Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
Discontinuity traces significantly impact the mechanical properties of rock masses,making their rapid and accurate identification crucial for stability analysis.We propose a framework using the multi-scale surface var...Discontinuity traces significantly impact the mechanical properties of rock masses,making their rapid and accurate identification crucial for stability analysis.We propose a framework using the multi-scale surface variation index(MsSVI)and transfer-learning enhanced artificial neural network(ANN)for efficient discontinuity trace extraction from rock mass point clouds.Leveraging the similarity between regular geometric bodies and engineering rock masses,we extract trace feature points without manual threshold selection.Our contributions include:(1)An adaptive radius MsSVI calculation method based on density information;(2)a universal trace feature point classification model trained using MsSVI and ANN via inductive transfer learning;and(3)a random sampling L1-medial skeleton algorithm for precise trace feature point extraction,bypassing point cloud triangulation.Experimental results show that our model achieves a 90.2%F1-score on test sets,demonstrating its accuracy and robustness.Furthermore,our method excels in trace detail extraction on two datasets,surpassing existing models and highlighting its potential for rock mass structural analysis.展开更多
The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial in...The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.展开更多
Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learni...Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.展开更多
With the rapid development of artificial intelligence(AI)technology,the teaching mode in the field of education is undergoing profound changes.Especially the design and implementation of personalized learning paths ha...With the rapid development of artificial intelligence(AI)technology,the teaching mode in the field of education is undergoing profound changes.Especially the design and implementation of personalized learning paths have become an important direction of intelligent teaching reform.The traditional“one-size-fits-all”teaching model has gradually failed to meet the individualized learning needs of students.However,through the advantages of data analysis and real-time feedback,AI technology can provide tailor-made teaching content and learning paths based on students’learning progress,interests,and abilities.This study explores the innovation of the personalized learning path model based on AI technology,and analyzes the potential and challenges of this model in improving teaching effectiveness,promoting the all-round development of students,and optimizing the interaction between teachers and students.Through case analysis and empirical research,this paper summarizes the implementation methods of the AI-driven personalized learning path,the innovation of teaching models,and their application prospects in educational reform.Meanwhile,the research also discussed the ethical issues of AI technology in education,data privacy protection,and its impact on the teacher-student relationship,and proposed corresponding solutions.展开更多
Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for sp...Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for spinal pathologies,leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms,enabling faster and more accurate detection of abnormalities.AIpowered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries.Wearable devices and virtual platforms,designed with AI,offer personalized,adaptive therapies that improve treatment adherence and recovery outcomes.AI also enables preventive interventions by assessing spine condition risks early.Despite progress,challenges remain,including limited healthcare datasets,algorithmic biases,ethical concerns,and integration into existing systems.Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care.Future developments include multimodal AI systems integrating imaging,clinical,and genetic data for holistic treatment approaches.AI and ML promise significant improvements in diagnostic accuracy,treatment personalization,service accessibility,and cost efficiency,paving the way for more streamlined and effective spine care,ultimately enhancing patient outcomes.展开更多
Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transforma...Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.展开更多
Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide ...Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.展开更多
Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes ev...Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes evidence on explainable ensemble learning approaches for HCC classification,emphasizing their integration with clinical workflows and multi-omics data.A systematic analysis[including datasets such as The Cancer Genome Atlas,Gene Expression Omnibus,and the Surveillance,Epidemiology,and End Results(SEER)datasets]revealed that explainable ensemble learning models achieve high diagnostic accuracy by combining clinical features,serum biomarkers such as alpha-fetoprotein,imaging features such as computed tomography and magnetic resonance imaging,and genomic data.For instance,SHapley Additive exPlanations(SHAP)-based random forests trained on NCBI GSE14520 microarray data(n=445)achieved 96.53%accuracy,while stacking ensembles applied to the SEER program data(n=1897)demonstrated an area under the receiver operating characteristic curve of 0.779 for mortality prediction.Despite promising results,challenges persist,including the computational costs of SHAP and local interpretable model-agnostic explanations analyses(e.g.,TreeSHAP requiring distributed computing for metabolomics datasets)and dataset biases(e.g.,SEER’s Western population dominance limiting generalizability).Future research must address inter-cohort heterogeneity,standardize explainability metrics,and prioritize lightweight surrogate models for resource-limited settings.This review presents the potential of explainable ensemble learning frameworks to bridge the gap between predictive accuracy and clinical interpretability,though rigorous validation in independent,multi-center cohorts is critical for real-world deployment.展开更多
Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical h...Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical history,clinical assessment,biochemical markers,and imaging techniques,often present limitations in sensitivity and specificity,especially in atypical cases.In recent years,artificial intelligence(AI)has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning(ML)and deep learning(DL)models.This review evaluates the current applications of AI in both adult and pediatric AAp,focusing on clinical data-based models,radiological imaging analysis,and AI-assisted clinical decision support systems.ML models such as random forest,support vector machines,logistic regression,and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems,achieving sensitivity and specificity rates exceeding 90%in multiple studies.Additionally,DL techniques,particularly convolutional neural networks,have been shown to outperform radiologists in interpreting ultrasound and computed tomography images,enhancing diagnostic confidence.This review synthesized findings from 65 studies,demonstrating that AI models integrating multimodal data including clinical,laboratory,and imaging parameters further improved diagnostic precision.Moreover,explainable AI approaches,such as SHapley Additive exPlanations and local interpretable model-agnostic explanations,have facilitated model transparency,fostering clinician trust in AI-driven decision-making.This review highlights the advancements in AI for AAp diagnosis,emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases.While preliminary results are promising,further prospective,multicenter studies are required for large-scale clinical implementation,given that a great proportion of current evidence derives from retrospective designs,and existing prospective cohorts exhibit limited sample sizes or protocol variability.Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.展开更多
The manufacturing sector has been transformed owing to additive manufacturing(AM),which has made it possible to create intricate,personalized items with little material waste.However,optimizing and enhancing AM proces...The manufacturing sector has been transformed owing to additive manufacturing(AM),which has made it possible to create intricate,personalized items with little material waste.However,optimizing and enhancing AM processes remain challenging owing to the intricacies involved in design,material selection,and process parameters.This review explores the integration of artificial intelligence(AI),machine learning(ML),and deep learning(DL)techniques to improve and innovate in the field of AM.AI-driven design optimization procedures offer innovative solutions for the 3D printing of complex geometries and lightweight structures.By leveraging machine learning(ML)algorithms,these procedures analyze extensive data from previous manufacturing processes to enhance efficiency and productivity.ML models facilitate design and production automation by learning from historical data and identifying intricate patterns that human operators may miss.Deep learning(DL)further augments this capacity by utilizing sophisticated neural networks to manage and interpret complex information and provide deeper insights into the manufacturing process.Integrating AI,ML,and DL into AM enables the creation of optimized,lightweight components that are crucial for reducing fuel consumption in the automotive and aviation industries.These advanced AI techniques optimize the design and production processes and enhance predictive modeling for process optimization and defect detection,leading to improved performance and reduced manufacturing costs.Therefore,integrating AI,ML,and DL into AM improves precision in component fabrication,enabling advanced material design innovations and opening new possibilities for innovation in product design and material science.This review discusses and highlights significant advancements and identifies future directions for applying AI,ML,and DL in AM.By leveraging these technologies,AM processes can achieve unprecedented levels of precision,customization,and productivity for analysis and modification.展开更多
The previous affiliation“Department of Computer Engineering,Cyprus International University,Nicosia,99258,Turkey”is for the Cyprus International University.
