BAYESIAN ADDITIVE REGRESSION TREE APPLICATION FOR PREDICTING MATERNITY RECOVERY RATE OF GROUP LONG-TERM DISABILITY INSURANCE by Stevanny Budiana, Felivia Kusnadi, Robyn Irawan

“…The decision tree-based models such as Gradient Boosting Machine, Random Forest, Decision Tree, and Bayesian Additive Regression Tree model are compared to find the best model by comparing mean squared error and program runtime. …”
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Digital mapping of soil erodibility factor in response to land use change using machine learning models by Wudu Abiye, Orhan Dengiz

“…These models were trained using the repeated tenfold cross-validation method and evaluated based on root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). …”
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Drug-Resistant Tuberculosis Hotspots in Oliver Reginald Tambo District Municipality, Eastern Cape, South Africa by Lindiwe Modest Faye, Mojisola Clara Hosu, Teke Apalata

“…The Linear Regression model predicts a continued decline to zero cases by 2026, with an R² = 0.865, a mean squared error (MSE) of 507.175, and a mean absolute error (MAE) of 18.65. …”
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Position Accuracy Improvement of the Inertial Navigation System using LSTM Algorithm without GNSS Signals by Mohammad Sabzevari, MasoudReza Aghabozorgi Sahaf

“…This system works by modeling errors and correcting them when GNSS signals are absent. …”
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Pressurized Water Reactor Transient Detection With Artificial Intelligence to Support Reactor Operators by Ceyhun Yavuz, Senem Şentürk Lüle

“…Detecting transients as fast and accurately as possible is essential to reactor safety especially to reduce the human error of operators. In order to enhance this process, artificial intelligence (AI) offers strong opportunities. …”
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Estimation of state of health for lithium-ion batteries using advanced data-driven techniques by Smitanjali Rout, Sudhansu Kumar Samal, Demissie Jobir Gelmecha, Satyasis Mishra

“…A comprehensive comparison using performance metrics such as root mean squared error, mean absolute error, and R2 scores highlights the LSTM model’s superiority while evaluating the suitability of other approaches. …”
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Correlation Analysis and Prediction of the Physical and Mechanical Properties of Coastal Soft Soil in the Jiangdong New District, Haikou, China by Yongchang Yang, Xinying Song, Shuai Zhang, Jun Hu, Ming Ruan, Dongling Zeng, Han Luo, Jiangsi Wang, Zhixin Wang

“…The results indicate that the established model exhibits strong predictive capabilities, with the mean squared error values of compression modulus (0.012), compression coefficient (1.21× 10−6), cohesion (0.081), and internal friction angle (0.003). …”
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Machine Learning‐Based Failure Prediction in Concrete Slabs and Cubes Under Impact Loading by Mohammad Hematibahar, Ahmed Deifalla, Adham E. Ragab, Gebre Tesfaldet

“…Design standards‐based statistical comparisons such as coefficient of determination and root mean square error are used to assess the efficacy of the generated models. …”
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TECs (v1): A Terrestrial Ecosystem Carbon Cycle Simulator Integrated With Spectral Reflection and Emission by Haoran Liu, Min Chen

“…After calibrating parameters, TECs accurately simulates net ecosystem exchange (NEE) (hourly: R2 = 0.80, mean absolute error (MAE) = 1.85 μmol/m2/s; daily: R2 = 0.71, MAE = 1.25 μmol/m2/s), hyperspectral reflectance (R2: 0.85, MAE: 0.04), and land surface temperature (LST) (R2: 0.85, MAE: 3.04°C). …”
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LIMITING THE VERTICAL VARIANCE OF ANNUAL RING AREA OF POPULUS NIGRA PLANTATION IN NINEVAH by Muzahem Younis

“…More over it could be explaining the translocation along the bole estimated of annual ring area at any location . it is very important for the forest management to table discern manger related with siliviculture activities, which apply in the forest, for this reason, we chose (12) tree of populous nigra grown normally in the forest plantation of Nineveh. …”
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Analyzing the Accuracy of Satellite-Derived DEMs Using High-Resolution Terrestrial LiDAR by Aya Hamed Mohamed, Mohamed Islam Keskes, Mihai Daniel Nita

