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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

4th International Conference on Advanced Informatics for Com...

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

ISBN: (纸本)9789811636592

The proceedings contain 23 papers. The special focus in this conference is on Advanced Informatics for computing Research. The topics include: Internet of Things in Healthcare: A Review;inter-Vehicular Communication for intelligent Collision Avoidance Using Machine Learning: An Overview;wireless Sensor Networks Source Location Privacy Preservation Mechanism;a Study on Secure data Aggregation and Routing for Wireless Sensor Networks;comparative Analysis of Internet of Things (IoT) Security Models;study of Energy Efficient Routing Protocols for Adhoc-Wireless Sensor Networks;ioT Based smart Transport Management System;vehicular Ad Hoc Network: Routing Protocols;a Reluctant Checksum Scheme Based on the Inherent Characteristic of Wireless Networks;Optimised Multi Energy System (OMES) for Electric Vehicle;anomaly Based Detection of Cross Site Scripting Attack in Web Applications Using Gradient Boosting Classifier;crypto Key Protection Generated from Images and Chaotic Logistic Maps;an Effıcıent Fault Management in Communication Networks Usıng On Demand Link State Algorıthm;application and Analysis of Phishing Website Detection in Machine Learning and Neural Networks;modeling of Microstrip Patch Antenna Using Artificial Neural Network Algorithms;smart PDM: A Novel smart Meter for Design Measurement and data Collection for smart Grid;GSM Based Remote Distribution Transformer Condition Monitoring System;Performance Analysis and Classification of Node Capture Attack Models in WSN;an Efficient IoT Based Framework for Missed Call Messaging Services for Electricity Consumers;A Divide and Conquer Based data Aggregation Technique for WBANs;open Research Challenges and Blockchain Based Security Solution for 5G Enabled IoT.

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

4th International Conference on Advanced Informatics for Com...

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

ISBN: (纸本)9789811636523

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Workshop on Impact and Opportunities of Artificial Intelligence in the Steel Industry, 2020

Workshop on Impact and Opportunities of Artificial Intellige...

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Workshop on Impact and Opportunities of Artificial Intelligence in the Steel Industry, 2020

ISBN: (纸本)9783030693664

The proceedings contain 12 papers. The special focus in this conference is on Workshop on Impact and Opportunities of Artificial Intelligence in the Steel Industry, 2020. The topics include: smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery;TSorage: A Modern and Resilient Platform for Time Series Management at Scale;data Pre-processing for Efficient Design of Machine Learning-Based Models to be Applied in the Steel Sector;quantifying Uncertainty in Physics-Informed Variational Autoencoders for Anomaly Detection;mapping of Standardized State Machines to Utilize Machine Learning Models in Process Control Environments;quality 4.0 - Transparent Product Quality Supervision in the Age of Industry 4.0;AI and ML Techniques for Generation and Assessment of Products Properties data;the Use of Advanced data analytics to Monitor Process-Induced Changes to the Microstructure and Mechanical Properties in Flat Steel Strip;unsupervised Deep Learning for Detection of Non-uniform Surface Defect Distributions in Flat Steel Production;machine Learning-Based Models for Supporting Optimal Exploitation of Process Off-Gases in Integrated Steelworks.

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Big data Technology in intelligent Distribution Network: Demand and Applications 6th

Big Data Technology in Intelligent Distribution Network: De...

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6th international conference on Advanced intelligent Systems and Informatics, AISI 2020

作者： Ye, Zhi-Peng Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fuzhou University No. 33 Xuefu South Road New District FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

Nowadays, with the development and maturity of big data technology, big data analysis technology is more and more widely used in practice, and more and more data are gradually applied to the smart grid of our country. Since the data in the smart grid meets the 4V characteristics of big data (large quantity, fast speed, many types, low value density), the use of big data technology can provide more accurate and cheaper data for power information generation Economic value and significance. Through the analysis of the development process of Chinas power industry, the development of distribution network in China obviously lags behind the development of power generation and transmission network. At present, more than 95% of the blackouts are caused by the distribution network, and half of the power loss occurs in the distribution network, so the automation of the distribution network system urgently needs the support of new technologies. This paper first enumerates several key points of big data technology, including big data collection, storage and analysis, and then expounds several methods of big data analysis. On this basis, big data technology is applied to the field of intelligent distribution network. Especially in the application of distribution forecasting, it can provide more powerful technical support for the operation of smart distribution network, continuously improve the technical level of Chinas smart distribution network, and promote the optimization and upgrading of smart grid system. Finally, an optimized prediction model is proposed, and the application of the new technology (5G technology) developed at the present stage is prospected, and its contribution to the data acquisition and application of big data technology is analyzed. © 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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Analysis, Evaluation, and Upgrading of a data analytics Methodology Through a Qualitative Evaluation Technique and a User-Centered Design Process 3rd

Analysis, Evaluation, and Upgrading of a Data Analytics Meth...