With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathemati...With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathematics)education,AI demonstrates significant advantages through adaptive learning pathways,instant feedback,and individualized resource allocation.However,current research predominantly focuses on the technical architecture and application effectiveness of such systems,with insufficient exploration of how AI-enabled personalized learning systems influence university students’learning motivation and academic achievement through educational psychological mechanisms.This paper adopts an educational psychology perspective to construct a causal mechanism model linking“learning motivation-learning behavior-academic achievement.”Findings indicate that AI-powered personalized learning systems enhance learning autonomy,boost self-efficacy,and optimize feedback mechanisms.These effects collectively stimulate university students’learning motivation in STEM disciplines,thereby promoting academic achievement.Building upon empirical research,this paper proposes implications for educational practice and policy formulation,emphasizing the necessity of advancing higher education reform through the dual influence of technology and psychological mechanisms.展开更多
The nutritional management of patients with esophageal cancer(EC)presents significant complexities,with traditional approaches facing inherent limitations in data collection,real-time decision-making,and personalized ...The nutritional management of patients with esophageal cancer(EC)presents significant complexities,with traditional approaches facing inherent limitations in data collection,real-time decision-making,and personalized care.This narrative review explores the transformative potential of artificial intelligence(AI)and machine learning(ML),particularly deep learning(DL)and reinforcement learning(RL),in revolutionizing nutritional support for this vulnerable patient population.DL has demonstrated remarkable capabilities in enhancing the accuracy and objectivity of nutritional assessment through precise,automated body composition analysis from medical imaging,offering valuable prognostic insights.Concurrently,RL enables the dynamic optimization of nutritional interventions,adapting them in real time to individual patient responses,paving the way for truly personalized care paradigms.Although AI/ML offers potential advantages in efficiency,precision,and personalization by integrating multidimensional data for superior clinical decision support,its widespread adoption is accompanied by critical challenges.These include safeguarding data privacy and security,mitigating algorithmic bias,ensuring transparency and accountability,and establishing rigorous clinical validation.Early evidence suggests the feasibility of applying AI/ML in nutritional risk stratification and workflow optimization,but highquality prospective studies are needed to demonstrate the direct impact on clinical outcomes,including complications,readmissions,and survival.Overcoming these hurdles necessitates robust ethical governance,interdisciplinary collaboration,and continuous education.Ultimately,the strategic integration of AI/ML holds immense promise to profoundly improve patient outcomes,enhance quality of life,and optimize health care resource utilization in the nutritional management of esophageal cancer.展开更多
Artificial intelligence(AI)serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution.Currently,deep learning techniques,s...Artificial intelligence(AI)serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution.Currently,deep learning techniques,such as convolutional neural networks,enable intelligent information collection in fields such as tongue and pulse diagnosis owing to their robust feature-processing capabilities.Natural language processing models,including long short-term memory and transformers,have been applied to traditional Chinese medicine(TCM)for diagnosis,syndrome differentiation,and prescription generation.Traditional machine learning algorithms,such as neural networks,support vector machines,and random forests,are also widely used in TCM diagnosis and treatment because of their strong regression and classification performance on small structured datasets.Future research on AI in TCM diagnosis and treatment may emphasize building large-scale,high-quality TCM datasets with unified criteria based on syndrome elements;identifying algorithms suited to TCM theoretical data distributions;and leveraging AI multimodal fusion and ensemble learning techniques for diverse raw features,such as images,text,and manually processed structured data,to increase the clinical efficacy of TCM diagnosis and treatment.展开更多
This paper delves into the application of artificial intelligence in daily life and the basic concepts of machine learning.Firstly,the concept and background of artificial intelligence were introduced,as well as machi...This paper delves into the application of artificial intelligence in daily life and the basic concepts of machine learning.Firstly,the concept and background of artificial intelligence were introduced,as well as machine learning as an important branch of artificial intelligence.Subsequently,the specific applications of artificial intelligence in daily life were elaborated,including intelligent voice assistants,recommendation systems,as well as image recognition and facial recognition technologies.Subsequently,a preliminary exploration was conducted on the basic concepts of machine learning,including paradigms such as supervised learning,unsupervised learning,and reinforcement learning,as well as common algorithms such as linear regression,logistic regression,decision trees,and support vector machines.Finally,the application of artificial intelligence in daily life and the basic concepts of machine learning were summarized,and the development trends and future prospects of artificial intelligence technology were discussed,as well as the possibility of guiding readers to continue in-depth learning and exploration in the field of artificial intelligence.展开更多
