As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by...As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.展开更多
Efficient surface passivation is critical for achieving high-performance perovskite solar cells(PSCs),yet the discovery of optimal passivators remains a time-consuming,trial-and-error process.Here,we report a synergis...Efficient surface passivation is critical for achieving high-performance perovskite solar cells(PSCs),yet the discovery of optimal passivators remains a time-consuming,trial-and-error process.Here,we report a synergistic machine learning(ML)and density functional theory(DFT)approach that enables predictive and rapid identification of effective passivation materials.By training an XGBoost model(91.3%accuracy)with DFT-derived molecular descriptors and activity calculations,we identify 2-(4-aminophenyl)-3H-benzimidazol-5-amine(APBIA)as a promising passivator.Experimental validation demonstrates that APBIA effectively removes surface impurities and passivates defects within perovskite films,leading to a significant increase in power conversion efficiency(PCE)from 22.48%to 25.55%(certified as 25.02%).This ML-DFT framework provides a generalizable pathway for accelerating the development of advanced functional materials for photovoltaic applications.展开更多
The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threa...The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.展开更多
The fast increase of online communities has brought about an increase in cyber threats inclusive of cyberbullying, hate speech, misinformation, and online harassment, making content moderation a pressing necessity. Tr...The fast increase of online communities has brought about an increase in cyber threats inclusive of cyberbullying, hate speech, misinformation, and online harassment, making content moderation a pressing necessity. Traditional single-modal AI-based detection systems, which analyze both text, photos, or movies in isolation, have established useless at taking pictures multi-modal threats, in which malicious actors spread dangerous content throughout a couple of formats. To cope with these demanding situations, we advise a multi-modal deep mastering framework that integrates Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to become aware of and mitigate online threats effectively. Our proposed model combines BERT for text class, ResNet50 for photograph processing, and a hybrid LSTM-3-d CNN community for video content material analysis. We constructed a large-scale dataset comprising 500,000 textual posts, 200,000 offensive images, and 50,000 annotated motion pictures from more than one platform, which includes Twitter, Reddit, YouTube, and online gaming forums. The system became carefully evaluated using trendy gadget mastering metrics which include accuracy, precision, remember, F1-score, and ROC-AUC curves. Experimental outcomes demonstrate that our multi-modal method extensively outperforms single-modal AI classifiers, achieving an accuracy of 92.3%, precision of 91.2%, do not forget of 90.1%, and an AUC rating of 0.95. The findings validate the necessity of integrating multi-modal AI for actual-time, high-accuracy online chance detection and moderation. Future paintings will have consciousness on improving hostile robustness, enhancing scalability for real-world deployment, and addressing ethical worries associated with AI-driven content moderation.展开更多
Membrane fouling is a persistent challenge in membrane-based technologies,significantly impacting efficiency,operational costs,and system lifespan in applications like water treatment,desalination,and industrial proce...Membrane fouling is a persistent challenge in membrane-based technologies,significantly impacting efficiency,operational costs,and system lifespan in applications like water treatment,desalination,and industrial processing.Foul-ing,caused by the accumulation of particulates,organic compounds,and microorganisms,leads to reduced permeability,increased energy demands,and frequent maintenance.Traditional fouling control approaches,relying on empirical models and reactive strategies,often fail to address these issues efficiently.In this context,artificial intelligence(AI)and machine learning(ML)have emerged as innovative tools offering predictive and proactive solutions for fouling man-agement.By utilizing historical and real-time data,AI/ML techniques such as artificial neural networks,support vector machines,and ensemble models enable accurate prediction of fouling onset,identification of fouling mechanisms,and optimization of control measures.This review provides a detailed examination of the integration of AI/ML in membrane fouling prediction and mitigation,discussing advanced algorithms,the role of sensor-based monitoring,and the importance of robust datasets in enhancing predictive accuracy.Case studies highlighting successful AI/ML applications across various membrane processes are presented,demonstrating their transformative potential in improving system performance.Emerging trends,such as hybrid modeling and IoT-enabled smart systems,are explored,alongside a criti-cal analysis of research gaps and opportunities.This review emphasizes AI/ML as a cornerstone for sustainable,cost-effective membrane operations.展开更多
