Optimizing Sunflower Husk Pellet Combustion for B2B Bioenergy Commercialization by Penka Zlateva, Nevena Mileva, Mariana Murzova, Kalin Krumov, Angel Terziev

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Interpretable AI for Short-Term Water Demand Forecasting by Aly-Joy Ulusoy, Carlos Jara-Arriagada, Yuanyang Liu, Bradley Jenks, Ivan Stoianov

“…In this work, we forecast the hourly water demand of ten operational district metered areas using optimal trees, a machine learning model which has been shown to combine the interpretability of regression approaches and the accuracy of ANNs. …”
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Construction and application of a digital twin model for multi-objectiveoptimization of intelligent tape conveyor system by Wei CHEN, Jingzhao LI, Qing SHI, Jichao LIU, Huashun LI

“…Responding to this problem, we propose an effective and environmentally friendly intelligent conveyor system leveraging Digital Twin (DT) technology. In this conceptual model, an Online Sequential Extreme Learning Machine (OS-ELM) is applied within the virtual component to construct a near real-time model predicting sweeping force, using physical feedback data as input parameters. …”
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Machine Learning-Driven Prediction of CO₂ Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach by Seyed Hossein Hashemi, Farshid Torabi, Paitoon Tontiwachwuthikul

“…In this study, Gaussian Process Regression (GPR) with eight different kernels was optimized using the Grey Wolf Optimizer (GWO) algorithm to model this important phase behavior. …”
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Optimizing Urban Thermal Environments Through 2D/3D Landscape Pattern Analysis: A Machine Learning-Driven Approach for the Yangtze River Delta Urban Agglomeration by Haoshan Zhou, Ruci Wang, Hao Hou, Bin Xie, Tangao Hu

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Improved Correlation of Oil Recovery Factor for Water Driven Reservoirs in the Niger Delta by Ayodele Daniel, Faisal Olasubomi Olanipekun, Sunday Oloruntoba Isehunwa

“…After data cleaning and quality checking, cleaned data was used to train the machine learning model using multiple linear regression algorithms optimized with batch gradient descent method. …”
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Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

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Inversion and validation of soil water-holding capacity in a wild fruit forest, using hyperspectral technology combined with machine learning by Tingwei Song, Liang Guo, Qian Sun, Guizhen Gao, Jing Chen, Qikun Zhang

“…This study integrated hyperspectral technology with machine learning algorithms to model complex nonlinear relationships and to select the optimal SWHC model. …”
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Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis by Zemin Li, Yulun Wu, Tao Chen, Bo Yan, Chaohai Wei

“…Three datasets were constructed based on organic types: biodegradable organic compounds, biorefractory organic compounds and combined two types organic compounds. Two machine learning models were employed to predict Anammox performance, with Random Forest (RF) identified as the optimal model, subsequently validated using real coking industry wastewater treatment data. …”
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A hybrid model to overcome landslide inventory incompleteness issue for landslide susceptibility prediction by Jiayao Tan, Chi Yang, Yuzhou Wang, Hanxiang Xiong, Chuanming Ma

“…However, traditional methods, including heuristic, statistical and deterministic models, cannot address LII issue. In this work, we introduce a novel hybrid LEO-MAHP model, blending landslide frequency, empirical adjustments, optimization functions, and multi-participated analytic hierarchy process to address it by taking Badong County as the study area. …”
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Enhanced Credit Card Fraud Detection Using Deep Hybrid CLST Model by Madiha Jabeen, Shabana Ramzan, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

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Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft by Yanhui Guo, Yanpeng Chen, Peibo Li, Xinfu Chi, Yize Sun

“…In order to solve the optimization problem, 108 sets of process experiments were designed, and then the experimental data were used to train a Back Propagation Neural Network (BPNN), a Least Squares Support Vector Machine (LSSVM), and Random Forest (RF) to obtain the best prediction model for the process parameters. …”
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High entropy alloy property predictions using a transformer-based language model by Spyros Kamnis, Konstantinos Delibasis

“…Abstract This study introduces a language transformer-based machine learning model to predict key mechanical properties of high-entropy alloys (HEAs), addressing the challenges due to their complex, multi-principal element compositions and limited experimental data. …”
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Classification of Brain Tumors by Using a Hybrid CNN-SVM Model by Talitha Safa Nabila, Abu Salam

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An Efficient Framework for Personalizing EMG-Driven Musculoskeletal Models Based on Reinforcement Learning by Joseph Berman, I-Chieh Lee, Jie Yin, He Huang

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Optimized landslide susceptibility prediction based on SBAS-InSAR: case study of the Jiuzhaigou Ms7.0 earthquake by Shiqian Yin, Zebing Dai, Ying Zeng

“…The results indicate that the integration of SAR-derived surface deformation data significantly enhances the accuracy of Landslide Susceptibility Mapping (LSM). Comparing the model performance with the receiver operating characteristic curve and landslide density, the reliability and prediction performance of the RF-I model are outstanding, reflecting that the improved method based on the InSAR collaborative machine learning model with shape variables along the slope direction can optimize the accuracy of the LSM, and has better performance and robustness in earthquake landslide susceptibility evaluation.…”
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Development and application of advanced learning models for predicting the land subsidence due to coal mining by Shirin Jahanmiri, Majid Noorian-Bidgoli

“…Three hybrid models—biogeography-based optimization with gene expression programming (BBO-GEP), gray wolf optimizer with gene expression programming (GWO-GEP), and salp swarm algorithm with gene expression programming (SSA-GEP)—are applied to assess subsidence risks. …”
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Harnessing machine learning for transmembrane pressure prediction in MBR systems during textile wastewater treatment by Onaira Zahoor, Sher Jamal Khan, Muhammad Usama, Henry J. Tanudjaja, Noreddine Ghaffour, Muhammad Saqib Nawaz

“…This study focuses on predicting TMP in a bench-scale MBR by employing advanced regression models such as Lasso, support vector machines, and random forest. …”
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Sustainable approach of strength measurement for soil’s stabilized with geo-polymer with hybrid ensemble models by Ishwor Thapa, Sufyan Ghani, Nishant Kumar, Megha Gupta, Sunil Saharan, Prabhu Paramasivam, Abinet Gosaye Ayanie

“…Five machine learning models Random Forest, Support Vector Regression, Extreme Learning Machine, Artificial Neural Networks, and Multivariate Adaptive Regression Splines were developed and combined in a unique hybrid ensemble. …”
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EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model by Weikang He, Xizhe Li, Yujin Wan, Honming Zhan, Nan Wan, Sijie He, Yaoqiang Lin, Longyi Wang, Wenxuan Yu, Liqing Chen

“…The performance of four machine learning models was compared, and the optimal model was selected. …”
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