatility predictions. TCN captures both short-term and long-term dependencies using convolutional operati

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Yuan, Jianming. "Predicting Death Risk of COVID-19 Patients Leveraging Machine Learning Algorithm." Applied and Computational Engineering 8, no. 1 (2023): 186–90. http://dx.doi.org/10.54254/2755-2721/8/20230122.

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The first instance of COVID-19 was found in Wuhan, China, which mainly caused damage to human body in the form of respiratory diseases. In this study, an XGBoost prediction model was put forward according to the analysis on age, pneumonia, diabetes, and other attributes in the dataset, which was employed to estimate the COVID-19 patients' risk of death. In this study, a lot of preprocessing was carried out on the dataset, such as deleting null values in the dataset. In addition, there are strong correlation between sex, pnueumonia and death probability. In this study, XGBoost, CatBoost, logist

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Jose, Abin. "Phishing URL Detection Using XGBoost." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1255–60. http://dx.doi.org/10.22214/ijraset.2024.61807.

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Abstract: Phishing attacks are a major threat to cybersecurity, affecting individuals and organizations around the world. In this project we are developing a phishing site detection system using XGBoost, a widely used machine learning algorithm that is well-known for its effectiveness and precision in classification tasks. Our approach involves extracting features from URLs and related domains, preprocessing that data, and training XGBoost’s model. We test our system’s performance by using a dataset of both phishing and normal websites to see how well our system detects phishing attempts. The

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Wahyuningsih, Tri, Ade Iriani, Hindriyanto Dwi Purnomo, and Irwan Sembiring. "Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting." Computer Science and Information Technologies 5, no. 1 (2024): 23–31. http://dx.doi.org/10.11591/csit.v5i1.pp23-31.

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This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing

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Wahyuningsih, Tri, Ade Iriani, Hindriyanto Dwi Purnomo, and Irwan Sembiring. "Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting." Computer Science and Information Technologies 5, no. 1 (2024): 23–31. http://dx.doi.org/10.11591/csit.v5i1.p23-31.

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This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing

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Wahyuningsih, Tri, Ade Iriani, Hindriyanto Dwi Purnomo, and Irwan Sembiring. "Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting." Computer Science and Information Technologies 5, no. 1 (2024): 29–37. http://dx.doi.org/10.11591/csit.v5i1.pp29-37.

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This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing

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Wahyuningsih, Tri, Ade Iriani, Hindriyanto Dwi Purnomo, and Irwan Sembiring. "Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting." Computer Science and Information Technologies 5, no. 1 (2024): 29–37. http://dx.doi.org/10.11591/csit.v5i1.p29-37.

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This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing

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Al-Taie, Mohammed Zuhair. "Comparative Study of Machine Learning Approaches for Detecting Fake News in Arabic Text." IETI Transactions on Data Analysis and Forecasting (iTDAF) 3, no. 1 (2025): 18–31. https://doi.org/10.3991/itdaf.v3i1.53575.

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It is evident that fake news remains a critical global problem, especially in the Arabic language, although there is an absence of vast amounts of annotated datasets required for effective stateof- the-art natural treatment. In this paper, we compare deep neural networks (DNNs), XGBoost, gradient boosting (GB), and long short-term memory (LSTM) networks on the task of distinguishing real and fake Arabic news. When we applied special preprocessing for AFND with specific approaches to tackle the class imbalance problem, we observed that XGBoost was found to be the best method, performing with an

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Wahyuningsih, Tri, Ade Iriani, Purnomo Hindriyanto Dwi, and Irwan Sembiring. "Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting." Computer Science and Information Technologies 5, no. 1 (2024): 23–31. https://doi.org/10.11591/csit.v5i1.pp23-31.

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This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing

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Kandi, Kianeh, and Antonio García-Dopico. "Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms." Machine Learning and Knowledge Extraction 7, no. 1 (2025): 20. https://doi.org/10.3390/make7010020.

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This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the potential of LSTM networks for their ability to understand sequential dependencies in transaction data. This research sheds light on which model is more effective in addressing the challenges posed by imbalanced datasets in credit risk assessment. The methodology utilized for imbalanced datasets includes the use of the Synthetic Minority Oversampli

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Lin, Hsio-Yi, and Bin-Wei Hsu. "Empirical Study of ESG Score Prediction through Machine Learning—A Case of Non-Financial Companies in Taiwan." Sustainability 15, no. 19 (2023): 14106. http://dx.doi.org/10.3390/su151914106.