Let’s review the role of gut microbiota in pathogenesis of chronic hepatitis B infection as addressed in by Zhu et al.Zhu et al used high-throughput technology to characterize the microbial ecosystems,which led to an...Let’s review the role of gut microbiota in pathogenesis of chronic hepatitis B infection as addressed in by Zhu et al.Zhu et al used high-throughput technology to characterize the microbial ecosystems,which led to an explosion of various types of molecular profiling data,such as metagenomics,metatranscriptomics,and metabolomics.To analyze such data,machine learning(ML)algorithms have shown to be useful for identifying key molecular signatures,discovering potential patient stratifications,and,particularly,for generating models that can accurately predict phenotypes.Strong evidence suggests that such gut microbiome-based stratification could guide customized interventions to benefit human health.Supervised learning includes designing an algorithm to fix a pre-identified pro-blem.To get an answer,ML software must access data that have been nominated.On the other hand,unsupervised learning does not address any pre-defined prob-lems.Bias should be eliminated as much as possible.In unsupervised learning,an ML algorithm works to identify data patterns without any prior operator input.This can subsequently lead to elements being identified that could not be concei-ved by the operator.At the intersection between supervised and unsupervised learning is semi-supervised ML.Semi-supervised learning includes using a par-tially labeled data set.The ML algorithm utilizes unsupervised learning to label data(that has not yet been labelled)by drawing findings from the labeled data.Then,supervised techniques can be used to solve defined problems involving the labeled data.Reinforcement learning,which is similar to supervised learning in the meaning,is goal-oriented.Reinforcement learning does not need labeled data,instead,it is provided with a set of regulations on a problem.An algorithm will carry out operations to try to answer questions involving the problem.Based on obtained data of gut microbiota,various therapeutic modalities can be applied:Prebiotics,probiotics,postbiotics,engineered bacteria,bacteriophage,and novel microbe-materials therapeutic system and fecal transplantation.In conclusion,ML is an artificial intelligence application that helps in providing new perspectives on tailored therapy.Furthermore,assessing the impact of gut microbiota modification is a critical step in advanced liver disease management.These new artificial intelligence techniques although promising,still require further analysis and validation in future studies.展开更多
Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including...Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including non-alcoholic fatty liver disease,cirrhosis,and hepatocellular carcinoma,often progress silently and pose diagnostic challenges due to reliance on invasive biopsies and operatordependent imaging.This review explores the integration of AI across key domains such as big data analytics,deep learning-based image analysis,histopathological interpretation,biomarker discovery,and clinical prediction modeling.AI algorithms have demonstrated high accuracy in liver fibrosis staging,hepatocellular carcinoma detection,and non-alcoholic fatty liver disease risk stratification,while also enhancing survival prediction and treatment response assessment.For instance,convolutional neural networks trained on portal venous-phase computed tomography have achieved area under the curves up to 0.92 for significant fibrosis(F2-F4)and 0.89 for advanced fibrosis,with magnetic resonance imaging-based models reporting comparable performance.Advanced methodologies such as federated learning preserve patient privacy during cross-center model training,and explainable AI techniques promote transparency and clinician trust.Despite these advancements,clinical adoption remains limited by challenges including data heterogeneity,algorithmic bias,regulatory uncertainty,and lack of real-time integration into electronic health records.Looking forward,the convergence of multi-omics,imaging,and clinical data through interpretable and validated AI frameworks holds great promise for precision liver care.Continued efforts in model standardization,ethical oversight,and clinician-centered deployment will be essential to realize the full potential of AI in hepatopathy diagnosis and treatment.展开更多
The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combi...The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.展开更多
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2025XJSB01)the Founda-tion of State Key Laboratory for Geomechanics and Deep Under-ground Engineering,China University of Mining&Technology,Beijing.(Grant No.SKLGDUEK 2217)the Collaborative Inno-vation Center for Prevention and Control of Mountain Geological Hazards of Zhejiang Province(PCMGH-2022-03).
