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model view controller (MVC) architecture and client-to-client file transfer protocol with binarized spiking neural network for building a smart home appliances control in IoT

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3189-3200页

作者： Yadawad, Ranganath Kulkarni, Umakant P. Department of Computer Science and Engineering SDM College of Engineering and Technology Karnataka Dharwad 580002 India

In the time Internet of Things (IoT) the term “Smart” is mostly used because of its application in various entities such as smart cities, smart homes, and smart cars. These are done by integrating Machine Learning (ML) algorithms along with IoT devices. In the context of the smart home, many researchers use IoT designs with Deep Learning (DL) techniques, although the majority of them are limited because of hardware issues of the end devices. So, the transport of detected messages from IoT devices to the application layer takes longer when object detection techniques are used in real-time systems, which lowers the DL’s accuracy. To overcome these issues a model view controller (MVC) architecture and client-to-client file transfer protocol (C2CFTP) with binarized spiking neural network (BS4NN) for building a smart home appliances control in IoT is proposed in this manuscript. In this smart home automation system, users can remotely monitor their houses by employing an object detection algorithm built on the client-to-client model view controller (C2CMVC) architecture. C2CMVC architecture consists of three layers such as bottom Layer, Repository layer, and application layer. This, IoT-based sensing device, collects data from a laptop or personal computer (PC) that has a camera attached using the bottom layer. For object detection, the BS4NN is employed, and when an object is discovered, a message is delivered to the C2CFTP. The middle layer, also known as the repository layer, in this, the C2CFTP broker used. C2CFTP is a novel protocol which is efficiently transfers messages to the application layer. Users receive notifications by short message service (SMS) and email, and the system's application layer sends a command message. The performance of the proposed method attains 32.0%, 1.0%, 15.0%, and 15.0% lower delay is likened to the existing methods. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management

关键词： Binarized Spiking Neural Network Client-to-client file transfer protocol Internet of Things model view controller architecture Object detection

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Epiviewer: An Epidemiological Application For Exploring Time Series Data

EpiViewer: An Epidemiological Application For Exploring Time...

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作者： Thorve, Swapna Virginia Tech | University

Visualization plays an important role in epidemic time series analysis and forecasting. viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form. However,there are challenges in reviewing data sets from multiple data sources (data can be aggregated in different ways and measure different criteria which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data. In this thesis, we develop Epiviewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. Epiviewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, Epiviewer provides a built-in statistical Epi-features module to help users interpret the epidemiological curves.

关键词： web srevices model view controller architecture catalogues epidemiology metrics charts Thesis

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