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Relevant bibliographies by topics / XG boost

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Academic literature on the topic 'XG boost'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "XG boost"

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Kumavat,, Aditya. "Phishing URL and Website Detection using MI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41473.

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Phishing attacks remain a significant threat in the digital landscape, with cybercriminals constantly developing sophisticated techniques to deceive users into revealing sensitive information. This study presents a robust framework for phishing URLs and website detection utilizing the XG-Boost (Extreme Gradient Boosting) algorithm, known for its superior performance and efficiency in classification tasks. The proposed system focuses on analyzing various features extracted from URLs and webpage content, including lexical, structural, and host-based attributes, to distinguish between legitimate

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Nisha Gurung, MD Rokibul Hasan, Md Sumon Gazi, and Md Zahidul Islam. "Algorithmic Trading Strategies: Leveraging Machine Learning Models for Enhanced Performance in the US Stock Market." Journal of Business and Management Studies 6, no. 2 (2024): 132–43. http://dx.doi.org/10.32996/jbms.2024.6.2.13.

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In the recent past, algorithmic trading has become exponentially predominant in the American stock market. The principal objective of this research was to explore the employment of machine learning frameworks in formulating algorithmic trading strategies tailored for the US stock market. For this investigation, an array of software tools was employed, comprising the Pandas library for data manipulation and analysis, the Python programming language, the Scikit-learn library for machine learning algorithms and analysis metrics, and the LIME library for explainable AI. In this study, the research

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Zheng, Ziwei, Yuanyu Chen, Yongzhong Yang, et al. "A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers." International Journal of Environmental Research and Public Health 19, no. 15 (2022): 9165. http://dx.doi.org/10.3390/ijerph19159165.

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The dark and humid environment of underground coal mines had a detrimental effect on workers’ skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boo

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Zheng, Ziwei, Zhikang Si, Xuelin Wang, et al. "Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset." International Journal of Environmental Research and Public Health 20, no. 4 (2023): 3411. http://dx.doi.org/10.3390/ijerph20043411.

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OBJECTIVE: Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic. METHODS: We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed from March to September 2019. A group of 2992 steelworkers was used as the study population. Three models of Logistic regression, CNN, and XG Boost were established to predict HUA incidence in steelworkers, respectively. The predictive effects of the three models were evaluated in terms of discriminat

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Vemulapalli, Saritha, M. Sushma Sri, P. Varshitha, P. Pranay Kumar, and T. Vinay. "An experimental analysis of machine learning techniques for crop recommendation." Nigerian Journal of Technology 43, no. 2 (2024): 301–8. http://dx.doi.org/10.4314/njt.v43i2.13.

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Taking a country into consideration where agriculture remains the primary occupation and farming still happens using conventional methods, the farmers are not able to produce anticipated yields. Modern farming strategies called precision farming play a vital role in improving crop yield and generating more profit for the farmers. This includes recommendations of crops that are suitable for specific fields based on soil conditions, temperature, rainfall, and humidity. To solve this problem, crop recommendation systems play an important role. In this research work, a crop recommendation system (

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I., Gede Pajar Bahari. "Hyperparameter Optimization in XG Boost for Insurance Claim Prediction." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1510–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201630.

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Pavithraa, G. "Analysis and Comparison of Prediction of Heart Disease Using Novel Genetic Algorithm and XGBoost Algorithm." CARDIOMETRY, no. 25 (February 14, 2023): 778–82. http://dx.doi.org/10.18137/cardiometry.2022.25.778782.

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Aim: Prediction of coronary sickness utilizing novel genetic algorithm and contrasting its accuracy with XG boost algorithm. Materials and methods: Two models are proposed for foreseeing the accuracy (%) of coronary infection. To be unequivocal, a novel genetic algorithm and XG boost algorithm. Here we take 20 samples each for evaluation and analysis. Result: The novel genetic algorithm gives better accuracy (88.35%) than the XG boost accuracy (81.88%). Along these lines the genuine meaning of novel genetic algorithms is superior to XGBoost calculation with significance value of 0.115 Conclusi

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Shen, Siyin. "A Review of Risks Associated with Machine Learning in Application to Quantitative Investment." Academic Journal of Science and Technology 3, no. 3 (2022): 35–38. http://dx.doi.org/10.54097/ajst.v3i3.2538.

