The impact of globalization and the recent advancements in Information and Communication Technologies has pushed the manufacturing sector towards a new transformation. Current manufacturers with the help of recent adv...
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data is ubiquitously generated each microsecond from heterogeneous sources. These sources are connected devices in the form of mobiles, telemetry machines, sensors, clickstreams, weather forecasting devices, and many ...
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With the rapid development of the Internet of things and communication technology, the arrival of the era of big data, the construction of smart tourism is no longer an impossible slogan, the significance of this pape...
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Objective To develop smart Shoes with obstacle detection system for visually impaired people. Method The obstacle detection system adopts a triple-axis accelerometer to collect feets acceleration data, and uses an ult...
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In recent years, smart city has gradually become popular. smart city of big data plays an active role in urban fine management, easing traffic jams, improving emergency security system, improving urban environment, el...
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Although shill bidding is a common fraud in online auctions, it is however very tough to detect because there is no obvious evidence of it happening. There are limited studies on SB classification because training dat...
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ISBN:
(纸本)9789813299498;9789813299481
Although shill bidding is a common fraud in online auctions, it is however very tough to detect because there is no obvious evidence of it happening. There are limited studies on SB classification because training data are difficult to produce. In this study, we build a high-quality labeled shill bidding dataset based on recently scraped auctions from eBay. Labeling shill biding instances with multidimensional features is a tedious task but critical for developing efficient classification models. For this purpose, we introduce a new approach to effectively label shill bidding data with the help of the robust hierarchical clustering technique CURE. As illustrated in the experiments, our approach returns remarkable results.
data can be scaled as well as transacted with modularity based on specific applications. The term Internet of things in this domain is conceptualized based on smart virtual applications toward large segment of data wi...
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The Cancer research is the utmost important research field nowadays for the well being of human being. Every year, thousands of people die in Cancer. Therefore, there is a high demand for Cancer research. The Cancer r...
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The rapid development of IoT, cloud and fog computing has increased the potential for developing smart services for IoT devices. Such services require not only connectivity and high computing capacity, but also fast r...
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ISBN:
(纸本)9781728189567
The rapid development of IoT, cloud and fog computing has increased the potential for developing smart services for IoT devices. Such services require not only connectivity and high computing capacity, but also fast response time and throughput of inferencing results. In this paper we present our ongoing work, investigating the potential for implementing smart services in the context of industrial robot applications with focus on analytic inferencing on fog and cloud computing platforms. We review different use cases that we have found in the literature and we divide them into two suggested categories, "distributed deep models" and "distributed interconnected models". We analyze the characteristics of IoT data in industrial robot applications and present two concrete use cases of smart services where inferencing in a fog and a cloud architecture, respectively, is needed. We also reason about important considerations and design decisions for the development process of analytic services.
This research makes a focus on evolutionary computing environments i.e. Pervasive, Internet of Things, Artificially intelligent (AI) and Big data risks management. The contribution of paper lies in identifying, managi...
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This research makes a focus on evolutionary computing environments i.e. Pervasive, Internet of Things, Artificially intelligent (AI) and Big data risks management. The contribution of paper lies in identifying, managing risks in advanced computing paradigms, mitigating security risks and benchmarking smart softwares. As the security and privacy of big volume data is heated discussion these days due to several vulnerabilities in data posed by its pervasive nature so the paper has implemented the security risks mitigation in big data projects. In addition, the benchmarking of pervasive software is done with the help of an intelligent tool for detecting application frameworks and security vulnerabilities. The application is scanned, and the intelligent tool quantifies the severity levels to provide the possible solutions. This tool also collects application metrices to benchmark it against technology, application business drivers and properties to improve the quantitative Performance of the software. This research also puts an analytical foundation for various risks management concepts in evolutionary environments i.e. Pervasive, AI, Internet of Things and Big data. (C) 2020 The Authors. Published by Elsevier B.V.
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