Invasion dynamics of Acer negundo L. in ribbon forests of the Altai Krai: ecological impacts and predictive habitat modeling by Natalia V. Ovcharova, Marina M. Silantyeva, Alexey V. Vaganov, Anastasia A. Masanina

“…This leads to low-diversity communities increasingly dominated by the invasive species and highlights the exacerbating role of logging and land-use changes. Using predictive modeling techniques, we assessed habitat suitability for A. negundo across Eurasia, identifying temperature as the primary limiting factor for its distribution. …”
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A nomogram for predicting prognosis for young cervical neuroendocrine carcinoma: A SEER-based study and external validation by Ning Xie, Haijuan Yu, Jie Lin, Sufang Deng, Linying Liu, Yang Sun

“…Based on the nomogram, gynecologic oncologists can accurately and easily predict the prognosis of young NECC and provide scientific guidance for individualized treatment.…”
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Identification of HOXA9 methylation as an epigenetic biomarker predicting prognosis and guiding treatment choice in acute myeloid leukemia by Fei Xie, Ting-juan Zhang, Xin-long Zhang, Zi-jun Xu, Liang Qiao, Yun Wang, Yang-jing Zhao, Jun Qian, Jing-dong Zhou

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Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques by Marium Mehmood, Nasser Alshammari, Saad Awadh Alanazi, Fahad Ahmad

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Automated Cattle Monitoring System for Calving Time Prediction Using Trajectory Data Embedded Time Series Analysis by Wai Hnin Eaindrar Mg, Thi Thi Zin, Pyke Tin, Masaru Aikawa, Kazayuki Honkawa, Yoichiro Horii

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Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target by Hao Wu, Yuan Li, Yuxuan Jiang, Xinran Li, Shenglan Wang, Changle Zhao, Changle Zhao, Ximiao Yang, Baocheng Chang, Juhong Yang, Juhong Yang, Jianjun Qiao, Jianjun Qiao

“…The top 40 bacterial species were utilized to develop ML models, with XGBoost demonstrating the highest predictive accuracy. SHAP analysis indicated a negative association between the relative abundance of six bacterial species, including B. pseudocatenulatum, and body mass index (BMI). …”
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Methods of risk prediction and monitoring of treatment response rate of bleedings from esophageal varices in patients with liver cirrhosis by D. V. Garbuzenko

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Digital Transformation: Improving Operation Efficiencies Through AI-Predictive Analysis Network at a Vancouver Catering Services by Innocent Mafiele, Rustam Nigmatullin, Farzaneh Tajikfilestan, Gaurav Pawar, Anu Chawla

Subjects: “…ai predictive analysis network…”
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Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments by Shixi Tang, Jinan Gu, Keming Tang, Wei Ding, Zhengyang Shang

“…And when the output traits involving in prediction increase, more output traits can be predicted. …”
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Improved prediction model for ceiling maximum smoke temperature in the uphill tunnel fires using water spray system by Jie Wang, Dan Huang, Yanlong Song, Kaihua Lu

“…The flow rate is more important than the atomization angle in controlling smoke temperature. This predictive model is a reliable tool for estimating the maximum temperature rise in the upstream and downstream areas of the fire source under various water spray conditions.…”
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Systematic Review with Meta-Analysis: Fecal Calprotectin as a Surrogate Marker for Predicting Relapse in Adults with Ulcerative Colitis by Jiajia Li, Xiaojing Zhao, Xueting Li, Meijiao Lu, Hongjie Zhang

“…Recent studies revealed that fecal calprotectin (FC) could predict clinical relapse for UC patients in remission, which has not yet been well accepted. …”
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Prediction of mechanical behavior of epoxy polymer using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) by Khalissa Saada, Salah Amroune, Moussa Zaoui

“…Afterwards, the nonlinear functional relationship of input parameters between epoxy sample geometries and sections was established using the response surface model (RSM) and the artificial neural network (ANN) to predict the output parameters of mechanical properties (Young's Modulus and stress). …”
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A Golgi apparatus‑related subtype and risk signature predicts prognosis and evaluates immunotherapy response in gastric cancer by Ruyue Chen, Zengwu Yao, Lixin Jiang, Jinchen Hu

“…GARRS, validated for its prognostic, immune infiltration, and drug sensitivity predictive abilities, offers new insights into gastric cancer treatment strategies.…”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

Subjects: “…diabetes prediction…”
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Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation by Li Hu, Xilingyuan Chen, Rentao Yu

“…This study was designed to develop and compare different models for predicting patients with cellulitis developing sepsis during hospitalisation.Design This is a retrospective cohort study.Setting This study included both the development and the external-validation phases from two independent large cohorts internationally.Participants and methods A total of 6695 patients with cellulitis in the Medical Information Mart for Intensive care (MIMIC)-IV database were used to develop models with different machine-learning algorithms. …”
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Long-Term Monitoring Reliability and Life Prediction of Fiber Bragg Grating-Based Self-Sensing Steel Strands by Heying Qin, Quanxi Shen, Jinping Ou, Wanxu Zhu

“…When the sensitivity dropped to about 80% of its initial value, the FBG sensor suddenly failed. The life-prediction model indicates that the predicted monitoring life of an FBG sensor is about 56 years in an unstressed condition but about 27 years under the stressful conditions that FBG-based steel strands are subjected to in their working environment. …”
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The Role of P‐Wave Variables in Enhancing Prediction of New‐Onset Atrial Fibrillation in Patients With Acute Myocardial Infarction by Na Yang, Xiaoyan Li, Bo Wu, Longhao Dai, Shaobin Yang, Qinning Zhang, Shaobin Jia

“…Some P‐wave variables (P‐wave duration [PWD], P‐wave amplitude, and interatrial block [IAB]), reflecting the process of electrical and structural remodeling, could predict the risk of atrial fibrillation (AF). This study aimed to assess the predictive value of P‐wave variables for post‐AMI NOAF. …”
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Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural Network by Jie Bu, FanZhen Zhang, Meng Zhu, Zhiyang He, Qigao Hu

“…Then, based on the Levenberg–Marquardt (LM) algorithm and using the nonlinear function of the backpropagation neural network (BPNN), a prediction model of the punching capacity of slab-column connections without transverse reinforcement is established. …”
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Tumor immune dysfunction and exclusion evaluation and chemoimmunotherapy response prediction in lung adenocarcinoma using pathomic-based approach by Wei Nie, Liang Zheng, Yinchen Shen, Yao Zhang, Haohua Teng, Runbo Zhong, Lei Cheng, Guangyu Tao, Baohui Han, Tianqing Chu, Hua Zhong, Xueyan Zhang, Xiangxiang Pan

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Kansas City Cardiomyopathy Questionnaire Utility in Prediction of 30-Day Readmission Rate in Patients with Chronic Heart Failure by Shengchuan Dai, Manoucher Manoucheri, Junhong Gui, Xiang Zhu, Divyanshu Malhotra, Shenjing Li, Jason D’souza, Fnu Virkram, Aditya Chada, Haibing Jiang

“…The KCCQ score determined before hospital discharge was significantly associated with 30-day readmission rate in patients with HF, which may provide a clinically useful measure and could significantly improve readmission prediction reliability when combined with other clinical components.…”
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