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In recent years, ESG (Environmental, Social, and Governance) has become a critical indicator for evaluating sustainable companies. However, the actual logic used for ESG score calculation remains exclusive to rating agencies. Therefore, with the advancement of AI, using machine learning to establish a reliable ESG score prediction model is a topic worth exploring. This study aims to build ESG score prediction models for the non-financial industry in Taiwan using random forest (RF), Extreme Learning Machines (ELM), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) and invest

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Zheng, Runze. "Bayesian Optimization of Lasso and XGBoost Models for Comparative Analysis in Housing Price Prediction." ITM Web of Conferences 73 (2025): 03005. https://doi.org/10.1051/itmconf/20257303005.

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Fluctuations in housing prices have a profound impact on the broader economy and people's livelihoods. Accurate housing price predictions contribute to enhanced market transparency and the formulation of evidence-based policies. This paper focuses on optimizing two machine learning models, Lasso Regression and XGBoost, using Bayesian optimization for predicting housing prices. By leveraging economic features such as Average Earnings, Gross Domestic Product (GDP), Mortgage rates, Population, and Unemployment Rate, the models aim to improve prediction accuracy in the housing market. The Lasso mo

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Sanjaykumar, Swamynathan, Karthikeyan Udaichi, Gowtham Rajendiran, Marian Cretu, and Zhanneta Kozina. "Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023." Health, sport, rehabilitation 10, no. 1 (2024): 6–19. http://dx.doi.org/10.58962/hsr.2024.10.1.6-19.

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Background and purpose Cricket, a globally renowned bat and ball sport, is the second most popular sport worldwide. The objective of the study is to utilize machine learning algorithms to predict the performance probabilities of Indian cricket players participating in the ODI Cricket World Cup 2023. Furthermore, we aim to assess and compare the predictive precision of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost. Materials and Methods Data collection centered on Indian One Day International cricket statistics, encompassing match

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Abdualjabar, Rana Dhia’a, and Osama A. Awad. "Parallel extreme gradient boosting classifier for lung cancer detection." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1610. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1610-1617.

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Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles

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Abdu-Aljabar, Rana Dhiaa, and Osama A. Awad. "Parallel extreme gradient boosting classifier for lung cancer detection." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1610–17. https://doi.org/10.11591/ijeecs.v24.i3.pp1610-1617.

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Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles

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Velasquez-Vasconez, Pedro Alexander, and Danita Andrade Díaz. "LeafArea Package: A Tool for Estimating Leaf Area in Andean Fruit Species." International Journal of Plant Biology 15, no. 1 (2024): 102–9. http://dx.doi.org/10.3390/ijpb15010009.

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The LeafArea package is an innovative tool for estimating leaf area in six Andean fruit species, utilizing leaf length and width along with species type for accurate predictions. This research highlights the package’s integration of advanced machine learning algorithms, including GLM, GLMM, Random Forest, and XGBoost, which excels in predictive accuracy. XGBoost’s superior performance is evident in its low prediction errors and high R2 value, showcasing the effectiveness of machine learning in leaf area estimation. The LeafArea package, thus, offers significant contributions to the study of pl

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Kannan, Deeba, Balakrishnan Amutha, Sattianadan Dasarathan, et al. "Virtual analysis of machine learning models for diseases prediction in muskmelon." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1748. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1748-1759.

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Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host p

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Kannan, Deeba, Balakrishnan Amutha, Sattianadan Dasarathan, et al. "Virtual analysis of machine learning models for diseases prediction in muskmelon." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1748–59. https://doi.org/10.11591/ijeecs.v33.i3.pp1748-1759.

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Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host p

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Luo, Xiong, Lijia Xu, Peng Huang, et al. "Nondestructive Testing Model of Tea Polyphenols Based on Hyperspectral Technology Combined with Chemometric Methods." Agriculture 11, no. 7 (2021): 673. http://dx.doi.org/10.3390/agriculture11070673.

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Nondestructive detection of tea’s internal quality is of great significance for the processing and storage of tea. In this study, hyperspectral imaging technology is adopted to quantitatively detect the content of tea polyphenols in Tibetan teas by analyzing the features of the tea spectrum in the wavelength ranging from 420 to 1010 nm. The samples are divided with joint x-y distances (SPXY) and Kennard-Stone (KS) algorithms, while six algorithms are used to preprocess the spectral data. Six other algorithms, Random Forest (RF), Gradient Boosting (GB), Adaptive boost (AdaBoost), Categorical Bo

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Guo, RuYan, MinFang Peng, ZhenQi Cao, and RunFu Zhou. "Transformer graded fault diagnosis based on neighborhood rough set and XGBoost." E3S Web of Conferences 243 (2021): 01002. http://dx.doi.org/10.1051/e3sconf/202124301002.

