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Uncertainty-aware Forecasting of Renewable Energy Sources

Uncertainty-aware Forecasting of Renewable Energy Sources

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

作者： Pekaslan, Direnc Wagner, Christian Garibaldi, Jonathan M. Marin, Luis G. Saez, Doris Univ Nottingham Sch Comp Sci Intelligent Modelling & Anal IMA Grp Nottingham England Univ Nottingham Sch Comp Sci Lab Uncertainty Data & Decis Making LUCID Nottingham England Univ Chile Dept Elect Engn Santiago Chile

ISBN: (纸本)9781728160344

smart grid systems are designed to enable the efficient capture and intelligent distribution of electricity across a distributed set of utilities. They are an essential component of increasingly important renewable energy sources, where it is vital to forecast the levels of energy being fed into and drawn from the grid. However, because of the high levels of uncertainty affecting real-world environments, accurate forecasting for example of wind power generation - being directly dependent on meteorological parameters and climatic conditions - is extremely challenging. Fuzzy Logic systems are frequently used in control systems to leverage their capacity for handling varying levels of uncertainty. In most cases, while uncertainty affecting the systems is captured in fuzzy sets (FSs), the final output of such systems is reduced to a crisp number (e.g. a control output). The latter process, while providing an efficient pathway to generating a specific control output, at the same time implies substantial information loss, as the uncertainty information captured in the FS outputs of these systems is effectively discarded. In this paper, we explore the potential of Mamdani fuzzy logic system based forecasting in order to generate not only a numeric forecast of the energy generated, but to also generate uncertainty intervals around said forecast indicating the level of uncertainty associated with the prediction. The proposed model is explored using both synthetic and smart-grid specific real-world (wind power) time series datasets. The results of the study indicate that utilising the `complete' FS output can provide valuable additional information in terms of the reliability of the forecast without any extra computational cost. At a general level, the approach indicates strong potential for leveraging the uncertainty information in fuzzy system outputs - which is commonly discarded - in real world applications.

关键词： Forecasting Uncertainty Intervals smart-Grid Renewable Energy

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Role of data analytics in Human Resource Management for Prediction of Attrition Using Job Satisfaction 3rd

Role of Data Analytics in Human Resource Management for Pred...

引用

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

作者： Aswale, Neerja Mukul, Kavya MIT Sch Technol Management Vishwashanti Marg Pune 411038 Maharashtra India

ISBN: (纸本)9789813299498;9789813299481

The reputed management publications like Harvard Business Review (HBR) have started stressing upon the emergence of data-driven management decisions. The enhancing investments in data and analytics are underlining the aforementioned emergence. According to international data Corporation, this investment is expected to grow up to $200 billion by 2020. In such a data lead management world collecting, managing, and analysing the human resources-related data becomes a key for any rather every organization. Human resource analytics is changing into necessary as strategic personnel designing is the need of the hour and helps organizations to investigate each side of HR metrics. HR analytics could be a holist approach. According to KPMG-India's Annual Compensation Trends Survey 2018-19 the average annual voluntary attrition across sectors is 13.1%. This is a considerably high percentage. Hence, antecedents leading to attrition are needed to be explored in order to propose appropriate HR policies, strategies, and practices. In relevance to these facts, this study focused on proposing a data-driven predictive approach that examines the relationship between the attrition (dependent variable) and other demographic and psychographic independent variables (Antecedents). The present study found that there is a strong relationship between job satisfaction and attrition. Further, there is a higher probability that the employees having work experience between 0-5 years may leave the organizations. Such data-based outcomes may offer help to HR managers in addressing the problems like attrition which intern may increase ROI. Thus, this paper underlines the emergence and relevance of analytics with special reference to human resource management domain.

关键词： analytics Human resources management Attrition Job satisfaction

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Application of Artificial Intelligence Technology in international Trade Finance

Application of Artificial Intelligence Technology in Interna...

