Predicting social welfare in Madrid neighbourhoods using machine learning by Carlos Alberto Lastras Rodríguez

“…A comprehensive dataset representing various socioeconomic metrics of Madrid’s neighbourhoods is analysed utilising different linear regression models and the XGBoost machine learning algorithm. The findings indicate that demographic variables play a crucial role in shaping social welfare and inequality in Madrid's neighbourhoods, with the percentage of women, and the percentage of children under 14 years old and adults over 65 years old being the most important variables for predicting social welfare and inequality in the studied neighbourhoods. …”
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Slope deformation prediction based on GA–BP neural networks by Wenhui TAN, Kai LI, Huimin LIU, Meifeng CAI, Qifeng GUO

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Enhancing Education with Machine Learning: Predicting Student Readability Scores by Claire Bell

“…The research leverages a dataset of 1,000 English texts to evaluate and compare the performance of RFC, the Sooty Tern Optimization Algorithm (STOA), and the Gold Rush Optimizer (GRO) in predicting readability ratings. …”
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Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model by Kerang Cao, Chang Liu, Siqi Yang, Yuxin Zhang, Lili Li, Hoekyung Jung, Shuo Zhang

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Application of Machine Learning Algorithms for Predicting the Dynamic Stiffness of Rail Pads Based on Static Stiffness and Operating Conditions by Isaac Rivas, Jose A. Sainz-Aja, Diego Ferreño, Víctor Calzada, Isidro Carrascal, Jose Casado, Soraya Diego

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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms by Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh

“…Results he RF algorithm showed high performance in both prediction approaches. …”
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Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms by Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu

“…Herein, the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates, with R 2 of 0.9219 and 0.9243, respectively. …”
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Predicting the Compressive Strength of High-Performance Concrete utilizing Radial Basis Function Model integrating with Metaheuristic Algorithms by LiWei Hu

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Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes by Junwei Peng, Xiaoyujie Geng, Yiyue Zhao, Zhijin Hou, Xin Tian, Xinyi Liu, Yuanyuan Xiao, Yang Liu

“…Four machine learning (ML) algorithms including random forest, extreme gradient boosting, light gradient boosting machine and binary logistic regression were used to construct prediction models. …”
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Application of interpretable machine learning algorithms to predict macroangiopathy risk in Chinese patients with type 2 diabetes mellitus by Ningjie Zhang, Yan Wang, Hui Zhang, Huilong Fang, Xinyi Li, Zhifen Li, Zhenghang Huan, Zugui Zhang, Yongjun Wang, Wei Li, Zheng Gong

“…This study establish an approach based on machine learning algorithm in features selection and the development of prediction tools for diabetic macroangiopathy.…”
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Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor by Iman Setiawan, Mohammad Dahlan Th. Musa, Dini Aprilia Afriza, Siti Nur Hafidah

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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…Therefore, this paper takes the star-rated hotels in the six districts of Tianjin as the research subject and proposes a few-shot hotel location prediction method based on meta-learning algorithms and transportation accessibility. …”
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Integrating Hyperspectral, Thermal, and Ground Data with Machine Learning Algorithms Enhances the Prediction of Grapevine Yield and Berry Composition by Shaikh Yassir Yousouf Jewan, Deepak Gautam, Debbie Sparkes, Ajit Singh, Lawal Billa, Alessia Cogato, Erik Murchie, Vinay Pagay

“…The use of multimodal data and machine learning (ML) algorithms could overcome these challenges. Our study aimed to assess the potential of multimodal data (hyperspectral vegetation indices (VIs), thermal indices, and canopy state variables) and ML algorithms to predict grapevine yield components and berry composition parameters. …”
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A study on method for bearing residual life prediction based on optimized Bray-Curtis dissimilarity and PSO algorithms by LI Quanfu, JIA Chen, SONG Dongli, XU Xiao

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End‐To‐End Deep Learning Temperature Prediction Algorithms of a Phase Change Materials From Experimental Photos by Mohammad Hassan Ranjbar, Kobra Gharali, Artie Ng

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Prediction of barite scale formation and inhibition in hydrocarbon reservoirs using AI modeling: Focus on different optimization algorithms by Ouafa Belkacem, Ahmed Rezrazi, Kamel Aizi, Lokmane Abdelouahed, Maamar Laidi, Abdelhafid Touil, Leila Cherifi, Salah Hanini

“…This study introduces a novel and highly effective approach for predicting barite scale formation and inhibition by leveraging advanced artificial intelligence (AI) techniques. …”
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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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