Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction by Chandan Kumar, Gabriel Walton, Paul Santi, Carlos Luza

“…This experiment was conducted on regional landslide susceptibility prediction using different ML models: logistic regression (LR), k-nearest neighbor (KNN), linear discriminant analysis (LDA), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and C5.0. The experimental results showed that R-CV often produces optimistic performance estimates, e.g., 6–18% higher than those obtained using the S-CV. …”
Get full text

Predicting the thickness of shallow landslides in Switzerland using machine learning by C. Schaller, C. Schaller, L. Dorren, M. Schwarz, C. Moos, A. C. Seijmonsbergen, E. E. van Loon

“…We tested three machine learning (ML) models based on random forest (RF) models, generalised additive models (GAMs), and linear regression models (LMs). …”
Get full text

Structural performance of additive manufactured wood-sodium silicate composite beams for sustainable construction by Hojat Hematabadi, Adefemi A Alade, Robert HR Carne, Daniel Robertson, Milinda Yapa, Armando G. McDonald, Ahmed A. Ibrahim

On Using a Mobile Application to Support Teledermatology: A Case Study in an Underprivileged Area in Colombia by Juan Pablo Sáenz, Mónica Paola Novoa, Darío Correal, Bell Raj Eapen

“…This approach was found to be pertinent in the Colombian rural context, particularly in forest regions, where dermatology specialists are not available. …”
Get full text

Studying Summer Season Drought in Western Russia by Anthony R. Lupo, Igor I. Mokhov, Yury G. Chendev, Maria G. Lebedeva, Mirseid Akperov, Jason A. Hubbart

“…The record heat, high humidity, dry weather, and smoke from forest fires caused increased human mortality rates in the Moscow region during the summer. …”
Get full text

Antiviral and Immunoenhancing Properties of 7-Thia-8-Oxoguanosine and Related Guanosine Analogues by Donald F Smee, Howard B Cottam, Brahma S Sharma, Ganesh D Kini, Ganapathi R Revankar, Emmanuel A Ojo-Amaize, Robert W Sidwell, Weldon B Jolley, Roland K Robins

“…The protective effect of TOGuo against Semliki Forest and Punta Toro viruses can be eliminated by co-treatment with antibody to alpha/ beta-interferon. indicating that interferon induction is of prime importance for antiviral activity against these two viruses. …”
Get full text

Temporal variability of dissolved inorganic nitrogen and key environmental drivers in a dam-induced subtropical urban lake by Peng Tang, Boyu Ren, Tianyang Li, Qiwen Xu, Baoxiang Yang, Shunyao Zhu, Binghui He

“…The results indicated that DIN concentration was seasonally significantly different, showing higher values in winter and spring than that in summer and autumn. Random Forest modelling indicated that the temporal variations in DIN concentration were closely related to the notable seasonal fluctuations in key water quality indicators such as the temperature (T), Secchi depth (SD), and concentrations of dissolved phosphorus (DP), dissolved silica (DSi), chlorophyll-a (Chl-a), which were predominantly attributable to hydrological alterations associated with reservoir management and external pollutant inputs from agricultural fertilization. …”
Get full text

Climatic Trends in Hail Precipitation in France: Spatial, Altitudinal, and Temporal Variability by Lucía Hermida, José Luis Sánchez, Laura López, Claude Berthet, Jean Dessens, Eduardo García-Ortega, Andrés Merino

“…We found 177 pads with a negative trend, which were largely south of a pine forest in Landes. The remainder of the study area showed an elevated spatial variability with no pattern, even between relatively close hailpads. …”
Get full text

An ultrasonic-AI hybrid approach for predicting void defects in concrete-filled steel tubes via enhanced XGBoost with Bayesian optimization by Shuai Wan, Shipan Li, Zheng Chen, Yunchao Tang

“…The BO-XGBoost model demonstrated superior performance compared to baseline models (Random Forest, AdaBoost, and Gradient Boosting Decision Tree), achieving an overall prediction accuracy of 0.92, precision and recall of 0.90, and an AUC of 0.98. …”
Get full text

Avian species and functional diversity in the Yellow River Wetland Nature Reserve of Henan Province by Wanying Li, Kaiyue Wang, Peisen Yan, Tianen Chen, Haipeng Zhao, Xunling Lu

