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smart Manufacturing with Prescriptive analytics 8

Smart Manufacturing with Prescriptive Analytics

引用

8th international conference on Industrial Technology and Management, ICITM 2019

作者： Vater, Johannes Harscheidt, Lars Knoll, Alois Planning and Production of Electrified Powertrains BMW Group Munich Germany Robotics Artificial Intelligence and Real-time Systems Technical University of Munich Munich Germany

ISBN: (纸本)9781728132686

Automotive industry faces challenges in manufacturing like increasingly individualized products with a short lead-time to market and higher quality. Additionally to that, new technologies, such as Internet of Things (IoT), big data, data analytics and cloud computing, are changing the production into the next generation of industry. To address these challenges intelligent manufacturing in combination with data analytics plays an important role. In this sense, the integration of prescriptive analytics in manufacturing may help industry to increase productiveness. This paper provides first a comprehensive review of key elements for prescriptive analytics in manufacturing. Furthermore, this paper highlights requirements for a prescriptive analytics based production control, so called prescriptive automation, and finally points out field of activities in this topic. © 2019 IEEE.

关键词： data analytics

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Deep Learning-Based intelligent Chatbot in a Digital Campus 2

Deep Learning-Based Intelligent Chatbot in a Digital Campus

引用

2nd international conference on data analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Yang, Xinrong Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350339765

With the rapid development of artificial intelligence technology, all walks of life have started the process of intelligent construction. The intelligent Q&A system in the field of artificial intelligence has the characteristics of all-weather and intelligent, and exploring the deep integration of intelligent Q&A system and campus business in the process of smart campus construction has practical significance for campus service model innovation. Based on the shortcomings of the traditional information service model, this paper elaborates from the overall framework and core modules of the campus intelligent question and answer system, and summarizes and analyzes its application advantages and subsequent development directions in smart campus services, so as to explore its more application potential and practical value in the field of campus intelligent services. This paper presents the implementation of a digital campus intelligent chatbot using two distinct technologies. The chatbot is made up of three main modules: the chat module, the campus information module, and the virtual teaching assistant module. The Encoder-Decoder framework is used in the chat module, while the campus information module and virtual teaching assistant module prioritize accuracy and employ a retrieval mode. Through the integration of these two modules, employing different technical approaches, the overall user experience for students is significantly enhanced. © 2023 IEEE.

关键词： Deep learning

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Review of neural network models for air quality prediction 1

引用

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

作者： Zhou, Kai Xie, Ruichao Army Academy of Artillery and Air Defense Hefei China Hefei Anhui230031 China

ISBN: (数字)9789811525681

ISBN: (纸本)9789811525674

With the diversified development of air quality data acquisition and processing technology, people began to gradually adopt the new technology represented by machine learning to predict the air quality data to make up for the shortcomings of traditional forecasting methods. However, many machine learning models applied to air quality prediction generally use batch learning and prediction methods, that is, after a sample study and prediction, new samples will not be learned, and air quality prediction will be increased. The error, which deviates from the track of real-time prediction, is difficult to apply effectively to actual engineering. Therefore, in view of the problems existing in the current air quality prediction field, we review the previous research on the air quality neural network prediction model. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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A Knowledge Management-extended Gamified Customer Relationship Management System 5

A Knowledge Management-extended Gamified Customer Relationsh...

引用

international conference on Soft computing, intelligent System and Information Technology (ICSIIT)

作者： Lee, Chien-Sing Foo, Jun-Jie Sangar, Vinudha A. P. Jeya Chan, Pei-Yee Hor, Weng-Keen Chan, Eng-Keong Sunway Univ Sch Sci & Technol Kuala Lumpur Malaysia

ISBN: (纸本)9780769561639

The growth of smart Cities often depends on global trends, human capital, foreign investments and partnerships, as well as citizen participation. With these drivers in mind, we focus on the question of how to attract and maintain customers first through a Gamified Customer Relationship Management System and subsequently, its enhancement via a Knowledge Management approach. The design of the gamified customer relationship management system and subsequently, its extension via a Knowledge Management approach are presented along with simple text mining on data from the Website's forum. Findings towards user acceptance and user satisfaction are positive and directly lend towards possible Big data and possibly, IoT applications in the future.

关键词： smart cities gamified customer relationship management system knowledge management text mining applications to analytics IoT

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Application of Bayes Rule for Precise Analysis of Class-Imbalanced Stroke data

Application of Bayes Rule for Precise Analysis of Class-Imba...

引用

2023 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2023

作者： Viswapriya, S.E. Rajeswari, D. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350319200

The interpretability of stroke risk prediction is still lagging and it can be solved by using deep analysisofdata with the application of the Bayes rule. The prediction model has to be produced with accuracy, clarity, and comprehensibility without ambiguity, especially in machine learning and data analytics. The class imbalance problem is a common issue affecting the raw data, which means the imbalanced categorization of outcome feature value. In many fields, such as fraud detection, network intrusion detection, and medical diagnostics, this class imbalance issue is present where negative class samples outnumber positive class samples. If some datasets have class imbalance and imperfect issues, there will be no proper classification pattern. There are many resampling methods to address this class imbalance problem. However, these resampling methods can make an even distribution of classes by generating or omitting some samples, but theycannot handle imperfect data. The probabilistic analysis can lead to the identification of issues that exist in the dataset and provide direction to medical experts on dealing with the condition to avoid *** strength of applying the Bayes rule will provide a meaningful interpretation of stroke data. This paper investigates the Baye rule's significance in stroke data analysis. © 2023 IEEE.