Against the background of the continuous reform in medical education,biochemistry,as a fundamental medical course,maintains a close connection with clinical practice.However,under the traditional teaching model,the ef...Against the background of the continuous reform in medical education,biochemistry,as a fundamental medical course,maintains a close connection with clinical practice.However,under the traditional teaching model,the effectiveness of the“basic-clinical”connection is relatively poor,which hinders the improvement of educational outcomes.In the practical teaching of higher vocational medical education,the integration of the AI Case-Guided Learning System can enhance students’enthusiasm for knowledge exploration and effectively improve teaching quality.Starting from the perspective of the“basic-clinical”connection teaching in the biochemistry course,this paper analyzes the application value of the AI Case-Guided Learning System and proposes specific application strategies,aiming to accumulate experience for the innovation of biochemistry teaching.展开更多
This study explores a novel educational model of generative AI-empowered interdisciplinary project-based learning(PBL).By analyzing the current applications of generative AI technology in information technology curric...This study explores a novel educational model of generative AI-empowered interdisciplinary project-based learning(PBL).By analyzing the current applications of generative AI technology in information technology curricula,it elucidates its advantages and operational mechanisms in interdisciplinary PBL.Combining case studies and empirical research,the investigation proposes implementation pathways and strategies for the generative AI-enhanced interdisciplinary PBL model,detailing specific applications across three phases:project preparation,implementation,and evaluation.The research demonstrates that generative AI-enabled interdisciplinary project-based learning can effectively enhance students’learning motivation,interdisciplinary thinking capabilities,and innovative competencies,providing new conceptual frameworks and practical approaches for educational model innovation.展开更多
In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and ...In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and machine learning(ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics.Furthermore,newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable.Hence,effective detection depends on identifying the most critical features.Traditional feature selection(FS)methods often struggle to enhance ML model performance and instead decrease it.To combat these issues,we propose an innovative method using explainable AI(XAI)to enhance FS in ML models and improve the identification of phishing websites.Specifically,we employ SHapley Additive exPlanations(SHAP)for global perspective and aggregated local interpretable model-agnostic explanations(LIME)to deter-mine specific localized patterns.The proposed SHAP and LIME-aggregated FS(SLA-FS)framework pinpoints the most informative features,enabling more precise,swift,and adaptable phishing detection.Applying this approach to an up-to-date web phishing dataset,we evaluate the performance of three ML models before and after FS to assess their effectiveness.Our findings reveal that random forest(RF),with an accuracy of 97.41%and XGBoost(XGB)at 97.21%significantly benefit from the SLA-FS framework,while k-nearest neighbors lags.Our framework increases the accuracy of RF and XGB by 0.65%and 0.41%,respectively,outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset,showcasing its potential.展开更多
Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predispositio...Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predisposition,environmental exposure,unhealthy lifestyle habits,and existing medical conditions.Although existing machine learning-based methods for predicting stroke patients’health status have made significant progress,limitations remain in terms of prediction accuracy,model explainability,and system optimization.This paper proposes a multi-task learning approach based on Explainable Artificial Intelligence(XAI)for predicting the health status of stroke patients.First,we design a comprehensive multi-task learning framework that utilizes the task correlation of predicting various health status indicators in patients,enabling the parallel prediction of multiple health indicators.Second,we develop a multi-task Area Under Curve(AUC)optimization algorithm based on adaptive low-rank representation,which removes irrelevant information from the model structure to enhance the performance of multi-task AUC optimization.Additionally,the model’s explainability is analyzed through the stability analysis of SHAP values.Experimental results demonstrate that our approach outperforms comparison algorithms in key prognostic metrics F1 score and Efficiency.展开更多
Skin cancer is among the most common malignancies worldwide,but its mortality burden is largely driven by aggressive subtypes such as melanoma,with outcomes varying across regions and healthcare settings.These variati...Skin cancer is among the most common malignancies worldwide,but its mortality burden is largely driven by aggressive subtypes such as melanoma,with outcomes varying across regions and healthcare settings.These variations emphasize the importance of reliable diagnostic technologies that support clinicians in detecting skin malignancies with higher accuracy.Traditional diagnostic methods often rely on subjective visual assessments,which can lead to misdiagnosis.This study addresses these challenges by developing HybridFusionNet,a novel model that integrates Convolutional Neural Networks(CNN)with 1D feature extraction techniques to enhance diagnostic accuracy.Utilizing two extensive datasets,BCN20000 and HAM10000,the methodology includes data preprocessing,application of Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors(SMOTEENN)for data balancing,and optimization of feature selection using