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Visualization and Analysis of Knowledge Structure of smart Agriculture in the Context of Big data 9

Visualization and Analysis of Knowledge Structure of Smart A...

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9th international conference on Computer and Communication Systems (ICCCS)

作者： Sun, Jun Yang, Meng Dalian Polytech Univ Sch Management Dalian Peoples R China

ISBN: (纸本)9798350350227;9798350350210

Big data boosts agricultural production through intelligent transformation;data will become the emerging element of modern agricultural production. Therefore, applying big data technology to agriculture is a new trend in the development of modern agriculture. It can promote the development and progress of agricultural information service technology to a large extent. It can also promote the overall development process of the agricultural field to a large extent. Developing smart agriculture can improve agricultural modernization and promote agricultural transformation and upgrading. smart agriculture can improve the yield and quality of agricultural products, reduce the waste of natural resources, and reduce the pollution of the environment. There is an increasing number of research results related to smart agriculture. However, there is a lack of research on using big data analytics tools to sort out smart agriculture fully. Therefore, based on knowledge mapping, this paper analyzes intelligent agriculture's research hotspots and development trends. This study concludes that precision agriculture, the Internet of Things, big data, artificial intelligence, and cloud computing are the current research hotspots in intelligent agriculture. Furthermore, it is moving toward intelligence and sustainability.

关键词： smart agriculture big data cloud computing artificial intelligence CiteSpace

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Research on the Value of smarter Education in the Era of Big data 5

Research on the Value of Smarter Education in the Era of Big...

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5th IEEE international conference on Big data analytics (ICBDA)

作者： Jiang, Jie Chen, Tong Jiang Xi Sci & Technol Normal Univ Inst Educ Nanchang Jiangxi Peoples R China

ISBN: (数字)9781728141114

ISBN: (纸本)9781728141114

With the rapid development of science and technology, the change of "Internet +", cloud computing, cloud storage and other new generation of information technology makes the information highly circulating, travel and daily life are very convenient, and Big data is the product of the current era. Education as a continuation of thousands of years of behavior, how to comply with the times? How to exploit the intrinsic value of big data? There is also a broader space for thinking and development. The essence of intelligent education lies in the analysis of the current mainstream education model, the combination of specific education behaviors, the analysis of the mutual integration of big data and education, the promotion of the integration and development of education of big data.

关键词： smart education big data education information

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User and Event Behavior analytics on Differentially Private data for Anomaly Detection 7

User and Event Behavior Analytics on Differentially Private ...

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7th IEEE international conference on Big data Security on Cloud, 7th IEEE international conference on High Performance and smart computing, and 6th IEEE international conference on intelligent data and Security, BigdataSecurity/HPSC/IDS 2021

作者： Rashid, Fatema Miri, Ali Ryerson University Department of Computer Science Toronto Canada

ISBN: (纸本)9781665439275

In today's world of digitization, anomaly detection has become one of the most important issues in our lives. User and Entity Behavior analytics (UEBA) is a security solution for anomaly detection. UEBA minimizes the impact of attacks on data and decreases the risk of privacy breeches by keeping track of normal user and entity behaviors. Organizations need to deploy security control through UEBA in order to achieve security for their data by detecting attacks in their early stages, but the cost of such security control is often out of reach for many small and medium enterprises. One possible solution for such organizations is to outsource UEBA to third parties. However, such an approach can result in disclosure of private data. In this paper, we propose a novel scheme that integrates differential privacy into UEBA. We will show that by introducing noise to the data before publishing it to the third party for anomaly detection, we can ensure that the privacy of the data remains intact, while allowing the third party to preform accurate UEBA analysis. Our experimental results and security analysis will show the validity of our approach. © 2021 IEEE.

关键词： Anomaly detection

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smart Meter data Analysis Issues: A data analytics Perspective

Smart Meter Data Analysis Issues: A Data Analytics Perspecti...

