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Analytic for Temperature and Humidity - Cloud based Forecasting and Dashboard

Analytic for Temperature and Humidity - Cloud based Forecast...

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international conference on intelligent computing and Control Systems (ICICCS)

作者： Kolhe, Mukul Paturkar, Roshan Sahu, Umesh Pillai, Sofia Titarmare, Abhijit GH Raisoni Coll Engn Dept ETC Nagpur Maharashtra India GH Raisoni Coll Engn Dept CSE Nagpur Maharashtra India Galgotias Univ In Charge Ctr Excellence AIME Greater Noida India

ISBN: (纸本)9781728148762

Various factors affect the growth of the plants, which will later result in low productivity and quality, the foremost necessary factors that affects the growth of plants are temperature, humidity, water, and light. The aim of this study is analytics of temperature and humidity, as these are necessary for better crop yield. Therefore, in this work, low cost smart autonomous remote monitored Agri system for polyhouse and greenhouse is developed. In smart autonomous remote monitored system, collected temperature and humidity data is stored in cloud for further analytics. DHT22 temperature and humidity sensor are the main component of setup which is connected to nodemcu and nodemcu wirelessly communicates with raspberry pi. Also, developed an android application as a dashboard for monitoring. Experiments have been conducted to validate the performance of proposed monitoring system in polyhouse and greenhouse of G H Raisoni University Saikheda.

关键词： data Analytic Prediction smart farming

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A Novel Approach for Clinical data Harmonization

A Novel Approach for Clinical Data Harmonization

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

作者： Chondrogiannis, Efthymios Andronikou, Vassiliki Karanastasis, Efstathios Varvarigou, Theodora Natl Tech Univ Athens 9 Heroon Politech Str Athens Greece

ISBN: (纸本)9781538677896

A city transforms into a "smart" one when it utilizes the data from smart devices, cloud infrastructures, applications and repositories in order to develop and provide new services, products and insights with the goal being to offer a safer, more efficient and robust environment for government, citizens and businesses, and accelerate sustainable economic growth. Although the variability across cities in terms of cultural background, demographics, current infrastructures, topology, among others, might drive significant variations in the individual goals of a smart city, the role of health in achieving the aforementioned goals is of paramount importance in any city. One of the starting points of introducing a health-related orientation into a smart city developments and operations is the intelligent processing of the overwhelming amount of clinical data being continuously generated during healthcare provision, daily activities and clinical research. Great challenges that need to be met in order to offer big data analytics over clinical data for the purposes of a smart city lie in their high heterogeneity at system, syntactic, structural and semantic level as well as their sensitive nature from the legal and ethical perspective, which may prevent and/or limit access to and analysis of the data. In this work we will present a novel approach for expressing clinical data using a common formalism with explicit semantics of the terms being used. Based on this established semantic ground, questions on health-related data can be placed for disease prevention, treatment monitoring, post-marketing surveillance, policy making, among others.

关键词： data Harmonization Heterogeneity Issues Semantic Web Clinical Studies

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The intelligent systems for language phonetic interfaces 14

The intelligent systems for language phonetic interfaces

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14th IADIS international conference Computer Graphics, Visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and 5th IADIS international conference Big data analytics, data Mining and Computational Intelligence 2020, BigDaCI 2020 and 9th IADIS international conference Theory and Practice in Modern computing 2020, TPMC 2020, Part of the 14th Multi conference on Computer Science and Information Systems, MCCSIS 2020

作者： Yanova, Natalia Altai State University Lenina 61 Barnaul Russia

ISBN: (纸本)9789898704214

The project aims to develop intelligent natural language analysis tools. The aim of the project is to formalize the interpretative approach in the study of perception and understanding of language interfaces. The project implements an expert system of psychophonetic analysis in the field of onomastics. The system is designed for psychological diagnostics of anthroponyms in the Russian language. The project offers a research technique for the psychophonetic study of naming in natural language. The project discusses the formalization of psychophonetic functions of the mental lexicon. The subject of the analysis is the psychological structure of phonosemantic decoding of nouns. A psychometric model is proposed for calculating the interpretation functions of the psychophonic meaning of names. The research solution includes mobile software application for IOS and Android (smart-test 'What's Name'). © Proceedings of the 14th IADIS international conference Computer Graphics, Visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and Proceedings of the 5th IADIS international conference Big data analytics, data Mining and Computational Intelligence 2020, BigDaCI 2020 and Proceedings of the 9th IADIS international conference Theory and Practice in Modern computing 2020, TPMC 2020 - Part of the 14th Multi conference on Computer Science and Information Systems, MCCSIS 2020. All rights reserved.

关键词： Sentiment analysis

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A Review on Several Critical Issues and Challenges in IoT based e-Healthcare System

A Review on Several Critical Issues and Challenges in IoT ba...

