Presenting a Prediction Model for CEO Compensation Sensitivity using Meta-heuristic Algorithms (Genetics and Particle Swarm) by Saeed Khaljastani, Habib Piri, Reza Sotoudeh

“…Given these points, the aim of this research is to provide a model for predicting the sensitivity of CEO compensation using meta-heuristic algorithms, specifically genetic algorithms and particle swarm optimization. …”
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Machine learning algorithms for prediction of cerebrospinal fluid leakage after posterior surgery for thoracic ossification of the ligamentum flavum by Ruizhou Guo, Ben Liu, Yunqi Wu, Yilu Zhang, Xiyang Wang, Dingyu Jiang, Zheng Liu

“…Abstract To develop and validate a machine-learning (ML) model that pre-operatively predicts cerebrospinal-fluid leakage (CSFL) after posterior decompression for thoracic ossification of the ligamentum flavum (TOLF), and to elucidate the key risk factors driving model decisions. …”
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Tropical cyclone track prediction harnessing deep learning algorithms: A comparative study on the Northern Indian Ocean by Sabbir Rahman, M. Fahim Faisal, Prianka Mondal, Md. Mahfuzur Rahman

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Enhanced Prediction and Evaluation of Hydraulic Concrete Compressive Strength Using Multiple Soft Computing and Metaheuristic Optimization Algorithms by Tianyu Li, Xiamin Hu, Tao Li, Jie Liao, Lidan Mei, Huiwen Tian, Jinlong Gu

“…To address this issue, this study introduces a novel hybrid method for predicting concrete compressive strength by integrating multiple soft computing algorithms and the stacking ensemble learning strategy. …”
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Stress–Strain Prediction for Steam-Cured Steel Slag Fine Aggregate Concrete Based on Machine Learning Algorithms by Chuanshang Wang, Di Hu, Qiang Jin

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Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset by Menghay Phoeuk, Minho Kwon

“…To address this challenge, four machine learning models based on ensemble learning algorithms, including CatBoost regressor (CatBoost), light gradient-boosting machine regressor (LGBM), random forest regressor (RFR), and extreme gradient-boosting regressor (XGBoost), were employed to predict the compressive strength of recycled aggregate concrete. …”
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Improved Monthly Runoff Prediction of OSELM Based on Secondary Decomposition Technique and Optimization of Ten "Bird" Swarm Algorithms by DENG Zhiyu, CUI Dongwen

Subjects: “…monthly runoff prediction…”
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Predicting the Color of Archaeological <i>Littorina obtusata/fabalis</i> Shells Using Raman Spectroscopy and Clustering Algorithms by Andrea Perez-Asensio, María Gabriela Fernández-Manteca, David Cuenca-Solana, Igor Gutiérrez-Zugasti, Asier García-Escárzaga, Jesús Mirapeix, José Miguel López-Higuera, Luis Rodríguez-Cobo, Adolfo Cobo

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SARS-CoV-2 Prediction Strategy Based on Classification Algorithms from a Full Blood Examination by C. F. Choukhan, I. Lasri, R. El Hatimi, M. R. Lemnaouar, M. Esghir

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A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry by Yashar Shahedi, Mohsen Zandi, Mandana Bimakr

“…ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90 % accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties.…”
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Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity by Ramon M. Salazar, PhD, Saurabh S. Nair, MS, Alexandra O. Leone, MBS, Ting Xu, PhD, Raymond P. Mumme, BS, Jack D. Duryea, BA, Brian De, MD, Kelsey L. Corrigan, MD, Michael K. Rooney, MD, Matthew S. Ning, MD, Prajnan Das, MD, Emma B. Holliday, MD, Zhongxing Liao, MD, Laurence E. Court, PhD, Joshua S. Niedzielski, PhD

“…Purpose: To evaluate the efficacy of prominent machine learning algorithms in predicting normal tissue complication probability using clinical data obtained from 2 distinct disease sites and to create a software tool that facilitates the automatic determination of the optimal algorithm to model any given labeled data set. …”
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An Investigation Towards Resampling Techniques and Classification Algorithms on CM1 NASA PROMISE Dataset for Software Defect Prediction by Agung Fatwanto, Muh Nur Aslam, Rebbecah Ndugi, Muhammad Syafrudin

Subjects: “…software defect prediction…”
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Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction by Jing-xian Wang, Chang-ping Li, Zhuang Cui, Yan Liang, Yu-hang Wang, Yu Zhou, Yin Liu, Jing Gao, Jing Gao, Jing Gao, Jing Gao

“…This study aims to develop a model based on a machine learning algorithm that can predict the risk of in-hospital HFpEF in patients with PMI early and quickly.MethodsThis prospective study consecutively included PMI patients from January 2017 to December 2022. …”
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P336: Testing concordance of pathogenicity predictions from orthogonal machine learning algorithms and signal to noise analysis by Trevor Williams, Leonie Kurzlechner, Yuya Kobayashi, Keith Nykamp, Andrew Landstrom, Flavia M. Facio

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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms by Ao Yang, Shirui Sun, Lu Qi, Zong Yang Kong, Jaka Sunarso, Weifeng Shen

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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms. …”
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Robust-tuning machine learning algorithms for precise prediction of permeability impairment due to CaCO3 deposition by Mohammad Javad Khodabakhshi, Masoud Bijani, Masoud Hasani

“…Using machine learning models—Support Vector Regression (SVR), Extra Trees (ET), and Extreme Gradient Boosting (XGB)—the research aims to predict how much permeability is lost due to scaling. …”
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Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region by Temitope Seun Oluwadare, Marina Pannunzio Ribeiro, Dongmei Chen, Masoud Babadi Ataabadi, Saba Hosseini Tabesh, Abiodun Esau Daomi

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Cervical Cancer Prediction Based on Imbalanced Data Using Machine Learning Algorithms with a Variety of Sampling Methods by Mădălina Maria Muraru, Zsuzsa Simó, László Barna Iantovics

“…Data imbalance is frequent in healthcare data and has a negative influence on predictions made using ML algorithms. Cancer data, in general, and cervical cancer data, in particular, are frequently imbalanced. …”
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Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities by Manoj Lamichhane, Sushant Mehan, Kyle R. Mankin

“…Machine learning (ML) has gained significant attention for unraveling the complex, nonlinear relationships between soil moisture (SM) and various predictive variables, including remote sensing (RS; reflectance, brightness temperature, backscatter coefficients) and biophysical (topographic, soil, vegetation, and weather) variables. …”
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