Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study by Yanfei Chen, Bing Wang, Yankai Shi, Wenhao Qi, Shihua Cao, Bingsheng Wang, Ruihan Xie, Jiani Yao, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu, Danni He

“…The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. …”
Get full text

Prediction of Length of Stay After Colorectal Surgery Using Intraoperative Risk Factors by Daitlin Esmee Huisman, MD, Erik Wouter Ingwersen, MD, Joanna Luttikhold, MD, PhD, Gerrit Dirk Slooter, MD, PhD, Geert Kazemier, MD, PhD, Freek Daams, MD, PhD, LekCheck Study Group, Audrey Jongen, Carlo V. Feo, Simone Targa, Hidde M. Kroon, Emmanuel A. G. L. Lagae, Aalbert K. Talsma, Johannes A. Wegdam, Bob van Wely, Dirk J. A. Sonneveld, Sanne C. Veltkamp, Emiel G. G. Verdaasdonk, Rudi M. H. Roumen, Freek Daams

“…This study included patients who underwent colorectal surgery in 14 different hospitals between January 2016 and December 2020. Two distinct random forest models were developed: one solely based on preoperative variables (preoperative prediction model [PP model]) and the other incorporating both preoperative and intraoperative variables (intraoperative prediction model [IP model]). …”
Get full text

Land Use Modeling and Predicted Ecosystem Service Value Under Different Development Scenarios: A Case Study of the Upper–Middle Yellow River Basin, China by Mingwei Ma, Yuhuai He, Yanwei Sun, Huijuan Cui, Hongfei Zang

“…Under the EPS scenario, the area of forests, grasslands, and water would increase by 16.57%, 10.59%, and 4%, respectively. …”
Get full text

Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete by Xuyong Chen, Xuan Liu, Shukai Cheng, Xiaoya Bian, Xixuan Bai, Xin Zheng, Xiong Xu, Zhifeng Xu

“…On this basis, six machine learning models were employed to predict RAC carbonation depth: Artificial Neural Network, Decision Tree, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting. …”
Get full text

Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter Emission by Aleksandr Šabanovič, Jonas Matijošius, Dragan Marinković, Aleksandras Chlebnikovas, Donatas Gurauskis, Johannes H. Gutheil, Artūras Kilikevičius

“…In this paper, a random forest machine learning model developed to predict particulate concentrations post-cleaning demonstrated robust performance (MAE = 0.49 P/cm³, <i>R</i>² = 0.97). …”
Get full text

Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China by Deji Wuyun, Liang Sun, Zhongxin Chen, Luís Guilherme Teixeira Crusiol, Jinwei Dong, Nitu Wu, Junwei Bao, Ruiqing Chen, Zheng Sun, Hasituya, Hongwei Zhao

“…By employing a binary classification strategy and adaptive optimization, the efficiency of sample generation improved, providing more effective samples for the Random Forest algorithm. Cropland status maps were successfully generated for Inner Mongolia from 2000 to 2022 with annual accuracy between 97% and 99%. …”
Get full text

Machine Learning Models for Spring Discharge Forecasting by Francesco Granata, Michele Saroli, Giovanni de Marinis, Rudy Gargano

“…Three different machine learning algorithms were used for spring discharge forecasting in this comparative study: M5P regression tree, random forest, and support vector regression. The spring of Rasiglia Alzabove, Umbria, Central Italy, was selected as a case study. …”
Get full text

Epidemiological and sociodemographic description of snakebite envenoming cases in Paraguay reported between 2015 and 2021 by Guillermo Sequera, Sofia Ardiles-Ruesjas, Edgar Sanabria, Victor Hugo Segovia Portillo, Lorena Jara Oroa, Viviana de Egea, Julio Alonso-Padilla, Irene Losada, María Jesús Pinazo

“…Introduction Snakebite envenoming (SBE) is a public health problem in Paraguay where the presence of 15 medically important snake species has been reported. Blessed with large forested areas, its economy largely relies on agricultural production which increases the exposure of outdoor workers to the morbidity and mortality of SBE. …”
Get full text

Spatial heterogeneity in deployment and upscaling of wind power in Swedish municipalities by Yodefia Rahmad, Fredrik Hedenus, Jessica Jewell, Vadim Vinichenko

“…Municipalities with the highest large-scale deployment typically have extensive forest cover, low population density and wind speeds within a lower median range relative to the national median. …”
Get full text

Long-Term Assessment of Soil Salinization Patterns in the Yellow River Delta Using Landsat Imagery from 2003 to 2021 by Yu Fu, Pengyu Wang, Wengeng Cao, Shiqian Fu, Juanjuan Zhang, Xiangzhi Li, Jiju Guo, Zhiquan Huang, Xidong Chen

“…In this study, we constructed and evaluated three soil salinization indices—NDSI, SI, and S5—using measured soil conductivity data and three machine learning methods: Random Forest, Support Vector Machine, and XGBoost. The results indicate that the Support Vector Machine achieved the best inversion effect on regional salinization levels, with an Area Under Curve (AUC) value of 0.88. …”
Get full text

Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia by Kang Junmei, Yang Fengshuo, Wang Jun, Liu Yang, Fang Dengmao, Jiang Chengcheng

“…The results show that (1) during 2000–2022, forest in Cambodia covered a wide range, showing a landscape pattern mainly distributed in the east and west. (2) The degree of fragmentation of impervious landscape pattern increased gradually from 2000 to 2022, indicating that it was seriously affected by human activities and natural factors, and with the increase in elevation and slope, the area of various types converted to impervious decreased. (3) During 2000–2022, high habitat quality areas were concentrated in Tonle SAP Lake in the east, southwest, and central part of Cambodia, while low habitat quality areas were concentrated in the central part. (4) Natural factors, socio-economic factors, policies, and regulations all have an impact on the change in land use pattern and habitat quality in Cambodia.…”
Get full text

Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data by Johnny Downs, Robert Stewart, Alice Wickersham, Sumithra Velupillai, Lucile Ter-Minassian, Natalia Viani, Lauren Cross

“…Ethnic group and language biases were weighted using a fair pre-processing algorithm.Results Random forest and logistic regression prediction models provided the highest predictive accuracy for ADHD in population samples (AUC 0.86 and 0.86, respectively) and clinical samples (AUC 0.72 and 0.70). …”
Get full text

Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning by Mithila Akter Mim, M. R. Khatun, Muhammad Minoar Hossain, Wahidur Rahman, Arslan Munir

“…These include logistic regression (LRC), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), and bagging classification models. …”
Get full text

Analysis on the Spatio-Temporal Characteristics of Urban Expansion and the Complex Driving Mechanism: Taking the Pearl River Delta Urban Agglomeration as a Case by Luo Liu, Jianmei Liu, Zhenjie Liu, Xuliang Xu, Binwu Wang

“…From 2000 to 2015, the most important source of urban land expansion was farmland, followed by forest land. Meanwhile, the decline in the proportion of outlying expansion type indicated that the urban land has gradually become more compact. (2) From 2000 to 2015, the socio-economic factors had a greater effect on UEI than natural factors. …”
Get full text

Urban green infrastructure index: Assessing supply of regulating and cultural ecosystem services at a megacity scale by Yury Dvornikov, Valentina Grigorieva, Vyacheslav Vasenev, Mikhail Varentsov, Olga Romzaykina, Olga Maximova, Anastasia Konstantinova, Victor Matasov, Ekaterina Kozlova

“…., differences between central districts and suburbs or neighborhoods with higher and lower accessibility of the nearest urban forest) challenges the city’s environmental strategy, which aims to provide an equal access to green spaces for all citizens. …”
Get full text

Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search by Jürgen Landauer, Simon Maddison, Giacomo Fontana, Axel G. Posluschny

“…The AI classifier achieved F1 scores ranging from 34–38%, demonstrating its adaptability to diverse landscapes, including the Mediterranean terrain of Molise and Hesse’s densely forested regions. Case studies identified new potential hillforts in England and promising candidates in Hesse and Molise, underscoring the effectiveness of the approach. …”
Get full text

Effects of organic amendment on some soil physicochemical characteristics and vegetative properties of Zea mays in wetland soils of the Niger Delta impacted with crude oil by Francis E. Egobueze, Josiah M. Ayotamuno, Chukwujindu M. A. Iwegbue, Chibogwu Eze, Reuben N. Okparanma

“…Methods Two soil types were investigated, namely, fadama soil (oxisol) and swamp forest soil (utisol). For each soil type, 48 treatment cells and 1 control containing 1 kg of soil each were spiked with crude oil at concentrations ranging from 50 to 200 g kgâ1, representing 5â20% (m/m) contamination levels, respectively. …”
Get full text

Novel Indices of Glucose Homeostasis Derived from Principal Component Analysis: Application for Metabolic Assessment in Pregnancy by Tina Stopp, Michael Feichtinger, Ingo Rosicky, Gülen Yerlikaya-Schatten, Johannes Ott, Hans Christian Egarter, Christian Schatten, Wolfgang Eppel, Peter Husslein, Martina Mittlböck, Andrea Tura, Christian S. Göbl

“…PCS1 to 3 assessed at early pregnancy were also associated with development of GDM, whereby random forest analysis revealed the highest variable importance for PCS1. …”
Get full text

Assessing Short-term Flood Impact on Land Use Dynamics in Iran’s Central Zagros: A Case Study of Sefid Kuh Protected Area by Soheyl Moradi, Hossein Moradi, Jafar Dolatshah, Azita Rezvani

“…Results revealed that floods reduced the diversity and heterogeneity of the landscape, increased the fragmentation and isolation of forest patches, and enhanced the aggregation and clumpiness of bare soil patches. …”
Get full text

Identification of Plasma Proteins Associated with Alzheimer's Disease Using Feature Selection Techniques and Machine Learning Algorithms by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…The SBFS technique generated all possible combinations of protein groups from the 146 proteins, which were then trained and tested using five machine learning models: Decision Tree, Random Forest, Extremely Randomized Trees, Extreme Gradient Boosting, and Adaptive Boosting. …”
Get full text