文摘Discontinuity traces significantly impact the mechanical properties of rock masses,making their rapid and accurate identification crucial for stability analysis.We propose a framework using the multi-scale surface variation index(MsSVI)and transfer-learning enhanced artificial neural network(ANN)for efficient discontinuity trace extraction from rock mass point clouds.Leveraging the similarity between regular geometric bodies and engineering rock masses,we extract trace feature points without manual threshold selection.Our contributions include:(1)An adaptive radius MsSVI calculation method based on density information;(2)a universal trace feature point classification model trained using MsSVI and ANN via inductive transfer learning;and(3)a random sampling L1-medial skeleton algorithm for precise trace feature point extraction,bypassing point cloud triangulation.Experimental results show that our model achieves a 90.2%F1-score on test sets,demonstrating its accuracy and robustness.Furthermore,our method excels in trace detail extraction on two datasets,surpassing existing models and highlighting its potential for rock mass structural analysis.
基金support from the National Key Research and Development Program of China(No.2024YFB3713705)is acknowledgedWangzhong Mu would like to acknowledge the Strategic Mobility,Sweden(SSF,No.SM22-0039)+1 种基金the Swedish Foundation for International Cooperation in Research and Higher Education(STINT,No.IB2022-9228)the Jernkontoret(Sweden)for supporting this clean steel research.Gonghao Lian would like to acknowledge China Scholarship Council(CSC,No.202306080032).
文摘The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.
基金funded by Ongoing Research Funding Program for Project number(ORF-2025-648),King Saud University,Riyadh,Saudi Arabia.
文摘Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.
基金The 2024 Guangdong University of Science and Technology Teaching,Science and Innovation Project(GKJXXZ2024028)。
文摘With the rapid development of artificial intelligence(AI)technology,the teaching mode in the field of education is undergoing profound changes.Especially the design and implementation of personalized learning paths have become an important direction of intelligent teaching reform.The traditional“one-size-fits-all”teaching model has gradually failed to meet the individualized learning needs of students.However,through the advantages of data analysis and real-time feedback,AI technology can provide tailor-made teaching content and learning paths based on students’learning progress,interests,and abilities.This study explores the innovation of the personalized learning path model based on AI technology,and analyzes the potential and challenges of this model in improving teaching effectiveness,promoting the all-round development of students,and optimizing the interaction between teachers and students.Through case analysis and empirical research,this paper summarizes the implementation methods of the AI-driven personalized learning path,the innovation of teaching models,and their application prospects in educational reform.Meanwhile,the research also discussed the ethical issues of AI technology in education,data privacy protection,and its impact on the teacher-student relationship,and proposed corresponding solutions.
文摘Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for spinal pathologies,leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms,enabling faster and more accurate detection of abnormalities.AIpowered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries.Wearable devices and virtual platforms,designed with AI,offer personalized,adaptive therapies that improve treatment adherence and recovery outcomes.AI also enables preventive interventions by assessing spine condition risks early.Despite progress,challenges remain,including limited healthcare datasets,algorithmic biases,ethical concerns,and integration into existing systems.Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care.Future developments include multimodal AI systems integrating imaging,clinical,and genetic data for holistic treatment approaches.AI and ML promise significant improvements in diagnostic accuracy,treatment personalization,service accessibility,and cost efficiency,paving the way for more streamlined and effective spine care,ultimately enhancing patient outcomes.
文摘Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00460621,Developing BCI-Based Digital Health Technologies for Mental Illness and Pain Management).
文摘Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.
