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). …”
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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. …”
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Land Use Change Detection Between Tarsus - Karataş in Lower Seyhan Plain with Spectral Angle Mapper Technique by Mamadou Traore, Senem Tekin, Tolga Çan

“…According to the results obtained, growth of 192%, 37%, 7% and 8% growth in settlement, non-cultivated agriculture, forest and semi-natural and lagoon / lakes areas between 1985-2019, and 43% and 21% in bare and cultivated agricultural areas Decreases in rates of 21 have occurred. …”
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Nonlinear Autoregressive Neural Network for Antimicrobial Waste Water Treatment by Anwer Mustafa Hilal, Mashael M. Asiri, Shaha Al-Otaibi, Faisal Mohammed Nafie, Amal Al-Rasheed, Mohammed Rizwanullah, Ishfaq Yaseen, Abdelwahed Motwakel

“…In addition, the proposed method use the learning under supervision technique of a nonlinear autoregressive for estimating the CO2 concentration and flows in units of rate of a reaction characteristics, an exogenous (NARX) neural network model with two activation functions was used (Log-sigmoid and hyperbolic tangent) and for both the findings of a TC and SMX absorption simulations showed the random forest performed support vector tree and nonlinear autoregressive exogenous neural networks and machine learning methods. …”
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La disputa por el territorio en el cambio en la Política de Ordenamiento Territorial de Manizales (2003-2017). Un estudio a partir del marco de análisis y desarrollo institucional... by Manuela Carmona Bedoya

“…Se realiza un análisis de la política de Ordenamiento Territorial en la ciudad de Manizales a partir de 2003, a raíz del cambio de la norma para el uso del suelo en La Aurora, área colindante a la Reserva Forestal de Río Blanco. Mediante un método de investigación cualitativa y usando el marco de análisis y desarrollo institucional (ADI) se establece que si bien las reglas constitucionales determinan la formulación de los planes de ordenamiento territorial (POT) estas desencadenan un espacio de interacción, en el que los actores locales desarrollan estrategias para modificar dichas reglas. …”
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A deep learning‐based framework to identify and characterise heterogeneous secure network traffic by Faiz Ul Islam, Guangjie Liu, Weiwei Liu, Qazi Mazhar ul Haq

“…The state‐of‐the‐art machine learning strategies (C4.5, random forest, and K‐nearest neighbour) are investigated for comparison. …”
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A novel machine learning approach for spatiotemporal prediction of EMS events: A case study from Barranquilla, Colombia by Dionicio Neira-Rodado, Juan Camilo Paz-Roa, John Willmer Escobar, Miguel Ángel Ortiz-Barrios

“…The model outperforms a Random Forest trained solely on time-series data, boosting accuracy by up to 26.9 % in Barranquilla's case study zones, with a mean improvement of 16.4 %. …”
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Quantifying urbanization-induced dynamics of urban sprawl using spatial metrics method in Adama City, Ethiopia by Shimelis Sishah Dagne, Hurgesa Hundera Hirpha, Addisu Teshome Tekoye, Zenebe Reta Roba, Mitiku Badasa Moisa

“…The results demonstrate that agricultural land and forest land were negatively impacted by urbanization-induced changes in land use and cover in Adama City. …”
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Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III by Mingzhang Pan, Xinxin Cao, Changcheng Fu, Shengyou Liao, Xiaorong Zhou, Wei Guan

“…Firstly, a data cleaning method based on isolated forest and correlation analysis is designed to improve the stability of the system. …”
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IoT-based automated system for water-related disease prediction by Bhushankumar Nemade, Kiran Kishor Maharana, Vikram Kulkarni, Surajit mondal, G S Pradeep Ghantasala, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…Classification is performed using Random Forest, XGBoost, and AdaBoost, which have accuracy rates of 99.66%, 99.52%, and 99.64%, respectively. …”
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Surveillance of pathogenic Leptospira among rodents and small mammals in enzootic areas of plague in Pasuruan Indonesia by Siti Amanah Febriani, Kurnia Ritma Dhanti, Kurniawan Kurniawan, Ristiyanto Ristiyanto, Arief Junaedi, Caecilia Hapsari Ceriapuri Sukowati, Farida Dwi Handayani