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Aiming at the uncertainty of fault type reasoning based on fault data in transformer fault diagnosis model, this paper proposed a hierarchical diagnosis model based on neighborhood rough set and XGBoost. The model used arctangent transformation to preprocess the DGA data, which could reduce the distribution span of data features and the complexity of model training. Using 5 characteristic gases and 16 gas ratios as the input characteristic parameters of the XGBoost model at all levels, reduction was performed on these 21 input feature attributes, features that had a high contribution to fault

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Wang, Yuqi. "Bank Marketing Prediction Based on XGBoost." Advances in Economics, Management and Political Sciences 193, no. 1 (2025): 14–23. https://doi.org/10.54254/2754-1169/2025.lh24150.

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Under the dual challenges of fintech evolution and digital transformation, commercial banks face increasing limitations in traditional marketing prediction methods, which struggle with static customer profiling, low data utilization, and poor adaptability to real-time demands. This study addresses these gaps by proposing an XGBoost-based predictive framework to enhance precision marketing and risk-adjusted returns in banking scenarios. We integrate multidimensional features, including static attributes (e.g., age, occupation) and dynamic behavioral indicators (e.g., consumer confidence index,

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Mandhar, Kartikeya. "A Machine Learning Approach for Study of Emission Standards on Used Car Prices in India." Journal of Engineering Research and Reports 26, no. 8 (2024): 381–401. http://dx.doi.org/10.9734/jerr/2024/v26i81253.

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This study investigates the influence of emission standards on second-hand car prices in India using advanced machine learning techniques. Utilizing a comprehensive dataset from CarDekho, we performed two distinct analyses to explore this relationship. Initially, we excluded emission standards, employing various regression algorithms, with Random Forest and XGBoost achieving accuracies close to 94%. Upon introducing emission standards into the models, Random Forest's accuracy slightly improved to 94.25%, while XGBoost's accuracy decreased to 88.08%, highlighting different algorithmic responses

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Abbas, Adel T., Mohamed O. Helmy, Abdulhamid A. Al-Abduljabbar, Mahmoud S. Soliman, Ali S. Hasan, and Ahmed Elkaseer. "Precision Face Milling of Maraging Steel 350: An Experimental Investigation and Optimization Using Different Machine Learning Techniques." Machines 11, no. 11 (2023): 1001. http://dx.doi.org/10.3390/machines11111001.

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Maraging steel, characterized by its superior strength-to-weight ratio, wear resistance, and pressure tolerance, is a material of choice in critical applications, including aerospace and automotive components. However, the machining of this material presents significant challenges due to its inherent properties. This study comprehensively examines the impacts of face milling variables on maraging steel’s surface quality, cutting temperature, energy consumption, and material removal rate (MRR). An experimental analysis was conducted, and the gathered data were utilized for training and testing

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Gayaker, Savaş. "Türkiye'de Ekonomik Şoklar ve Krizler Bağlamında Enflasyon Öngörüsü: XGBOOST ve ARMA Yöntemlerinin Karşılaştırması." Ekonomi Politika ve Finans Arastirmalari Dergisi 9, no. 4 (2024): 877–95. https://doi.org/10.30784/epfad.1560378.

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Enflasyon, ekonomik istikrar ve büyüme üzerinde derin etkiler yaratan, temel bir makroekonomik göstergedir. Fiyatlar genel düzeyindeki süreklilik arz eden artışlar, yalnızca bireylerin satın alma güçlerini zayıflatmakla kalmayıp, ulusal ekonominin çeşitli sektörleri üzerinde de ciddi tehditler oluşturmaktadır. Dolayısıyla, enflasyonun doğru tahmini hem merkez bankaları hem de hükümetler için stratejik bir önem taşımaktadır. Bu çalışma, Türkiye’deki ekonomik şoklar ve kriz dönemlerinde, enflasyon tahmininde XGBoost ve ARMA modellerinin performansını incelemektedir. 1994 ekonomik krizi, 2001 fin

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Chimphlee, Witcha, and Siriporn Chimphlee. "Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 817. http://dx.doi.org/10.11591/ijai.v13.i1.pp817-826.

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<p>With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performan

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Champahom, Thanapong, Chinnakrit Banyong, Thananya Janhuaton, et al. "Deep Learning vs. Gradient Boosting: Optimizing Transport Energy Forecasts in Thailand Through LSTM and XGBoost." Energies 18, no. 7 (2025): 1685. https://doi.org/10.3390/en18071685.

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Thailand’s transport sector faces critical challenges in energy management amid rapid economic growth, with transport accounting for approximately 30% of total energy consumption. This study addresses research gaps in transport energy forecasting by comparing Long Short-Term Memory (LSTM) neural networks and XGBoost models for predicting transport energy consumption in Thailand. Utilizing a comprehensive dataset spanning 1993–2022 that includes vehicle registration data by size category, vehicle kilometers traveled, and macroeconomic indicators, this research evaluates both modeling approaches

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Chimphlee, Witcha, and Siriporn Chimphlee. "Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 817–26. https://doi.org/10.11591/ijai.v13.i1.pp817-826.

