Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images ...
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Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and ***,these methods suffer from biased results and inaccurate detection of the ***,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT *** proposed methodology works on the basis of two stages such as pre-processing and ***,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT *** collected images are then preprocessed using Gaussian *** filter can be utilized in the removal of unwanted noise from the collected CT scan ***,the preprocessed images are sent to prediction *** this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed *** proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed ***,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or *** projected method was implemented in MATLAB and the performances were evaluated through statistical *** proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)*** results established the supremacy of the proposed model.
Wireless sensor networks generate volumes of scientific observations. However, gathered data is typically not published for use by other scientists. This paper analyses the reasons that so little sensor network data i...
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Wireless sensor networks generate volumes of scientific observations. However, gathered data is typically not published for use by other scientists. This paper analyses the reasons that so little sensor network data i...
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Wireless sensor networks generate volumes of scientific observations. However, gathered data is typically not published for use by other scientists. This paper analyses the reasons that so little sensor network data is published online in a usable form, and identifies gaps in currently available publication systems. To address these gaps we present WebSense, a lightweight and configurable web-based tool for sensor network researchers to publish their collected data. The design goals and implementation of WebSense are described and the system is evaluated against the goals. WebSense is available from http://***/.
Today's collaborative applications over the web span domains as diverse as enterprise e-commerce applications and biomedical applications. We have previously developed and formally investigated "Web Coordinat...
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ISBN:
(纸本)0769523129
Today's collaborative applications over the web span domains as diverse as enterprise e-commerce applications and biomedical applications. We have previously developed and formally investigated "Web Coordination Bonds," a theoretically powerful artifact for interconnecting autonomous web objects for control and data flows, and for enforcing dependencies. Here, we describe BondFlow, a system that enables the configuration and execution of workflows using web bonds over heterogeneous web objects. The system automatically generates lightweight proxy wrapper objects to enable encapsulated web services to be interconnected through web bonds. The small footprint wrappers (less than 10 KB) can reside on Java-enabled handheld devices. We describe the design, architecture and performance of the Bond-Flow system. Our initial performance results indicate that both wrapper creation time and web bond related execution time are small. In contrast to our BondFlow system, existing systems need expert programming to create workflows, do not easily allow distributed coordination of workflows, and usually do not support heterogeneity of device, data and network. The system is preliminary, and further research is ongoing to extend its configuration and execution platforms.
While current view technology assumes that information systems (ISs) do not change their schemas, our Evolvable View Environment (EVE) project addresses this problem by evolving the view definitions affected by IS sch...
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While current view technology assumes that information systems (ISs) do not change their schemas, our Evolvable View Environment (EVE) project addresses this problem by evolving the view definitions affected by IS schema changes, which we call view synchronization. In EVE, the view synchronizer rewrites the view definitions by replacing view components with suitable components from other ISs. However, after such a view redefinition process, the view extents, if materialized, must also be brought up to date. In this paper, we propose strategies to address this incremental adaptation of the view extent after view synchronization. One key idea of our approach is to regard the complex changes done to a view definition after synchronization as an atomic unit; another is to exploit knowledge of how the view definition was synchronized, especially the containment information between the old and new views. Our techniques would successfully adapt views under the unavailability of base relations, while currently known maintenance strategies from the literature would fail.
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