“…Rattus tanezumi was identified as the Leptospirosis reservoir in settlements habitats with a percentage of 13.2%, Rattus tiomanicus was detected at 28.6% in forest habitats, and Rattus exulans was found at 4.4% in both habitats. …”
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Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation by Ruijie Huang, Chenji Wei, Jian Yang, Xin Xu, Baozhu Li, Suwei Wu, Lihui Xiong

“…A total of 10 machine learning algorithms are tested and compared in the dataset. Random forest (RF) and gradient boosting (GBT) are optimal and selected to conduct quantitative analysis of the main controlling factors. …”
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Application of the Different Machine Learning Algorithms to Predict Dry Matter Intake in Feedlot Cattle by Hayati Köknaroğlu, Özgür Koşkan, Malik Ergin

“…The multivariate linear regression (LR), random forest (RF), gradient boosting regressor (GBR), and light gradient boosting machine (LGBR) algorithms were compared in terms of several performance metrics (MAE, MAPE, MSE, and RMSE). …”
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A Novel Hybrid Machine Learning Framework for Wind Speed Prediction by Rhafes Mohamed Yassine, Moussaoui Omar, Raboaca Maria Simona, Mihaltan Traian Candin

“…In this study, we investigate the potential of machine learning to improve wind power forecasting by conducting a comparison of three regression models: K-Nearest Neighbor regression, Random Forest regression, and Support Vector regression. …”
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Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning by Kang Huang, Jianjun Wu, Xin Yang, Ziyou Gao, Feng Liu, Yuting Zhu

“…Then, two typical machine learning algorithms, random forest regression (RFR) algorithm and support vector machine regression (SVR) algorithm, are used to identify the importance degree of velocity in the different positions of profile and calculate the traction energy consumption. …”
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Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations by Deepanshu Lakra, Deepanshu Lakra, Shobhit Pipil, Prashant K. Srivastava, Suraj Kumar Singh, Manika Gupta, Rajendra Prasad

“…The performance analysis of RMSE, R-squared, and correlation coefficients revealed that the Random Forest (RF) and Convolutional Neural Network (CNN) models demonstrated superior performance for SM estimation over the wheat field. …”
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Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load D... by Hafeez Ur Rehman Siddiqui, Robert Brown, Adil Ali Saleem, Muhammad Amjad Raza, Sandra Dudley

“…A comparative evaluation of models including Decision Trees (DT), Random Forest (RF), Extra Tree Classifier (ETC), Gradient Boosting Classifier (GBC), and K-Nearest Neighbors (KNN) revealed the Extra Tree Classifier achieved the highest testing accuracy of 93.85%. …”
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Deciphering the Immune Subtypes and Signature Genes: A Novel Approach Towards Diagnosing and Prognosticating Severe Asthma Through Interpretable Machine Learning by Yue Hu, Yating Lin, Bo Peng, Chunyan Xiang, Wei Tang

“…We employ single-sample gene set enrichment analysis (ssGSEA) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms to identify differentially expressed immune cells and utilize machine learning techniques, including Extreme Gradient Boosting (XGBoost) and random forest, to predict severe asthma outcomes and identify key genes associated with immune cells. …”
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Evolution and Attribution Analysis of Habitat Quality in China’s First Batch of National Parks by Pengyue Dai, Yanfang Wang, Jinhong Ye, Jing Chen, Runze Li, Xiping Cheng

“…This study highlights the ecological benefits of forestland restoration and the risks posed by the conversion of forest to cultivated or construction land, providing valuable insights for optimizing conservation strategies in China’s national parks.…”
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A Data-Driven Review of the Financial Performance and Environmental Compliance of Shariah-Compliant Businesses by Hassan Syed, Rahmi Deniz Özbay, Klemens Katterbauer, Sema Yılmaz Genç

“…The deep learning system uses an unsupervised-random forest learning method to classify environmental compliance while also measuring these firms' financial performance. …”
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