BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning ofte...BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Human Activity Recognition(HAR)is a novel area for computer vision.It has a great impact on healthcare,smart environments,and surveillance while is able to automatically detect human behavior.It plays a vital role in ...Human Activity Recognition(HAR)is a novel area for computer vision.It has a great impact on healthcare,smart environments,and surveillance while is able to automatically detect human behavior.It plays a vital role in many applications,such as smart home,healthcare,human computer interaction,sports analysis,and especially,intelligent surveillance.In this paper,we propose a robust and efficient HAR system by leveraging deep learning paradigms,including pre-trained models,CNN architectures,and their average-weighted fusion.However,due to the diversity of human actions and various environmental influences,as well as a lack of data and resources,achieving high recognition accuracy remain elusive.In this work,a weighted average ensemble technique is employed to fuse three deep learning models:EfficientNet,ResNet50,and a custom CNN.The results of this study indicate that using a weighted average ensemble strategy for developing more effective HAR models may be a promising idea for detection and classification of human activities.Experiments by using the benchmark dataset proved that the proposed weighted ensemble approach outperformed existing approaches in terms of accuracy and other key performance measures.The combined average-weighted ensemble of pre-trained and CNN models obtained an accuracy of 98%,compared to 97%,96%,and 95%for the customized CNN,EfficientNet,and ResNet50 models,respectively.展开更多
Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Par...Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks.展开更多
This study presents a physics-informed modelling framework that combines finite element method(FEM)simulations and supervised machine learning(ML)to predict the self-healing performance of microbial concrete.A FEniCS-...This study presents a physics-informed modelling framework that combines finite element method(FEM)simulations and supervised machine learning(ML)to predict the self-healing performance of microbial concrete.A FEniCS-based FEM platform resolves multiphysics phenomena including nutrient diffusion,microbial CaCO_(3) precipitation,and stiffness recovery.These simulations,together with experimental data,are used to train ML models(Random Forest yielding normalized RMSE≈0.10)capable of predicting performance over a wide range of design parameters.Feature importance analysis identifies curing temperature,calcium carbonate precipitation rate,crack width,bacterial strain,and encapsulation method as the most influential parameters.The coupled FEM-ML approach enables sensitivity analysis,design optimization,and prediction beyond the training dataset(consistently exceeding 90%healing efficiency).Experimental validation confirms model robustness in both crack closure and strength recovery.This FEM–ML pipeline thus offers a generalizable,interpretable,and scalable strategy for the design of intelligent,self-adaptive construction materials.展开更多
Amorphous materials represent a promising platform for advancing CO_(2)electrochemical reduction due to their inherently diverse coordination environments.In this study,we demonstrate computationally the superior perf...Amorphous materials represent a promising platform for advancing CO_(2)electrochemical reduction due to their inherently diverse coordination environments.In this study,we demonstrate computationally the superior performance of amorphous CuNi alloys for CO_(2)electrochemical reduction.By integrating machine learning forcefields for efficient structure generation and density functional theory for subsequent structural refinement and property calculations,we reveal the potential of these disordered systems to outperform their crystalline counterparts.Machine learning forcefields can generate a bulk structure containing a mixture of Cu and Ni atoms,resulting in enhanced catalytic performance.Effective screening of the amorphous surfaces is used to identify undercoordinated Cu and Ni sites in the amorphous structure to synergistically promote selective CO production and favor ethanol formation over ethylene via the stabilization of the*COCHO intermediate,resulting in significantly lower Gibbs free energy changes compared to the crystalline counterpart.The varying atomic coordination environments on amorphous surfaces promote both C–C bond formation and subsequent proton-electron transfer,leading to ethanol formation.These findings demonstrate the superior catalytic performance of amorphous CuNi,highlighting its potential for efficient and selective electroreduction of CO_(2).展开更多
This paper investigates the adaptive optimal tracking control(AOTC)for underactuated surface vessels(USVs).Compared to the majority of existing studies,the control strategy in this paper innovatively combines an exten...This paper investigates the adaptive optimal tracking control(AOTC)for underactuated surface vessels(USVs).Compared to the majority of existing studies,the control strategy in this paper innovatively combines an extended state observer(ESO)with reinforcement learning(RL).The designed ESO has high estimation accuracy and robust disturbance rejection capabilities for the unmeasurable information for USVs.To obtain the AOTC,the actor–critic(AC)networks based on RL are constructed to solve the Hamilton–Jacobi–Bellman(HJB)equations.Due to the uncertainties,it is challenging to obtain the optimal controller by directly solving the HJB equations.To address this issue,this paper employs neural networks(NNs)to approximate the uncertainties and solves the optimal controller via AC-RL and ESO.In addition,the adaptive parameters of the optimal controller is trained in parallel with AC networks,which can ensure that the trained networks can further improve tracking performance.The boundedness of AOTC for USVs is shown by Lyapunov stability theorem.Finally,simulation results demonstrate the effectiveness of the proposed algorithm.展开更多