Get full text

Predicting turbidity dynamics in small reservoirs in central Kenya using remote sensing and machine learning by Stefanie Steinbach, Anna Bartels, Andreas Rienow, Bartholomew Thiong’o Kuria, Sander Jaap Zwart, Andrew Nelson

“…We found distinct monthly turbidity patterns. Random forest and gradient boosting models showed that annual turbidity outcomes depend on meteorological variables, topography, and land cover (R2 = 0.46 and 0.43 respectively), while longer-term turbidity was influenced more strongly by land management and land cover (R2 = 0.88 and 0.72 respectively). …”
Get full text

Hydrologic responses of watershed assessment to land cover and climate change using soil and water assessment tool model by R.C.C. Puno, G.R. Puno, B.A.M. Talisay

“…Meanwhile, urbanization had influenced the increase in surface runoff, evapotranspiration, and baseflow. The increase of forest vegetation resulted in a minimal decrease in baseflow and surface runoff. …”
Get full text

Forecasting yield and market classes of Vidalia sweet onions: A UAV-based multispectral and texture data-driven approach by Marcelo Rodrigues Barbosa Júnior, Lucas de Azevedo Sales, Regimar Garcia dos Santos, Rônega Boa Sorte Vargas, Chris Tyson, Luan Pereira de Oliveira

“…Afterward, the random forest (RF) was selected to perform the forecasting models for each individual date. …”
Get full text

Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms by Zhichao Wang, Long Cheng, Guanghui Li, Huiyan Cheng

“…Several machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), bagged trees, and random forest (RF), were used to select immune-related signaling genes closely associated with the occurrence of PE. …”
Get full text

Design of an iterative method for enhanced early prediction of acute coronary syndrome using XAI analysis by Shital Hajare, Rajendra Rewatkar, K.T.V. Reddy

“…The study harnesses diverse algorithms—Support Vector Machines, Logistic Regression, Gradient Boosting Machines, and Deep Forest—tailored for nuanced ACS detection, balancing simplicity with computational depth to optimize performance metrics. …”
Get full text

Using Inverse Distance Weighting to Determine Spatial Distributions of Airborne Chemical Elements. Case Study: Douala, Cameroon by Joseph Magloire Fossokeng Mouafo, Claudiu Tănăselia, André Nana Yakam, Jules Richard Priso, Mohammed Ketata, Alexandru-Ionut Petrisor

Get full text

Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning by Xiaoqing Liu, Miaoran Wang, Rui Wen, Haoyue Zhu, Ying Xiao, Qian He, Yangdi Shi, Zhe Hong, Bing Xu

“…An 80/20 train-test split was implemented for model development and validation, employing various machine learning classifiers, including artificial neural networks (ANN), random forest (RF), XGBoost (XGB), and LASSO regression. …”
Get full text

Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches by Samina Amin, Muhammad Irfan Uddin, Duaa H. alSaeed, Atif Khan, Muhammad Adnan

“…This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT), with the help of Term Frequency and Inverse Document Frequency (TF-IDF). …”
Get full text

Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms by Jinmei Liu, Juan Luo, Xu Chen, Jiyi Xie, Cong Wang, Hanxiang Wang, Qi Yuan, Shijun Li, Yu Zhang, Jianli Hu, Chen Shi

“…Five ML algorithms, such as logistic regression (LR), random forest, eXtreme Gradient Boosting, multilayer perceptron, and support vector machine, were used to predict opioid nonadherence in patients with cancer pain using 43 demographic and clinical factors as predictors. …”
Get full text

A Novel Rapeseed Mapping Framework Integrating Image Fusion, Automated Sample Generation, and Deep Learning in Southwest China by Ruolan Jiang, Xingyin Duan, Song Liao, Ziyi Tang, Hao Li

“…The NeXt-TDNN model showed an overall accuracy (OA) of 90.12% and a mean Intersection over Union (mIoU) of 81.96% in Santai County, outperforming other models such as random forest, XGBoost, and UNet-LSTM. These results highlight the effectiveness of the proposed automatic rapeseed mapping framework in accurately identifying rapeseed. …”
Get full text