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international conference on intelligent computing and Control Systems (ICCS)

作者： Shamila, M. Vinuthna, K. Tyagi, Amit Kumar Koneru Lakshmaiah Educ Fdn Comp Sci & Engn Guntur Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Comp Sci & Engn Hyderabad Telangana India Lingayas Vidyapeeth Comp Sci & Engn Faridabad Haryana India

ISBN: (纸本)9781538681138

Due to recent development in smart/ electronic devices or computing devices, a lot of data (called Big data) is being generated. This data in simple terms is called as "Large data" or "Big data". Today's Big data is highly useful for predicting future (with respect to an application) after having a proper and valid analysis. Together this, several researchers have been tried to make efficient analytics tools (or techniques like data mining or machine learning) to analysis this Big data, but some tool fail due to the volume and veracity feature of the big data. So, there are several problems, issues and challenges have been rectified which are need to overcome in near future. Hence, this article gives insight to where and how big data can be used in health care system, also provide several challenges and issues with respect to genomic analytics (on this collected data).

关键词： Big data Genomic analytics smart Devices data Mining and Machine Learning Techniques

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A smart Agricultural Model by Integrating IoT, Mobile and Cloud-based Big data analytics

A Smart Agricultural Model by Integrating IoT, Mobile and Cl...

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international conference on intelligent computing and Control (I2C2)

作者： Rajeswari, S. Suthendran, K. Rajakumar, K. Kalasalingam Univ Dept Comp Applicat Srivilliputtur Tamil Nadu India Kalasalingam Univ Dept Informat Technol Srivilliputtur Tamil Nadu India

ISBN: (纸本)9781538603741

Nowadays, the traditional database paradigm does not have enough storage for the data produced by Internet of Things (IoT) devices leads to the need of cloud storage. These data's are analyzed with the help of Big data mining techniques. Cloud based big data analytics and the IoT technology performs an important role in the feasibility study of smart agriculture. smart or precision agricultural systems are estimated to play an essential role in improving agriculture activities. Mobile device usage is very common by everyone, including the farmers. In that, in the daily life of farmers the Information and Communication Technologies (ICT) play a vital role to get the agricultural Information. The IoT has various applications in Digital Agriculture domain like monitoring the crop growth, selection of the fertilizer, irrigation decision support system, etc. In this paper, IoT device is used to sense the agricultural data and it is stored into the Cloud database. Cloud based Big data analysis is used to analyze the data viz. fertilizer requirements, analysis the crops, market and stock requirements for the crop. Then the prediction is performed based on data mining technique which information reaches the farmer via mobile app. Our ultimate aim is to increase the crop production and control the agricultural cost of the products using this predicted information.

关键词： IoT WSN Cloud computing Big data Hadoop MapReduce Mobile computing data mining Precision Agriculture

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A Demo of a Software Platform for Ubiquitous Big data Engineering, Visualization, and analytics, via Reconfigurable Micro-Services, in smart Factories 8

A Demo of a Software Platform for Ubiquitous Big Data Engine...

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8th IEEE international conference on smart computing (smartCOMP)

作者： Soderi, Mirco Kamath, Vignesh Breslin, John G. Natl Univ Ireland Data Sci Inst Galway Ireland

ISBN: (纸本)9781665481526

intelligent, smart, Cloud, reconfigurable manufacturing, and remote monitoring, all intersect in modern industry and mark the path toward more efficient, effective, and sustainable factories. Many obstacles are found along the path, including legacy machineries and technologies, security issues, and software that is often hard, slow, and expensive to adapt to face unforeseen challenges and needs in this fast-changing ecosystem. Lightweight, portable, loosely coupled, easily monitored, variegated software components, supporting Edge, Fog and Cloud computing, that can be (re)created, (re)configured and operated from remote through Web requests in a matter of milliseconds, and that rely on libraries of ready-to-use tasks also extendable from remote through sub-second Web requests, constitute a fertile technological ground on top of which fourth-generation industries can be built. In this demo it will be shown how starting from a completely virgin Docker Engine, it is possible to build, configure, destroy, rebuild, operate, exclusively from remote, exclusively via API calls, computation networks that are capable to (i) raise alerts based on configured thresholds or trained ML models, (ii) transform Big data streams, (iii) produce and persist Big datasets on the Cloud, (iv) train and persist ML models on the Cloud, (v) use trained models for one-shot or stream predictions, (vi) produce tabular visualizations, line plots, pie charts, histograms, at real-time, from Big data streams. Also, it will be shown how easily such computation networks can be upgraded with new functionalities at real-time, from remote, via API calls.

