The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population,especially in relation to falls.While falls can lead to significant cognitive impairments,timely i...The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population,especially in relation to falls.While falls can lead to significant cognitive impairments,timely intervention can mitigate their adverse effects.In this context,the need for non-invasive,efficient monitoring systems becomes paramount.Although wearable sensors have gained traction for monitoring health activities,they may cause discomfort during prolonged use,especially for the elderly.To address this issue,we present an intelligent,non-invasive Software-Defined Radio Frequency(SDRF)sensing system,tailored red for monitoring elderly people’s falls during routine activities.Harnessing the power of deep learning and machine learning,our system processes the Wireless Channel State Information(WCSI)generated during regular and fall activities.By employing sophisticated signal processing techniques,the system captures unique patterns that distinguish falls from normal activities.In addition,we use statistical features to streamline data processing,thereby optimizing the computational efficiency of the system.Our experiments,conducted for a typical home environment while using treadmill,demonstrate the robustness of the system.The results show high classification accuracies of 92.5%,95.1%,and 99.8%for three Artificial Intelligence(AI)algorithms.Notably,the SDRF-based approach offers flexibility,cost-effectiveness,and adaptability through software modifications,circumventing the need for hardware overhaul.This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring,providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.展开更多
Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics,particularly in dermatology.This study presents an ensemble-based skin lesion classification frame...Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics,particularly in dermatology.This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks(DNNs)with transfer learning,a customized DNN,and an optimized self-learning binary differential evolution(SLBDE)algorithm for feature selection and fusion.Leveraging computational techniques alongside medical imaging modalities,the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness.The methodology is evaluated on benchmark datasets,including ISIC 2017 and the Argentina Skin Lesion dataset,demonstrating superior accuracy,precision,and F1-score in melanoma detection.The proposed method achieved a classification accuracy of 98.5%,evaluated using an LSVM classifier on the Argentina Skin Lesion dataset,underscoring the robustness of the proposed methodology.The proposed approach offers a scalable and computationally efficient solution for automated skin lesion classification,thereby contributing to improved clinical decision-making and enhanced patient outcomes.By aligning artificial intelligence with radiation-based medical imaging and bioinformatics,this research advances dermatological computer-aided diagnosis(CAD)systems,minimizing misclassification rates and supporting early skin cancer detection.The proposed approach provides a scalable and computationally efficient solution for automated skin lesion analysis,contributing to improved clinical decision-making and enhanced patient outcomes.展开更多
基金supported in part by the Institute of Advanced Technology,University of Science and Technology of China (USTC) under Grant PF02023001Ythe Zayed Health Center at United Arab Emirates University (UAEU) under Grant G00003476COMSATS University Islamabad,Attock Campus。
文摘The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population,especially in relation to falls.While falls can lead to significant cognitive impairments,timely intervention can mitigate their adverse effects.In this context,the need for non-invasive,efficient monitoring systems becomes paramount.Although wearable sensors have gained traction for monitoring health activities,they may cause discomfort during prolonged use,especially for the elderly.To address this issue,we present an intelligent,non-invasive Software-Defined Radio Frequency(SDRF)sensing system,tailored red for monitoring elderly people’s falls during routine activities.Harnessing the power of deep learning and machine learning,our system processes the Wireless Channel State Information(WCSI)generated during regular and fall activities.By employing sophisticated signal processing techniques,the system captures unique patterns that distinguish falls from normal activities.In addition,we use statistical features to streamline data processing,thereby optimizing the computational efficiency of the system.Our experiments,conducted for a typical home environment while using treadmill,demonstrate the robustness of the system.The results show high classification accuracies of 92.5%,95.1%,and 99.8%for three Artificial Intelligence(AI)algorithms.Notably,the SDRF-based approach offers flexibility,cost-effectiveness,and adaptability through software modifications,circumventing the need for hardware overhaul.This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring,providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project under grant number RGP2/283/46funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R748),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics,particularly in dermatology.This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks(DNNs)with transfer learning,a customized DNN,and an optimized self-learning binary differential evolution(SLBDE)algorithm for feature selection and fusion.Leveraging computational techniques alongside medical imaging modalities,the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness.The methodology is evaluated on benchmark datasets,including ISIC 2017 and the Argentina Skin Lesion dataset,demonstrating superior accuracy,precision,and F1-score in melanoma detection.The proposed method achieved a classification accuracy of 98.5%,evaluated using an LSVM classifier on the Argentina Skin Lesion dataset,underscoring the robustness of the proposed methodology.The proposed approach offers a scalable and computationally efficient solution for automated skin lesion classification,thereby contributing to improved clinical decision-making and enhanced patient outcomes.By aligning artificial intelligence with radiation-based medical imaging and bioinformatics,this research advances dermatological computer-aided diagnosis(CAD)systems,minimizing misclassification rates and supporting early skin cancer detection.The proposed approach provides a scalable and computationally efficient solution for automated skin lesion analysis,contributing to improved clinical decision-making and enhanced patient outcomes.
