Machine learning algorithms for maize yield prediction with multispectral imagery: Assessing robustness across varied growing environments by Bala Ram Sapkota, Gurjinder S. Baath, K. Colton Flynn, Kabindra Adhikari, Chad Hajda, Douglas R. Smith

“…The research utilizes multispectral imagery and maize yield data from diverse growing environments, comprising seven maize planting dates tested across three field locations over two years. Among five ML algorithms tested, the Extra Trees Regressor (ETR) showed superior performance at predicting maize yield across most maize growth phases. …”
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Using Prediction Confidence Factors to Enhance Collaborative Filtering Recommendation Quality by Dionisis Margaris, Dimitris Spiliotopoulos, Kiriakos Sgardelis, Costas Vassilakis

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Prediction of Electric Vehicle Mileage According to Optimal Energy Consumption Criterion by Oleksii Chkalov, Roman Dropa

“…Within this context, a novel model-based predictive approach is introduced for estimating electric vehicle energy consumption. …”
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Sensor-validated simulations predict fracture healing outcomes in an ovine model by Alicia Feist, Carla Hetreau, Manuela Ernst, Peter Varga, Peter Schwarzenberg

“…The potential of the simulation to predict healing patterns and to be used as a tool for non-union risk assessment was illustrated. …”
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Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam by HUANG Song, WU Jie, FANG Zhanchao, CHU Huaping, WU Yan'gang, XUE Zilong, HE Linbo

“…However, there are some deficiencies in the predictive power of the former and the theoretical explanation of the latter. …”
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Dynamic Error Modeling and Predictive Compensation for Direct-Drive Turntables Based on CEEMDAN-TPE-LightGBM-APC Algorithm by Manzhi Yang, Hao Ren, Shijia Liu, Bin Feng, Juan Wei, Hongyu Ge, Bin Zhang

“…This study presents a dynamic continuous error compensation model for direct-drive turntables, based on an analysis of positioning error mechanisms and the implementation of a “decomposition-modeling-integration-correction” strategy, which features high flexibility, adaptability, and online prediction-correction capabilities. Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. …”
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Research on multi-algorithm and explainable AI techniques for predictive modeling of acute spinal cord injury using multimodal data by Jiaojiao Tai, Linbang Wang, Yijun Xie, Yang Li, Hua Fu, Xiaowen Ma, Haiyan Li, Xinying Li, Ziqiang Yan, Jingkun Liu

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A Servo Control Algorithm Based on an Explicit Model Predictive Control and Extended State Observer with a Differential Compensator by Zhuobo Dong, Shuai Chen, Zheng Sun, Benyi Tang, Wenjun Wang

“…This paper introduces a novel two-degree-of-freedom (2-DOF) control algorithm that integrates explicit model predictive control (EMPC) with a differential-compensated extended state observer (DCESO). …”
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Multi-layer perceptron-particle swarm optimization: A lightweight optimization algorithm for the model predictive control local planner by Xiaoqing Guan, Tao Hu, Ziang Zhang, Yixu Wang, Yifan Liu, You Wang, Jie Hao, Guang Li

“…This letter reports a lightweight and efficient two-stage solving algorithm for the model predictive control planner. …”
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Investigating the Predictive Performance of Process Data and Result Data in Complex Problem Solving Using the Conditional Gradient Boosting Algorithm by Fatma Nur Aydin, Kubra Atalay Kabasakal, Ismail Dilek

“…This study aims to examine the predictive performance of process data and result data in complex problem-solving skills using the conditional gradient boosting algorithm. …”
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An Effective Approach to Promote Air Traveler Repurchasing Using the Random Forest Algorithm: Predictive Model Design and Utility Evaluation by Zhenyuan Zhang, Yanhong Chen, Luning Liu

“…The results show that the proposed model framework is better than the prediction results of the other algorithms. In addition, the proposed model framework was verified through a real case of an airline in China. …”
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SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection by M. Z. Naser, Ahmad Z. Naser

“…Abstract This paper presents a novel anomaly and outlier detection algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. …”
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Optimising islanded AC microgrid control: A hierarchical approach with FCS-VMPC and consensus algorithm by Zawar Khan, Muhammad Aamir, Rizwanullah Khan, Mohd Fairouz Mohd Yousof

Subjects: “…Consensus algorithm…”
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Machine learning algorithms for predictive modeling of dyslipidemia-associated cardiovascular disease risk in pregnancy: a comparison of boosting, random forest, and decision tree regression by Idris Zubairu Sadiq, Fatima Sadiq Abubakar, Muhammad Auwal Saliu, Babangida Sanusi katsayal, Aliyu Salihu, Aliyu Muhammad

“…Methods In this study, we utilized three different machine learning algorithms (boosting, random forest, and decision tree regression) to predict dyslipidemia-associated cardiovascular disease using atherogenic index and lipid profile parameters based on a cross-sectional study datasets of 112 pregnant women aged between 15 and 49 conducted at Aminu Kano Teaching Hospital. …”
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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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Experimental Application of Predictive Controllers by C. H. F. Silva, H. M. Henrique, L. C. Oliveira-Lopes

“…The classical algorithms Infinite Horizon Model Predictive Control (IHMPC) and Model Predictive Control with Reference System (RSMPC) were used for the experimental application in the multivariable control of the pilot plant (level and pH). …”
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Portable XRF and Vis-NIR spectrometry for predicting chemical properties of forest soils in the Amazon: Insights into sensor data dimensionality reduction by Quésia Sá Pavão, Paula Godinho Ribeiro, Gutierre Pereira Maciel, Sérgio Henrique Godinho Silva, Suzana Romeiro Araújo, Antonio Rodrigues Fernandes, José Alexandre Melo Demattê, Pedro Walfir Martins e Souza Filho, Silvio Junio Ramos

“…The following objectives were set: i) to compare the efficacy of individual and combined Vis-NIR and pXRF data for the prediction of chemical attributes, using the Random Forest (RF) algorithm, and ii) to compare two methods (Boruta and Principal Component Analysis - PCA) for dimensionality reduction. …”
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Machine learning algorithms to predict the risk of admission to intensive care units in HIV-infected individuals: a single-centre study by Jialu Li, Yi Ding, Yiwei Hao, Chengyu Gao, Jinjing Xiao, Ying Liu, Yining Zhao, Qinlan Li, Lulu Xing, Hongyuan Liang, Liang Ni, Fang Wang, Sa Wang, Di Yang, Guiju Gao, Jiang Xiao, Hongxin Zhao

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Optimized ANN Model for Predicting Buckling Strength of Metallic Aerospace Panels Under Compressive Loading by Shahrukh Khan, Saiaf Bin Rayhan, Md Mazedur Rahman, Jakiya Sultana, Gyula Varga

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Leveraging Machine Learning for Exchange Rate Prediction: A Business and Financial Management Perspective in Nigeria by Adedeji Daniel Gbadebo

“…Methodology The paper employs Logistic Linear Regression, Support Vector Machine, Random Forest, and XGBoost algorithms to predict the univariate time series of Nigeria's exchange rate against the US dollar, using both hourly and daily data. …”
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