作者：
Ribeiro, LucasOliveira, Helder P.Hu, XiaoPereira, TaniaUniversity of Porto
Inesc Tec Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal University of Porto
Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal Emory University
Nell Hodgson Woodruff School of Nursing Department of Biomedical Informatics School of Medicine Department of Computer Science College of Arts and Sciences Atlanta United States Technology and Science
Inesc Tec - Institute for Systems and Computer Engineering Porto Portugal
PPG signal is a valuable resource for continuous heart rate monitoring;however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal dif...
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Federated learning (FL) is widely used in edge-cloud collaborative training due to its distributed architecture and privacy-preserving properties without sharing local data. FLTrust, the most state-of-the-art FL defen...
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It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these ...
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Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in...
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Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident ...
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Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.
3D bioprinting is gaining immense importance in the biofabrication of organs, cartilages, bones, tissue repair or regeneration in tissue-engineered applications, personalized medicine, tailored therapies, and pharmace...
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The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signa...
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The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keep...
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The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret
Statistical models, enhanced by deep learning techniques, have become pivotal in various predictive tasks, including financial forecasting. This paper addresses the challenge of predicting cryptocurrency prices, utili...
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ISBN:
(数字)9798350368833
ISBN:
(纸本)9798350368840
Statistical models, enhanced by deep learning techniques, have become pivotal in various predictive tasks, including financial forecasting. This paper addresses the challenge of predicting cryptocurrency prices, utilizing a dataset comprising various cryptocurrencies treated as time series. We employ several deep neural network architectures-including Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Bidirectional LSTM networks-to forecast future prices. Our methodology involves a detailed analysis of cryptocurrency time series to inform the design of these networks. The performance of each model is rigorously compared, highlighting their predictive capabilities in the context of cryptocurrency markets. This study not only contributes to the empirical literature by applying advanced neural networks to high-volume financial data but also provides a comparative analysis that may guide future applications of deep learning in economic forecasting.
Mobile edge cloud (MEC) has emerged as a critical technology for enabling low-latency and real-time mobile device applications. However, an efficient resource allocation framework for improving the user experience in ...
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