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Advanced autism detection and visualization through xgboost algorithm for fNIRS hemo-dynamic signals

EXPERT SYSTEMS WITH APPLICATIONS 2025年 275卷

作者： Farooq, Sajid He, Hailun Guo, Deyu Feng, Yuyi Hang, Jingguo Kong, Dexing He, Sailing Zhejiang Normal Univ Coll Math Med Jinhua 321004 Peoples R China Zhejiang Univ Taizhou Hosp Taizhou Peoples R China Zhejiang Univ Natl Engn Res Ctr Opt Instruments Ctr Opt & Electromagnet Res Hangzhou Peoples R China Wenzhou Med Univ Taizhou Hosp Zhejiang Prov Taizhou 318050 Peoples R China Royal Inst Technol Sch Elect Engn Dept Electromagnet Engn S-10044 Stockholm Sweden

Early detection of Autism Spectrum Disorder (ASD), a neuro-developmental condition characterized by impairments in social communication, is critical for prompt intervention and care. Recent advancements in digital medicine have significantly enhanced the precision and efficiency of ASD diagnosis, management, and care coordination. These technologies offer considerable potential for optimizing treatment pathways and improving patient outcomes. However, challenges remain in sustaining long-term user engagement and effectively integrating these innovations into routine clinical practice for ASD management. In this study, we report the findings from a clinic-based, prospective investigation aimed at evaluating the diagnostic validity of ASD employing xgboost algorithm. Of the 51 cases assessed, 24 were diagnosed with ASD, while 27 were identified as having developmental delay without autism. Functional near-infrared spectroscopy (fNIRS) was employed to measure resting-state hemo-dynamic fluctuations in the bilateral temporal lobes, revealing connectivity differences through computer vision and machine learning analysis. Agglomerative hierarchical clustering (AgHC) was employed to evaluate the active coupling between fluctuations of deoxygenated hemoglobin (HbR) and oxygenated hemoglobin (HbO) across specific channels, elucidating inter-channel relationships in neurovascular coupling. The algorithm, using a range of significant features, achieved robust diagnostic performance with area under receiver operating characteristic curve of 0.99 +/- 1, demonstrating sensitivity of 98%, specificity of 95%, negative predictive value (NPV) of 98%, and positive predictive value (PPV) of 95%, indicating high diagnostic accuracy in distinguishing relevant cases. These findings underscore the potential of digital medicine to offer objective and scalable framework for the diagnosis of autism in real-world clinical environments.

关键词： Autism xgboost algorithm Machine learning fNIRS UMAP

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Long tunnel group driving fatigue detection model based on xgboost algorithm

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Journal of Traffic and Transportation Engineering(English Edition) 2025年第1期12卷 167-179页

作者： Huazhi Yuan Kun Zhao Ying Yan Li Wan Zhending Tian Xinqiang Chen School of Civil Engineering Lanzhou University of Technology School of Transportation Engineering Chang'an University Shandong Transportation Planning and Design Institute Group Co. Ltd. China Railway First Survey and Design Institute Group Co. Ltd. Institute of Logistics Science and Engineering Shanghai Maritime University

Driving fatigue is one of the important causes of accidents in tunnel(group) sections. In this paper, in order to effectively identify the driving fatigue of tunnel(group) drivers, an eye tracker and other instruments were used to conduct real vehicle tests on long tunnel(group)expressways and thus obtain the eye movement, driving duration, and Karolinska sleepiness scale(KSS) data of 30 drivers. The impacts of the tunnel and non-tunnel sections on drivers were compared, and the relationship between blink indexes, such as the blink frequency,blink duration, mean value of blink duration, driving duration, and driving fatigue, was studied. A paired t-test and a Spearman correlation test were performed to select the indexes that can effectively characterize the tunnel driving fatigue. A driving fatigue detection model was then developed based on the xgboost algorithm. The obtained results show that the blink frequency, total blink duration, and mean value of blink duration gradually increase with the deepening of driving fatigue, and the mean value of blink duration is the most sensitive in the tunnel environment. In addition, a significant correlation exists between the driving duration index and driving fatigue, which can provide a reference for improving the tunnel safety. Using the mean value of blink duration and driving duration as the characteristic indexes, the accuracy of the driving fatigue detection model based on the xgboost algorithm reaches 98%. The cumulative and continuous tunnel proportion effectively estimates the driving fatigue state in a long tunnel(group) environment.