文摘Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes evidence on explainable ensemble learning approaches for HCC classification,emphasizing their integration with clinical workflows and multi-omics data.A systematic analysis[including datasets such as The Cancer Genome Atlas,Gene Expression Omnibus,and the Surveillance,Epidemiology,and End Results(SEER)datasets]revealed that explainable ensemble learning models achieve high diagnostic accuracy by combining clinical features,serum biomarkers such as alpha-fetoprotein,imaging features such as computed tomography and magnetic resonance imaging,and genomic data.For instance,SHapley Additive exPlanations(SHAP)-based random forests trained on NCBI GSE14520 microarray data(n=445)achieved 96.53%accuracy,while stacking ensembles applied to the SEER program data(n=1897)demonstrated an area under the receiver operating characteristic curve of 0.779 for mortality prediction.Despite promising results,challenges persist,including the computational costs of SHAP and local interpretable model-agnostic explanations analyses(e.g.,TreeSHAP requiring distributed computing for metabolomics datasets)and dataset biases(e.g.,SEER’s Western population dominance limiting generalizability).Future research must address inter-cohort heterogeneity,standardize explainability metrics,and prioritize lightweight surrogate models for resource-limited settings.This review presents the potential of explainable ensemble learning frameworks to bridge the gap between predictive accuracy and clinical interpretability,though rigorous validation in independent,multi-center cohorts is critical for real-world deployment.
文摘Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical history,clinical assessment,biochemical markers,and imaging techniques,often present limitations in sensitivity and specificity,especially in atypical cases.In recent years,artificial intelligence(AI)has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning(ML)and deep learning(DL)models.This review evaluates the current applications of AI in both adult and pediatric AAp,focusing on clinical data-based models,radiological imaging analysis,and AI-assisted clinical decision support systems.ML models such as random forest,support vector machines,logistic regression,and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems,achieving sensitivity and specificity rates exceeding 90%in multiple studies.Additionally,DL techniques,particularly convolutional neural networks,have been shown to outperform radiologists in interpreting ultrasound and computed tomography images,enhancing diagnostic confidence.This review synthesized findings from 65 studies,demonstrating that AI models integrating multimodal data including clinical,laboratory,and imaging parameters further improved diagnostic precision.Moreover,explainable AI approaches,such as SHapley Additive exPlanations and local interpretable model-agnostic explanations,have facilitated model transparency,fostering clinician trust in AI-driven decision-making.This review highlights the advancements in AI for AAp diagnosis,emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases.While preliminary results are promising,further prospective,multicenter studies are required for large-scale clinical implementation,given that a great proportion of current evidence derives from retrospective designs,and existing prospective cohorts exhibit limited sample sizes or protocol variability.Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.
文摘The manufacturing sector has been transformed owing to additive manufacturing(AM),which has made it possible to create intricate,personalized items with little material waste.However,optimizing and enhancing AM processes remain challenging owing to the intricacies involved in design,material selection,and process parameters.This review explores the integration of artificial intelligence(AI),machine learning(ML),and deep learning(DL)techniques to improve and innovate in the field of AM.AI-driven design optimization procedures offer innovative solutions for the 3D printing of complex geometries and lightweight structures.By leveraging machine learning(ML)algorithms,these procedures analyze extensive data from previous manufacturing processes to enhance efficiency and productivity.ML models facilitate design and production automation by learning from historical data and identifying intricate patterns that human operators may miss.Deep learning(DL)further augments this capacity by utilizing sophisticated neural networks to manage and interpret complex information and provide deeper insights into the manufacturing process.Integrating AI,ML,and DL into AM enables the creation of optimized,lightweight components that are crucial for reducing fuel consumption in the automotive and aviation industries.These advanced AI techniques optimize the design and production processes and enhance predictive modeling for process optimization and defect detection,leading to improved performance and reduced manufacturing costs.Therefore,integrating AI,ML,and DL into AM improves precision in component fabrication,enabling advanced material design innovations and opening new possibilities for innovation in product design and material science.This review discusses and highlights significant advancements and identifies future directions for applying AI,ML,and DL in AM.By leveraging these technologies,AM processes can achieve unprecedented levels of precision,customization,and productivity for analysis and modification.
文摘The previous affiliation“Department of Computer Engineering,Cyprus International University,Nicosia,99258,Turkey”is for the Cyprus International University.
