Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems by Anna O. Basile, Anurag Verma, Leigh Anne Tang, Marina Serper, Andrew Scanga, Ava Farrell, Brittney Destin, Rotonya M. Carr, Anuli Anyanwu‐Ofili, Gunaretnam Rajagopal, Abraham Krikhely, Marc Bessler, Muredach P. Reilly, Marylyn D. Ritchie, Nicholas P. Tatonetti, Julia Wattacheril

“…Our rule‐based algorithm provides an accurate, automated approach for rapidly identifying, stratifying, and sub‐phenotyping NAFLD patients within a large EHR system.…”
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Research on Air Quality Monitoring System and Evaluation Method in Closed Environment by LI Bo-chen, DING Xi-bo, CAI Qing-yao

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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…This mechanism first uses the bilinear interpolation algorithm to adjust the spatial resolution of different scale feature maps to be consistent. …”
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Efficient IDS for IoT Networks Using Host-Based Data Aggregation and Multi-Entropy Analysis by Yusei Katsura, Arata Endo, Ismail Arai, Kazutoshi Fujikawa

“…This enables the reduction of computational resources during detection processing while maintaining detection accuracy, even when using fewer features and lightweight machine learning algorithms. …”
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Gas adsorption meets geometric deep learning: points, set and match by Antonios P. Sarikas, Konstantinos Gkagkas, George E. Froudakis

“…Recently, machine learning (ML) pipelines have been established as the go-to method for large scale screening by means of predictive models. These are typically built in a descriptor-based manner, meaning that the structure must be first coarse-grained into a 1D fingerprint before it is fed to the ML algorithm. …”
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Substituted 1,4-naphthoquinones for potential anticancer therapeutics: In vitro cytotoxic effects and QSAR-guided design of new analogs by Veda Prachayasittikul, Prasit Mandi, Ratchanok Pingaew, Supaluk Prachayasittikul, Somsak Ruchirawat, Virapong Prachayasittikul

“…Four QSAR models were successfully constructed using multiple linear regression (MLR) algorithm providing good predictive performance (R: training set = 0.8928–0.9664; testing set = 0.7824–0.9157; RMSE: training set = 0.1755–0.2600; testing set = 0.2726–0.3748). …”
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Soft computing approaches of direct torque control for DFIM Motor's by Zakariae Sakhri, El-Houssine Bekkour, Badre Bossoufi, Nicu Bizon, Mishari Metab Almalki, Thamer A.H. Alghamdi, Mohammed Alenezi

“…Our evaluation focuses on various aspects: torque and flux ripple reduction, speed tracking improvement, switching losses minimization, algorithmic complexity simplification, and sensitivity reduction to parameter variations. …”
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Duty of care, data science, and gambling harm: A scoping review of risk assessment models by Virve Marionneau, Kim Ristolainen, Tomi Roukka

“…Online operators often employ risk detection algorithms to accomplish this task. This scoping review focuses on how such data science applications can perform from a duty of care perspective. …”
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Emergency Medical Care for Victims with Crush Syndrome at the Pre-Hospital Stage by V. Orlyk

“…The informative materials outlined in this article align with continuing medical education programs and training curricula for medical students, interns, and master's students in higher medical education institutions. …”
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Integrative single-cell and metagenomic analysis dissects SARS-CoV-2 shedding modes in human respiratory tract by Xiangxing Jin, Lili Ren, Xianwen Ren, Jianwei Wang

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Analysis of influencing factors and equity in hospitalization expense reimbursement for mobile populations based on random forest model: a cross-sectional study from China by Lisheng Shen, Xinan Lu, Yanyun Zhang, Lin Fei, Bo Dong

“…Secondly, the random forest algorithm was applied to evaluate the importance of the factors influencing hospitalization expense reimbursement. …”
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YOLOv8-OCHD: A Lightweight Wood Surface Defect Detection Method Based on Improved YOLOv8 by Zuxing Chen, Junjie Feng, Xueyan Zhu, Bin Wang

“…Compared with the YOLOv8n algorithm, the model size is significantly reduced, with the parameter count at only 2.9M and the computational load at 7.0G, representing reductions of 21.6% and 15.6%, respectively. …”
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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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Exploration for the physical origin and impact of chemical short-range order in high-entropy alloys: Machine learning-assisted study by Panhua Shi, Zhen Xie, Jiaxuan Si, Jianqiao Yu, Xiaoyong Wu, Yaojun Li, Qiu Xu, Yuexia Wang

“…In this study, we introduced a set of interpretable ML workflows and determined the best algorithm (kernel ridge regression (KRR)) for predicting the atomic stress in HEAs, which can deepen the understanding of the formation mechanism of CSRO. …”
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A Hybrid Grey Based KOHONEN Model and Biogeography-Based Optimization for Project Portfolio Selection by Farshad Faezy Razi, Abbas Toloie Eshlaghy, Jamshid Nazemi, Mahmood Alborzi, Alireza Pourebrahimi

“…Next, each portfolio was evaluated using grey relational analysis (GRA) and then scheduled risk of the project was predicted using Mamdani fuzzy inference method. Finally, the multiobjective biogeography-based optimization algorithm was utilized for drawing risk and rank Pareto analysis. …”
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Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning by Jingjing Liu, Chang Liu, Zhangdaihong Liu, Yibin Zhou, Xiaoguang Li, Yang Yang

“…This study introduces a novel machine learning (ML) pipeline, an Algorithm for Spatial Relationships Analysis between Exposome and Metabolic Diseases (ASEMD), to analyze spatial associations between APs and MDs at the prefecture-level city scale in China. …”
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Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data by Jiafeng Peng, Hongxing Zhang, Huaize Wang, Ting Jiang, Minglei Gao, Xingfu Ma, Yingzong Xiong, Yingchun Li, Ran Xu, Junchen Zhu

“…First, the biomarkers associated with OP were identified through differential gene expression analysis, machine learning algorithms, expression validation, and receiver operating characteristic (ROC) curve analysis. …”
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Mapping Polyclonal HIV-1 Antibody Responses via Next-Generation Neutralization Fingerprinting. by Nicole A Doria-Rose, Han R Altae-Tran, Ryan S Roark, Stephen D Schmidt, Matthew S Sutton, Mark K Louder, Gwo-Yu Chuang, Robert T Bailer, Valerie Cortez, Rui Kong, Krisha McKee, Sijy O'Dell, Felicia Wang, Salim S Abdool Karim, James M Binley, Mark Connors, Barton F Haynes, Malcolm A Martin, David C Montefiori, Lynn Morris, Julie Overbaugh, Peter D Kwong, John R Mascola, Ivelin S Georgiev

“…Here, we present next-generation NFP algorithms that substantially improve prediction accuracy for individual donors and enable serologic analysis for entire cohorts. …”
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Proactive Frequency Stability Scheme Based on Bayesian Filters and Spectral Clustering by Gian Paramo, Mario D. Baquedano-Aguilar, Arturo Bretas, Sean Meyn

“…In this work, phasor measurement units are used to predict frequency values. When a disturbance is detected, the state of frequency is predicted a few seconds into the future via a particle filter algorithm. …”
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