Multi-model ensemble machine learning-based downscaling and projection of GRACE data reveals groundwater decline in Saudi Arabia throughout the 21st century by Arfan Arshad, Muhammad Shafeeque, Thanh Nhan Duc Tran, Ali Mirchi, Zaichen Xiang, Cenlin He, Amir AghaKouchak, Jessica Besnier, Md Masudur Rahman

“…This was accomplished by using multi-model ensemble machine learning (ML) approach leveraging Random Forest, CART, and Gradient Tree Boosting algorithms within Google Earth Engine (GEE). Additionally, we used the downscaled GWS and CMIP6 climate data with the Generalized Additive Model (GAM) to project the future GWS changes under climate change. …”
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Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning by Jiming Tang, Yao Huang, Dingli Liu, Liuyuan Xiong, Rongwei Bu

“…Decision tree, XGBoost, and random forest algorithms, respectively, were applied for the secondary prediction. …”
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Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning by Jinyu Zheng, Xiaoyi Xu, Xianguo Chen, Xianshuai Li, Miao Fu, Yiping Zheng, Jie Yang

“…Three machine learning algorithms—Random Forest, LASSO, and SVM-RFE—were applied to identify key hub genes. …”
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Inertial-Based Dual-Task Gait Normalcy Index at Turns: A Potential Novel Gait Biomarker for Early-Stage Parkinson’s Disease by Lin Meng, Xiaofei Zhang, Yu Shi, Xinge Li, Jun Pang, Lei Chen, Xiaodong Zhu, Rui Xu, Dong Ming

“…However, few studies have explored gait deviation evaluation algorithms under a complex dual task that reflect disease specificity. …”
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Multimodal data integration with machine learning for predicting PARP inhibitor efficacy and prognosis in ovarian cancer by Xi’an Xiong, Li Cai, Li Cai, Zhen Yang, Zhongping Cao, Nayiyuan Wu, Nayiyuan Wu, Qianxi Ni

“…Patient-specific efficacy and prognosis prediction models were then constructed using various machine learning algorithms.ResultsTotal bile acids (TBAs) and CA-199 present as an independent risk factor in Cox multivariate analysis for primary and recurrent ovarian cancer patients respectively (P < 0.05). …”
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SA-MARL: Novel Self-Attention-Based Multi-Agent Reinforcement Learning With Stochastic Gradient Descent by Rabbiya Younas, Hafiz Muhammad Raza Ur Rehman, Ingyu Lee, Byung-Won On, Sungwon Yi, Gyu Sang Choi

“…Second, to address the local optima problem in MARL algorithms, we leverage the Grey Wolf Optimizer for weight and bias selection, adding a stochastic element for improved optimization. …”
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Synergizing remote sensing, support vector machine, and aeromagnetic data for precise lithological and mineral potential mapping: a case study from Egypt by Hatem M. El-Desoky, Ahmed M. Abdel-Rahman, Hamada El-Awny, Wael Fahmy, Reda Abdu Yousef El-Qassas, Kamal Abdelrahman, Hassan Alzahrani, Peter Andráš, Yahia Z. Amer, Ahmed M. Eldosouky

“…Therefore, we effectively mapped the exposed rock units in the Hamash region of the Eastern Desert of Egypt by using Support Vector Machine (SVM) to Sentinel 2 data through executing machine learning algorithms (MLAs). This goal involves extensive fieldwork, petrographical examinations, and image processing remote sensing techniques to accurately reveal the exposed lithologies distribution besides the potential relic areas. …”
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GastroEndoNet: Comprehensive endoscopy image dataset for GERD and polyp detectionMendeley Data by Abu Kowshir Bitto, Md. Hasan Imam Bijoy, Kamrul Hassan Shakil, Aka Das, Khalid Been Badruzzaman Biplob, Imran Mahmud, Syed Md. Minhaz Hossain

“…This dataset is designed to facilitate research in automated detection and classification of these conditions through machine learning algorithms. The dataset consists of 4006 primary images collected following endoscopic procedures, which were augmented using six distinct techniques, expanding the total number of images to 24,036. …”
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Functional Connectivity Metrics in Temporal Lobe Epilepsy: A Machine Learning Perspective With MEG by M. V. Suhas, N. Mariyappa, A. Karunakar Kotegar, M. Ravindranadh Chowdary, K. Raghavendra, Ajay Asranna, L. G. Viswanathan, H. Anitha, Sanjib Sinha

“…We employ machine learning algorithms to classify TLE and HC groups based on these metrics. …”
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Empowering Sustainability: The Crucial Role of IoT-Enabled Distributed Learning Systems in Reducing Carbon Footprints by Anjana M S, Aryadevi Remanidevi Devidas, Maneesha Vinodini Ramesh