关键词： Software Platform Micro-service API Edge Fog Cloud Big data data Engineering data Visualization data analytics intelligent Manufacturing smart Manufacturing Reconfigurable Manufacturing Remote Monitoring

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BigClue analytics: A middleware component for modeling sensor data in IoT systems 20

BigClue Analytics: A middleware component for modeling senso...

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20th IEEE international conference on High Performance computing and Communications (HPCC) / 16th IEEE international conference on smart City (smartCity) / 4th IEEE international conference on data Science and Systems (DSS)

作者： Huru, Dan Leordeanu, Catalin Apostol, Elena Mocanu, Mariana Cristea, Valentin Univ Politehn Bucuresti Fac Automat Control & Comp Bucharest Romania

ISBN: (纸本)9781538666142

The typology of most IoT projects requires data processing to be handled in a low latency scenario. In this article we first introduce BigClue, a middle-ware capable of collecting, processing and modeling sensor data in such a scenario. Then we present BigClue analytics, a specialized component which is part of the middle-ware that has multiple features: data modeling, data prediction, anomaly detection and sensor correlation. While our experimental work is validated in a greenhouse environment the intention is to extend its applicability to other problem domains.

关键词： IoT machine learning time series modeling analytics anomaly detection

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Real-Time Big data analytics in smart Cities from LoRa-Based IoT Networks 14th

Real-Time Big Data Analytics in Smart Cities from LoRa-Based...

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14th international conference on Soft computing Models in Industrial and Environmental Applications (SOCO)

作者： Fernandez, Antonio M. Gutierrez-Aviles, David Troncoso, Alicia Martinez-Alvarez, Francisco Pablo de Olavide Univ Data Sci & Big Data Lab ES-41013 Seville Spain

ISBN: (纸本)9783030200558;9783030200541

The currently burst of the Internet of Things (IoT) technologies implies the emergence of new lines of investigation regarding not only to hardware and protocols but also to new methods of produced data analysis satisfying the IoT environment constraints: a real-time and a big data approach. The Real-time restriction is about the continuous generation of data provided by the endpoints connected to an IoT network;due to the connection and scaling capabilities of an IoT network, the amount of data to process is so high that Big data techniques become essential. In this article, we present a system consisting of two main modules. In one hand, the infrastructure, a complete LoRa based network designed, tested and deployment in the Pablo de Olavide University and, on the other side, the analytics, a big data streaming system that processes the inputs produced by the network to obtain useful, valid and hidden information.

关键词： IoT LoRaWAN Real-time Big data data streaming

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Big data analytics Adoption Model for Malaysian SMEs 4th

Big Data Analytics Adoption Model for Malaysian SMEs

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4th international conference of Reliable Information and Communication Technology (IRICT)

作者： Tien, Eu Lay Ali, Nazmona Mat Miskon, Suraya Ahmad, Norasnita Abdullah, Norris Syed Univ Teknol Malaysia Johor Baharu Malaysia

ISBN: (纸本)9783030335823;9783030335816

Big data analytics (BDA) was utilized to analyze and examine big data sets. This system is able to analyze enormous data sets which exist in different formats and to extract useful information within the data which may be used to improve business decision-making, predict sales, enhance customer relationships, and ultimately lead to generating increased revenues and profits. Multinational and large companies are starting to adopt BDA to acquire the benefits and advantages from this technology. However, the rate of adoption of BDA by Small and Medium Enterprises (SMEs) is low. There is a need and desire that SMEs should start to adopt BDA in order to stay one step ahead of their rivals, and at the same time, to remain competitive in the market. Hence, this study aims to identify the factors influencing the adoption of BDA in Malaysian SMEs and propose a BDA adoption model for Malaysian SMEs.

关键词： Big data analytics Adoption model Malaysian SMEs

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Interactive analytics of Massive Spatial Vector data via Display-driven computing

Interactive Analytics of Massive Spatial Vector Data via Dis...

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

作者： Ma, Mengyu Wu, Ye Chen, Luo Li, Jun Jing, Ning Du, Chun Natl Univ Def Technol Coll Elect Sci Changsha Peoples R China

ISBN: (纸本)9781728160344

In the big data era, real-time analysis of large-scale spatial data remains a challenging problem since the computational scales of conventional data-driven methods expand rapidly with data volumes. This paper presents display-driven computing (DisDC), a new computing model for interactive analysis of large-scale spatial vector data. The computing units in DisDC are screen pixels for display rather than spatial objects in traditional data-driven methods, and the core task of DisDC is to judge the spatial relationships between pixels and spatial objects. As the number of pixels for display is stable, DisDC is less sensitive to data volumes and can be used to provide real-time data analysis of large-scale spatial vector data. Experiments show that DisDC is capable of handling billion-scale spatial vector data, and an online demonstration is provided (https://***/MemoryMmy/DisDC).

关键词： display-driven computing real-time big data parallel computing

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