The expansion of 5G-enabled Internet of Things(IoT)networks,while enabling transformative applications,significantly increases the attack surface and necessitates security solutions that extend beyond traditional intr...The expansion of 5G-enabled Internet of Things(IoT)networks,while enabling transformative applications,significantly increases the attack surface and necessitates security solutions that extend beyond traditional intrusion detection.Existing intrusion detection systems(IDSs)mainly operate in an open-loop manner,excelling at classification but lacking the ability for autonomous,safety-aware remediation.This gap is particularly critical in 5G environments,where manual intervention is too slow and naive automation can lead to severe service disruptions.To address this issue,we propose a novel Self-Healing Intrusion Detection System(SH-IDS)framework that develops a closed-loop cyber defense mechanism.The main technical contribution is the integration of a deep neural networkbased threat detector,which offers uncertainty-quantified predictions,with a safety-aware reinforcement learning(RL)engine formulated as a Constrained Markov Decision Process(CMDP).The CMDP explicitly models operational safety as cost constraints,and a new runtime safety shield actively adjusts any unsafe action proposed by the RL agent to the nearest safe alternative,ensuring operational integrity.Additionally,we introduce a composite utility function for the comprehensive evaluation of the system.Empirical analysis on the 5G-NIDD dataset demonstrates the superior performance of our framework:the detector achieves 98.26%accuracy,while the safe RL agent learns effective mitigation policies.Importantly,the safety shield blocked up to 70 unsafe actions under strict constraints,and analysis of the learned Q-tables confirms that the agent internalizes safety,avoiding overly disruptive actions,such as isolating nodes for minor threats.The system also maintains high efficiency with a compact model size of 121.7 KB and sub-millisecond latency,confirming its practical deployability for real-time 5G-IoT security.展开更多
Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements...Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements at transceivers and RIS,the codebook based beamforming can be utilized in a computationally efficient manner.However,the codeword selection for analog beamforming is an intractable combinatorial optimization(CO)problem.To this end,by taking the CO problem as a classification problem,a multi-task learning based analog beam selection(MTL-ABS)framework is developed to implement cooperative beam selection concurrently at transceivers and RIS.In addition,residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features.Finally,the network convergence is analyzed from a blockwise perspective,and numerical results demonstrate that the MTL-ABS framework greatly decreases the beam selection overhead and achieves near optimal sum-rate compared with heuristic search based counterparts.展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
Recent years have witnessed significant progress in deep learning for remote sensing image Super-Resolution(SR).However,in real-world applications,paired data is often unavailable,making supervised training infeasible...Recent years have witnessed significant progress in deep learning for remote sensing image Super-Resolution(SR).However,in real-world applications,paired data is often unavailable,making supervised training infeasible,while unknown degradation factors constrain reconstruction performance and impair detail recovery.To this end,we propose a Degradation-Adaptive Self-supervised SR method,named DASSR,which recovers high-fidelity details from low-resolution remote sensing images without requiring supervision from high-resolution groundtruth.DASSR employs a dual-path closed-loop architecture,enabling joint learning of SR reconstruction and blur kernel estimation through cycle consistency in the main branch and regularization in the auxiliary branch.Specifically,we incorporate an Edge-Preserving SR network(EPSRN)into DASSR,whose core Hybrid Attention Enhancement Block(HAEB)captures precise structural representations to guide accurate detail reconstruction.Furthermore,a composite loss function is designed,integrating spatial reconstruction consistency,frequencydomain spectrum alignment,and kernel sparsity constraints to ensure stable and efficient self-supervised learning.Experiments on both simulated and real-world remote sensing datasets demonstrate that the proposed DASSR method outperforms competitive deep learning-based SR methods,notably achieving approximately 9%and 15%improvements in the Average Gradient(AG)and Spatial Frequency(SF)metrics,respectively,over the best-performing competitor.展开更多
Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the pro...Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.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
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
