A SHAP algorithm-based prediction of the interlaminar shear strength degradation of G/BFRP bars embedded in concrete exposed to marine environment by Xuan Zhao, Pei-Fu Zhang, Qi Zhao, Daxu Zhang, Yiliyaer Tuerxunmaimaiti, Heng Cao

“…To address this issue, a dataset collected from published literature was compiled and SHAP algorithm-based method was employed to predict the ILSS retention (ILSSR). …”
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Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm by Hongjun Fan, Xiaoqing Zhao, Zongjun Wang, Zheqing Zhang, Ao Chang

“…Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. …”
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Medium- and Long-term Runoff Prediction Based on SMA-LSSVM by TIAN Jinghuan, LI Congxin, LI Ang

Subjects: “…slime mold algorithm (SMA)…”
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Artificial Intelligence in Paediatric Urology: Transforming Diagnosis and Treatment by Wesam Khan

Subjects: “…algorithms…”
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The integration of artificial intelligence in assisted reproduction: a comprehensive review by Pragati Kakkar, Shruti Gupta, Kasmiria Ioanna Paschopoulou, Ilias Paschopoulos, Ioannis Paschopoulos, Vassiliki Siafaka, Orestis Tsonis

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Necessary and inherent limits to internet surveillance by Joss Wright

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A Data-Driven Strategy for Long-Term Agrarian Sustainability using the Application of Machine Learning Algorithms to Predictive Models for Pest and Disease Management by Almusawi Muntather, Ameer S. Abdul, Lalitha Yaragudipati Sri

“…PDM-MLA based on predictive modeling predicts infestations with high accuracy by analyzing weather, parameters of soil, history of outbreaks of pests, and crop health data. …”
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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. …”
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Using Supervised Machine Learning Algorithms to Predict Bovine Leukemia Virus Seropositivity in Florida Beef Cattle: A 10‐Year Retrospective Study by Ameer A. Megahed, Y. Reddy Bommineni, Michael Short, João H. J. Bittar

“…Objectives To compare six different supervised machine‐learning (SML) algorithms used to identify the most important risk factors for predicting BLV seropositivity in beef cattle in Florida. …”
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Development and Validation of a Neonatal Hypothermia Prediction Model for In-Hospital Transport Using Machine Learning Algorithms: A Single-Center Retrospective Study by Zhang W, Gu X, Gu C, Yao L, Zhang Y, Wang K

“…Six machine learning algorithms—Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Naive Bayes (NB)—were used to develop predictive models. …”
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Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression by Xuelin Wang, Qinqin Huang, Dong Wu, Jinhua Xie, Ming Cao, Jun Liu

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Construction of Clinical Predictive Models for Heart Failure Detection Using Six Different Machine Learning Algorithms: Identification of Key Clinical Prognostic Features by Qu FZ, Ding J, An XF, Peng R, He N, Liu S, Jiang X

“…Following the elimination of features with significant missing values, the remaining features were utilized to construct predictive models employing six machine learning algorithms. …”
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Prediction of the Gross Motor Function Measure-66 in Ambulant Children with Cerebral Palsy Based on Instrumental Gait Analysis Using Machine-Learning Algorithms by Stephanie Gross, Karoline Spiess, Stefanie Steven, Maja Zimmermann, Eckhard Schoenau, Ibrahim Duran

“…The IGA was performed with a Zebris FDM pressure plate. For the prediction of the GMFM-66, different statistical models were used (multiple linear regression and machine learning algorithms). …”
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Enhancing Attendance Management Through Face Recognition Technology: A Case Study at Rugarama School of Nursing and Midwifery. by Taremwa, Benjamin

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An Optimization‐Simulation Method for Low‐Impact‐Development (LID) Facilities Based on CCMO Algorithm Combining an Integrated Finite Volume Coastal Ocean and Drainage Pipe Model... by Fei Liu, Qiming Cheng, Zijian Zeng, Shuyi Wei, Shaochun Yuan, Zhen Liu, Yao Chen

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Design and Implementation of Mobile Experiment Algorithm Designer Interface for Network Control System Laboratory by Jahan Zaib Bhatti, Guo-Ping Liu, Shafqat Ali, Nasim Ullah, Ivo Pergl, Lukas Prokop, Vojtech Blazek

Subjects: “…Control engineering education…”
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Validation of a deep learning–based AI system for HER2-targeted breast cancer assessment using ultrasound imaging in a clinical setting by Kathryn Malherbe

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Prediction of Liquefaction-Induced Lateral Displacements Using Hybrid GBRT and EOA by Arash Ziaie, Bahareh Mehdizadeh, Farzad Safi Jahanshahi, Nazanin Ahmadi, Ali Reza Ghanizadeh

“…This research employs the Gradient Boosting Regression Tree (GBRT) approach, tuned through the Equilibrium Optimization Algorithm (EOA), to estimate LILD. A dataset containing 247 data points was used to build the predictive model. …”
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Cooperative Sleep and Energy-Sharing Strategy for a Heterogeneous 5G Base Station Microgrid System Integrated with Deep Learning and an Improved MOEA/D Algorithm by Ming Yan, Tuanfa Qin, Wenhao Guo, Yongle Hu

“…Numerical results indicate that our approach achieves significant energy savings while ensuring accurate predictions of BSMG energy demands through a multi-objective evolutionary algorithm based on decomposition.…”
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Mopidip: a modular real-time pipeline for machinery diagnosis and prognosis based on deep learning algorithms by Mattia Pujatti, Davide Calzà, Andrea Gobbi, Piergiorgio Svaizer, Marco Cristoforetti

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