Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques by Hamed Shokrnia, Ashkan KhodabandehLou, Peyman Hamidi, Fedra Ashrafzadeh

“…The sensitivity assessment approves that the training and generalization abilities of the ELM and ANFIS models for the CS prediction of FRC are improved by their integration with the GWO algorithm. The best model (i.e., ELM-GWO) predicts the testing datasets with the R2 (coefficient of determination), RMSE (root mean square error), SI (scatter index), RPD (relative percent deviation), and PMARE (percent mean absolute relative error) values of 0.9510, 3.985 MPa, 0.061, 0.8, and 5.421, respectively.…”
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Sustainable Epoxidation of Unsaturated Fatty Acid through Peracid Mechanism with Amberlite Resin as a Catalyst by Siti Mazlifah Ismail, Siti Nadia Abdullah, Nurul Izzah Md Zain, Siti Juwairiyah A. Rahman, Mohd Jumain Jalil, Intan Suhada Azmi

“…The Runge-Kutta Fourth Order method, in conjunction with genetic algorithm optimisation for numerical integration, was used to establish a mathematical model.…”
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The Least Limiting Water Range to Estimate Soil Water Content Using Random Forest Integrated with GIS and Geostatistical Approaches by Orhan Dengiz, Pelin Alaboz

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A Novel Voltage–Current Characteristic Model for Understanding of Electric Arc Furnace Behavior Using Experimental Data and Grey Wolf Optimization Algorithm by Mustafa Şeker, Emre Ünsal, Ahmet Aksoz, Mahir Dursun

“…The proposed model integrates polynomial curve fitting, the modified Heidler function, and double S-curves, with the grey wolf optimization (GWO) algorithm applied for parameter optimization, enhancing accuracy in predicting arc dynamics. …”
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Compressive Strength Prediction Using Coupled Deep Learning Model with Extreme Gradient Boosting Algorithm: Environmentally Friendly Concrete Incorporating Recycled Aggregate by Mayadah W. Falah, Sadaam Hadee Hussein, Mohammed Ayad Saad, Zainab Hasan Ali, Tan Huy Tran, Rania M. Ghoniem, Ahmed A. Ewees

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stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learning by Xin Lu, Murong Zhou, Bo Gao, Fang Wang, Shuilin Jin, Qiaoming Liu, Guohua Wang

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Ensemble prediction modeling of flotation recovery based on machine learning by Guichun He, Mengfei Liu, Hongyu Zhao, Kaiqi Huang

“…First, the outliers are processed using the box chart method and filtering algorithm. Then, the decision tree (DT), support vector regression (SVR), random forest (RF), and the bagging, boosting, and stacking integration algorithms are employed to construct a flotation recovery prediction model. …”
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Inverse Scattering Integrability and Fractional Soliton Solutions of a Variable-Coefficient Fractional-Order KdV-Type Equation by Sheng Zhang, Hongwei Li, Bo Xu

“…Firstly, according to Ablowitz et al.’s fractional-order algorithm and the anomalous dispersion relation, we derive the vcfKdV-type equation contained in a new class of integrable fNLEEs, which can be used to describe the dispersion transport in fractal media. …”
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A Deep Reinforcement Learning Framework for Last-Mile Delivery with Public Transport and Traffic-Aware Integration: A Case Study in Casablanca by Amine Mohamed El Amrani, Mouhsene Fri, Othmane Benmoussa, Naoufal Rouky

“…The pickup and delivery operations are optimized with the proximal policy optimization algorithm within this environment, and experiments are conducted to assess the effectiveness of public transportation integration and three different exploration strategies. …”
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A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies by Yingchun He, Yi-haw Jan, Fan Yang, Yunru Ma, Chun Pei

“…Abstract This study proposes a deep learning-based motion assessment method that integrates the pose estimation algorithm (Keypoint RCNN) with signal processing techniques, demonstrating its reliability and effectiveness.The reliability and validity of this method were also verified.Twenty college students were recruited to pedal a stationary bike. …”
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Control System Strategy for Ring Thrower Robot Based on PID-CSA for ABU Robocon 2023 by Aris Budiyarto, Ridwan Ridwan, Rizky Andhika Akbar

“…The implemented system uses Proportional, Integral and Derivative (PID) control based on the Cuckoo Search Algorithm (CSA). …”
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Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement by Qingmeng Shen, Yuming Wu, Limin Wan, Qian Chen, Yue Li, Zichao Liao, Wenbo Wang, Feng Li, Tao Li, Jiajun Shu

“…The results show that the prediction model with the integrated decomposition algorithm reduces the RMSE and MAE by 33% and 37%, respectively, which significantly improves the prediction accuracy and generalization ability of the neural network to meet the demand of practical engineering prediction and simultaneously enhances the risk warning ability of the model.…”
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Synergistic coupling of ultrasonic cavitation with tailored deep eutectic solvent systems for intensified extraction of Scutellaria Radix flavonoids: Modelling and optimization by... by Qi Cui, Jia-Yi Shi, Le-Le Wen, Wei-Hua Kong, Li-Jie Jiang, Ju-Zhao Liu

“…These values represented significant improvements over conventional extraction methods. …”
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Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study by Qi Huang, Xiantong Zou, Zhouhui Lian, Xianghai Zhou, Xueyao Han, Yingying Luo, Shuohua Chen, Yanxiu Wang, Shouling Wu, Linong Ji

“…Both the trajectory and machine learning algorithm contributed significantly to the enhancement of model performance. …”
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Identification of ageing-associated gene signatures in heart failure with preserved ejection fraction by integrated bioinformatics analysis and machine learning by Guoxing Li, Qingju Zhou, Ming Xie, Boying Zhao, Keyu Zhang, Yuan Luo, Lingwen Kong, Diansa Gao, Yongzheng Guo

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A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes by Hossein Moayedi, Atefeh Ahmadi Dehrashid, Binh Nguyen Le

“…The AUC values were computed for every optimisation algorithm used in this study. …”
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Modeling peak ground acceleration for earthquake hazard safety evaluation by Fatima Khalid, Milad Razbin

“…The ANN architecture comprises 4 nodes in the input layer, two hidden layers each containing 25 nodes, and a single-node output layer, resulting in 750 unknown weight and bias values that the model must optimize. Following the model assessment, a genetic algorithm (GA) was integrated with the ANN model to enhance its predictive capabilities. …”
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Toward Trustworthy Machine Learning for Daily Sediment Modeling in the Riverine Systems: An Integrated Framework With Enhanced Uncertainty Quantification and Interpretability by Z. J. Yue, N. N. Wang, B. D. Xu, X. Huang, D. M. Yang, H. B. Xiao, Z. H. Shi

“…To achieve trustworthy ML for riverine sediment timeseries predictions, this study proposes an integrated ML framework, enhancing key steps: feature selection, UQ, and interpretation. …”
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Enhancement of Breast Cancer Classification Using Bat Feature Selection with Recurrent Deep Learning by Ali Nafaa Jaafar

“…Data preprocessing involves removing unnecessary columns and filling out missing values with the median value. The result was a comparative study using recurrent deep learning with the bat algorithm to classify breast cancer. …”
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Coverage intensity of optimal sensors for common, isolated, and integrated steel structures using novel approach of FEM-MAC-TTFD by Mehdi Firoozbakht, Hamidreza Vosoughifar, Alireza Ghari Ghoran

“…In this article, a novel algorithm for optimal sensor placement in various steel frames was evaluated. …”
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