the Tree-based Pipeline Optimization Tool(TPOT).The results demonstrate significant performance improvements over traditional CNN models,achieving an accuracy of 0.9693 on the BCN20000 dataset and 0.9909 on the HAM10000 dataset.The HybridFusionNet model not only outperforms conventionalmethods but also effectively addresses class imbalance.To enhance transparency,it integrates post-hoc explanation techniques such as LIME,which highlight the features influencing predictions.These findings highlight the potential of HybridFusionNet to support real-world applications,including physician-assist systems,teledermatology,and large-scale skin cancer screening programs.By improving diagnostic efficiency and enabling access to expert-level analysis,the modelmay enhance patient outcomes and foster greater trust in artificial intelligence(AI)-assisted clinical decision-making.展开更多
Tungsten carbide-based(WC-based)cemented carbides are widely recognized as high-performance tool materials.Traditionally,single metals such as cobalt(Co)or nickel(Ni)serve as the binder phase,providing toughness and s...Tungsten carbide-based(WC-based)cemented carbides are widely recognized as high-performance tool materials.Traditionally,single metals such as cobalt(Co)or nickel(Ni)serve as the binder phase,providing toughness and structural integrity.Replacing this phase with high-entropy alloys(HEAs)offers a promising approach to enhancing mechanical properties and addressing sustainability challenges.However,the complex multi-element composition of HEAs complicates conventional experimental design,making it difficult to explore the vast compositional space efficiently.Traditional trial-and-error methods are time-consuming,resource-intensive,and often ineffective in identifying optimal compositions.In contrast,artificial intelligence(AI)-driven approaches enable rapid screening and optimization of alloy compositions,significantly improving predictive accuracy and interpretability.Feature selection techniques were employed to identify key alloying elements influencing hardness,toughness,and wear resistance.To enhance model interpretability,explainable artificial intelligence(XAI)techniques—SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)—were applied to quantify the contributions of individual elements and uncover complex elemental interactions.Furthermore,a high-throughput machine learning(ML)–driven screening approach was implemented to optimize the binder phase composition,facilitating the discovery of HEAs with superiormechanical properties.Experimental validation demonstrated strong agreement between model predictions and measured performance,confirming the reliability of the ML framework.This study underscores the potential of integrating ML and XAI for data-driven materials design,providing a novel strategy for optimizing high-entropy cemented carbides.展开更多
With the rapid popularization of artificial intelligence technology in the field of higher education,college students are increasingly dependent on AI tools such as ChatGPT,automatic writing assistants,and intelligent...With the rapid popularization of artificial intelligence technology in the field of higher education,college students are increasingly dependent on AI tools such as ChatGPT,automatic writing assistants,and intelligent translators.Behind the convenience and efficiency,a decline trend in students’core learning abilities such as autonomous learning ability,critical thinking ability,and knowledge construction ability has gradually emerged.This study aims to explore the interactive logical mechanism between college students’reliance on AI tools and the weakening of their learning abilities,and on this basis,propose practical and feasible educational intervention strategies.Research has found that while AI tools lower the learning threshold,they also weaken students’cognitive investment and independent thinking abilities,further intensifying their reliance on technology.In this regard,this paper proposes a three-dimensional intervention path based on guided usage,ability compensation,and value reconstruction to achieve the collaborative improvement of students’technical usage ability and learning ability.This research has certain theoretical value and practical enlightenment significance for solving the structural predicament of higher education in the intelligent era.展开更多
In response to the pain points of rapid iteration of front-end education technology,large differences in learner foundations,and a lack of practical scenarios,this paper combines generative artificial intelligence and...In response to the pain points of rapid iteration of front-end education technology,large differences in learner foundations,and a lack of practical scenarios,this paper combines generative artificial intelligence and AI agents to analyze the empowerment logic from three dimensions:knowledge ecology reconstruction,cognitive collaborative upgrading,and teaching methodology innovation.It explores its application scenarios in teaching and learning,sorts out challenges such as technology adaptation and learning dependence,and proposes paths such as building an exclusive AI ecosystem and optimizing the guidance mechanism of intelligent agents to provide support for the digital transformation of front-end education.展开更多