This study explores the integration of Synthetic Aperture Radar(SAR)imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection.This study proposes a novel hybrid appr...This study explores the integration of Synthetic Aperture Radar(SAR)imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection.This study proposes a novel hybrid approach for oil spill detection.The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization(BHHO)and Principal Component Analysis(PCA)for improved feature extraction and selection from input SAR imagery.Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images.The BHHO and PCA algorithms were implemented to identify subsets of optimal features from the entire feature dataset extracted by MobileNet.A supplemented hybrid feature set was constructed from the PCA and BHHO-generated features.It was used as input for oil spill detection using the logistic regression supervised machine learning classification algorithm.Several feature set combinations were implemented to test the classification performance of the logistic regression classifier in comparison to that of the proposed hybrid feature set.Results indicate that the highest oil spill detection accuracy of 99.2%has been achieved using the logistic regression classification algorithm,with integrated feature input from subsets identified using the PCA and the BHHO feature selection techniques.The proposed method yielded a statistically significant improvement in the classification performance of the used machine learning model.The significance of our study lies in its unique integration of deep learning with optimized feature selection,unlike other published studies,to enhance oil spill detection accuracy.展开更多
A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumor...A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.展开更多
Contemporary higher education prioritizes cultivating students’key competencies and comprehensive problem-solving abilities,specifically fostering innovation,goal orientation,and initiative.This study investigates a ...Contemporary higher education prioritizes cultivating students’key competencies and comprehensive problem-solving abilities,specifically fostering innovation,goal orientation,and initiative.This study investigates a pedagogical framework that synergizes Research-Led Learning(RLL)and Project-Based Learning(PBL)to establish an open,exploratory learning environment.Employing a case study methodology,the research tracked architecture students engaging in a structured PBL process involving rigorous research activities—ranging from theoretical analysis to field investigations—to develop evidence-based design solutions.Evaluations from both student and faculty perspectives assessed the pedagogical effectiveness regarding learning outcomes and competency development.The findings indicate that this methodology effectively bridges the gap between research and practice,significantly bolstering students’capacity to address authentic challenges and propelling self-directed learning in architectural education.展开更多
基金Supported by Chongqing Medical Scientific Research Project(Joint Project of Chongqing Health Commission and Science and Technology Bureau),No.2023MSXM060.
文摘BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R765),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human Activity Recognition(HAR)is a novel area for computer vision.It has a great impact on healthcare,smart environments,and surveillance while is able to automatically detect human behavior.It plays a vital role in many applications,such as smart home,healthcare,human computer interaction,sports analysis,and especially,intelligent surveillance.In this paper,we propose a robust and efficient HAR system by leveraging deep learning paradigms,including pre-trained models,CNN architectures,and their average-weighted fusion.However,due to the diversity of human actions and various environmental influences,as well as a lack of data and resources,achieving high recognition accuracy remain elusive.In this work,a weighted average ensemble technique is employed to fuse three deep learning models:EfficientNet,ResNet50,and a custom CNN.The results of this study indicate that using a weighted average ensemble strategy for developing more effective HAR models may be a promising idea for detection and classification of human activities.Experiments by using the benchmark dataset proved that the proposed weighted ensemble approach outperformed existing approaches in terms of accuracy and other key performance measures.The combined average-weighted ensemble of pre-trained and CNN models obtained an accuracy of 98%,compared to 97%,96%,and 95%for the customized CNN,EfficientNet,and ResNet50 models,respectively.
基金supported by National Natural Science Foundation of China(62106092)Natural Science Foundation of Fujian Province(2024J01822,2024J01820,2022J01916)Natural Science Foundation of Zhangzhou City(ZZ2024J28).
文摘Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks.
基金funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No.945478(SASPRO2)supported by the ReBuilt project:Circular and Digital Renewal of Central Europe Construction and Building Sector CE0100390 ReBuiltthe Slovak Research and Development Agency under APVV-23-0383 and the Slovak Grant Agency VEGA No.2/0080/24.
文摘This study presents a physics-informed modelling framework that combines finite element method(FEM)simulations and supervised machine learning(ML)to predict the self-healing performance of microbial concrete.A FEniCS-based FEM platform resolves multiphysics phenomena including nutrient diffusion,microbial CaCO_(3) precipitation,and stiffness recovery.These simulations,together with experimental data,are used to train ML models(Random Forest yielding normalized RMSE≈0.10)capable of predicting performance over a wide range of design parameters.Feature importance analysis identifies curing temperature,calcium carbonate precipitation rate,crack width,bacterial strain,and encapsulation method as the most influential parameters.The coupled FEM-ML approach enables sensitivity analysis,design optimization,and prediction beyond the training dataset(consistently exceeding 90%healing efficiency).Experimental validation confirms model robustness in both crack closure and strength recovery.This FEM–ML pipeline thus offers a generalizable,interpretable,and scalable strategy for the design of intelligent,self-adaptive construction materials.