引用

international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Guo, Shuangmei Department of Economics and Management Yunnan Technology and Business University KunmingYunnan China

ISBN: (纸本)9783030627423

Artificial intelligence technology is a new method proposed by people inspired by the biological evolution mechanism and some natural phenomena. Because it can well solve the modeling and optimization problems of complex systems, it has received extensive attention and applications in various fields. As the scale, quantity, and scope of international trade continue to expand, the complexity and uncertainty of trade increase, so artificial intelligence is more widely used in international trade. The purpose of this article is the application of artificial intelligence technology in international trade finance. Aiming at the deficiencies of Artificial Bee Colony (ABC), this paper proposes two types of improvement strategies: In order to improve the convergence accuracy and operational stability of ABC, this paper proposes an improved artificial bee colony algorithm, and also proposed a hybrid artificial bee colony algorithm with predictive selection ability, this paper tested it through 23 benchmark optimization problems The simulation results show that the improved algorithm has a good global search capability and convergence speed, so that it can play an important role in the healthy development of China’s international trade and has practical significance. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： international trade

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Outdoor Sports Industry Platform Construction Based on Big data Technology

Outdoor Sports Industry Platform Construction Based on Big D...

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international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Wang, Yuyan Department of Physical Education Huazhong Agricultural University WuhanHubei China

ISBN: (纸本)9783030627423

The development of my country’s sports big data industry is still in the preliminary stage of development, but its application prospects are broad and it has extremely high R&D value. Through literature, logical analysis and other research methods, and on the basis of reviewing relevant research on the application of big data technology in the sports industry, a platform architecture for the outdoor sports industry of big data technology is constructed. After the outdoor sports industry data platform is built, the application of big data analysis can achieve the following expected results: First, it can be opened to provincial mountaineering associations and outdoor sports clubs, and provide guidance for them;Second, provide future sports brands for related sports Product development suggestions to achieve precise marketing;third, a precise positioning system for outdoor sports rescue can be established to provide technical support for emergency rescue. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Proceedings - 2020 IEEE international conference on smart computing, smartCOMP 2020

Proceedings - 2020 IEEE International Conference on Smart Co...

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6th IEEE international conference on smart computing, smartCOMP 2020

ISBN: (纸本)9781728169972

The proceedings contain 74 papers. The topics discussed include: multi-modal adversarial training for crisis-related data classification on social media; learning mobility flows from urban features with spatial interaction models and neural networks; LAXARY: a trustworthy explainable twitter analysis model for post-traumatic stress disorder assessment; from smart city to smart citizen: rewarding waste recycle by designing a data-centric IoT based garbage collection service; MobIntel: sensing and analytics infrastructure for urban mobility intelligence; data ingestion and inspection for smart city applications; a scalable distributed system for precision irrigation; towards AI conversing : FloodBot using deep learning model stacks; and privacy-aware sensor data upload management for securely receiving smart home service.

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Use of hadoop for sentiment analysis on twitter’s big data 3rd

Use of hadoop for sentiment analysis on twitter’s big data

引用

3rd international conference on smart Innovations in Communications and Computational Sciences, ICSICCS 2020

作者： Rathor, Sandeep GLA University MathuraUP281406 India

ISBN: (纸本)9789811553448

Nowadays, everyone is connected with social media like WhatsApp, Facebook, Instagram, Twitter, etc. Social media is an important platform for sharing ideas, information, opinion, and knowledge among human being. In spite of these basic attributes, we can also analyze the sentiments and emotions. However, handling such big data is a challenging task. Therefore, such type of analysis is efficiently possible only through the Hadoop. In the proposed research, we are going to analyze nature of a particular person on the basis of their behavior on social sites using Hadoop. For analysis purpose in our research, we have taken Twitter data. The sentiment analysis is used as positive, negative, and neutral by using the concept of decision dictionary. The result shows sentiment analysis with good accuracy. © Springer Nature Singapore Pte Ltd 2021.