Blood cell disorders are among the leading causes of serious diseases such as leukemia,anemia,blood clotting disorders,and immune-related conditions.The global incidence of hematological diseases is increasing,affecti...Blood cell disorders are among the leading causes of serious diseases such as leukemia,anemia,blood clotting disorders,and immune-related conditions.The global incidence of hematological diseases is increasing,affecting both children and adults.In clinical practice,blood smear analysis is still largely performed manually,relying heavily on the experience and expertise of laboratory technicians or hematologists.This manual process introduces risks of diagnostic errors,especially in cases with rare or morphologically ambiguous cells.The situation is more critical in developing countries,where there is a shortage of specialized medical personnel and limited access to modern diagnostic tools.High testing costs and delays in diagnosis hinder access to quality healthcare services.In this context,the integration of Artificial Intelligence(AI),particularly Explainable AI(XAI)based on deep learning,offers a promising solution for improving the accuracy,efficiency,and transparency of hematological diagnostics.In this study,we propose a Ghost Residual Network(GRsNet)integrated with XAI techniques such as Gradient-weighted Class Activation Mapping(Grad-CAM),Local Interpretable Model-Agnostic Explanations(LIME),and SHapley Additive exPlanations(SHAP)for automatic blood cell classification.These techniques provide visual explanations by highlighting important regions in the input images,thereby supporting clinical decision-making.The proposed model is evaluated on two public datasets:Naturalize 2K-PBC and Microscopic Blood Cell,achieving a classification accuracy of up to 95%.The results demonstrate the model’s strong potential for automated hematological diagnosis,particularly in resource-constrained settings.It not only enhances diagnostic reliability but also contributes to advancing digital transformation and equitable access to AI-driven healthcare in developing regions.展开更多
Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing de...Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.展开更多
文摘As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.
基金supported by the National Key Research and Development Program of China (Grant No. 2024YFB4205101)the National Natural Science Foundation of China (No. 62274098 and No. 62074084)+2 种基金the Natural Science Foundation of Tianjin (No.22JCYBJC01300, No. 23JCYBJC01620 and No. 21JCYBJC00270)the Overseas Expertise Introduction Project for Discipline Innovation of Higher Edu cation of China (Grant No. B16027)the Fundamental Research Funds for the Central Universities,Nankai University (No. 63241568)
文摘Efficient surface passivation is critical for achieving high-performance perovskite solar cells(PSCs),yet the discovery of optimal passivators remains a time-consuming,trial-and-error process.Here,we report a synergistic machine learning(ML)and density functional theory(DFT)approach that enables predictive and rapid identification of effective passivation materials.By training an XGBoost model(91.3%accuracy)with DFT-derived molecular descriptors and activity calculations,we identify 2-(4-aminophenyl)-3H-benzimidazol-5-amine(APBIA)as a promising passivator.Experimental validation demonstrates that APBIA effectively removes surface impurities and passivates defects within perovskite films,leading to a significant increase in power conversion efficiency(PCE)from 22.48%to 25.55%(certified as 25.02%).This ML-DFT framework provides a generalizable pathway for accelerating the development of advanced functional materials for photovoltaic applications.
文摘The increasing number of interconnected devices and the incorporation of smart technology into contemporary healthcare systems have significantly raised the attack surface of cyber threats.The early detection of threats is both necessary and complex,yet these interconnected healthcare settings generate enormous amounts of heterogeneous data.Traditional Intrusion Detection Systems(IDS),which are generally centralized and machine learning-based,often fail to address the rapidly changing nature of cyberattacks and are challenged by ethical concerns related to patient data privacy.Moreover,traditional AI-driven IDS usually face challenges in handling large-scale,heterogeneous healthcare data while ensuring data privacy and operational efficiency.To address these issues,emerging technologies such as Big Data Analytics(BDA)and Federated Learning(FL)provide a hybrid framework for scalable,adaptive intrusion detection in IoT-driven healthcare systems.Big data techniques enable processing large-scale,highdimensional healthcare data,and FL can be used to train a model in a decentralized manner without transferring raw data,thereby maintaining privacy between institutions.This research proposes a privacy-preserving Federated Learning–based model that efficiently detects cyber threats in connected healthcare systems while ensuring distributed big data processing,privacy,and compliance with ethical regulations.To strengthen the reliability of the reported findings,the resultswere validated using cross-dataset testing and 95%confidence intervals derived frombootstrap analysis,confirming consistent performance across heterogeneous healthcare data distributions.This solution takes a significant step toward securing next-generation healthcare infrastructure by combining scalability,privacy,adaptability,and earlydetection capabilities.The proposed global model achieves a test accuracy of 99.93%±0.03(95%CI)and amiss-rate of only 0.07%±0.02,representing state-of-the-art performance in privacy-preserving intrusion detection.The proposed FL-driven IDS framework offers an efficient,privacy-preserving,and scalable solution for securing next-generation healthcare infrastructures by combining adaptability,early detection,and ethical data management.
