Artificial Intelligence(AI)and Computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged people.Object detection includes a variety of challenges,fo...Artificial Intelligence(AI)and Computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged people.Object detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so on.The test images include all these variants as well.These detection models alert them about their surroundings when they want to walk independently.This study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition applications.Based on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed.To further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the work.Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization approach.Additionally,Inception V3 can handle images of different sizes.This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.展开更多
Vision impairment is a latent problem that affects numerous people across the globe.Technological advancements,particularly the rise of computer processing abilities like Deep Learning(DL)models and emergence of weara...Vision impairment is a latent problem that affects numerous people across the globe.Technological advancements,particularly the rise of computer processing abilities like Deep Learning(DL)models and emergence of wearables pave a way for assisting visually-impaired persons.The models developed earlier specifically for visually-impaired people work effectually on single object detection in unconstrained environment.But,in real-time scenarios,these systems are inconsistent in providing effective guidance for visually-impaired people.In addition to object detection,extra information about the location of objects in the scene is essential for visually-impaired people.Keeping this in mind,the current research work presents an Efficient Object Detection Model with Audio Assistive System(EODM-AAS)using DL-based YOLO v3 model for visually-impaired people.The aim of the research article is to construct a model that can provide a detailed description of the objects around visually-impaired people.The presented model involves a DL-based YOLO v3 model for multi-label object detection.Besides,the presented model determines the position of object in the scene and finally generates an audio signal to notify the visually-impaired people.In order to validate the detection performance of the presented method,a detailed simulation analysis was conducted on four datasets.The simulation results established that the presented model produces effectual outcome over existing methods.展开更多
Adelegation led by Prof. Chen Shiqiu, Vice President of the China Society for Human Rights Studies (CSHRS) andexpert of the UN Sub-Commission on the Promotion and Protection of Human Rights, attended ASEM People’s Fo...Adelegation led by Prof. Chen Shiqiu, Vice President of the China Society for Human Rights Studies (CSHRS) andexpert of the UN Sub-Commission on the Promotion and Protection of Human Rights, attended ASEM People’s Forum V held in Hanoi, Vietnam on 6-9 September, 2004.展开更多
目的分析社区老年人衰弱问题的研究现状及研究热点以促进国内社区老年人衰弱研究的发展。方法运用CiteSpaceV软件对2007-2017年Web of Science数据库收录的社区老年人衰弱问题的相关文献进行总结与分析。结果共检出695篇有效文献,美国...目的分析社区老年人衰弱问题的研究现状及研究热点以促进国内社区老年人衰弱研究的发展。方法运用CiteSpaceV软件对2007-2017年Web of Science数据库收录的社区老年人衰弱问题的相关文献进行总结与分析。结果共检出695篇有效文献,美国发文量最多,占25.5%,其次为荷兰(10.4%)和加拿大(9.6%),中国大陆排名第十三,发文量仅占总体的3.6%。该领域的研究热点和前沿集中在疾病状态分析、危险因素评估、生理机制、行为干预方式与效果四个领域。结论社区老年人衰弱问题已引起学者的广泛关注,但是相关领域的研究深度和广度有待拓展,尚需加强科研机构之间的合作,促进社区老年人衰弱研究深化发展为促进健康老龄化提供科学依据。展开更多
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R191)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR61)This study is supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1444).
文摘Artificial Intelligence(AI)and Computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged people.Object detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so on.The test images include all these variants as well.These detection models alert them about their surroundings when they want to walk independently.This study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition applications.Based on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed.To further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the work.Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization approach.Additionally,Inception V3 can handle images of different sizes.This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.
文摘Vision impairment is a latent problem that affects numerous people across the globe.Technological advancements,particularly the rise of computer processing abilities like Deep Learning(DL)models and emergence of wearables pave a way for assisting visually-impaired persons.The models developed earlier specifically for visually-impaired people work effectually on single object detection in unconstrained environment.But,in real-time scenarios,these systems are inconsistent in providing effective guidance for visually-impaired people.In addition to object detection,extra information about the location of objects in the scene is essential for visually-impaired people.Keeping this in mind,the current research work presents an Efficient Object Detection Model with Audio Assistive System(EODM-AAS)using DL-based YOLO v3 model for visually-impaired people.The aim of the research article is to construct a model that can provide a detailed description of the objects around visually-impaired people.The presented model involves a DL-based YOLO v3 model for multi-label object detection.Besides,the presented model determines the position of object in the scene and finally generates an audio signal to notify the visually-impaired people.In order to validate the detection performance of the presented method,a detailed simulation analysis was conducted on four datasets.The simulation results established that the presented model produces effectual outcome over existing methods.
文摘Adelegation led by Prof. Chen Shiqiu, Vice President of the China Society for Human Rights Studies (CSHRS) andexpert of the UN Sub-Commission on the Promotion and Protection of Human Rights, attended ASEM People’s Forum V held in Hanoi, Vietnam on 6-9 September, 2004.
文摘目的分析社区老年人衰弱问题的研究现状及研究热点以促进国内社区老年人衰弱研究的发展。方法运用CiteSpaceV软件对2007-2017年Web of Science数据库收录的社区老年人衰弱问题的相关文献进行总结与分析。结果共检出695篇有效文献,美国发文量最多,占25.5%,其次为荷兰(10.4%)和加拿大(9.6%),中国大陆排名第十三,发文量仅占总体的3.6%。该领域的研究热点和前沿集中在疾病状态分析、危险因素评估、生理机制、行为干预方式与效果四个领域。结论社区老年人衰弱问题已引起学者的广泛关注,但是相关领域的研究深度和广度有待拓展,尚需加强科研机构之间的合作,促进社区老年人衰弱研究深化发展为促进健康老龄化提供科学依据。
