Leveraging Advanced Mathematical Methods in Artificial Intelligence to Explore Heterogeneity and Asymmetry in Cross-Border Travel Satisfaction by Yan Xu, Huajie Yang, Zibin Ye, Xiaobo Ma, Lei Tong, Xinyi Yu

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A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis by Pan Lu, Zijian Zheng, Yihao Ren, Xiaoyi Zhou, Amin Keramati, Denver Tolliver, Ying Huang

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Study on privacy preserving encrypted traffic detection by Xinyu ZHANG, Bingsheng ZHANG, Quanrun MENG, Kui REN

“…Existing encrypted traffic detection technologies lack privacy protection for data and models, which will violate the privacy preserving regulations and increase the security risk of privacy leakage.A privacy-preserving encrypted traffic detection system was proposed.It promoted the privacy of the encrypted traffic detection model by combining the gradient boosting decision tree (GBDT) algorithm with differential privacy.The privacy-protected encrypted traffic detection system was designed and implemented.The performance and the efficiency of proposed system using the CICIDS2017 dataset were evaluated, which contained the malicious traffic of the DDoS attack and the port scan.The results show that when the privacy budget value is set to 1, the system accuracy rates are 91.7% and 92.4% respectively.The training and the prediction of our model is efficient.The training time of proposed model is 5.16 s and 5.59 s, that is only 2-3 times of GBDT algorithm.The prediction time is close to the GBDT algorithm.…”
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Experimental investigation and laser control in Ti10Mo6Cu powder bed fusion: optimizing process parameters with machine learning by Ouf A. Shams, Hanan B. Matar Al-Baity, Luttfi A. Al-Haddad

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Kombinasi K-NN dan Gradient Boosted Trees untuk Klasifikasi Penerima Program Bantuan Sosial by Elly Firasari, Umi Khultsum, Monikka Nur Winnarto, Risnandar Risnandar

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Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Sani I. Abba, Jamil Hussain, Soo-Mi Choi

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Engine Mass Flow Estimation through Neural Network Modeling in Semi-Transient Conditions: A New Calibration Approach by T. Savioli, M. Pampanini, G. Visani, L. Esposito, C. A. Rinaldini

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Spatial Differentiation Mechanism of Urban Housing Prices from the Perspective of Amenity: A Case Study of Nanjing by Guangyuan Feng, Xiaopu Bi, Jingxiang Zhang, Tianhan Cheng

“…An evaluation indicator system of human environment quality was established under the amenity connotation based on three dimensions of natural amenity, artificial amenity and social atmosphere amenity, and the Gradient Boosting Decision Tree (GBDT) algorithm was applied to investigate the impact of different amenity factors on housing prices. …”
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Associations between life’s essential 8 and arthritis among adults in United States: a national-wide longitudinal study by Feiying He, Xiangchun Xu, Hongwen Yu, Ziyang Miao, Zhaolin Fu, Lei Shi

“…Subgroup analyses ideallighted poorer scores for smoking (P < 0.002, poor_socre and intermediate_score) and physical activity(P = 0.001, poor_score) as significant risk factors. Gradient Boosting Decision Trees predicted disease risk, with age, HDL cholesterol, and blood pressure identified as the three most significant predictive factors. …”
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Development of a machine learning prognostic model for early prediction of scrub typhus progression at hospital admission based on clinical and laboratory features by Youguang Lu, Zixu Wang, Junhu Wang, Yingqing Mao, Chuanshen Jiang, Jinpiao Wu, Haizhou Liu, Haiming Yi, Chao Chen, Wei Guo, Liguan Liu, Yong Qi

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Methodology for detecting anomalies in cyber attack assessment data using Random Forest and Gradient Boosting in machine learning by A. S. Kechedzhiev, O. L. Tsvetkova, A. I. Dubrovina

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Examining Nasdaq Market Data and Presenting an Optimized Model by Extreme Gradient Boosting Regression and Artificial Bee Colony by Ali Ahmadpour

“…The study introduces an Extreme Gradient Boosting Regression (XGBR) model optimized with three distinct metaheuristic algorithms: Battle Royal Optimization (BRO), Moth Flame Optimization (MFO), and Artificial Bee Colony (ABC). …”
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Target Threat Assessment Method for UUVs Based on EBM by Shuwei LIU, Jianqing CHENG, Kai LIU

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Attention-driven multi-model architecture for unbalanced network traffic intrusion detection via extreme gradient boosting by Oluwadamilare Harazeem Abdulganiyu, Taha Ait Tchakoucht, Ahmed El Hilali Alaoui, Yakub Kayode Saheed

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Comparative Evaluation of Machine Learning Models for Mobile Phone Price Prediction: Assessing Accuracy, Robustness, and Generalization Performance by Saima Anwar Lashari, Muhammad Muntazir Khan, Abdullah Khan, Sana Salahuddin, Muhammad Noman Ata

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Explainable tuned machine learning models for assessing the impact of corrosion on bond strength in concrete by Maryam Bypour, Alireza Mahmoudian, Mohammad Yekrangnia, Mahdi Kioumarsi

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Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm by Qiyuan Cui, Jianhong Pu, Wei Li, Yun Zheng, Jiaxi Lin, Lu Liu, Peng Xue, Jinzhou Zhu, Mingqing He

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Explainable artificial intelligence visions on incident duration using eXtreme Gradient Boosting and SHapley Additive exPlanations by Khaled Hamad, Emran Alotaibi, Waleed Zeiada, Ghazi Al-Khateeb, Saleh Abu Dabous, Maher Omar, Bharadwaj R.K. Mantha, Mohamed G. Arab, Tarek Merabtene

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Impacts of characteristics of service facilities in metro station area on housing prices by Minqing Zhu, Chengwang Zhou, Yizhe Yang, Hongjun Cui, Xinwei Ma

“…The results showed that: (1) the development of metro stations encourages the evolution of housing prices and service facilities, and there is a more pronounced concentration effect of service facility allocation intensity on the urban central circle; (2) the characteristics of service facilities in metro station areas have a nonlinear effect and threshold effect on housing prices; (3) the gradient boosting decision tree model explains a premium effect, which is better than the ordinary least square model; (4) kernel the density is the most significant, and affects housing prices, with per capita quantity decreasing as diversity increases over time. …”
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Моделі для аналізу успішності стартапів та прогнозування їх виживання на ринку by Д. Чорнобривець, К. Сергійчук, Т. Ліхоузова

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