关键词： Sentiment analysis

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An Integrated Platform for Collaborative data analytics 13

An Integrated Platform for Collaborative Data Analytics

引用

2020 IEEE Congress on Cybermatics: 13th IEEE international conferences on Internet of Things, iThings 2020, 16th IEEE international conference on Green computing and Communications, GreenCom 2020, 13th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE international conference on smart data, smartdata 2020

作者： Oesch, Sean Gillen, Rob Karnowski, Tom Oak Ridge National Laboratory Oak RidgeTN37830 United States

ISBN: (纸本)9781728176475

While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and a unified method of sharing knowledge. Each of these barriers to collaboration is related to the fundamental question of knowledge management 'how can organizations use knowledge more effectively?'. In this paper, we consider the problem of knowledge management in collaborative data analytics and present ShareAL, an integrated knowledge management platform, as a solution to that problem. The ShareAL platform consists of three core components: a full stack web application, a dashboard for analyzing streaming data and a High Performance computing (HPC) cluster for performing real time analysis. Prior research has not applied knowledge management to collaborative analytics or developed a platform with the same capabilities as ShareAL. ShareAL overcomes the barriers data scientists face to collaboration by providing intuitive sharing of data and analytics via the web application, a shared computing environment via the HPC cluster and knowledge sharing and collaboration via a real time messaging application. © 2020 IEEE.

关键词： Knowledge management

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Multimodal and Multidimensional Interactive Teaching Mode of Comprehensive English Under the Background of Big data

Multimodal and Multidimensional Interactive Teaching Mode of...

引用

international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Duan, Yongjia Department of Applied Foreign Language Chengdu Neusoft University Sichuan China

ISBN: (纸本)9783030627423

As the core basic course of English major, Comprehensive English also needs corresponding transformation and innovation in the context of big data. Multimodal and multidimensional interactive teaching has the conditionality of depending on the sustainable development of modern technology. Therefore, taking the innovation of Comprehensive English teaching as the starting point, making full use of all kinds of information technology in big data era, fully exploring the multidimensional interactive channels between teachers and students in the background of big data and developing multimodal interactive teaching, can further meet the needs of Comprehensive English teaching and students’ learning in the context of big data. This paper mainly explores the multimodal and multidimensional interactive teaching mode of Comprehensive English in big data. Through multimodal sensory integration, the creation of multimodal interactive practice activities, the improvement of teaching methods and means, and the creation of diversified evaluation system, a more scientific and complete multimodal multidimensional interactive mode is established to further promote the Comprehensive English multi-modal interactive teaching coverage and coverage quality, and finally improve the teaching effect. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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smart Property Insurance using IoT

Smart Property Insurance using IoT

引用

international conference on intelligent computing and Control Systems (ICICCS)

作者： Parameshwari, Prarthana Revathy, S. Mani, Vignesh Sathyabama Inst Sci & Technol Informat Technol Chennai Tamil Nadu India Sathyabama Inst Sci & Technol Sch Comp Chennai Tamil Nadu India TCS Chennai Tamil Nadu India

ISBN: (纸本)9781728148762

IoT has already enforced in many industries including insurance domain. In this paper, a smart reliable insurance system is proposed, for both the insured and the insurer. The main objective of the paper is to detect fire and water clogging incidents by using smart connected sensors. Billions of data is being generated from the IoT enabled connected devices like sensors, to measure temperature, bumidity, smoke etc., These information will be sent to the insurance underwriter to contact on emergency and can permit insurers to dynamically process insurance policies and to calculate premium based on risks.

关键词： Internet of Thing flame sensor ultrasonic sensors Arduino raspberry pi Wi-Fi module data database

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A Way to Explore the Lower Bound of Adversarial Perturbation

A Way to Explore the Lower Bound of Adversarial Perturbation

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

作者： Han, Keji Bai, Yuxuan Li, Yun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160344

In the big data era, Deep Neural Networks (DNNs) have been being applied to many learning tasks. However, they are demonstrated to be vulnerable to adversarial examples, which are crafted by adding imperceptible adversarial perturbation to the corresponding legitimate example. Although there are couples of adversarial attack methods that have been proposed, few attack methods focus on investigating the lower bound of the adversarial perturbation. Present optimization-based attacks have many super-parameters and high lime complexity. In this paper, a novel attack method is proposed to evaluate the robustness of DNNs. Our method aims to explore the lower bound of the adversarial perturbation, and it can dynamically adjust part of super-parameters. Moreover, the lower bound of the adversarial perturbation can be employed to measure the gap of the inter-class distance, which can improve the interpretability of DNNs. Experimental results show that our method achieves the smallest perturbation under l(p), norm metric, compared to some state-of-the-art attack methods.

关键词： Perturbation methods Time complexity Mathematical model Measurement Optimization Training Convergence

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