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Based on inspirations and ideas from relevant literatures, this paper evaluated the risks associated with using random forest, XG Boost and logistic regression for quantitative investment from the perspective of its accuracy, adaptability, efficiency, simplicity and interpretability. Overall, the random forest and the XG Boost contains better accuracy and have higher adaptability than the logistic regression as they are susceptible to different data types. The XG Boost have the fastest processing speed which gives it higher efficiency over the other two, however it is also the most difficult t

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Kumar, Hemant, Soumyabrata Chakravarty, Nitesh Kuamr, and Nikhil Kumar. "Prediction of laser welding qualities of Al alloys using regression and machine learning techniques." Materials Research Express 12, no. 6 (2025): 066501. https://doi.org/10.1088/2053-1591/addd68.

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Abstract This work compared different machine learning models such as linear regression, polynomial regression and XG-Boost for the prediction of laser welding qualities in aluminum alloys. The key weld quality parameters are ultimate load, weld width and penetration depth. Each model was trained and validated based on data experimentally collected by varying laser power, scanning speed and offset distance to compare them. Quantitative results are shown to prove that XG-Boost produces a better predictive accuracy, as it gives a root mean square error (RMSE) of 0.05 for ultimate load, 0.03 for

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P. K., Rajani, Kalyani Patil, Bhagyashree Marathe, Prerna Mhaisane, and Atharva Tundalwar. "Heart Disease Prediction using Different Machine Learning Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 354–59. http://dx.doi.org/10.17762/ijritcc.v11i9s.7430.

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Identifying a person's potential for developing heart disease is one of the most challenging tasks medical professionals faces today. With nearly one death from heart disease every minute, it is the leading cause of death in the modern era [4]. The database is taken from Kaggle. Various machine learning algorithms are used for prediction of heart disease detection here are Random Forest, XG-Boost, K- Nearest Neighbors (KNN), Logistic Regression, Support Vector Machines (SVM). All these algorithms are implemented using Python programming with Google collab. The performance evaluation parameters

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Book chapters on the topic "XG boost"

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Chunamari, Abhishek, M. Yashas, Aparajitha Basu, D. K. Anirudh, and C. S. Soumya. "Quora Question Pairs Using XG Boost." In Emerging Research in Computing, Information, Communication and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1342-5_55.

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Shankar, Aryan Vinod, Megha Ramamurthy, and Golda Dilip. "Credit Card Fraud Detection Using XG Boost." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82383-1_23.

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Raj, S. Naga Mallik, Eali Stephen Neal Joshua, K. Swathi, S. Neeraja, and Debnath Bhattacharyya. "Analyzing Comments on Social Media with XG Boost Mechanism." In Advances in Intelligent Systems and Computing. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8364-0_5.

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Mishra, Sakshi, Ritwik Kapoor, Yukti, and G. Mahesh. "Prediction of Health Insurance Premium Using XG Boost Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9112-5_26.

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Nandha, Gopal J., S. Muthukaruppasamy, Arun Sampaul Thomas, Doss Adaikalam I. Arul, and Anand A. Jose. "Precision Poverty Evaluation: Leveraging Generalized Linear Models and XG Boost Algorithms." In Smart Technologies for Sustainable Development Goals. CRC Press, 2024. http://dx.doi.org/10.1201/9781003519010-13.

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Zhu, Tianlei, Yingke Yang, Shu Bao, and Hassan Raza. "House Price Prediction Using XG-Boost Grid Search and Cross-Validation Methods." In Springer Proceedings in Mathematics & Statistics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52965-8_47.