基金partially funded by EPSRC (EP/T022213/1, EP/W032260/1 and EP/P020194/1) via our membership of the UK’s HEC Materials Chemistry Consortium, which is funded by EPSRC (EP/L000202)part of the “Advancing Solid Interface and Lubricants by First Principles Material Design (SLIDE)” project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 865633)
文摘Amorphous materials represent a promising platform for advancing CO_(2)electrochemical reduction due to their inherently diverse coordination environments.In this study,we demonstrate computationally the superior performance of amorphous CuNi alloys for CO_(2)electrochemical reduction.By integrating machine learning forcefields for efficient structure generation and density functional theory for subsequent structural refinement and property calculations,we reveal the potential of these disordered systems to outperform their crystalline counterparts.Machine learning forcefields can generate a bulk structure containing a mixture of Cu and Ni atoms,resulting in enhanced catalytic performance.Effective screening of the amorphous surfaces is used to identify undercoordinated Cu and Ni sites in the amorphous structure to synergistically promote selective CO production and favor ethanol formation over ethylene via the stabilization of the*COCHO intermediate,resulting in significantly lower Gibbs free energy changes compared to the crystalline counterpart.The varying atomic coordination environments on amorphous surfaces promote both C–C bond formation and subsequent proton-electron transfer,leading to ethanol formation.These findings demonstrate the superior catalytic performance of amorphous CuNi,highlighting its potential for efficient and selective electroreduction of CO_(2).
基金supported by the National Natural Science Foundation of China under Grants 62203338,62173259 and U1913602Zhejiang Provincial Natural Science Foundation of China under Grant LZ24F0390006the Postdoctoral Science Foundation of China under Grant 2022M722485.
文摘This paper investigates the adaptive optimal tracking control(AOTC)for underactuated surface vessels(USVs).Compared to the majority of existing studies,the control strategy in this paper innovatively combines an extended state observer(ESO)with reinforcement learning(RL).The designed ESO has high estimation accuracy and robust disturbance rejection capabilities for the unmeasurable information for USVs.To obtain the AOTC,the actor–critic(AC)networks based on RL are constructed to solve the Hamilton–Jacobi–Bellman(HJB)equations.Due to the uncertainties,it is challenging to obtain the optimal controller by directly solving the HJB equations.To address this issue,this paper employs neural networks(NNs)to approximate the uncertainties and solves the optimal controller via AC-RL and ESO.In addition,the adaptive parameters of the optimal controller is trained in parallel with AC networks,which can ensure that the trained networks can further improve tracking performance.The boundedness of AOTC for USVs is shown by Lyapunov stability theorem.Finally,simulation results demonstrate the effectiveness of the proposed algorithm.
基金appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Group Project under grant number(RGP2/245/46)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R333)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The expansion of 5G-enabled Internet of Things(IoT)networks,while enabling transformative applications,significantly increases the attack surface and necessitates security solutions that extend beyond traditional intrusion detection.Existing intrusion detection systems(IDSs)mainly operate in an open-loop manner,excelling at classification but lacking the ability for autonomous,safety-aware remediation.This gap is particularly critical in 5G environments,where manual intervention is too slow and naive automation can lead to severe service disruptions.To address this issue,we propose a novel Self-Healing Intrusion Detection System(SH-IDS)framework that develops a closed-loop cyber defense mechanism.The main technical contribution is the integration of a deep neural networkbased threat detector,which offers uncertainty-quantified predictions,with a safety-aware reinforcement learning(RL)engine formulated as a Constrained Markov Decision Process(CMDP).The CMDP explicitly models operational safety as cost constraints,and a new runtime safety shield actively adjusts any unsafe action proposed by the RL agent to the nearest safe alternative,ensuring operational integrity.Additionally,we introduce a composite utility function for the comprehensive evaluation of the system.Empirical analysis on the 5G-NIDD dataset demonstrates the superior performance of our framework:the detector achieves 98.26%accuracy,while the safe RL agent learns effective mitigation policies.Importantly,the safety shield blocked up to 70 unsafe actions under strict constraints,and analysis of the learned Q-tables confirms that the agent internalizes safety,avoiding overly disruptive actions,such as isolating nodes for minor threats.The system also maintains high efficiency with a compact model size of 121.7 KB and sub-millisecond latency,confirming its practical deployability for real-time 5G-IoT security.
