Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscul...Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability.To counter this limitation,this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat.We employ FastAI vision-learner-based convolutional neural networks(CNNs)that include ResNet,DenseNet,VGG,ConvNeXt,SqueezeNet,and AlexNet to classify heart sound recordings.Instead of raw waveform analysis,the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms,which are suited for capturing temporal and frequency-wise patterns.The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations,noise levels,and acoustic distortions.To demonstrate generalization,external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds.Comparative analysis revealed that DenseNet-201,ConvNext Large,and ResNet-152 could deliver superior performance to the other architectures,achieving an accuracy of 81.50%,a precision of 85.50%,and an F1-score of 84.50%.In the process,we performed statistical significance testing,such as the Wilcoxon signed-rank test,to validate performance improvements over traditional classification methods.Beyond the technical contributions,the research underscores clinical integration,outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows.We also discuss in detail issues of computational efficiency,model interpretability,and ethical considerations,particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings.The study describes a scalable,automated system combining deep learning,feature extraction using spectrograms,and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease.AI-driven solutions can be viable in improving access,reducing delays in diagnosis,and ultimately even the continued global burden of heart disease.展开更多
As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)system...As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)systems.These systems are essential for monitoring and controlling industrial operations,making their security paramount.A key threat arises from Shor’s algorithm,a powerful quantum computing tool that can compromise current hash functions,leading to significant concerns about data integrity and confidentiality.To tackle these issues,this article introduces a novel Quantum-Resistant Hash Algorithm(QRHA)known as the Modular Hash Learning Algorithm(MHLA).This algorithm is meticulously crafted to withstand potential quantum attacks by incorporating advanced mathematical and algorithmic techniques,enhancing its overall security framework.Our research delves into the effectiveness ofMHLA in defending against both traditional and quantum-based threats,with a particular emphasis on its resilience to Shor’s algorithm.The findings from our study demonstrate that MHLA significantly enhances the security of SCADA systems in the context of quantum technology.By ensuring that sensitive data remains protected and confidential,MHLA not only fortifies individual systems but also contributes to the broader efforts of safeguarding industrial and infrastructure control systems against future quantumthreats.Our evaluation demonstrates that MHLA improves security by 38%against quantumattack simulations compared to traditional hash functionswhilemaintaining a computational efficiency ofO(m⋅n⋅k+v+n).The algorithm achieved a 98%success rate in detecting data tampering during integrity testing.These findings underline MHLA’s effectiveness in enhancing SCADA system security amidst evolving quantum technologies.This research represents a crucial step toward developing more secure cryptographic systems that can adapt to the rapidly changing technological landscape,ultimately ensuring the reliability and integrity of critical infrastructure in an era where quantum computing poses a growing risk.展开更多
In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading...In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.展开更多
基金funded by the deanship of scientific research(DSR),King Abdulaziz University,Jeddah,under grant No.(G-1436-611-309).
文摘Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability.To counter this limitation,this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat.We employ FastAI vision-learner-based convolutional neural networks(CNNs)that include ResNet,DenseNet,VGG,ConvNeXt,SqueezeNet,and AlexNet to classify heart sound recordings.Instead of raw waveform analysis,the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms,which are suited for capturing temporal and frequency-wise patterns.The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations,noise levels,and acoustic distortions.To demonstrate generalization,external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds.Comparative analysis revealed that DenseNet-201,ConvNext Large,and ResNet-152 could deliver superior performance to the other architectures,achieving an accuracy of 81.50%,a precision of 85.50%,and an F1-score of 84.50%.In the process,we performed statistical significance testing,such as the Wilcoxon signed-rank test,to validate performance improvements over traditional classification methods.Beyond the technical contributions,the research underscores clinical integration,outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows.We also discuss in detail issues of computational efficiency,model interpretability,and ethical considerations,particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings.The study describes a scalable,automated system combining deep learning,feature extraction using spectrograms,and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease.AI-driven solutions can be viable in improving access,reducing delays in diagnosis,and ultimately even the continued global burden of heart disease.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R343),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia for funding this research work through the project number NBU-FFR-2025-1092-10.
文摘As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)systems.These systems are essential for monitoring and controlling industrial operations,making their security paramount.A key threat arises from Shor’s algorithm,a powerful quantum computing tool that can compromise current hash functions,leading to significant concerns about data integrity and confidentiality.To tackle these issues,this article introduces a novel Quantum-Resistant Hash Algorithm(QRHA)known as the Modular Hash Learning Algorithm(MHLA).This algorithm is meticulously crafted to withstand potential quantum attacks by incorporating advanced mathematical and algorithmic techniques,enhancing its overall security framework.Our research delves into the effectiveness ofMHLA in defending against both traditional and quantum-based threats,with a particular emphasis on its resilience to Shor’s algorithm.The findings from our study demonstrate that MHLA significantly enhances the security of SCADA systems in the context of quantum technology.By ensuring that sensitive data remains protected and confidential,MHLA not only fortifies individual systems but also contributes to the broader efforts of safeguarding industrial and infrastructure control systems against future quantumthreats.Our evaluation demonstrates that MHLA improves security by 38%against quantumattack simulations compared to traditional hash functionswhilemaintaining a computational efficiency ofO(m⋅n⋅k+v+n).The algorithm achieved a 98%success rate in detecting data tampering during integrity testing.These findings underline MHLA’s effectiveness in enhancing SCADA system security amidst evolving quantum technologies.This research represents a crucial step toward developing more secure cryptographic systems that can adapt to the rapidly changing technological landscape,ultimately ensuring the reliability and integrity of critical infrastructure in an era where quantum computing poses a growing risk.
基金This project was funded by Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah underGrant No.(IFPIP-1127-611-1443)the authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.
