Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques by J. Priskilla Angel Rani, C. Yesubai Rubavathi

“…This technique does not assume data follows a distribution, which may reduce the model’s predictive effectiveness. Instead, it forecasts aquatic quality using RNN-MLP. …”
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Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes by Liu Yang, Li Du, Yuanyuan Ge, Muhui Ou, Wanyan Huang, Xianmei Wang

“…Abstract Objective This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) using a machine learning (ML) algorithm. …”
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From Surveillance to Sentencing: Evaluating AI's Role in Indian Criminal Justice by Kumar Navin

“…Predictive algorithms can identify crime-prone areas, while facial recognition systems can swiftly pinpoint suspects—tasks that once required significant time and manpower. …”
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Capitalising on the Floristic Survey as a Non-Destructive Line of Evidence for Mineral Potential Modelling: A Case Study of Bauxite in South-Western Australia by Lewis Trotter, Grant Wardell-Johnson, Andrew Grigg, Sarah Luxton, Todd P. Robinson

“…Here, we combine plant species distributions with terrain metrics to produce predictive models showing the probability of bauxite presence. …”
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Geohazard impact and gas reservoir pressure dynamics in the Zagros Fold-Thrust Belt: An environmental perspective by Mahsa Asghari, Zahra Maleki, Ali Solgi, Mohammad Ali Ganjavian, Pooria Kianoush

“…A novel hybrid model is introduced that integrates geographic information system (GIS) mapping, decision support system (DSS) modeling, and machine learning algorithms. By analyzing a century's worth of seismic data alongside real-time environmental parameters, the model demonstrates a predictive accuracy of 92% using Random Forest algorithms, significantly outperforming traditional methods. …”
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Identifying opioid agonist treatment prescriber networks from health administrative data: A validation study. by Megan Kurz, Mark Tatangelo, Kristen A Morin, Michelle Zanette, Emanuel Krebs, David C Marsh, Bohdan Nosyk

“…Clinics were identified using modularity maximization, with sensitivity analyses applying Louvain, Walktrap, and Label Propagation algorithms. Concordance between network-identified facilities and the (gold standard) de-identified facility-level IDs was assessed using overall, positive and negative agreement, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).…”
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Theoretical basis and field experiment of low-temperature gathering and transportation for high water-cut crude trunk pipeline by Yang LYU, Hanwen ZHANG, Luoqian LIU, Fuqiang ZHANG

“…Conclusion The research findings can be applied to ensure safe operations and facilitate risk prediction and early warning for gathering and transportation pipelines undergoing temperature reduction. …”
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Efficient and Privacy-Preserving Decision Tree Inference via Homomorphic Matrix Multiplication and Leaf Node Pruning by Satoshi Fukui, Lihua Wang, Seiichi Ozawa

“…Additionally, we introduce a leaf node pruning (LNP) algorithm designed to identify and retain the most informative leaf nodes during prediction with a decision tree. …”
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Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review by Bo Sun, Bo Sun, Fei Cai, Huiman Huang, Bo Li, Bing Wei

“…Artificial intelligence provides advanced models and algorithms for better diagnosis, prediction and classification of attention deficit/hyperactivity disorder. …”
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Machine learning in stream and river water temperature modeling: a review and metrics for evaluation by C. R. Corona, T. S. Hogue, T. S. Hogue

“…The aim of this work is threefold: first, to provide a concise review of the use of ML algorithms in SWT modeling and prediction; second, to review ML performance evaluation metrics as they pertain to SWT modeling and prediction to find the commonly used metrics and suggest guidelines for easier comparison of ML performance across SWT studies; and, third, to examine how ML use in SWT modeling has enhanced our understanding of spatial and temporal patterns of SWT and examine where progress is still needed.…”
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Clinical Phenotype Identification and Validation of Patients with Sepsis in the Intensive Care Unit by GONG Chao, YU Na, CHEN Haoran

“…Then, supervised machine learning algorithms (lightweight gradient boosting machine) were used for the prediction of the patient's phenotypes, and were further combined with SHAP (Shapely Additive eXplanations) for the identification of important features. …”
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Evaluasi Algoritma Machine Learning untuk Klasifikasi dan Prediksi Penggunaan Lahan by Fajar Nugraha, Dwi Putro Tejo Baskoro, Suria Darma Tarigan

“…This study aimed to evaluate machine learning algorithms in land use classification and prediction and analyzed land use change from 2002 to 2032. …”
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Diagnosis of Device Exception Based on Causality of Device Indicators by Zhaohui Wang, Yan Wei, Longhua Shang, Shiwei Zhang, Shixiong Bao, Zhengren Li

“…Experimental validation on the extended-TE dataset demonstrates that our CGNN framework outperforms baseline algorithms, achieving faster fault identification and higher prediction accuracy. …”
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A Systematic Survey of Sparse Clustering by Josephine Bernadette M. Benjamin, Miin-Shen Yang

“…Feature selection and feature reduction algorithms have been proposed to process the data. …”
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Identification model of mine water inrush source based on XGBoost and SHAP by Bencong Kou, Tingxin Wen

“…Verified by 160 sample sets in Xinzhuangzi Mine, the average prediction precision of the CLSSA-XGBoost is 97.78%, the average prediction recall rate is 97.59% and the F1 is 97.61%, which are better than other comparison models. …”
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Machine Learning Techniques In Wdm-Fso Systems: Comparative Study by Ranim Younes, Mohammad Nassr

“…Furthermore, machine learning algorithms were used to predict the quality factor of the proposed system and then the performance metrics R2 and RMSE (Root Mean Square Error) were used to compare between the algorithms. …”
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Simulation of Minefield Installation in a Video Game Engine by Maksym Maksymov, Oleksii Neizhpapa, Oleksandr Toshev, Maksym Kiriakidi

“…The first objective of this research is to improve damage prediction algorithms, enabling the simulation to more accurately estimate the consequences of ships passing through a minefield. …”
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Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models by Wei Chen, Haotian Zheng, Binglin Ye, Tiefeng Guo, Yude Xu, Zhibin Fu, Xing Ji, Xiping Chai, Shenghua Li, Qiang Deng

“…Based on these rankings, predictive models were constructed using Logistic Regression (LR), Random Forest (RF), eXtreme Gradient Boosting (xGBoost), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) algorithms. …”
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Mutational landscape and DNA methylation-based classification of squamous cell carcinoma and urothelial carcinoma by Min Ren, Midie Xu, Chen Chen, Ran Wei, Qianlan Yao, Liqing Jia, Peng Qi, Qifeng Wang, Qianming Bai, Xiaoli Zhu, Sheng Wu, Qinghua Xu, Xiaoyan Zhou

“…The predictive accuracy for the primary samples (89.66%, 78/87) was obviously greater than that for the metastatic samples (71.88%, 23/32). …”
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Improving maize water stress diagnosis accuracy by integrating multimodal UAVs data and leaf area index inversion model by Qi Liu, Xiaolong Hu, Yiqiang Zhang, Liangsheng Shi, Wei Yang, Yixuan Yang, Ruxin Zhang, Dongliang Zhang, Ze Miao, Yifan Wang, Zhongyi Qu

“…Although models built using the random forest regression (RFR) algorithm and the combination of MIs+TIs+LAI performed best (R2 ≥ 0.575, RMSE ≤ 0.073, and RRMSE ≤ 0.18) across growth stages, their predictive advantages for PMC and NGS varied with the growth stage: PMC predictions were more accurate during stages V9 and R3, whereas NGS predictions were more accurate during stages VT and R1. …”
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