Mathematical modeling of the initial stage of a fire in the storage of military equipment and weapons by A. L. Akhtulo, L. N. Akhtulova, A. E. Lyubako, L. A. Ivanova

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Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models by Junchen Pan, Zhen Yue, Jing Ji, Yongping You, Liqing Bi, Yun Liu, Xinglin Xiong, Genying Gu, Ming Chen, Shen Zhang

“…Patients were divided into training, and validation sets. The primary outcome was nosocomial pneumonia infection during ICU stay. …”
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Improving care pathways for children with severe illness through implementation of the ASPIRE mHealth primary ETAT package in Malawi. by Nicola Desmond, Marc Y R Henrion, Mtisunge Gondwe, Thomasena O'Byrne, Pui-Ying Iroh Tam, Deborah Nyirenda, Louisa Pollock, Maureen Daisy Majamanda, Martha Makwero, Marije Geldof, Queen Dube, Chimwemwe Phiri, Chimwemwe Banda, Rabson Kachala, Prof Robert S Heyderman, Clemens Masesa, Norman Lufesi, David G Lalloo

“…We describe the development, implementation and mixed methods evaluation of a mobile health (mHealth) triage algorithm based on the WHO Emergency, Triage, Assessment, and Treatment (ETAT) for primary-level care. …”
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Real-time ocean wave prediction in time domain with autoregression and echo state networks by Karoline Holand, Henrik Kalisch

“…This study evaluates the potential of applying echo state networks (ESN) and autoregression (AR) for dynamic time series prediction of free surface elevation for use in wave energy converters (WECs). …”
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Metabolomics and lipidomics of plasma biomarkers for tuberculosis diagnostics using UHPLC-HRMS by Gaofeng Sun, Quan Wang, Xinjie Shan, Maierheba Kuerbanjiang, Ruiying Ma, Wensi Zhou, Lin Sun, Qifeng Li

“…Differential metabolites were screened using principal component analysis and machine learning algorithms including LASSO, Random Forest, and XGBoost. …”
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Automated chest CT three-dimensional quantification of body composition: adipose tissue and paravertebral muscle by Akinori Hata, Yohei Muraguchi, Minoru Nakatsugawa, Xinan Wang, Jiyeon Song, Noriaki Wada, Takuya Hino, Kota Aoyagi, Masami Kawagishi, Takuo Negishi, Vladimir I. Valtchinov, Mizuki Nishino, Akihiro Koga, Naoki Sugihara, Masahiro Ozaki, Gary M. Hunninghake, Noriyuki Tomiyama, Mark L. Schiebler, Yi Li, David C. Christiani, Hiroto Hatabu

“…Muscle area, mean attenuation value, and intermuscular adipose tissue percentage (IMAT%) were calculated in the paravertebral muscle segmentation. The AI algorithm was trained on the training sets, and its performance was evaluated on the test sets. …”
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Acoustic-based machine learning approaches for depression detection in Chinese university students by Yange Wei, Yange Wei, Shisen Qin, Fengyi Liu, Rongxun Liu, Yunze Zhou, Yuanle Chen, Xingliang Xiong, Wei Zheng, Guangjun Ji, Yong Meng, Fei Wang, Fei Wang, Ruiling Zhang

“…For training and testing, ten-fold cross-validation was employed. …”
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Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking by Baojie Fan, Yingkui Du, Yang Cong

“…Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy, and robustness.…”
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Monitored reconstruction improved by post-processing neural network by A.V. Yamaev

“…A novel training method specifically designed for neural network algorithms within the Monitored reconstruction framework is proposed. …”
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An MRI-based fusion model for preoperative prediction of perineural invasion status in patients with intrahepatic cholangiocarcinoma by Zuochao Qi, Hao Yuan, Qingshan Li, Pengyu Chen, Dongxiao Li, Kunlun Chen, Bo Meng, Peigang Ning, Haibo Yu, Deyu Li

“…Methods A retrospective collection of 192 ICC patients from three medical centers (training set: n = 147; external test set: n = 45) was performed. …”
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A Synergistic Framework for Coupling Crop Growth, Radiative Transfer, and Machine Learning to Estimate Wheat Crop Traits in Pakistan by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Aamir Ali, Syed Roshaan Ali Shah, Cheng Jiang, Zhongqi Ma, Kang Sun, Hongzhi Jiang

