Landslide susceptibility evaluation and determination of critical influencing factors in eastern Sichuan mountainous area, China by Lin Zhang, Zhengxi Guo, Shi Qi, Tianheng Zhao, Bingchen Wu, Peng Li

“…Furthermore, we employed SHAP algorithm and Structural Equation Models to quantify the relative importance and explanatory power of these factors on shallow landslide susceptibility and to clarify the interaction mechanisms among various factors in Huaying Mountain. …”
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Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary, Muhammad Salman Saeed

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Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas by Ruiling Xu, Jinxin Tang, Chenbei Li, Hua Wang, Lan Li, Yu He, Chao Tu, Zhihong Li

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Overhead line path planning based on deep reinforcement learning and geographical information system by Jiahui Chen, Yi Yang, Ling Peng, Lina Yang, Yinhui Han, Xingtong Ge

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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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Predicting tensile and fracture parameters in polypropylene-based nanocomposites using machine learning with sensitivity analysis and feature impact evaluation by Pouya Rajaee, Faramarz Ashenai Ghasemi, Amir Hossein Rabiee, Mohammad Fasihi, Behnam Kakeh, Alireza Sadeghi

“…This study examines the efficacy of decision tree and AdaBoost algorithms in predicting mechanical and fracture parameters of polypropylene nanocomposites toughened with ethylene-based and propylene-based thermoplastic elastomers and reinforced with fumed silica and halloysite nanotube nanoparticles. …”
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Predicting tilling and seeding operation times in grain production: A comparison of machine learning and mechanistic models by Luca Scheurer, Tobias Zimpel, Joerg Leukel

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Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction by Kuo-Yang Huang, Ying-Lin Hsu, Che-Liang Chung, Huang-Chi Chen, Ming-Hwarng Horng, Ching-Hsiung Lin, Ching-Sen Liu, Jia-Lang Xu

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Supervised methods of machine learning for email classification: a literature survey by Muath AlShaikh, Yasser Alrajeh, Sultan Alamri, Suhib Melhem, Ahmed Abu-Khadrah

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Reinforcement learning applications in water resource management: a systematic literature review by Linus Kåge, Vlatko Milić, Vlatko Milić, Maria Andersson, Magnus Wallén

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Reinforcement learning for vehicle-to-grid: A review by Hongbin Xie, Ge Song, Zhuoran Shi, Jingyuan Zhang, Zhenjia Lin, Qing Yu, Hongdi Fu, Xuan Song, Haoran Zhang

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A Machine Learning-Enabled System for Crop Recommendation by Pedina Sasi Kiran, Gembali Abhinaya, Smaraneeka Sruti, Neelamadhab Padhy

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Integrating IoT sensors and machine learning for sustainable precision agroecology: enhancing crop resilience and resource efficiency through data-driven strategies, challenges, an... by Val Hyginus Udoka Eze, Esther Chidinma Eze, George Uwadiegwu Alaneme, Pius Erheyovwe BUBU, Ezekiel Oluwaseun Ejiofor Nnadi, Michael Ben Okon

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Distinction between the concepts of mathematical and logical modeling by E. N. Kolybenko

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A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data by Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie

“…Subsequently, this model was transformed into a 3D AGM using the SML technique. Four algorithms, namely, random forest (RF), decision tree (DT), support vector machine (SVM), and extreme gradient boosting (XGBoost) were implemented. …”
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AI-enhanced automation of building energy optimization using a hybrid stacked model and genetic algorithms: Experiments with seven machine learning techniques and a deep neural net... by Mohammad H. Mehraban, Samad ME Sepasgozar, Alireza Ghomimoghadam, Behrouz Zafari

“…Seven machine learning (ML) models, including Linear Regression (LR), Decision Trees (DT), Random Forest Regressor (RFR), Gradient Boosting Machines (GBM), Support Vector Regressor (SVR), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGB), and a deep Feedforward Neural Network (FNN) are developed and assessed in predicting three key performance metrics: Energy Use Intensity (EUI), Predicted Percentage Dissatisfied (PPD), and Heating Load.A hybrid stacked model, combining FNN with XGB, using GBM meta learner, emerged as the top performer, achieving an impressive Coefficient of Determination (R²) of 0.99 and Mean Absolute Percentage Error (MAPE) of 0.02 across all targets. …”
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Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3–5 and end-stage kidney disease by Chia-Tien Hsu, Chin-Yin Huang, Cheng-Hsu Chen, Ya-Lian Deng, Shih-Yi Lin, Ming-Ju Wu

“…Nine ML algorithms were applied to predict osteoporosis: logistic regression, XGBoost, LightGBM, CatBoost, SVM, decision tree, random forest, k-nearest neighbors, and an artificial neural network (ANN). …”
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Application of data mining to extract knowledge about the occurrence of fistulas after palatoplasty by Patrick Pedreira Silva, Elvio Gilberto da Silva, Vinicius Santos Andrade, Telma Vidotto de Sousa Brosco, Gabriela Aparecida Prearo, Maria Inês Pegoraro-Krook, Jeniffer de Cassia Rillo Dutka

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The Application of Machine Learning Algorithms to Predict HIV Testing Using Evidence from the 2002–2017 South African Adult Population-Based Surveys: An HIV Testing Predictive Mode... by Musa Jaiteh, Edith Phalane, Yegnanew A. Shiferaw, Haruna Jallow, Refilwe Nancy Phaswana-Mafuya

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Re-Supplying Autonomous Mobile Parcel Lockers in Last-Mile Distribution by Sajjad Hedayati, Mostafa Setak, Tom Van Woensel, Emrah Demir

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