文摘The fast increase of online communities has brought about an increase in cyber threats inclusive of cyberbullying, hate speech, misinformation, and online harassment, making content moderation a pressing necessity. Traditional single-modal AI-based detection systems, which analyze both text, photos, or movies in isolation, have established useless at taking pictures multi-modal threats, in which malicious actors spread dangerous content throughout a couple of formats. To cope with these demanding situations, we advise a multi-modal deep mastering framework that integrates Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to become aware of and mitigate online threats effectively. Our proposed model combines BERT for text class, ResNet50 for photograph processing, and a hybrid LSTM-3-d CNN community for video content material analysis. We constructed a large-scale dataset comprising 500,000 textual posts, 200,000 offensive images, and 50,000 annotated motion pictures from more than one platform, which includes Twitter, Reddit, YouTube, and online gaming forums. The system became carefully evaluated using trendy gadget mastering metrics which include accuracy, precision, remember, F1-score, and ROC-AUC curves. Experimental outcomes demonstrate that our multi-modal method extensively outperforms single-modal AI classifiers, achieving an accuracy of 92.3%, precision of 91.2%, do not forget of 90.1%, and an AUC rating of 0.95. The findings validate the necessity of integrating multi-modal AI for actual-time, high-accuracy online chance detection and moderation. Future paintings will have consciousness on improving hostile robustness, enhancing scalability for real-world deployment, and addressing ethical worries associated with AI-driven content moderation.
文摘Membrane fouling is a persistent challenge in membrane-based technologies,significantly impacting efficiency,operational costs,and system lifespan in applications like water treatment,desalination,and industrial processing.Foul-ing,caused by the accumulation of particulates,organic compounds,and microorganisms,leads to reduced permeability,increased energy demands,and frequent maintenance.Traditional fouling control approaches,relying on empirical models and reactive strategies,often fail to address these issues efficiently.In this context,artificial intelligence(AI)and machine learning(ML)have emerged as innovative tools offering predictive and proactive solutions for fouling man-agement.By utilizing historical and real-time data,AI/ML techniques such as artificial neural networks,support vector machines,and ensemble models enable accurate prediction of fouling onset,identification of fouling mechanisms,and optimization of control measures.This review provides a detailed examination of the integration of AI/ML in membrane fouling prediction and mitigation,discussing advanced algorithms,the role of sensor-based monitoring,and the importance of robust datasets in enhancing predictive accuracy.Case studies highlighting successful AI/ML applications across various membrane processes are presented,demonstrating their transformative potential in improving system performance.Emerging trends,such as hybrid modeling and IoT-enabled smart systems,are explored,alongside a criti-cal analysis of research gaps and opportunities.This review emphasizes AI/ML as a cornerstone for sustainable,cost-effective membrane operations.
文摘Against the background of the continuous reform in medical education,biochemistry,as a fundamental medical course,maintains a close connection with clinical practice.However,under the traditional teaching model,the effectiveness of the“basic-clinical”connection is relatively poor,which hinders the improvement of educational outcomes.In the practical teaching of higher vocational medical education,the integration of the AI Case-Guided Learning System can enhance students’enthusiasm for knowledge exploration and effectively improve teaching quality.Starting from the perspective of the“basic-clinical”connection teaching in the biochemistry course,this paper analyzes the application value of the AI Case-Guided Learning System and proposes specific application strategies,aiming to accumulate experience for the innovation of biochemistry teaching.
文摘This study explores a novel educational model of generative AI-empowered interdisciplinary project-based learning(PBL).By analyzing the current applications of generative AI technology in information technology curricula,it elucidates its advantages and operational mechanisms in interdisciplinary PBL.Combining case studies and empirical research,the investigation proposes implementation pathways and strategies for the generative AI-enhanced interdisciplinary PBL model,detailing specific applications across three phases:project preparation,implementation,and evaluation.The research demonstrates that generative AI-enabled interdisciplinary project-based learning can effectively enhance students’learning motivation,interdisciplinary thinking capabilities,and innovative competencies,providing new conceptual frameworks and practical approaches for educational model innovation.