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2019 IEEE international conference on intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2019

作者： Sulaiman, S.M. Jeyanthy, P. Aruna Devaraj, D. Asia Pacific University of Technology and Innovation School of Computing Bukit Jalil Malaysia Kalasalingam Academy of Research and Eductaion Department Electrical and Electronics Krishnankoil Tamil Nadu India

ISBN: (纸本)9781538695425

in recent years, the power sector has seen major developments in terms of infrastructure. Incorporating Information and Communication Technologies (ICT) is one of the significant move that has modernized the power industry to what is called smart Grid. As a result of this, an enormous amount of data have been generated by the devices that are installed in the smart Grid system especially the smart Meters which is considered to be the backbone of the smart Grid. Analyzing the smart Meter data for the mutual benefit of both the energy suppliers and its consumers is a data science problem. This paper attempts to address the challenges and issues relevant to data acquisition from a real-time setup and different data analytics perspective of smart Meter data namely descriptive analytics, predictive analytics and prescriptive analytics. The discussion is extended to some of the publicly available smart Meter datasets including residential customers. The paper also dictates possible immediate requirements and future directions in this area. © 2019 IEEE.

关键词： smart meters

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Big data in Mobile and Pervasive computing

Big Data in Mobile and Pervasive Computing

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international conference on intelligent Sustainable Systems (ICISS)

作者： Sharma, Anuradha Farooq, Omar Misra, Praveen Kumar Amity Univ Amity Sch Engn & Technol Dept Comp Sci & Engn Lucknow Campus Lucknow Uttar Pradesh India Dr Shakuntala Misra Natl Rehab Univ Lucknow Dept Stat Lucknow Uttar Pradesh India

ISBN: (纸本)9781538619599

The data from the pervasive devices/computing can be taken from the servers of the respective service providers or application oriented services. The data can be further processed by analytics using Big data with different tools for the analytics like Bluemix, Hadoop and Matlab so, that we can get useful information in real time without further processing and delay. The data in pervasive computing is mostly unstructured. As the data generated from sensor networks and data in mobile and smart devices is increasing day by day, data analytics in unstructured data like this is difficult with limited processing power. But the tools like Hadoop, Bluemix, and Matlab with good connectivity to cloud can provide us some important data analysis tools. Big data can help in many ways which will ensure better services and responsiveness which is lacking in most of the devices right now. The inference from the captured data can be further processed for various applications like business, decisions making, forecasting etc.

关键词： Hadoop MapReduce HDFS Hive Pig Flume

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Calculating AQI Using Secondary Pollutants for smart Air Management System 3rd

Calculating AQI Using Secondary Pollutants for Smart Air Man...

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international conference on data Management, analytics and Innovation (ICDMAI)

作者： Jyoti, Gautam Nitima, Malsa Vikas, Singhal Komal, Malsa JSS Acad Tech Educ Noida India

ISBN: (纸本)9789811393648;9789811393631

With the onslaught of the industrial revolution, the environment is suffering from severe pollution leading to major imbalances. Air Quality Dispersion Modelling can be done through one of the most efficient model "Eulerian Grid based model". Various existing methods of prediction work on the basis of models resulting in satisfactory outcomes but with some certain loopholes. This project involves methods of predicting pollutants' concentration and air quality using machine learning. The data of different sites are collected and the pollutants contributing maximum to the pollution is elucidated using machine learning based methods. Also in this project, a user-friendly, smart application system is developed which can be used to monitor the pollution produced at an individual level. The analysis of the feature stimulating the pollution level (to reach at a dangerous level) can be done with the help of machine learning tools. This paper involves calculating the amount of harmful pollutants released by any individual during their journey. Further solutions can be identified at government level to reduce these pollutants raising the pollution level.

关键词： Air pollutants AQI Eulerian grid based model Machine learning smart air pollution system

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Towards Energy Efficiency smart Buildings Models based on intelligent data analytics 7

Towards Energy Efficiency Smart Buildings Models based on In...