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Bayesian surrogate assisted neural network model to predict the hydrogen storage in 9-ethylcarbazole by Ahsan Ali, Mohammad Usman, Hafiz Muhammad Ali, Uzair Sajjad, Md. Abdul Aziz, M. Nasiruzzaman Shaikh

“…The error density curve centered around zero emphasized the model’s accuracy and uniform error distribution.…”
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Group-Specific SVM With Bilevel Programming Methods for Parameter Optimization and Explainable AI in Urban Quality of Life Prediction by Gonzalo Rios-Vasquez, Hanns De La Fuente-Mella, Jose Ceroni-Diaz

“…The proposed approach is benchmarked against Linear Regression, Regression Tree, Random Forest, and Gradient Boosting models. The evaluation is conducted using a cross-validation procedure computing the Mean Absolute Error, the Mean Squared Error, and the Mean Absolute Percentage Error as performance metrics. …”
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The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection by Dong Dai, Zhenyu Wang, Hao Huang, Xu Mao, Yehong Liu, Hao Li, Du Chen

“…MC scanning experiments were conducted by synchronized moving waveguides; the maximum absolute error of MC prediction was 0.565%, with a maximum relative error of 3.166%. …”
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Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms by Ö. Şengül, Ş. Çelik

“…<span class="inline-formula"><i>R</i>²=0.633</span>, 0.633, 0.721, 0.637, 0.768, and 0.609), coefficient of variation (CV % <span class="inline-formula">=</span> 6.35 and 5.14, <span class="inline-formula"><i>P</i><i>&lt;</i>0.01</span>), mean square error (MSE <span class="inline-formula">=</span> 3.296, 3.296, 2.904, 4.461, 2.277, and 4.121), root mean square error (RMSE <span class="inline-formula">=</span> 1.815, 1.815, 1.704, 2.112, 1.509, and 2.030), mean absolute error (MAE <span class="inline-formula">=</span> 1.409, 1.409, 1.279, 1.702, 1.193, and 1.628), and mean absolute percentage error (MAPE <span class="inline-formula">=</span> 3.925, 3.925, 3.578, 4.002, 3.335, and 3.967), between actual and predicted values of live body weight. …”
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Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data by Shihua Li, Shunda Zhao, Zhilin Tian, Hao Tang, Zhonghua Su

“…Forest tree information is crucial for monitoring forest resources and developing forestry management strategies. …”
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Using natural vegetation succession to evaluate how natural restoration proceeds under different climate in Yunnan, Southwest China. by Weifeng Gui, Qingzhong Wen, Wenyuan Dong, Xue Ran, Xiaosong Yang, Guangqi Zou, Dechang Kong

“…Constant vigilance is required in the first five years following the implementation of restoration actions to avoid failure due to calculation errors.…”
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Pixel 5 Versus Pixel 9 Pro XL—Are Android Devices Evolving Towards Better GNSS Performance? by Julián Tomaštík, Jorge Hernández Olcina, Šimon Saloň, Daniel Tunák

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Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models by Kennedy C. Onyelowe, Viroon Kamchoom, Tammineni Gnananandarao, Krishna P. Arunachalam

“…In the same way, several experiments were conducted with a fixed value of C and ∂ kernel specific parameters in order to determine an appropriate value of error-insensitive zone (∋).Similarly, for the random forest classifier (RFC) model, the number of variables used (m) and the number of trees to be grown (k) are two user-defined parameters. …”
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Machine learning-based prediction of torsional behavior for ultra-high-performance concrete beams with variable cross-sectional shapes by Elhabyb Khaoula, Baina Amine, Bellafkih Mostafa, A. Deifalla, Amr El-Said, Mohamed Salama, Ahmed Awad

“…Existing prediction methods often fall short of accurately capturing the complex interplay between material characteristics, cross-sectional geometry, and reinforcement, leading to significant errors. This work introduces a unique Machine Learning (ML) method to accurately anticipate the torsional behavior of UHPCs. …”
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