Evaluating QoS in Dynamic Virtual Machine Migration: A Multi-Class Queuing Model for Edge-Cloud Systems by Anna Kushchazli, Kseniia Leonteva, Irina Kochetkova, Abdukodir Khakimov

“…The efficient migration of virtual machines (VMs) is critical for optimizing resource management, ensuring service continuity, and enhancing resiliency in cloud and edge computing environments, particularly as 6G networks demand higher reliability and lower latency. …”
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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…The performance of these deep learning models was compared to traditional machine learning approaches. …”
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Interpretable machine learning model for predicting anastomotic leak after esophageal cancer surgery via LightGBM by Xiaodong Yang, Fulin Dou, Guoshuo Tang, Ruipu Xiu, Xiaogang Zhao

“…Methods A retrospective case‒control study analyzed clinical and laboratory data from esophageal cancer patients obtained via a case management system. Nine machine learning (ML) models were compared to identify the best-performing model and its optimal feature set. …”
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X-ray based radiomics machine learning models for predicting collapse of early-stage osteonecrosis of femoral head by Yaqing He, Yang Chen, Yusen Chen, Pingshi Li, Le Yuan, Maoxiao Ma, Yuhao Liu, Wei He, Wu Zhou, Leilei Chen

“…After the optimal radiomics model was selected based on areas under the curve (AUC), its performance on the test set was compared with that of orthopaedists using receiver operating characteristic (ROC) curves and confusion matrices. …”
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An exploratory machine learning model for predicting advanced liver fibrosis in autoimmune hepatitis patients: A preliminary study by Qinglin Wei, Wen Li, Shubei He, Hongbo Wu, Qiaoling Xie, Ying Peng, Xingyue Zhang

“…Patients were categorized into groups with no/minimal/moderate fibrosis and advanced fibrosis. Six ML models were employed to identify the optimal model. …”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…These models, by enabling early interventions in hemodialysis membranes, could enhance patient safety and optimize the care of hemodialysis patients.…”
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Evaluating the efficacy and site-specific performance of machine learning approaches: A comprehensive review of autism detection models by Deblina Mazumder Setu, Tania Islam, Md Maklachur Rahman, Samrat Kumar Dey, Tazizur Rahman

“…From them, 18 studies are based on 14 popular machine learning (ML) models to identify the most effective prediction methods. …”
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Application of machine learning and temporal response function modeling of EEG data for differential diagnosis in primary progressive aphasia by Heather Dial, Lokesha S. Pugalenthi, G. Nike Gnanateja, Junyi Jessy Li, Maya L. Henry

“…Early diagnosis is essential for optimal provision of care but differential diagnosis by PPA subtype can be difficult and time consuming. …”
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Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus by Xiyao Wan, Yuan Wang, Ziyi Liu, Ziyan Liu, Shuting Zhong, Xiaohua Huang

“…Abstract This study sought to establish and validate an interpretable CT radiomics-based machine learning model capable of predicting post-acute pancreatitis diabetes mellitus (PPDM-A), providing clinicians with an effective predictive tool to aid patient management in a timely fashion. …”
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Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques by Atefeh Rostami, Mostafa Robatjazi, Amir Dareyni, Ali Ramezan Ghorbani, Omid Ganji, Mahdiye Siyami, Amir Reza Raoofi

“…Sixteen ML and one sequential DL models were created using the 5-fold cross-validation method and each model with its special optimized parameters trained using the training-validation datasets. …”
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Early PCOS Detection: A Comparative Analysis of Traditional and Ensemble Machine Learning Models With Advanced Feature Selection by Khandaker Mohammad Mohi Uddin, Md. Tofael Ahmed Bhuiyan, Md. Mahbubur Rahman, Md. Manowarul Islam, Md Ashraf Uddin

“…Importantly, our research highlights how effective machine learning can be in predicting PCOS. The logistic regression and support vector machine model stood out with its remarkable accuracy of 99.7753%. …”
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Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach. by Guo-Hua Ye, Mirxat Alim, Peng Guan, De-Sheng Huang, Bao-Sen Zhou, Wei Wu

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Machine-learning-derived prediction models of outcomes for patients with pseudomyxoma peritonei: development and validation in two retrospective cohorts by Yu-Run Cui, Xin-Li Liang, Yan Li

“…Recent advancements have shown that machine learning (ML) algorithms hold great promise in developing predictive models within the medical field. …”
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Development and validation of machine learning models to predict unplanned hospitalizations of patients with diabetes within the next 12 months by A. E. Andreychenko, A. D. Ermak, D. V. Gavrilov, R. E. Novitskiy, A. V. Gusev

“…The creation and inference of a machine learning model for predicting hospitalizations of patients with DM to an inpatient medical facility will make it possible to personalize the provision of medical care and optimize the load on the entire healthcare system.AIM: Development and validation of models for predicting unplanned hospitalizations of patients with diabetes due to the disease itself and its complications using machine learning algorithms and data from real clinical practice.MATERIALS AND METHODS: 170,141 depersonalized electronic health records of 23,742 diabetic patients were included in the study. …”
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Effects of SBAS-InSAR Deformation Integration Methods and Machine Learning Model Selection on Landslide Susceptibility Mapping by Zhen Yang, Xiangchao Jiang, Meinan Zheng, Qingbiao Guo

“…We selected Jinping Autonomous County as the study area and performed a landslide susceptibility evaluation based on 11 static evaluation factors using three machine learning models: Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). …”
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Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients by Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh

“…Model development employed 5-fold cross-validation, random oversampling, and hyperparameter tuning. …”
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Comparing the Effectiveness of Machine Learning and Deep Learning Models in Student Credit Scoring: A Case Study in Vietnam by Nguyen Thi Hong Thuy, Nguyen Thi Vinh Ha, Nguyen Nam Trung, Vu Thi Thanh Binh, Nguyen Thu Hang, Vu The Binh

“…Future research should consider longitudinal data, behavioral factors, and hybrid modeling approaches to further optimize predictive performance in educational finance.…”
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Early detection of positive urine culture in patients with urolithiasis: a machine learning model with dynamic online nomogram by Di Luo, Jingdong Zhang, Linguo Xie, Chunyu Liu, Rui Wang

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Identifying climate and environmental determinants of spatial disparities in wheat production using a geospatial machine learning model by Kai Ren, Yongze Song, Linchao Li, Francesco Mancini, Zhuoyao Xiao, Xueyuan Zhang, Rui Qu, Qiang Yu

“…Finally, the developed geospatial machine learning model is evaluated by comparing its effectiveness with the commonly used geographical detector model. …”
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TetraRNA, a tetra-class machine learning model for deciphering the coding potential derivation of RNA world by Hanrui Bai, Jie Wang, Xiaoke Jiang, Zhen Guo, Wenjing Yang, Zitian Yang, Jing Li, Changning Liu

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