文摘With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathematics)education,AI demonstrates significant advantages through adaptive learning pathways,instant feedback,and individualized resource allocation.However,current research predominantly focuses on the technical architecture and application effectiveness of such systems,with insufficient exploration of how AI-enabled personalized learning systems influence university students’learning motivation and academic achievement through educational psychological mechanisms.This paper adopts an educational psychology perspective to construct a causal mechanism model linking“learning motivation-learning behavior-academic achievement.”Findings indicate that AI-powered personalized learning systems enhance learning autonomy,boost self-efficacy,and optimize feedback mechanisms.These effects collectively stimulate university students’learning motivation in STEM disciplines,thereby promoting academic achievement.Building upon empirical research,this paper proposes implications for educational practice and policy formulation,emphasizing the necessity of advancing higher education reform through the dual influence of technology and psychological mechanisms.
文摘The nutritional management of patients with esophageal cancer(EC)presents significant complexities,with traditional approaches facing inherent limitations in data collection,real-time decision-making,and personalized care.This narrative review explores the transformative potential of artificial intelligence(AI)and machine learning(ML),particularly deep learning(DL)and reinforcement learning(RL),in revolutionizing nutritional support for this vulnerable patient population.DL has demonstrated remarkable capabilities in enhancing the accuracy and objectivity of nutritional assessment through precise,automated body composition analysis from medical imaging,offering valuable prognostic insights.Concurrently,RL enables the dynamic optimization of nutritional interventions,adapting them in real time to individual patient responses,paving the way for truly personalized care paradigms.Although AI/ML offers potential advantages in efficiency,precision,and personalization by integrating multidimensional data for superior clinical decision support,its widespread adoption is accompanied by critical challenges.These include safeguarding data privacy and security,mitigating algorithmic bias,ensuring transparency and accountability,and establishing rigorous clinical validation.Early evidence suggests the feasibility of applying AI/ML in nutritional risk stratification and workflow optimization,but highquality prospective studies are needed to demonstrate the direct impact on clinical outcomes,including complications,readmissions,and survival.Overcoming these hurdles necessitates robust ethical governance,interdisciplinary collaboration,and continuous education.Ultimately,the strategic integration of AI/ML holds immense promise to profoundly improve patient outcomes,enhance quality of life,and optimize health care resource utilization in the nutritional management of esophageal cancer.
基金supported by grants from the National Natural Science Foundation of China(Key Program)(No.82230124)Traditional Chinese Medicine Inheritance and Innovation“Ten million”talent project-Qihuang Project Chief Scientist Project(No.0201000401)+1 种基金State Administration of Traditional Chinese Medicine 2nd National Traditional Chinese Medicine Inheritance Studio Construction Project(Official Letter of the State Office of Traditional Chinese Medicine[2022]No.245)National Natural Science Foundation of China(General Program)(No.81974556).
文摘Artificial intelligence(AI)serves as a key technology in global industrial transformation and technological restructuring and as the core driver of the fourth industrial revolution.Currently,deep learning techniques,such as convolutional neural networks,enable intelligent information collection in fields such as tongue and pulse diagnosis owing to their robust feature-processing capabilities.Natural language processing models,including long short-term memory and transformers,have been applied to traditional Chinese medicine(TCM)for diagnosis,syndrome differentiation,and prescription generation.Traditional machine learning algorithms,such as neural networks,support vector machines,and random forests,are also widely used in TCM diagnosis and treatment because of their strong regression and classification performance on small structured datasets.Future research on AI in TCM diagnosis and treatment may emphasize building large-scale,high-quality TCM datasets with unified criteria based on syndrome elements;identifying algorithms suited to TCM theoretical data distributions;and leveraging AI multimodal fusion and ensemble learning techniques for diverse raw features,such as images,text,and manually processed structured data,to increase the clinical efficacy of TCM diagnosis and treatment.
文摘This paper delves into the application of artificial intelligence in daily life and the basic concepts of machine learning.Firstly,the concept and background of artificial intelligence were introduced,as well as machine learning as an important branch of artificial intelligence.Subsequently,the specific applications of artificial intelligence in daily life were elaborated,including intelligent voice assistants,recommendation systems,as well as image recognition and facial recognition technologies.Subsequently,a preliminary exploration was conducted on the basic concepts of machine learning,including paradigms such as supervised learning,unsupervised learning,and reinforcement learning,as well as common algorithms such as linear regression,logistic regression,decision trees,and support vector machines.Finally,the application of artificial intelligence in daily life and the basic concepts of machine learning were summarized,and the development trends and future prospects of artificial intelligence technology were discussed,as well as the possibility of guiding readers to continue in-depth learning and exploration in the field of artificial intelligence.
