Predicting Nitrous Oxide Emissions from China’s Upland Fields Under Climate Change Scenarios with Machine Learning by Tong Li, Yunpeng Li, Wenxin Cheng, Jufeng Zheng, Lianqing Li, Kun Cheng

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Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy by Abdelhadi Amahrouch, Youssef Saadi, Said El Kafhali

“…To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. …”
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Two-stage prediction of drift ratio limits of corroded RC columns based on interpretable machine learning methods by Yan Zhou, Yizhi Qiu, Liuzhuo Chen

“…To address this gap, this paper introduces a two-stage machine learning (ML) approach for the simultaneous prediction of DRLs in CRCCs, utilizing quasi-static test data from 290 corroded column specimens. …”
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Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses by Yahya Parvizi, Shahrokh Fatehi

“…The SOC changes were simulated using multivariate analysis and machine learning methods including generalized linear model (GLM), linear additive model (LAM), cubist, random forest (RF), and support vector machine (SVM) models. …”
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Residential Building Renovation Considering Energy, Carbon Emissions, and Cost: An Approach Integrating Machine Learning and Evolutionary Generation by Rudai Shan, Wanyu Lai, Huan Tang, Xiangyu Leng, Wei Gu

“…This study proposes an integrated artificial intelligence framework to facilitate multi-criteria energy renovation decision making by combining a surrogate-based machine learning (ML) model and an evolutionary generative algorithm to efficiently and accurately identify optimal renovation strategies. …”
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Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms by Rakesh Salakapuri, Panduranga Vital Terlapu, Kishore Raju Kalidindi, Ramesh Naidu Balaka, D. Jayaram, T. Ravikumar

“…It combines deep (DL) learning and machine (ML) learning techniques. The system uses advanced models like Inception-V3, ResNet-50, and VGG-16 for feature extraction, and for dimensional reduction, it uses the PCA model. …”
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Optimizing base fluid composition for PEMFC cooling: A machine learning approach to balance thermal and rheological performance by Praveen Kumar Kanti, Prashantha Kumar H. G, Nejla Mahjoub Said, V. Vicki Wanatasanappan, Prabhu Paramasivam, Leliso Hobicho Dabelo

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Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning–Based Multicohort (RIGIPREV) Study by Iván Cavero-Redondo, Arturo Martinez-Rodrigo, Alicia Saz-Lara, Nerea Moreno-Herraiz, Veronica Casado-Vicente, Leticia Gomez-Sanchez, Luis Garcia-Ortiz, Manuel A Gomez-Marcos

“…A multioutput regressor using 6 random forest models was used to predict the impact of each antihypertensive class on PWV reduction. …”
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Evaluation of vascular cognitive impairment and identification of imaging markers using machine learning: a multimodal MRI study by Haoying He, Dongwei Lu, Sisi Peng, Jiu Jiang, Fan Fan, Dong Sun, Tianqi Sun, Zhipeng Xu, Ping Zhang, Xiaoxiang Peng, Ming Lei, Junjian Zhang

“…Model reduction was undertaken to simplify models without sacrificing performance. …”
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Prediction of Hydrogen Production from Solid Oxide Electrolytic Cells Based on ANN and SVM Machine Learning Methods by Ke Chen, Youran Li, Jie Chen, Minyang Li, Qing Song, Yushui Huang, Xiaolong Wu, Yuanwu Xu, Xi Li

“…In recent years, the application of machine learning methods has become increasingly common in atmospheric science, particularly in modeling and predicting processes that impact air quality. …”
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Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis by Lengyun Wei, Dashuai Li, Hongjin Chen, Yajing Pu, Qun Wang, Jintao Li, Meng Zhou, Chenfeng Liu, Pengpeng Long

“…In this study, we employed a suite of machine learning algorithms and statistical methods to construct a robust prognostic model for BC based on insulin resistance-related genes (IRGs). …”
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An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability by Abdennabi Morchid, Abdennacer Elbasri, Zahra Oughannou, Hassan Qjidaa, Rachid El Alami, Badre Bossoufi, Saleh Mobayen, Pawel Skruch

“…The use of embedded systems and machine learning offers a solution for optimizing irrigation according to local conditions and actual crop needs while contributing to food security and environmental sustainability. …”
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Energy-Efficient Prediction of Carbon Deposition in DRM Processes Through Optimized Neural Network Modeling by Rui Fang, Tuo Zhou, Zhuangzhuang Xu, Xiannan Hu, Man Zhang, Hairui Yang

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Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain by Jay Gopal, Jonathan Bao, Tessa Harland, Julie G. Pilitsis, Steven Paniccioli, Rachael Grey, Michael Briotte, Kevin McCarthy, Ilknur Telkes

“…The present study applies machine learning to predict which patients will respond to SCS based on intraoperative electroencephalogram (EEG) data and recognized outcome measures. …”
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Flexural strengthening of corroded steel beams with CFRP by using the end anchorage: Experimental, numerical, and machine learning methods by Amin Shabani Ammari, Younes Nouri, Habib Ghasemi Jouneghani, Seyed Amin Hosseini, Arash Rayegani, Mehrdad Ebrahimi, Pooria Heydari

“…A new end anchorage system was developed to avoid CFRP slippage, ensuring full utilization of its tensile capacity. Numerical modeling further validated the experimental results and then numerical specimens were used for parametric and Machine Learning (ML) studies. …”
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Enhancing high pressure pulsation test bench performance: a machine learning approach to failure condition tracking by Aslı Aksoy, Ömer Haki

“…The objective of this study is to enhance the efficiency of HPPT benches by addressing specimen, bench, and test environment- based problems and to develop a failure condition tracking tool (FCTT) by using machine learning (ML) algorithms. The findings of this study provide a basis for the development of the company’s data-driven smart predictive maintenance applications while providing an increase in the operational efficiency of HPPT benches. …”
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A Hybrid Machine Learning Approach for Detecting and Assessing <i>Zyginidia pullula</i> Damage in Maize Leaves by Havva Esra Bakbak, Caner Balım, Aydogan Savran

“…Extracted features are then fused and subjected to Principal Component Analysis for dimensionality reduction. The classification task is performed using Support Vector Machines, Random Forest, and Artificial Neural Networks, ensuring robust and accurate detection. …”
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Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques by Rabiu Aminu, Samantha M. Cook, David Ljungberg, Oliver Hensel, Abozar Nasirahmadi

“…Results showed improved accuracy (92.62 % Random forest, 90.16 % Support vector machine, 83.61 % K-nearest neighbours, and 81.97 % Naïve Bayes) and a reduction in the number of model parameters and memory usage (7.22 × 107 Random forest, 6.23 × 103 Support vector machine, 3.64 × 104 K-nearest neighbours and 1.88 × 102 Naïve Bayes) compared to using all features. …”
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A Near-Real-Time Model for Predicting Electricity Disruptions in Texas During Winter Storms by Jangjae Lee, Sangkeun Lee, Supriya Chinthavali, Stephanie Paal

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Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning by Yuanyuan Wang, Yu Ji, Jie Liu, Lianjiang Lv, Zihe Xu, Meimei Yan, Jialu Chen, Zhijun Luo, Xianjun Zeng

“…Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs. …”
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