文摘In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and machine learning(ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics.Furthermore,newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable.Hence,effective detection depends on identifying the most critical features.Traditional feature selection(FS)methods often struggle to enhance ML model performance and instead decrease it.To combat these issues,we propose an innovative method using explainable AI(XAI)to enhance FS in ML models and improve the identification of phishing websites.Specifically,we employ SHapley Additive exPlanations(SHAP)for global perspective and aggregated local interpretable model-agnostic explanations(LIME)to deter-mine specific localized patterns.The proposed SHAP and LIME-aggregated FS(SLA-FS)framework pinpoints the most informative features,enabling more precise,swift,and adaptable phishing detection.Applying this approach to an up-to-date web phishing dataset,we evaluate the performance of three ML models before and after FS to assess their effectiveness.Our findings reveal that random forest(RF),with an accuracy of 97.41%and XGBoost(XGB)at 97.21%significantly benefit from the SLA-FS framework,while k-nearest neighbors lags.Our framework increases the accuracy of RF and XGB by 0.65%and 0.41%,respectively,outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset,showcasing its potential.
基金funded by the Excellent Talent Training Funding Project in Dongcheng District,Beijing,with project number 2024-dchrcpyzz-9.
文摘Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predisposition,environmental exposure,unhealthy lifestyle habits,and existing medical conditions.Although existing machine learning-based methods for predicting stroke patients’health status have made significant progress,limitations remain in terms of prediction accuracy,model explainability,and system optimization.This paper proposes a multi-task learning approach based on Explainable Artificial Intelligence(XAI)for predicting the health status of stroke patients.First,we design a comprehensive multi-task learning framework that utilizes the task correlation of predicting various health status indicators in patients,enabling the parallel prediction of multiple health indicators.Second,we develop a multi-task Area Under Curve(AUC)optimization algorithm based on adaptive low-rank representation,which removes irrelevant information from the model structure to enhance the performance of multi-task AUC optimization.Additionally,the model’s explainability is analyzed through the stability analysis of SHAP values.Experimental results demonstrate that our approach outperforms comparison algorithms in key prognostic metrics F1 score and Efficiency.
文摘Skin cancer is among the most common malignancies worldwide,but its mortality burden is largely driven by aggressive subtypes such as melanoma,with outcomes varying across regions and healthcare settings.These variations emphasize the importance of reliable diagnostic technologies that support clinicians in detecting skin malignancies with higher accuracy.Traditional diagnostic methods often rely on subjective visual assessments,which can lead to misdiagnosis.This study addresses these challenges by developing HybridFusionNet,a novel model that integrates Convolutional Neural Networks(CNN)with 1D feature extraction techniques to enhance diagnostic accuracy.Utilizing two extensive datasets,BCN20000 and HAM10000,the methodology includes data preprocessing,application of Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors(SMOTEENN)for data balancing,and optimization of feature selection using the Tree-based Pipeline Optimization Tool(TPOT).The results demonstrate significant performance improvements over traditional CNN models,achieving an accuracy of 0.9693 on the BCN20000 dataset and 0.9909 on the HAM10000 dataset.The HybridFusionNet model not only outperforms conventionalmethods but also effectively addresses class imbalance.To enhance transparency,it integrates post-hoc explanation techniques such as LIME,which highlight the features influencing predictions.These findings highlight the potential of HybridFusionNet to support real-world applications,including physician-assist systems,teledermatology,and large-scale skin cancer screening programs.By improving diagnostic efficiency and enabling access to expert-level analysis,the modelmay enhance patient outcomes and foster greater trust in artificial intelligence(AI)-assisted clinical decision-making.
文摘Tungsten carbide-based(WC-based)cemented carbides are widely recognized as high-performance tool materials.Traditionally,single metals such as cobalt(Co)or nickel(Ni)serve as the binder phase,providing toughness and structural integrity.Replacing this phase with high-entropy alloys(HEAs)offers a promising approach to enhancing mechanical properties and addressing sustainability challenges.However,the complex multi-element composition of HEAs complicates conventional experimental design,making it difficult to explore the vast compositional space efficiently.Traditional trial-and-error methods are time-consuming,resource-intensive,and often ineffective in identifying optimal compositions.In contrast,artificial intelligence(AI)-driven approaches enable rapid screening and optimization of alloy compositions,significantly improving predictive accuracy and interpretability.Feature selection techniques were employed to identify key alloying elements influencing hardness,toughness,and wear resistance.To enhance model interpretability,explainable artificial intelligence(XAI)techniques—SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)—were applied to quantify the contributions of individual elements and uncover complex elemental interactions.Furthermore,a high-throughput machine learning(ML)–driven screening approach was implemented to optimize the binder phase composition,facilitating the discovery of HEAs with superiormechanical properties.Experimental validation demonstrated strong agreement between model predictions and measured performance,confirming the reliability of the ML framework.This study underscores the potential of integrating ML and XAI for data-driven materials design,providing a novel strategy for optimizing high-entropy cemented carbides.