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7th international conference on Ambient Systems, Networks and Technologies (ANT) / 6th international conference on Sustainable Energy Information Technology (SEIT)

作者： Gonzalez-Vidal, Aurora Moreno-Cano, Victoria Terroso-Saenz, Fernando Skarmeta, Antonio F. Univ Murcia Fac Comp Sci E-30001 Murcia Spain

This work presents how to proceed during the processing of all available data coming from smart buildings to generate models that predict their energy consumption. For this, we propose a methodology that includes the application of different intelligent data analysis techniques and algorithms that have already been applied successfully in related scenarios, and the selection of the best one depending on the value of the selected metric used for the evaluation. This result depends on the specific characteristics of the target building and the available data. Among the techniques applied to a reference building, Bayesian Regularized Neural Networks and Random Forest are selected because they provide the most accurate predictive results. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： pervasive computing smart buildings energy efficiency intelligence data analysis techniques

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Some thoughts on ‘intelligent campus’ of military academy

Some thoughts on ‘intelligent campus’ of military academy

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1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Liu, Jun Zhang, Xuexin Jin, Meng Basic Department Changchun Institute of Engineering and Technology Changchun China

ISBN: (纸本)9789811525674

In recent years, smart campus is gradually prevailing in our country, many colleges and universities and even primary and secondary schools have carried out the construction of ‘smart campus’. In recent years, military academies have begun to carry out exploratory construction of ‘smart campus’, but the pace of construction is slow due to various reasons. This paper puts forward some Suggestions and reflections on the construction of ‘smart campus’ in military academies. © Springer Nature Singapore Pte Ltd. 2020.

关键词： intelligent campus intelligent terminal Military education

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The Role of Big data analytics and AI in smart Manufacturing: An Overview

The Role of Big Data Analytics and AI in Smart Manufacturing...

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international conference on Research in intelligent and computing in Engineering, RICE 2020

作者： My, Chu Anh Le Quy Don Technical University Hanoi Viet Nam

ISBN: (纸本)9789811575266

In recent years, smart manufacturing which is the core idea of the Fourth Industrial Revolution (Industry 4.0) has gained increasing attention worldwide. Recent advancements of several information technologies and manufacturing technologies, such as Internet of Things (IoT), big data analytics, artificial intelligent (AI), cloud computing, digital twin, cyber-physical System, have motivated the development of smart manufacturing. This paper presents a comprehensive review of the recent publications related to smart manufacturing, especially related to the particular role of big data analytics and AI in the optimization of process parameters for smart manufacturing shop floors consisting of CNC machines and robots. © 2021, Springer Nature Singapore Pte Ltd.

关键词： data analytics

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An Ultra Low Power, Configurable intelligent Biasing Calibration for Medical Sensor Applications in 130 nm CMOS Technology

An Ultra Low Power, Configurable Intelligent Biasing Calibra...

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IEEE international conference on Big data and smart computing (BigComp)

作者： Ali, Imran Yingge, Huo Asif, Muhammad Lee, Kang-Yoon Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea

ISBN: (纸本)9781728160344

In this paper, a configurable intelligent biasing calibration methodology is presented for medical sensor applications. The proposed biasing calibration algorithm is applicable for single as well as an array of nanopore sensors in a medical device. This technique compensates the variation among different nanopore sensors by calibrating the gate biasing voltages and improves accuracy and enhances reliability. It also identifies the faulty nanopore sensors. The presented biasing calibration controller is fully synthesizable and it needs only 1.184 K gates for its implementation. It draws only 76.3 nA current from 1.2 V supply its power consumption is only 91.56 nW. The proposed intelligent biasing calibration controller is integrated into a medical sensing system and it is implemented in a 130 nm CMOS process.

关键词： Nanopore calibration digital controller CMOS analog front end

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