关键词： Traffic safety Tunnel group Driving fatigue detection Eye movement data Driving duration xgboost algorithm

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Machine learning-based assessment of thermal comfort for the elderly in warm environments: Combining the xgboost algorithm and human body exergy analysis

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2025年 209卷

作者： He, Mengyuan Liu, Hong Zhou, Shan Yao, Yan Kosonen, Risto Wu, Yuxin Li, Baizhan Chongqing Univ Joint Int Res Lab Green Bldg & Built Environm Minist Educ Chongqing Peoples R China Chongqing Univ Natl Ctr Int Res Low carbon & Green Bldg Minist Sci & Technol Chongqing Peoples R China Aalto Univ Dept Mech Engn Espoo 02150 Finland Zhejiang Sci Tech Univ Sch Civil Engn & Architecture Hangzhou Peoples R China

Many elderly people rarely own or use air conditioners because of low income and economising habits, causing them to live in warm thermal environments when heat waves and hot weather occur. Living in warm conditions worsens thermal discomfort and poses health risks this group. To investigate the thermal comfort and adaptation of the elderly, a total of 38 participants were recruited for two parts of experiments in a climate chamber: Part A collected thermal sensation vote (TSV) and physiological parameters for 30 min at 28, 30, and 32 degrees C, and Part B presented a 20-min cooling with fans (air velocities of 0.6 and 1.4 m/s) at the same temperature. Furthermore, we constructed a thermal comfort model for the elderly based on human body exergy analysis and the GBDT, AdaBoost, and xgboost machine-learning algorithms. The results showed that the predicted mean vote considerably overestimated the actual TSV. The TSV and mean skin temperature were decreased by 0.1-0.5 scores and 0.4-0.5 degrees C by the behavioural adaptation of fan cooling. The predictive results showed that the xgboost model performed better, with R2 score, mean absolute error (MAE), and mean squared error (MSE) of 81 %, 0.10, and 0.01. Exergy transfer from evaporation (Ex-Esk), mean skin temperature (mtsk), air velocity (va), and convective exergy transfer (Ex-C) contributed more to the feature importance in the SHAP value analysis. The current study has implications for investigating physiological comfort and age-friendly environmental designs for the elderly, providing new perspectives for thermal comfort evaluations.

关键词： Elderly Human body exergy analysis Machine learning Thermal comfort xgboost algorithm

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Evaluation and Application of the PT-JPL Physical Model Optimized with xgboost algorithm in Latent Heat Flux Estimation

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WATER RESOURCES MANAGEMENT 2025年 1-18页

作者： Wang, Lizheng Kong, Jinling Zhang, Qiutong Dong, Lixin Zhong, Yanling Changan Univ Sch Geol Engn & Geomat Xian 710054 Peoples R China China Meteorol Adm Natl Satellite Meteorol Ctr Natl Ctr Space Weather Key Lab Radiometr Calibrat & Validat Environm Sate Beijing Peoples R China

The static parameterization scheme in the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model limits the dynamic capture of latent heat flux (LE) in different plant functional types (PFTs). Therefore, this study employs the Extreme Gradient Boosting (xgboost) algorithm to optimize the constraint factors and sub-models in the PT-JPL model that are influenced by sensitive prior parameters, thereby constructing five hybrid models under the PT-JPL physical constraint framework to achieve the dynamic response of fixed prior parameters and to integrate ensemble learning (EML) with the process-based framework, ensuring that physical mechanism and high precision coexist. Comparative analysis and validation across five PFTs in the Heihe River Basin of China reveal that the XGB-LEc-PT-JPL model, optimized for vegetation transpiration, exhibits the best comprehensive performance and outperforms the pure data-driven model in several aspects. Regarding overall accuracy, the MAE and RMSE are 15.47 W/m(2) and 23.85 W/m(2), respectively. Although hybrid models optimized for deeper constraint factors sometimes exceed the simulation accuracy of the XGB-LEc-PT-JPL model, they often exhibit reduced parameter generalization, increasing model uncertainty. Finally, the regional scale comparison of different models reveals a consistent spatial pattern, and the XGB-LEc-PT-JPL model can still achieve good simulation accuracy. This study combines EML with physical model, providing scientific insights for understanding hydrological processes under regional climate change, as well as for ecological water resource conservation and optimal water resource allocation.

