Evaluating Machine Learning-Based Soft Sensors for Effluent Quality Prediction in Wastewater Treatment Under Variable Weather Conditions by Daniel Voipan, Andreea Elena Voipan, Marian Barbu

“…Using the Benchmark Simulation Model no. 2 (BSM2) as the WWTP, we were able to obtain datasets for training the ML models and to evaluate their performance in dry weather scenarios, rainy episodes, and storm events. …”
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Solar shelter: Exploring architectural design input for industrially-crafted shading devices by Ulrich Pont, Sigrun Swoboda, Peter Schober

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Rapid prediction algorithm for flow field in fully mechanized excavation face based on POD and machine learning by JIN Bing, ZHANG Lang, LI Wei, ZHENG Yi, LIU Yanqing, ZHANG Yibin

“…The support vector machine (SVM) model outperformed the Random Forest and Neural Network models in predicting mode coefficients. …”
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Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models by Gholamhossein Sodeifian, Ratna Surya Alwi, Reza Derakhsheshpour, Nedasadat Saadati Ardestani

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Using electro-inductive sensor to trace moving and non-moving objects tracked by S. S. Kolmogorova, S. V. Biryukov

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Intelligent agent orchestration for climate-resilient energy systems: Autonomous management of renewable integration and distributed resources by Kravari Kalliopi, Roussi Maria, Ladomenou Kalliopil, Thysiadou Anna, Chalaris Michail

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Archetype Identification and Energy Consumption Prediction for Old Residential Buildings Based on Multi-Source Datasets by Chengliang Fan, Rude Liu, Yundan Liao

“…Building energy consumption data for each prototype was generated using EnergyPlus (V23.2.0) simulations. Furthermore, XGBoost and Random Forest machine learning algorithms were used to predict city-scale old residential building energy consumption. …”
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Climate Conundrum: A Wet or Dry European and Northern African Climate During the Middle Miocene by R. P. Acosta, N. J. Burls, M. J. Pound, C. D. Bradshaw, J. McCoy, M. Gibson, J. M. K. O’Keefe, S. J. Feakins

“…A vegetation model (BIOME4) forced with simulated climatologies predicts both regions were dominated by mixed forest, which is largely consistent with the paleobotanical record. …”
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Optimizing the dynamic treatment regime of outpatient rehabilitation in patients with knee osteoarthritis using reinforcement learning by Sijia Liu, Jiawei Luo, Chengqi He

“…Methods In this study, a dedicated knee osteoarthritis bank (KOADB) was constructed by collecting extensive clinical data from patients. Random forest was used to select the features that had the greatest impact on treatment decisions from 122 questionnaire items. …”
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Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games by Jun Zhao, Jintian Ji, Robail Yasrab, Shuxin Wang, Liang Yu, Lingzhen Zhao

“…Experimental results demonstrate that Enhanced-DDPG outperforms traditional methods such as Random Forest Regression (RFR), XGBoost, LightGBM, METRA, and SQIRL in terms of both prediction accuracy (MSE = 0.0134, R² = 0.8439) and robustness under noisy conditions. …”
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ANALISIS DAYA DUKUNG SUMBER DAYA AIR UNTUK MENINGKATKAN KETERSEDIAAN AIR DI KABUPATEN BANDUNG, JAWA BARAT by Fayra Parahita, Dwi Putro Tejo Baskoro, Darmawan Darmawan

“…The application of scenarios for improving land management by increasing the area of mixed dry land agriculturalwith agroforestry methods, reforestation on plantation forest land, applying infiltration wells in settlements and applying terraces to paddy fields provides a fairly good hydrological response to reduce the raw water deficit from 174,91 million to 63.98 million m3.…”
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Oilfield Production Prediction Method Based on Multi-Input CNN-LSTM With Attention Mechanism by Lihui Tang, Zhenpeng Wang, Yajun Gao, Hao Wu, Wenbo Zhang, Xiaoqing Xie

“…Additionally, to quantify the impact of different input features on production, we adopt a random forest algorithm to assess feature importance and optimize data input through assigned weights. …”
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Hybrid Random Feature Selection and Recurrent Neural Network for Diabetes Prediction by Oyebayo Ridwan Olaniran, Aliu Omotayo Sikiru, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Nada MohammedSaeed Alharbi

“…Furthermore, real-life data evaluation on three benchmark datasets—Pima Indian Diabetes, Diabetic Retinopathy Debrecen, and Early Stage Diabetes Risk Prediction—revealed that the framework achieves state-of-the-art results, surpassing conventional (random forest, support vector machine) and recent hybrid frameworks with an accuracy of up to 100%, AUC of 99.1–100%, and superior calibration (Brier score: 0.006–0.023). …”
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Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network. by Yeping Shi, Yunbo Shi, Haodong Niu, Jinzhou Liu, Pengjiao Sun

“…In tests comparing the performance of the SSA-BPNN, support vector machine (SVM), and random forest (RF) models, the SSA-BPNN achieves a 99.1% classification accuracy, better than the SVM and RF models. …”
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Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China by Zeqiang Chen, Lei Wu, Nengcheng Chen, Ke Wan

“…In this study, we propose the A-DMLP (attention-deep multilayer perceptron)-deep learning model for NEE simulation as well as an interpretability study using the SHapley Additive exPlanations (SHAP) model. …”
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Fuzzy PD control for a quadrotor with experimental results by Anh T. Nguyen, Nam H. Nguyen, Mien L. Trinh

“…Quadrotor is an unmanned aerial vehicle widely used in traffic construction monitoring, volcano monitoring, forest fire, power line inspection, missing person search and disaster relief. …”
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EXPLOSION OF ANNULAR CHARGE ON DUSTY SURFASE by V. A. Levin, V. V. Markov, G. B. Sizykh

“…This problem is related to the safety problem in the area of forest fires. It is well known that is possible to extinguish a fire, for example, by means of a powerful air stream. …”
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Evolution Pattern of Flash Drought in Hanjiang River Basin and Its Response to Middle Route of South-to-North Water Transfer Project by HUANG Kai, DUAN Kai

“…Moreover, three machine learning methods, partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF), were employed to establish regression models for the duration of flash droughts, and the impact of the Middle Route of the South-to-North Water Transfer Project on the duration of flash droughts was quantitatively analyzed. …”
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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

“…The dataset for this study was generated from a numerical simulation conducted at a location with a latitude of 22.55° N and a longitude of -14.33° E. …”
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Missing Categorical Data in Sociological Surveys: An Experimental Evaluation of Imputation Techniques by Yaroslav Kostenko, Andrii Gorbachyk

“…We systematically compared the performance of six imputation methods (Multinomial Logistic Regression, Random Forest, CART, KNN, Hot Deck, and Mode) across four distinct predictor set sizes, evaluating them using Accuracy, Cohen’s Kappa, and Macro F1-score with m=20 imputations. …”
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