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With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performance. This

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Abu Bakar, Nurul Asyiqin, Wan Shafrina Wan Mohd Jaafar, Hamdan Omar, Siti Mariam Muhammad Nor, Aisyah Marliza Muhmad Kamarulzaman, and Ricky Anak Kemarau. "Modelling Above-Ground Biomass Using Machine Learning Algorithms in Mangrove Forests of Peninsular Malaysia." E3S Web of Conferences 599 (2024): 03002. https://doi.org/10.1051/e3sconf/202459903002.

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Mangrove forests are crucial for carbon sequestration and biodiversity conservation but are threatened by anthropogenic effects and climate change. Although restoration efforts have been initiated, their effectiveness remains uncertain due to the absence of robust monitoring and evaluation mechanisms. This study investigates machine learning algorithms for modelling aboveground biomass (AGB) in mangrove forests across Peninsular Malaysia. Data on tree diameter at breast height (DBH) and species were collected in Sungai Pulai, Sungai Johor, and Sungai Merbok. Combined with remote sensing data,

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Celen, Burak, Melik Bugra Ozcelik, Furkan Metin Turgut, et al. "Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries." Open Research Europe 2 (August 12, 2022): 96. http://dx.doi.org/10.12688/openreseurope.14745.1.

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Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxi

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Celen, Burak, Melik Bugra Ozcelik, Furkan Metin Turgut, et al. "Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries." Open Research Europe 2 (February 22, 2023): 96. http://dx.doi.org/10.12688/openreseurope.14745.2.

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Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxi

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G. Mohan. "Silent Sentinel: The Unseen Battle of Prostate Cancer Early Diagnosis with Advanced Artificial Neural Network Technology." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 369–78. https://doi.org/10.52783/jisem.v10i36s.6451.

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Introduction: The condition of prostate cancer continues to represent a substantial medical issue because it necessitates precise predictive models that support healthcare decisions. A research study investigates how Extreme Gradient Boosting (XGBoost) performs when identifying significant biomarkers to anticipate prostate cancer risks. The research compares XGBoost to Sequential Minimal Optimization (SMO) in Support Vector Machines (SVM) to evaluate its better performance in classification. Prostate-Specific Antigen (PSA) levels together with Gleason scores and molecular markers form the clin

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Susanto, Hary, and Ema Utami. "COMPARATIVE ANALYSIS TO PREDICT READING LITERACY BASED ON PISA 2022 USING GRADIENT BOOSTED DECISION TREES AND EXTREME GRADIENT BOOSTING." G-Tech: Jurnal Teknologi Terapan 9, no. 1 (2025): 390–99. https://doi.org/10.70609/gtech.v9i1.6257.

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Reading is a fundamental skill essential for interdisciplinary understanding and serves as a crucial indicator of the educational quality of a nation. PISA provides an international evaluation of students' reading literacy across various countries, including Indonesia. Numerous studies have utilized machine learning algorithms to predict reading literacy; however, achieving high model accuracy remains a significant challenge. This study compares the performance of Gradient Boosting Decision Trees (GBDT) and Extreme Gradient Boosting (XGBoost), two widely recognized machine learning algorithms

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OUKHOUYA, HASSAN, HAMZA KADIRI, KHALID EL HIMDI, and RABY GUERBAZ. "Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models." Statistics, Optimization & Information Computing 12, no. 1 (2023): 200–209. http://dx.doi.org/10.19139/soic-2310-5070-1822.

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   Forecasting time series is crucial for financial research and decision-making in business. The nonlinearity of stock market prices profoundly impacts global economic and financial sectors. This study focuses on modeling and forecasting the daily prices of key stock indices - MASI, CAC 40, DAX, FTSE 250, NASDAQ, and HKEX, representing the Moroccan, French, German, British, US, and Hong Kong markets, respectively. We compare the performance of machine learning models, including Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost), and the hybrid LSTM-XGBoost, a

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M’hamdi, Oussama, Sándor Takács, Gábor Palotás, Riadh Ilahy, Lajos Helyes, and Zoltán Pék. "A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data." Plants 13, no. 5 (2024): 746. http://dx.doi.org/10.3390/plants13050746.

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The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato quality, utilizing data from 48 cultivars and 28 locations in Hungary over 5 seasons. It focused on °Brix, lycopene content, and colour (a/b ratio) using extreme gradient boosting (XGBoost) and artificial neural network (ANN) models. The results revealed that XGBoost consistently outperformed ANN, achieving high accuracy in predicting °Brix (R

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