关键词： Bayes rule Class imbalance probabilistic analysis Resampling Stroke data

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intelligent Network Services enabling Industrial IoT Systems for Flexible smart Manufacturing 14

Intelligent Network Services enabling Industrial IoT Systems...

引用

14th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Behnke, Daniel Mueller, Marcel Boek, Patrick-Benjamin Bonnet, Jose Weidmuller Grp Detmold Germany Altice Labs Aveiro Portugal

ISBN: (纸本)9781538668764

In recent years developments of Industrial IoT components and data analysis paved the way for optimization of manufacturing processes. Additional sensors provide manifold data of machine parks and individual machines. Due to the huge amount of data, communication and storage systems get into focus of research. In this work, we are introducing a concept for the usage of Network Services in smart manufacturing, creating the missing link between the closed machine architecture and flexible SG services.

关键词： Firewalls (computing) data analysis 5G mobile communication Wireless communication smart manufacturing Sensors Internet

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The Impact of data Quality on Neural Network Models

The Impact of Data Quality on Neural Network Models

引用

international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Li, Chunmei Li, Zhao Jun, Xu Pi, Wei Qinghai Univ Dept Comp Technol & Applicat Xining Qinghai Peoples R China China Mobile Commun Grp Guangxi Co Ltd Guilin Peoples R China

ISBN: (纸本)9783030152352;9783030152345

This paper introduces the neural network evaluates Sanjiangyuan grassland degradation degree evaluation model from three aspects: data source, the neural network design and experimental process analysis. It focuses on analyzing the impact of training data quality on neural network quality and points out that the importance of data quality in big data era.

关键词： Grassland degradation Evaluation Neural network Big data

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High-performance computing based big data analytics for smart manufacturing 13

High-performance computing based big data analytics for smar...

引用

ASME 2018 13th international Manufacturing Science and Engineering conference, MSEC 2018

作者： Yang, Yuhang Dora Cai, Y. Lu, Qiyue Zhang, Yifang Koric, Seid Shao, Chenhui Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States

ISBN: (纸本)9780791851371

With the rapid development of sensing, communication, and computing technologies and infrastructure, today's manufacturing industry is marching towards a big data era and a new generation of digitalization and intelligence. The availability of big data provides us with a golden opportunity to promote smart manufacturing. Nevertheless, the deployment and popularization of big data analytics in manufacturing is still at its nascent stage. One critical challenge results from the lack of highperformance computing (HPC) capability, which is crucial for responsive and intelligent decision-making in the modern manufacturing industry. To address this challenge, this paper proposes a framework and some general guidelines for implementing big data analytics in an HPC environment. The details of the whole workflow, from the prototype to the final application, are highlighted. A case study for intelligent 3D sensing with real-world manufacturing data is presented to demonstrate the effectiveness of the proposed framework. Copyright © 2018 ASME.

关键词： Genetic algorithms

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smart Farming

Smart Farming

引用

international conference on Recent Trends in Machine Learning, IOT, smart Cities and Applications, ICMISC 2020

作者： Sardal, Nihar Patel, Ankit Sawant, Vinaya Department of Information Technology Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (纸本)9789811572333

Agriculture plays a vital role in the development of an agricultural country like India. As the population soars from today’s 1.3 billion to an estimated 2 billion by 2050, the demand for food is expected to more than double. In an agricultural process, the farmer aims to achieve increased yield at the least cost. The number of factors affecting the farm is high which complicates the decision-making process. The proposed system aims to assist farmers in selecting the crop for cultivation using sensor data collected from the field (Shirsath et al. in 2017 international conference on intelligent computing and control (I2C2), Coimbatore, pp 1–5 2017 [1]). Sensors are connected to the Cloud using IoT. analytics is performed on the real-time data stored in the cloud to analyze sensor data and identify any outliers. In the cloud, Machine Learning based real-time analytics is performed to analyze sensor data and identify any outliers. This system uses Machine Learning and IoT to develop an intelligent and affordable farming product. The techniques incorporated within this system improve the precision of the result and automate crop monitoring thus reducing human involvement. Real-time data collected can be utilized to predict disease using machine learning algorithms. The real-time update will alert the farmer by indicating which crop is in trouble, so the expenses on insecticides, pesticides will reduce. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crops

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Application of the data Mining Technology in the Economic Management in the Age of Big data

Application of the Data Mining Technology in the Economic Ma...

引用

international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Luo, Wenbiao Guangzhou Vocat Coll Sci & Technol Guangzhou 510550 Guangdong Peoples R China

ISBN: (纸本)9783030152352;9783030152345

Under the background of the continuous acceleration of the reform process of the domestic market economy, relying on the continuous improvement of the information technology level, the Internet era has come in an allround way. Under this background, the traditional economic management model obviously cannot meet the needs of the current economic market. In view of the problems existing in the traditional economic management, this paper discusses the informatization of the economic management under the background of the big data era.

关键词： Big data era data mining technology Economic management Application mechanism

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