Predicting Metabolite-Disease Associations Based on Linear Neighborhood Similarity with Improved Bipartite Network Projection Algorithm by Xiujuan Lei, Cheng Zhang

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Utilizing an Innovative Gaussian Process Regression Machine Learning Algorithm for Estimating Unconfined Compressive Strength Predictions by Hazel Abraham

“…This article offers an advanced argument in using a Machine Learning algorithm known as Gaussian Process Regression to predict the UCS for mixtures of soils. …”
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Prediction of COVID-19 Effect on Patients during Six Month After Recovery, by Using AI Algorithm by hanan aldabagh, Ghaydaa Al-Talib

“…The GSO algorithm was used for feature selection, and for hyper-parameter tuning for the Random Forest (RF) algorithm used in the prediction process in order to make predictions to identify the effects that may accrue on recoveries persons. …”
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Prediction of Mechanical Properties of Cotton Fibers by a BP Neural Network Model Optimized by Genetic Algorithm by Junyang Wang, Limin Zhang, Xiang Liu, Jinchan Zhang, Wanxin Wang, Hong Xu

“…In this experiment, a general purpose BP neural network (BP) based on genetic algorithm (GA) was developed for predicting fiber properties. …”
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Algorithm for link prediction in self-regulating network with adaptive topology based on graph theory and machine learning by Evgeny Y. Pavlenko

“…On the basis of the developed model of network functioning with adaptive topology, a graph algorithm for link prediction is proposed, which is extended to the case of peer-to-peer networks. …”
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Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis by Adama Ndour, Gerald Blasch, João Valente, Bisrat Haile Gebrekidan, Tesfaye Shiferaw Sida

“…In this study, we conducted a comprehensive meta-analysis to analyze the relationship between the machine learning model performance and variables such crop type, the type of aerial phenotyping platform, the phenological stage, etc A trait-based comparison of the efficiency and popularity of machine learning algorithms was conducted. Our findings showed that the multiple linear regression is the most effective model in predicting biomass while artificial neural networks showed up as the top performing algorithm in determining nitrogen content. …”
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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

“…A baseline logistic-regression (LR) model and four ML algorithms—XGBoost, Random Forest, LightGBM and Support Vector Machine (SVM)—were tuned via Bayesian optimisation. …”
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