关键词： Evapotranspiration Combining ensemble learning algorithm with physical model PT-JPL model Physical constraints xgboost algorithm

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A multi-dimensional decision framework based on the xgboost algorithm and the constrained parametric approach

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SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Wang, Xinxin Zhang, Bingbing Xu, Zeshui Li, Ming Skare, Marinko Sichuan Univ Business Sch Chengdu 610064 Peoples R China Juraj Dobrila Univ Pula Fac Econ & Tourism Dr Mijo Mirkov Preradoviceva 1-1 Pula 52100 Croatia Univ Econ & Human Sci Warsaw Warsaw Poland

With the wide access to data and advanced technologies, organizations and firms prefer to use data-based and interpretable analytics to deal with uncertain and cognitive decision-making problems. In this regard, this study considers quantitative data and qualitative variables, to propose a multi-dimensional decision framework based on the nested probabilistic linguistic term sets. Under the framework, xgboost algorithm, one of the machine learning methods, is conducted to capture the importance of attributes by using the historical data, and further calculate the attribute weights. The constrained parametric approach is used to establish membership functions of linguistic variables, and then get the objective probabilities in the linguistic model, so that we can obtain a scientific decision matrix. A case study concerning the ranking of bank credit is applied to present the proposed decision framework, and the process of making a rational decision. According to the comparative analysis, the proposed framework is flexible and the result is stable. Managers and policymakers determine the attribute weights by real data and choose the suitable decision method for a certain application. The framework provides an opportunity for capturing, integrating, analyzing data, and interpreting linguistic variables in the model to consider uncertain and cognitive decision at the both theoretical and practical levels.

关键词： Multi-dimensional decision xgboost algorithm Nested probabilistic linguistic term sets Constrained parametric approach Bank credit

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xgboost algorithm-based prediction of concrete electrical resistivity for structural health monitoring

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AUTOMATION IN CONSTRUCTION 2020年 114卷 103155-000页

作者： Dong, Wei Huang, Yimiao Lehane, Barry Ma, Guowei Univ Western Australia Dept Civil Environm & Min Engn Perth WA 6009 Australia Hebei Univ Technol Dept Civil & Transportat Engn Tianjin 300401 Peoples R China

For structural health monitoring, electrical resistivity measurement (ERM) method is commonly employed for the detection of concrete's durability, as indicated by the chloride permeability and the corrosion of steel reinforcement. However, according to previous experimental studies, ERM results are susceptible to significant uncertainties due to multiple influencing factors such as concrete water/cement ratio and structure curing environment as well as their complex interrelationships. The present study therefore proposes an xgboost algorithm-based prediction model which considers all potential influential factors simultaneously. A database containing 800 experimental instances composed of 16 input attributes is constructed according to existing reported studies and utilized for training and testing the xgboost model. Statistical scores (RMSE, MAE and R-2) and the GridsearchCV feature are applied to evaluate and optimize the established model respectively. Results show that the proposed xgboost model achieves satisfactory predictive performance as demonstrated by high coefficients of regression fitting lines (0.991 and 0.943) and comparatively low RMSE values (4.6 and 11.3 k5 Omega cm) for both training and testing sets respectively. The analyses of the attribute importance ranking also reveal that curing age and cement content have the greatest influence on ERM results.

关键词： Concrete electrical resistivity Structural health monitoring Machine learning xgboost algorithm

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Fusion State-of-Health Estimation of Lithium-Ion Batteries Based on Improved xgboost algorithm and Adaptive Kalman Filter

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2024年第12期171卷 120506-120506页

作者： Tan, Piqiang Zhao, Luhao Yang, Xiaomei Yang, Aoji Liu, Xiaoyang Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China

Accurately predicting the state-of-health of lithium-ion batteries (LIBs) is of paramount significance for safety and stability of battery systems. This paper introduces a fusion model, which integrates the characteristic of data-driven model and equivalent circuit model to enhance precision. The first step is to preprocess data, including extracting health features, correlation screening, and compressing data. Subsequently, the hyperparameters of xgboost algorithm are optimized using a weighted artificial bee colony algorithm, resulting in an improved xgboost (IXGB) data-driven model. Finally, the observed values from the data-driven model and the prior values based on the equivalent circuit model are combined through adaptive Kalman filter (AKF), developing an improved xgboost and adaptive Kalman filter (IXGB-AKF) fusion model, which makes the most of historical experience and the current state of LIBs. Validation is conducted using publicly available NASA Li-ion Battery Aging Datasets, with different datasets under various operating conditions, including different battery cells, different discharge depths and rates of LIBs. The resulting root mean square error values of the former three operating conditions are 1.834%, 2.570%, and 3.456%, respectively. The results indicate that the IXGB-AKF fusion model exhibits good accuracy and robustness under different operating conditions. A novel SOH fusion model is established with XGBoot and AKFSix health features are finally selected and compressed for SOH estimationBattery cells, discharge depths and rates are conducted to verify modelsWeighted artificial bee colony algorithm is used to optimize hyperparameters