“…This innovative system combines advanced local processing with EQC optimization, providing a cutting-edge approach to sustainable energy management in high-occupancy scenarios. Furthermore, the algorithms driving occupant-centric automation and the indoor localization method demonstrate remarkable performance, achieving an efficiency of 92% and an accuracy of 90%, respectively. …”
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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. …”
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River Radii: A Comparative National Framework for Remote Monitoring of Environmental Change at River Mouths by Shane Orchard, Francois Thoral, Matt Pinkerton, Christopher N. Battershill, Rahera Ohia, David R. Schiel

“…Satellite remote sensing data products and algorithms present many new possibilities for monitoring these dynamic and often inaccessible environments. …”
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Improving EEG based brain computer interface emotion detection with EKO ALSTM model by R. Kishore Kanna, Preety Shoran, Meenakshi Yadav, Mohammad Nadeem Ahmed, Shrikant Burje, Garima Shukla, Anurag Sinha, Mohammad Rashid Hussain, Saifullah Khalid

“…The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. …”
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Modelling water table depth at rewetted peatlands with Sentinel-1 and Sentinel-2 by Eoin Reddin, Jennifer Hanafin, Mingming Tong, Laurence Gill, Mark G. Healy

“…The aims of this paper are to (1) systematically test the relationship between radar backscatter and water table depth (2) compare decision tree regression algorithms to evaluate the potential of multi-sensor remote sensing in peatland management, and (3) make novel estimations of site-wide water table depth using a multi-sensor approach. …”
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Radiomics for Machine Learning—A Multi-Class System for the Automatic Detection of COVID-19 and Community-Acquired Pneumonia from Computed Tomography Images by Vasileia Paschaloudi, Dimitris Fotopoulos, Ioanna Chouvarda

“…Results: Four two-class models and one three-class model were developed: Normal–COVID, COVID–CAP, Normal–CAP, Normal–Disease and Normal–COVID–CAP. Different algorithms and data augmentation were used to train each model 20 times on a different data set split, and, finally, the model with the best average performance was selected for each case. …”
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Validation of Reference Genes for Accurate RT-qPCR Normalization in <i>Aeluropus littoralis</i> Under Drought, Cold, and ABA Treatments by Seyyed Hamidreza Hashemipetroudi, Ali Rezaei, Markus Kuhlmann

“…In this study eight CRGs of <i>A. littoralis</i>, <i>AlEF1A</i>, <i>AlRPS3</i>, <i>AlGTFC</i>, <i>AlRPS12</i>, <i>AlUBQ2</i>, <i>AlTUB6</i>, <i>AlACT7</i>, and <i>AlGAPDH1</i>, were analyzed to assess their stability for the normalization of RT-qPCR data under polyethylene glycol (PEG, 20% <i>w</i>/<i>v</i> for drought simulation), abscisic acid (ABA, 100 μM), and cold stress (4 °C) treatments. The result of the algorithms suggested different CRGs for different treatments or tissue types. …”
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Accelerometers can correctly count orthopaedic patients' early post‐operative steps while using walking aids by Spiros Tsamassiotis, Michael Schwarze, Philipp Gehring, Roman F. Karkosch, Lars‐René Tücking, Ann‐Kathrin Einfeldt, Eike Jakubowitz

“…However, it remains unclear if commercially available wearables or accelerometers using simple algorithms can accurately count steps in early post‐operative conditions. …”
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Prognostic and therapeutic relevance of IL2RG-related LncRNAs in clear cell renal cell carcinoma by Weijing Hu, Bo Wu, Yongquan Chen, Xiaoling Guo, Xiaosong Wang, Dongwen Wang

“…Gene set enrichment analysis (GSEA), single-sample GSEA (ssGSEA), CIBERSORT algorithms, and TISCH2 database were applied to analyze immune function and immune cell infiltration in different risk groups. …”
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Transcriptomics-based exploration of ubiquitination-related biomarkers and potential molecular mechanisms in laryngeal squamous cell carcinoma by Qiu Chen, Zhimin Wu, Yifei Ma

“…Next, DEGs, key module genes, and UbRGs were taken to intersect to obtain candidate genes. And then machine algorithms were to screen potential biomarkers, further their diagnostic value were analyzed and validated. …”
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Integrated Multi-Omics Profiling Reveals That Highly Pyroptotic MDMs Contribute to Psoriasis Progression Through CXCL16 by Liping Jin, Xiaowen Xie, Mi Zhang, Wu Zhu, Guanxiong Zhang, Wangqing Chen

“…<b>Methods:</b> We integrated 21 transcriptomic datasets (from 2007 to 2020) obtained from the GEO database and two single-cell RNA sequencing datasets to quantify pyroptotic activity using Gene Set Variation Analysis and AUCell algorithms. Immune cell infiltration profiles were evaluated via CIBERSORT, while cell-cell communication networks were analyzed by CellChat. …”
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