文摘Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements at transceivers and RIS,the codebook based beamforming can be utilized in a computationally efficient manner.However,the codeword selection for analog beamforming is an intractable combinatorial optimization(CO)problem.To this end,by taking the CO problem as a classification problem,a multi-task learning based analog beam selection(MTL-ABS)framework is developed to implement cooperative beam selection concurrently at transceivers and RIS.In addition,residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features.Finally,the network convergence is analyzed from a blockwise perspective,and numerical results demonstrate that the MTL-ABS framework greatly decreases the beam selection overhead and achieves near optimal sum-rate compared with heuristic search based counterparts.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
基金National Natural Science Foundation of China(Nos.42501465,42471504)。
文摘Recent years have witnessed significant progress in deep learning for remote sensing image Super-Resolution(SR).However,in real-world applications,paired data is often unavailable,making supervised training infeasible,while unknown degradation factors constrain reconstruction performance and impair detail recovery.To this end,we propose a Degradation-Adaptive Self-supervised SR method,named DASSR,which recovers high-fidelity details from low-resolution remote sensing images without requiring supervision from high-resolution groundtruth.DASSR employs a dual-path closed-loop architecture,enabling joint learning of SR reconstruction and blur kernel estimation through cycle consistency in the main branch and regularization in the auxiliary branch.Specifically,we incorporate an Edge-Preserving SR network(EPSRN)into DASSR,whose core Hybrid Attention Enhancement Block(HAEB)captures precise structural representations to guide accurate detail reconstruction.Furthermore,a composite loss function is designed,integrating spatial reconstruction consistency,frequencydomain spectrum alignment,and kernel sparsity constraints to ensure stable and efficient self-supervised learning.Experiments on both simulated and real-world remote sensing datasets demonstrate that the proposed DASSR method outperforms competitive deep learning-based SR methods,notably achieving approximately 9%and 15%improvements in the Average Gradient(AG)and Spatial Frequency(SF)metrics,respectively,over the best-performing competitor.
基金supported by the Research Grant Fund from Kwangwoon University in 2023,the National Natural Science Foundation of China under Grant(62311540155)the Taishan Scholars Project Special Funds(tsqn202312035)the open research foundation of State Key Laboratory of Integrated Chips and Systems.
文摘Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.
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.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0456.
文摘This study explores the integration of Synthetic Aperture Radar(SAR)imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection.This study proposes a novel hybrid approach for oil spill detection.The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization(BHHO)and Principal Component Analysis(PCA)for improved feature extraction and selection from input SAR imagery.Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images.The BHHO and PCA algorithms were implemented to identify subsets of optimal features from the entire feature dataset extracted by MobileNet.A supplemented hybrid feature set was constructed from the PCA and BHHO-generated features.It was used as input for oil spill detection using the logistic regression supervised machine learning classification algorithm.Several feature set combinations were implemented to test the classification performance of the logistic regression classifier in comparison to that of the proposed hybrid feature set.Results indicate that the highest oil spill detection accuracy of 99.2%has been achieved using the logistic regression classification algorithm,with integrated feature input from subsets identified using the PCA and the BHHO feature selection techniques.The proposed method yielded a statistically significant improvement in the classification performance of the used machine learning model.The significance of our study lies in its unique integration of deep learning with optimized feature selection,unlike other published studies,to enhance oil spill detection accuracy.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:1055-829-2024).
文摘A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.
基金received approval from a committee named Innovation Institute for Sustainable Maritime Architecture Research and Technology(iSMART)(The certificate number was 2022-5-22-01).
文摘Contemporary higher education prioritizes cultivating students’key competencies and comprehensive problem-solving abilities,specifically fostering innovation,goal orientation,and initiative.This study investigates a pedagogical framework that synergizes Research-Led Learning(RLL)and Project-Based Learning(PBL)to establish an open,exploratory learning environment.Employing a case study methodology,the research tracked architecture students engaging in a structured PBL process involving rigorous research activities—ranging from theoretical analysis to field investigations—to develop evidence-based design solutions.Evaluations from both student and faculty perspectives assessed the pedagogical effectiveness regarding learning outcomes and competency development.The findings indicate that this methodology effectively bridges the gap between research and practice,significantly bolstering students’capacity to address authentic challenges and propelling self-directed learning in architectural education.