关键词： batteries - li-ion energy storage state-of-health fusion model adaptive kalman filter xgboost algorithm

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Research on orthopedic auxiliary classification and prediction model based on xgboost algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第7期32卷 1971-1979页

作者： Li, Shenglong Zhang, Xiaojing China Med Univ Liaoning Canc Hosp & Inst Canc Hosp Dept Bone & Soft Tissue Tumor Surg Shenyang Liaoning Peoples R China

In the big data environment, hospital medical data are also becoming more complex and diversified. The traditional method of manually processing data has not been able to meet the management needs of massive medical data. With the further development of big data technology and machine learning, the smart medical aided diagnosis model came into being. However, there is almost no auxiliary diagnosis mode for orthopedic diseases. In order to make up for the gap in the auxiliary diagnosis of orthopedics and promote the wisdom process of orthopedic disease diagnosis, this paper proposes an orthopedic auxiliary classification prediction model based on xgboost algorithm. The experimental data were obtained from the clinical case information of femoral neck patients from April 2016 to October 2018, Department of Bone and Soft Tissue Tumor Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute. In order to make the experimental results more convincing, while constructing the xgboost model, the orthopedic auxiliary classification prediction model is constructed based on the random forest algorithm and the associated classification algorithm, respectively, and the three models are compared and analyzed. The results show that compared with the random forest model and the associated classification model, the xgboost algorithm classification prediction model has higher accuracy, faster calculation speed, and more applicability in orthopedic clinical data. The xgboost algorithm can cope with complex and diverse medical data, and can better meet the requirements of timeliness and accuracy of auxiliary diagnosis. The classification and prediction model of orthopedic auxiliary diagnosis proposed in this paper helps to reduce the workload of medical workers, help patients prevent and recover early, and realize real auxiliary medical services.

关键词： Orthopedic diseases Assisted diagnosis Classification prediction xgboost algorithm

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Design of High Temperature Complex Dielectric Properties Measuring System Based on xgboost algorithm

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MATERIALS 2020年第6期13卷 1419页

作者： Wu, Yuanyuan Wu, Li Zhu, Huacheng Hong, Tao Chengdu Univ Technol Coll Informat Sci & Technol Chengdu 610000 Peoples R China Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China China West Normal Univ Sch Elect Informat Engn Nanchong 637002 Peoples R China

This paper aims to propose an online relative complex permittivity measurement system at high temperature based on microwave interferometer. A ridge waveguide with a TE10 mode was used in which the sample was heated and measured simultaneously at a frequency of 2450 MHz, and the microwave interferometer is used to collect the amplitude and phase difference of the incident signal. The Extreme Gradient Boosting (xgboost) algorithm trained by the corresponding simulation data is used to construct the inversion model to calculate the complex dielectric coefficient of the tested material. Besides, this paper uses linear regression algorithm (LR) to calibrate the measurement system in order to improve the measurement accuracy. The entire system was tested using different materials at room temperature, and the maximum error of the measurement accuracy is less than 8% compared to the theoretical data. The robustness of the entire system was also tested by measuring Macor materials up to 800 degrees C. This proposed method provides an effective way to understand the mechanism between microwaves and matter at high temperatures.

关键词： dielectric property microwave interferometer xgboost algorithm

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Prediction of weld bead geometry of MAG welding based on xgboost algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第9-12期101卷 2283-2295页

作者： Chen, Kai Chen, Huabin Liu, Liang Chen, Shanben Shanghai Jiao Tong Univ Sch Mat Sci & Engn Shanghai Key Lab Mat Laser Proc & Modificat Shanghai 200240 Peoples R China

Evaluating the welding joint quality in real time is difficult for chassis parts robotic gas-shielded welding. Series of metal active gas (MAG) joints were conducted in this paper to investigate the relationship between welding current, welding speed, energy input, and weld bead geometry. Bead width and bead reinforcement are obtained using a line-structured light measurement method, and the penetration depth of the bead is measured with the macroscopic metallurgical microscope. The ratio of penetration depth to the plate thickness and reinforcement is chosen as the evaluation criterion of the joint quality. Based on xgboost algorithm, two data-driven models are proposed to recognize penetration status and predict the bead reinforcement. In the prediction results, the absolute error of the penetration coefficient is 0.079 at the maximum, and the average relative error is 11.06%. For the test result of reinforcement prediction model, the relative error is 20.5% on average. The test results show that the xgboost-based models can be used for real-time prediction of welding quality.

关键词： MAG welding xgboost algorithm line-structured light measurement penetration coefficient

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