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Baby, Anna, Nasirul Haque, and P. Preetha. "Partial Discharge Localization for a Single Source Using Acoustic Sensors and XG-Boost Algorithm." In Emerging Technologies & Applications in Electrical Engineering. CRC Press, 2024. http://dx.doi.org/10.1201/9781003505181-8.

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Sindhuri, N., B. Sindhu, Neti Praveen, and Marisetti SriDurga. "Smart Guard: Unravelling Credit Card Fraud Patterns Using Decision Tree and XG-Boost Algorithms." In Algorithms in Advanced Artificial Intelligence. CRC Press, 2025. https://doi.org/10.1201/9781003641537-92.

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Vignesh, P. Jai, and G. Charlyn Pushpa Latha. "Improving accuracy in comprehensive analysis of educational institutions using XG Boost compared to DBSCAN algorithm." In Pedagogical Revelations and Emerging Trends. CRC Press, 2024. https://doi.org/10.1201/9781003587538-40.

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Chhabra, Aman, and Manoranjan Rai Bharti. "Mammography Image Classification and Detection by Bi-LSTM with Residual Network Using XG-Boost Approach." In Intelligent Data Engineering and Analytics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7524-0_15.

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Conference papers on the topic "XG boost"

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Nagarajan, G., N. Karthik Reddy, Y. Varun Kumar, A. Rithish Reddy, and T. Chandu. "Water Quality Classification Using XG Boost." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT). IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545085.

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Chowdary, K. Sruthi, L. Krishna Praneetha, S. Holika, D. Bindhu Priya, S. Venkatrama Phani Kumar, and K. Venkata Krishna Kishore. "Prediction of Food Wastage using XG Boost." In 2024 8th International Conference on Inventive Systems and Control (ICISC). IEEE, 2024. http://dx.doi.org/10.1109/icisc62624.2024.00059.

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Dubey, Ratnesh Kumar, Rinki Pakshwar, Aravendra Kumar Sharma, Suraj Sharma, and Shubha Mishra. "Stock Price Forecast Using XG Boost Classifier Algorithm." In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2024. https://doi.org/10.1109/icaccm61117.2024.11059057.

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Sri, P. Navya, G. Kranthi Kumar, Y. Hari Priya, and G. Pranav Chowdary. "Categorization of Non-Alcoholic Fatty Liver Disease Using XG-Boost." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837138.

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Luo, Shuang, Xinhua El, and Xiaoli Li. "Data Preprocessing Method for Landslide Displacement Prediction Based on XG Boost." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606761.

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Choudhari, Amey Adinath, Rajeswaran Ayyadurai, Jayant Maruti Hudar, V. K. Arthi, Tasriqul Islam, and G. Jayashree Hareesh. "Prediction of Stock Price Using XG Boost Integrated Bi-Channel LSTM." In 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT). IEEE, 2025. https://doi.org/10.1109/ce2ct64011.2025.10939258.

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Kavitha, K., K. Gopalakrishnan, S. Kavitha, K. Sobiya, M. Suveetha, and R. Sridhar. "A Data-Driven Approach to Heart Disease Prognosis using XG Boost Algorithm." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725827.

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Mehortra, Richa, Shilpi Bisht, Neeraj Bisht, and Anshul Srivastava. "Comparative Analysis of Multinomial Naïve Bayes and XG Boost for Sentiment Analysis." In 2024 International Conference on Signal Processing and Advance Research in Computing (SPARC). IEEE, 2024. https://doi.org/10.1109/sparc61891.2024.10828695.

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Chaithra, K. N., Neelappa, Aman Shukla, Harsh Kanodiya, and Supriya Ray. "Performance of XG Boost over other ML models for Prediction of CVD." In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET). IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10895004.

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Zhang, Jingyao, Wei Jin, Haiyan Zhang, Haoyu Wang, and Nanting Liu. "Research on the Application of XG Boost Based on Quantum Genetic Optimization." In 2025 2nd International Conference on Smart Grid and Artificial Intelligence (SGAI). IEEE, 2025. https://doi.org/10.1109/sgai64825.2025.11009356.

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Reports on the topic "XG boost"

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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accident

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