Fractional-Order System Identification: Efficient Reduced-Order Modeling with Particle Swarm Optimization and AI-Based Algorithms for Edge Computing Applications by Ignacio Fidalgo Astorquia, Nerea Gómez-Larrakoetxea, Juan J. Gude, Iker Pastor

“…These optimized parameters then serve as training data for several AI-based algorithms—including neural networks, support vector regression (SVR), and extreme gradient boosting (XGBoost)—to evaluate their inference speed and accuracy. …”
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Predicting filtration coefficient and formation damage coefficient for particle flow in porous media using machine learning by Xuejia Du, George K. Wong

“…These parameters are typically determined through coreflood tests. …”
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Investigating the Efficiency of Different Cover Crops on Weed Populations and Yield and Yield Components of Soybean (Glycine max L.) by Ali Jafari, Behnam Kamkar, Asieh Siahmarguee, Javid Gherekhloo, Ebrahim Zeinali, Parisa Alizadeh Dehkordi

“…Each plot measured 6 × 3 meters, and crops were manually sprayed 30 days after planting. Parameters measured included plant height, leaf area, and dry weight of cover crops. …”
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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. …”
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Green finance and investment index for assessing scenario and performance in selected countries by Sumedha Bhatnagar, Dipti Sharma, Rashmi Bundel

“…Green transitioning of the financial system includes boosting green finance (GF) and green investment (GI) in the country. …”
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Machine learning-based estimation of seismic structural damage via an accessible web application by Vasile Calofir, Mircea-Ștefan Simoiu, Ruben-Iacob Munteanu, Emil Calofir, Sergiu-Stelian Iliescu

“…The platform utilizes gradient boosting, a machine learning algorithm selected as the most effective after evaluating several alternatives. …”
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Prediction of coronary heart disease based on klotho levels using machine learning by Yuan Yao, Ying Zhao, Haifeng Li, Yanlin Han, Yue Wu, Renwei Guo, Mingfeng Ma, Lixia Bu

“…We randomly assigned the dataset of the National Health and Nutrition Examination Survey (NHANES) 2007–2016 to training and test sets at a ratio of 70:30. We evaluated the ability of five models constructed using logistic regression, neural networks, random forest, support vector machine, and eXtreme Gradient Boosting to predict CHD. …”
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Ulceroprotective Effects of <i>Epilobium angustifolium</i> Extract in DSS-Induced Colitis in Mice by Rumyana Simeonova, Rositsa Mihaylova, Reneta Gevrenova, Yonko Savov, Dimitrina Zheleva-Dimitrova

“…The severity and progression of colitis were evaluated through disease activity indices and a range of inflammatory and oxidative stress markers, assessed using multiple analytical methods. …”
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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…Each model was evaluated based on the model performance and XGBoost shows the most effective model for predicting the ASR expansion with R2 of 0.99 for training and R2 of 0.98 for testing. …”
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Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment by Hyeon Cheol Kim, Si Young Ha, Jae-Kyung Yang

“…The artificial neural network (ANN) model demonstrated the highest level of accuracy among the models evaluated, including random forest, support vector machine, and extreme gradient boosting. …”
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IoT enabled health monitoring system using rider optimization algorithm and joint process estimation by J. Prabin Jose, G. Jaffino, Mohammed Al Awadh, Koppula Srinivas Rao, Yan Yafang, Krishna Moorthy Sivalingam

“…In JPEROA algorithm line coefficients and delay coefficients parameters are estimated to improve the performance of the system. …”
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Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China by Tiezhu Li, Qidi Huang, Qigang Chen

“…Model performance was rigorously evaluated through ten-fold cross-validation, and hyperparameter optimization was employed to enhance predictive accuracy. …”
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Color and Grey-Level Co-Occurrence Matrix Analysis for Predicting Sensory and Biochemical Traits in Sweet Potato and Potato by Judith Ssali Nantongo, Edwin Serunkuma, Gabriela Burgos, Mariam Nakitto, Joseph Kitalikyawe, Thiago Mendes, Fabrice Davrieux, Reuben Ssali

“…With instrumental color and texture parameters as predictors, low to moderate accuracy was detected in the machine learning models developed to predict sensory panel traits. …”
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Advanced machine learning techniques for predicting mechanical properties of eco-friendly self-compacting concrete by Arslan Qayyum Khan, Syed Ghulam Muhammad, Ali Raza, Amorn Pimanmas

“…Six ML models-backpropagation neural network (BPNN), random forest regression (RFR), K-nearest neighbors (KNN), stacking, bagging, and eXtreme gradient boosting (XGBoost)-were trained and validated using a comprehensive dataset of 239 mix design parameters. …”
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Explainable AI and optimized solar power generation forecasting model based on environmental conditions. by Rizk M Rizk-Allah, Lobna M Abouelmagd, Ashraf Darwish, Vaclav Snasel, Aboul Ella Hassanien

“…The effectiveness of the proposed X-LSTM-EO model is evaluated through the use of five metrics; R-squared (R2), root mean square error (RMSE), coefficient of variation (COV), mean absolute error (MAE), and efficiency coefficient (EC). …”
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Design and Implementation of a Floating PV Model to Analyse the Power Generation by Mohamad Reda A. Refaai, Lavanya Dhanesh, Bibhu Prasad Ganthia, Monalisa Mohanty, Ram Subbiah, Endalkachew Mergia Anbese

“…The paper presents and discusses various design alternatives for boosting the profitability and efficiency of floating photovoltaic (FPV) systems. …”
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Enhanced Prediction and Uncertainty Modeling of Pavement Roughness Using Machine Learning and Conformal Prediction by Sadegh Ghavami, Hamed Naseri, Farzad Safi Jahanshahi

“…The performance of the methods was compared, and the light gradient boosting machine was identified as the best-performing method for IRI prediction. …”
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Shale volume estimation using machine learning methods from the southwestern fields of Iran by Parirokh Ebrahimi, Ali Ranjbar, Yousef Kazemzadeh, Ali Akbari

“…Sensitivity analysis further identifies PEFZ and SP as the most influential parameters in shale volume estimation. Finally, model validation was carried out by comparing the estimated shale volume values with actual measurements from the available datasets.…”
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Interpretable machine learning modeling of temperature rise in a medium voltage switchgear using multiphysics CFD analysis by Mahmood Matin, Amir Dehghanian, Amir Hossein Zeinaddini, Hossein Darijani

“…However, the complex interaction of geometrical and operational parameters presents significant challenges in interpreting these methods. …”
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Biochar effects on soil properties and yield of maize in Northern region, Ghana by Abdul-Latif Abdul-Aziz, Issah Alidu Abukari, Moustapha Mahamane Galadima, Abdulai Haruna, Mutari Abubakari, Rashidatu Abdulai

“…Soil chemical properties, including pH, organic matter, and nutrient availability, were analyzed alongside maize yield parameters. The study demonstrates that groundnut husk biochar is the most effective at enhancing soil fertility and boosting maize yields, with the highest application rate (8 t ha⁻¹) leading to remarkable grain yield increases, up to 218.2% in 2022 and 106.3% in 2023. …”
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