基金The 2024 Higher Education Teaching Reform Project of Guangdong University of Science and Technology,“Teaching Practice of Human Resource Management Course Based on SPOC+FC Hybrid Teaching Mode”(GKZLGC2024024)。
文摘With the rapid popularization of artificial intelligence technology in the field of higher education,college students are increasingly dependent on AI tools such as ChatGPT,automatic writing assistants,and intelligent translators.Behind the convenience and efficiency,a decline trend in students’core learning abilities such as autonomous learning ability,critical thinking ability,and knowledge construction ability has gradually emerged.This study aims to explore the interactive logical mechanism between college students’reliance on AI tools and the weakening of their learning abilities,and on this basis,propose practical and feasible educational intervention strategies.Research has found that while AI tools lower the learning threshold,they also weaken students’cognitive investment and independent thinking abilities,further intensifying their reliance on technology.In this regard,this paper proposes a three-dimensional intervention path based on guided usage,ability compensation,and value reconstruction to achieve the collaborative improvement of students’technical usage ability and learning ability.This research has certain theoretical value and practical enlightenment significance for solving the structural predicament of higher education in the intelligent era.
基金funded by two 2024 Ministry of Education supply-demand docking employment and education projects(Grant No.2024101679202,Grant No.2024121116066)2024“Innovation Strong Institute Project of Guangdong Polytechnic Institute”(Grant No.2024CQ-29)2022 Guangdong Province Undergraduate Online Open Course Guidance Committee Research Project(Grant No.2022ZXKC612).
文摘In response to the pain points of rapid iteration of front-end education technology,large differences in learner foundations,and a lack of practical scenarios,this paper combines generative artificial intelligence and AI agents to analyze the empowerment logic from three dimensions:knowledge ecology reconstruction,cognitive collaborative upgrading,and teaching methodology innovation.It explores its application scenarios in teaching and learning,sorts out challenges such as technology adaptation and learning dependence,and proposes paths such as building an exclusive AI ecosystem and optimizing the guidance mechanism of intelligent agents to provide support for the digital transformation of front-end education.
文摘Blood cell disorders are among the leading causes of serious diseases such as leukemia,anemia,blood clotting disorders,and immune-related conditions.The global incidence of hematological diseases is increasing,affecting both children and adults.In clinical practice,blood smear analysis is still largely performed manually,relying heavily on the experience and expertise of laboratory technicians or hematologists.This manual process introduces risks of diagnostic errors,especially in cases with rare or morphologically ambiguous cells.The situation is more critical in developing countries,where there is a shortage of specialized medical personnel and limited access to modern diagnostic tools.High testing costs and delays in diagnosis hinder access to quality healthcare services.In this context,the integration of Artificial Intelligence(AI),particularly Explainable AI(XAI)based on deep learning,offers a promising solution for improving the accuracy,efficiency,and transparency of hematological diagnostics.In this study,we propose a Ghost Residual Network(GRsNet)integrated with XAI techniques such as Gradient-weighted Class Activation Mapping(Grad-CAM),Local Interpretable Model-Agnostic Explanations(LIME),and SHapley Additive exPlanations(SHAP)for automatic blood cell classification.These techniques provide visual explanations by highlighting important regions in the input images,thereby supporting clinical decision-making.The proposed model is evaluated on two public datasets:Naturalize 2K-PBC and Microscopic Blood Cell,achieving a classification accuracy of up to 95%.The results demonstrate the model’s strong potential for automated hematological diagnosis,particularly in resource-constrained settings.It not only enhances diagnostic reliability but also contributes to advancing digital transformation and equitable access to AI-driven healthcare in developing regions.
基金The author Dr.Arshiya S.Ansari extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1538).
文摘Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.