文摘Let’s review the role of gut microbiota in pathogenesis of chronic hepatitis B infection as addressed in by Zhu et al.Zhu et al used high-throughput technology to characterize the microbial ecosystems,which led to an explosion of various types of molecular profiling data,such as metagenomics,metatranscriptomics,and metabolomics.To analyze such data,machine learning(ML)algorithms have shown to be useful for identifying key molecular signatures,discovering potential patient stratifications,and,particularly,for generating models that can accurately predict phenotypes.Strong evidence suggests that such gut microbiome-based stratification could guide customized interventions to benefit human health.Supervised learning includes designing an algorithm to fix a pre-identified pro-blem.To get an answer,ML software must access data that have been nominated.On the other hand,unsupervised learning does not address any pre-defined prob-lems.Bias should be eliminated as much as possible.In unsupervised learning,an ML algorithm works to identify data patterns without any prior operator input.This can subsequently lead to elements being identified that could not be concei-ved by the operator.At the intersection between supervised and unsupervised learning is semi-supervised ML.Semi-supervised learning includes using a par-tially labeled data set.The ML algorithm utilizes unsupervised learning to label data(that has not yet been labelled)by drawing findings from the labeled data.Then,supervised techniques can be used to solve defined problems involving the labeled data.Reinforcement learning,which is similar to supervised learning in the meaning,is goal-oriented.Reinforcement learning does not need labeled data,instead,it is provided with a set of regulations on a problem.An algorithm will carry out operations to try to answer questions involving the problem.Based on obtained data of gut microbiota,various therapeutic modalities can be applied:Prebiotics,probiotics,postbiotics,engineered bacteria,bacteriophage,and novel microbe-materials therapeutic system and fecal transplantation.In conclusion,ML is an artificial intelligence application that helps in providing new perspectives on tailored therapy.Furthermore,assessing the impact of gut microbiota modification is a critical step in advanced liver disease management.These new artificial intelligence techniques although promising,still require further analysis and validation in future studies.
基金Supported by the Science Planning Project of Liaoning Province,No.2019JH2/10300031-05the National Natural Science Foundation of China,No.12171074.
文摘Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including non-alcoholic fatty liver disease,cirrhosis,and hepatocellular carcinoma,often progress silently and pose diagnostic challenges due to reliance on invasive biopsies and operatordependent imaging.This review explores the integration of AI across key domains such as big data analytics,deep learning-based image analysis,histopathological interpretation,biomarker discovery,and clinical prediction modeling.AI algorithms have demonstrated high accuracy in liver fibrosis staging,hepatocellular carcinoma detection,and non-alcoholic fatty liver disease risk stratification,while also enhancing survival prediction and treatment response assessment.For instance,convolutional neural networks trained on portal venous-phase computed tomography have achieved area under the curves up to 0.92 for significant fibrosis(F2-F4)and 0.89 for advanced fibrosis,with magnetic resonance imaging-based models reporting comparable performance.Advanced methodologies such as federated learning preserve patient privacy during cross-center model training,and explainable AI techniques promote transparency and clinician trust.Despite these advancements,clinical adoption remains limited by challenges including data heterogeneity,algorithmic bias,regulatory uncertainty,and lack of real-time integration into electronic health records.Looking forward,the convergence of multi-omics,imaging,and clinical data through interpretable and validated AI frameworks holds great promise for precision liver care.Continued efforts in model standardization,ethical oversight,and clinician-centered deployment will be essential to realize the full potential of AI in hepatopathy diagnosis and treatment.
基金Guangzhou Metro Scientific Research Project(No.JT204-100111-23001)Chongqing Municipal Special Project for Technological Innovation and Application Development(No.CSTB2022TIAD-KPX0101)Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.(No.N2023G045)。
文摘The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.
