Machine learning model for prediction of palliative care phases in patients with advanced cancer: a retrospective study by Junchen Guo, Yunyun Dai, Sishan Jiang, Junqingzhao Liu, Xianghua Xu, Yongyi Chen

“…Conclusions The prediction model developed in this study based on the machine learning algorithm showed good performance, offering significant potential for facilitating timely interventions, enhancing symptom management, and optimizing palliative care resource allocation in advanced cancer patients in mainland China.…”
Get full text

The optimum sizing of zero-emission water-cooled VCR cycle based on exergo-economic-environmental assessment criteria by triple-objective MPSO by Seyedeh Mohadeseh Miri, Mahmood Farzaneh-Gord, Alireza Hosseinpour, Mohit Bajaj, Ievgen Zaitsev

“…By the help of databank of parametric analysis, the optimum design variables are determined by employing the GA optimization algorithm. In the following, an intelligence neural network is developed to learn the reliable correlation between the inputs and outputs data. …”
Get full text

Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality by Miguel A. Becerra, Diego H. Peluffo-Ordoñez, Johana Vela, Cristian Mejía, Juan P. Ugarte, Catalina Tobón

“…Finally, the IQ criteria were optimized through a particle swarm optimization algorithm. …”
Get full text

Using Partial Differential Equation Face Recognition Model to Evaluate Students’ Attention in a College Chinese Classroom by Xia Miao, Ziyao Yu, Ming Liu

“…In the sparse preserving nonnegative block alignment algorithm, a discriminant partial optimization model is constructed by using sparse reconstruction coefficients to describe local geometry and weighted distance to describe class separability. …”
Get full text

Study on energy saving strategy and Nash equilibrium of base station in cognitive radio network by Xiao-tong MA, Shun-fu JIN, Jian-ping LIU, Zhan-qiang HUO

“…From the perspective of economics, a profit function was constructed and a nonlinear optimization algorithm was designed to investigate the Nash equilibrium and the socially optimal behavior of the secondary user packets, then a pricing policy of licensed spectrum for secondary users was formulated. …”
Get full text

Study on energy saving strategy and Nash equilibrium of base station in cognitive radio network by Xiao-tong MA, Shun-fu JIN, Jian-ping LIU, Zhan-qiang HUO

“…From the perspective of economics, a profit function was constructed and a nonlinear optimization algorithm was designed to investigate the Nash equilibrium and the socially optimal behavior of the secondary user packets, then a pricing policy of licensed spectrum for secondary users was formulated. …”
Get full text

Wireless Channel Prediction Using Artificial Intelligence With Imperfect Datasets by Gowhar Javanmardi, Ramiro Samano Robles

“…This stress test leads to new conclusions on channel prediction: i) how and why algorithms behave in different ways under diverse conditions (optimality region), ii) derivation of new bounds linked to channel features (coherence time, channel correlation, etc.), iii) optimum parameter settings for ML also linked to channel statistics, and iv) proposal of potential improvements. …”
Get full text

STRUCTURAL SYNTHESIS OF NAVIGATION SUPPORT OF TRIAD INTEGRATED NAVIGATION SYSTEM ON THE BASIS OF INERTIAL AND SATELLITE TECHNOLOGIES by V. S. Maryukhnenko, V. V. Erokhin

“…The imitating statistical modeling of optimal filtering algorithm of the triad integrated system is carried out. …”
Get full text

Energy Services Demand Forecasting Combined with Feature Preferences and Bidirectional Long- and Short-Term Memory Networks by KANG Feng, TAN Huochao, SU Liwei, JIAN Donglin, WANG Shuai, QIN Hao, ZHANG Yongjun

Get full text

Development and clinical application of an automated machine learning-based delirium risk prediction model for emergency polytrauma patients by Zhenyi Liu, Yihao Huang, Long Li, Yisha Xu, Peng Wu, Zhigang Zhang, Tingyong Han, Liangjie Zhang, Ming Zhang

“…ObjectiveTo address the limitations of conventional delirium prediction models in emergency polytrauma care, this study developed an interpretable machine learning (ML) framework incorporating trauma-specific biomarkers and advanced optimization algorithms for risk stratification of delirium in emergency polytrauma patients.MethodsThis multi-center retrospective observational cohort study was conducted across six hospitals in the Ya’an region. …”
Get full text

Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

“…The researcher suggests increasing the data size, as it is possible to improve the accuracy of models by increasing the data size. …”
Get full text

Implementation of XGBoost Models for Predicting CO₂ Emission and Specific Tractor Fuel Consumption by Nebojša Balać, Zoran Mileusnić, Aleksandra Dragičević, Mihailo Milanović, Andrija Rajković, Rajko Miodragović, Olivera Ećim-Đurić

“…Although not optimized for high precision, these models offer a valuable basis for preliminary assessments and highlight the potential of data-driven approaches for improving energy efficiency and environmental sustainability in agricultural mechanization.…”
Get full text

Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model by Gang Li, Ruijiang Ran, Jun Fang, Hao Peng, Shengmin Wang

“…Then, the LSSVM with the strongest nonlinear modelling ability was selected to build the bridge construction early-warning model and adopted the SSA to optimize the LSSVM parameter combination, improving the early warning accuracy. …”
Get full text

XGBoost based enhanced predictive model for handling missing input parameters: A case study on gas turbine by Nagoor Basha Shaik, Kittiphong Jongkittinarukorn, Kishore Bingi

“…The model is built to anticipate the gas turbine's Energy Yield (EY) output, optimize energy production efficiency, improve maintenance schedules, and enable operational decision-making within the power plant. …”
Get full text

Fusion of multi-scale attention for aerial images small-target detection model based on PARE-YOLO by Huiying Zhang, Pan Xiao, Feifan Yao, Qinghua Zhang, Yifei Gong

“…Evaluation on the VisDrone2019 dataset indicates that PARE-YOLO achieves a 5.9% improvement in mean Average Precision (mAP) at a threshold of 0.5, compared to the original YOLOv8 model. …”
Get full text

A Recognition Method for Adzuki Bean Rust Disease Based on Spectral Processing and Deep Learning Model by Longwei Li, Jiao Yang, Haiou Guan

“…Second, the competitive adaptive reweighted sampling (CARS) algorithm was implemented in the range of 425–825 nm to determine the optimal characteristic wavenumbers, thereby reducing data redundancy. …”
Get full text

A Pythagorean fuzzy MCDM model for evaluating career happiness in sports by selecting a suitable sport by JiaYan Zhu, Zeng Jiao

“…We present the MCDM algorithm for AHP and the derived AOs, offering solutions to practical numerical examples and identifying optimal sports options that improve career happiness. …”
Get full text

AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes by Ang Li, Yunxin Wang, Hongxu Chen

“…This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in biomedical diagnostics, particularly for AI-driven arrhythmia detection, hypertrophic cardiomyopathy (HCM) in athletes, and personalized medicine. …”
Get full text

Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…This research addresses this gap by evaluating the predictability of machine learning approaches for evaluating the CS of rubberized mortar (RM) incorporating supplementary cementitious materials. Among the tested algorithms, including bagging, gradient boosting, and AdaBoost, the bagging model achieved the highest accuracy (R 2 = 0.975). …”
Get full text

Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems by Fukui Wu, Hanzhong Tan, Linfeng Zhang, Shuangbing Wen, Tao Hu

“…Real-time vehicle data are collected using cameras deployed along highways, and key traffic parameters such as flow, density, and speed are precisely extracted using the YOLOv8 object detection model. Subsequently, five machine learning algorithms and three deep learning algorithms are employed to predict traffic flow. …”
Get full text