Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. by M Isabel Ramos, Juan J Cubillas, Ruth M Córdoba, Lidia M Ortega

“…The methodology uses Machine Learning algorithms together with an exhaustive analysis of predictor variables. …”
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Prediction of the axial compression capacity of ECC-CES columns using adaptive sampling and machine learning techniques by Khaled Megahed

Subjects: “…Machine learning…”
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Identification of ubiquitination-related key biomarkers and immune infiltration in Crohn’s disease by bioinformatics analysis and machine learning by Wei Chen, Zeyan Xu, Haitao Sun, Wen Feng, Zhenhua Huang

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A Data-Driven Approach to Engineering Instruction: Exploring Learning Styles, Study Habits, and Machine Learning by Lauren Genith Isaza Dominguez, Antonio Robles-Gomez, Rafael Pastor-Vargas

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Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning by Yuanzhi Sun, Guangbao Mi, Peijie Li, Liangju He

“…Abstract A machine learning model was developed to predict the oxidation resistance of Ti-V-Cr burn-resistant titanium alloy, and the natural logarithm of the parabolic oxidation rate constant (lnk p ) was utilized as the model output. …”
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Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction by Chandan Kumar, Gabriel Walton, Paul Santi, Carlos Luza

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Retracted: A New Approach to Model Machine Learning by Using Complex Bipolar Intuitionistic Fuzzy Information by Journal of Function Spaces

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Analyzing Student Academic Achievement Using Machine Learning Techniques at Senior High School Darunnajah Jakarta by Supriadi Panggabean, Wahyu Joko Saputro

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Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing by Manik Rakhra, Amitabh Bhargava, Deepshikha Bhargava, Ramandeep Singh, Astha Bhanot, Abdul Wahab Rahmani

“…In India, a decision-based smart engine for the rental and sharing of tools and equipment has been developed, which leverages machine learning methods to proceed towards a selection of tools and equipment. …”
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Optimizing biochar for carbon sequestration: a synergistic approach using machine learning and natural language processing by Jiayi Li, Yixuan Chen, Chaojie Wang, Hanbo Chen, Yurong Gao, Jun Meng, Zhongyuan Han, Lukas Van Zwieten, Yi He, Caibin Li, Gerard Cornelissen, Hailong Wang

“…Abstract Biochar is a promising technology for carbon storage and greenhouse gas (GHG) reduction, but optimizing it is challenging due to the complexity of natural systems. Machine learning (ML) and natural language processing (NLP) offer solutions through enhanced data analysis and pattern recognition, ushering in a new era of biochar research. …”
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Machine learning‐based multi‐omics models for diagnostic classification and risk stratification in diabetic kidney disease by Xian Shao, Suhua Gao, Pufei Bai, Qian Yang, Yao Lin, Mingzhen Pang, Weixi Wu, Lihua Wang, Ying Li, Saijun Zhou, Hongyan Liu, Pei Yu

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An IoT and Machine Learning-Based Model to Monitor Perishable Food towards Improving Food Safety and Quality by Amina Batool, Souvik Ganguli, Hashem Ali Almashaqbeh, Muhammad Shafiq, A. L. Vallikannu, K. Sakthidasan Sankaran, Samrat Ray, F. Sammy

“…Then, using the principal component analysis technique, features are extracted from the photos, and the images are categorized using machine learning models that have been trained. This proposed model makes use of the Internet of Things, image processing, and machine learning to monitor perishable food.…”
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Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning by Mohammad Amin Habibi, Reza Omid, Shafaq Asgarzade, Sadaf Derakhshandeh, Ali Soltani Farsani, Zohreh Tajabadi

“…We systematically reviewed the studies reported the performance of machine learning (ML) algorithms for accurately discrimination of these two entities. …”
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Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal by Erica Shrestha, Suyog Poudyal, Anup Ghimire, Shrena Maharjan, Manoj Lamichhane, Sushant Mehan

“…The suitability of both empirical and machine learning (ML) models with limited climatic variables for estimating ET0 in Nepal remains unexplored. …”
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Basin dependencies of tropical cyclone genesis environment and possible future changes revealed by machine learning methods by QiFeng Qian, YeFeng Chen, XiaoJing Jia, Hao Ma, Wei Dong

“…Ocean basins are categorized into three groups based on PCA, and three MaxEnt machine learning (ML) models are developed to predict TC genesis under future scenarios. …”
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Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning by Hao Xu, Wenchao Wu, Yuntian Chen, Dongxiao Zhang, Fanyang Mo

“…This study explicitly elucidates how chemists use thin-layer chromatography (TLC) to determine column chromatography (CC) conditions, employing statistical analysis and machine learning techniques. An experimental dataset of the CC is generated from the automatic platform developed in this study. …”
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Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms by Jinmei Liu, Juan Luo, Xu Chen, Jiyi Xie, Cong Wang, Hanxiang Wang, Qi Yuan, Shijun Li, Yu Zhang, Jianli Hu, Chen Shi

“…We aimed to develop and validate a machine learning (ML) model and evaluate its feasibility to predict opioid nonadherence in patients with cancer pain. …”
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Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction by Emmanuel de-Graft Johnson Owusu-Ansah, Richard Doamekpor, Richard Kodzo Avuglah, Yaa Kyere Adwubi

Subjects: “…credit scoring, smote, oversampling, undersampling, class imbalance, machine learning algorithms…”
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Machine learning-based prediction of hemodynamic parameters in left coronary artery bifurcation: A CFD approach by Sara Malek, Arshia Eskandari, Mahkame Sharbatdar

“…Computational fluid dynamics (CFD) provides valuable insights but is computationally intensive, prompting exploration of machine learning (ML) models for efficient hemodynamics prediction. …”
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Predictive Analysis of Structural Damage in Submerged Structures: A Case Study Approach Using Machine Learning by Alexandre Brás dos Santos, Hugo Mesquita Vasconcelos, Tiago M. R. M. Domingues, Pedro J. S. C. P. Sousa, Susana Dias, Rogério F. F. Lopes, Marco L. P. Parente, Mário Tomé, Adélio M. S. Cavadas, Pedro M. G. P. Moreira

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