“…The integration of the Crop Growth Model (CGM), Radiative Transfer Model (RTM), and Machine Learning Algorithm (MLA) for estimating crop traits represents a cutting-edge area of research. …”
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Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis by Huili Zhao, Shenao Zhang, Lang Chen, Xin Liu, Aihong Cao, Peng Du

“…The cohort was randomly divided into training and validation sets (7:3 ratio). The C5.0 algorithm was employed to develop a decision tree model for predicting treatment response. …”
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Factors affecting refractoriness or recurrence in diffuse large B-cell lymphoma: development and validation of a novel predictive nomogram by Yiwei Guo, Jie Lian, Yao Chen, Lina Quan, Xiuchen Guo, Jingbo Zhang, Zhiqiang Liu, Aichun Liu

“…These variables were also prioritized using a random forest algorithm. The developed nomogram was evaluated with the receiver-operator characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) for its clinical utility.Results Univariable analysis pinpointed several factors significantly associated with refractoriness/recurrence, including pathological subtype, lactate dehydrogenase (LDH), International Prognostic Index (IPI), treatment, absolute lymphocyte count (ALC), lymphocyte/monocyte ratio (LMR), and prognostic nutritional index (PNI). …”
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Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application by Haibo Peng, Tao Liu, Pengcheng Li, Fang Yang, Xing Luo, Xiaoqing Sun, Dong Gao, Fengyu Lin, Lecheng Jia, Ningyue Xu, Huigang Tan, Xi Wang, Tao Ren

“…The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) were used to evaluate the segmentation performance. The ability of the segmentation algorithm to improve the efficiency of online adaptive radiation therapy (ART) was assessed via geometric indicators and a subjective evaluation of radiation oncologists (ROs) in prospective clinical applications. …”
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Development and Validation of Predictive Models for Differentiating Resectable Stage III Peripheral SCLC from NSCLC Using Radiomic Features and Clinical Parameters by Junjie Zhang MD, Ligang Hao MD, Qiuxu Zhang MD, Lina Zheng MD, Qian Xu PhD, Fengxiao Gao MD

“…The cohort was divided into a training set (n = 92) and a test set (n = 40). Radiomic feature selection was performed using the LASSO algorithm, and nine machine learning models were evaluated. …”
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The prognostic predictive value of indirect bilirubin-inflammation score in patients with nasopharyngeal carcinoma by JI Huojin, LI Jun, LUO Yonglin, QIN Weiling, YE Yinxin, CAI Yonglin

“…The C-index of the IBI score was 0.722 in the training set, 0.564 in the test set, and 0.672 in the entire set. …”
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A Deep Learning Approach for Fault Detection and Localization in MT-VSC-HVDC System Utilizing Wavelet Scattering Transform by Manohar Mishra, Debadatta Amaresh Gadanayak, Abha Pragati, Jai Govind Singh

“…A secondary LSTM model with a similar layer architecture is also trained and evaluated on WST-based features to identify the fault location within internal faults. …”
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MMLT: Efficient object tracking through machine learning-based meta-learning by Bibek Das, Asfak Ali, Suvojit Acharjee, Jaroslav Frnda, Sheli Sinha Chaudhuri

“…While Deep learning algorithms address these challenges, however, they typically require significant computational resources, exhibit high complexity, and demand large amounts of training data. …”
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Development of intelligent tools to predict neuroblastoma risk stratification and overall prognosis based on multiphase enhanced CT and clinical features by Wei Zhao, Yahui Han, Xiaokun Yu, Jianing Liu, Jiao Zhang, Juan Li

“…Four risk stratification classifiers were developed using the Swin Transformer model and evaluated in training and testing cohorts. Prognostic models were constructed using a combination of multiple machine learning algorithms in conjunction with CT image features and clinical characteristics.ResultsSwin-ART based on arterial phase images was the best risk stratification classifier with an AUC of 0.770 (95% CI: 0.613–0.909) and an accuracy of 0.780 in the testing cohort. …”
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Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer patients by Qian Deng, Shan Li, Yuxiang Zhang, Yuanyuan Jia, Yanhui Yang

“…Additionally, we utilized ML algorithm combinations to predict prognosis in MIBC patients with metastasis. …”
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