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3rd international conference on Human Systems Engineering and Design: Future Trends and Applications, IHSED 2020

作者： Astudillo, Boris Cajilema, Katherine Santórum, Marco Aguilar, Jose Departamento de Informática y Ciencias de la Computación Escuela Politécnica Nacional Quito Ecuador Universidad de Los Andes Mérida Venezuela

ISBN: (纸本)9783030582814

A data analytics (DA) methodology allows a proper treatment to obtain the best benefit from the data with the aim of allowing supported business decisions to be made. However, a DA methodology needs to be continually reviewed and updated to incorporate standpoint from researchers, in order to become the most efficient version. Our paper carried out an analysis and evaluation of the first phase of a selected DA methodology called MIDANO applying qualitative techniques to reach an improved version. The analysis and evaluation process were applied through a focus group discussion from the results obtained during the implementation of a case study. The upgraded DA methodology, as a result of the qualitative analysis, was related to the nine attributes extracted, classified, and validated from a user-centered design process previously studied, and based on ISO 9241:2010 standard, in order to obtain a DA methodology participatory, adaptable, efficient, collaborative, and easy-to-apply. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data analytics

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Deep Reinforcement Learning Based Resource Allocation for Heterogeneous Networks 17

Deep Reinforcement Learning Based Resource Allocation for He...

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17th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Yang, Helin Zhao, Jun Lam, Kwok-Yan Garg, Sahil Wu, Qingqing Xiong, Zehui Nanyang Technol Univ Strateg Ctr Res Privacy Preserving Technol & Syst Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Quebec Ecole Technol Super Elect Engn Dept Montreal PQ Canada Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Singapore Univ Technol & Design Pillar Informat Syst Technol & Design Singapore Singapore

ISBN: (纸本)9781665428545

This paper investigates the problem of distributed resource management (i.e., joint device association, spectrum allocation, and power allocation) in two-tier heterogeneous networks without any central controller. Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

关键词： Heterogeneous wireless networks distributed resource management dueling deep reinforcement learning

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smart watering system based on framework of low-bandwidth distributed applications (LBDA) in cloud computing 12th

Smart watering system based on framework of low-bandwidth di...

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12th international conference on intelligent Networking and Collaborative Systems, INCoS 2020

作者： Sirimorok, Nurdiansyah As, Mansur Yoshida, Kaori Köppen, Mario Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Creative Informatics Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9783030577957

The estimation of soil moisture content is required for agriculture, mainly to build the irrigation scheduling model. In this study, we present a smart watering system to deal with various factors derived from the stochastic information in the agricultural operational, i.e., air temperature, air humidity, soil moisture, soil temperature, and light intensity. The methodology consists of exploiting the Internet of Things (IoT) using Low-Bandwidth Distributed Applications (LBDA) in cloud computing to integrate the real data sets collected by the several sensor technologies. We conducted experiments for the watering system used two types of soil and different plant. Here, the Long Short Term Memory Networks (LSTMs) approach in deep learning techniques used to build smart decisions concerning watering requirements and deal with heterogeneous information coming from agricultural environments. Results show that our models can effectively improve the prediction accuracy for the watering system over various soil and plants. © Springer Nature Switzerland AG 2021.

关键词： Cloud computing

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Prediction of the Electrical Load for Egyptian Energy Management Systems: Deep Learning Approach 1st

Prediction of the Electrical Load for Egyptian Energy Manage...

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1st international conference on Artificial Intelligence and Computer Visions, AICV 2020

作者： El-Telbany, Mohammed E. Faculty of Information Technology and Computers Science University of Sinai North Sinai Egypt

ISBN: (纸本)9783030442880

In the era of the Internet of Things (IoT) and machine learning, energy utilities have now the possibility of providing many benefits to the customers. Using advanced metering infrastructure is an important step to optimize and manage energy consumption. smart meters are deployed in millions of households worldwide, representing an opportunity for monitoring systems and data analytics. Egypt has started its path into smart metering and needs to use big data analytics to endow the Egyptian grid with intelligence. In this paper, we investigate the role of big data analytics and available machine learning technologies that mine the data to generate insights into smart grid management process. We investigate a deep learning-based methodology such as long short-term memory (LSTM) network model to perform prediction of the electrical load for energy management systems. The LSTM model presents a decrease of 14% in forecasting error compared to deep forward neural networks. © 2020, Springer Nature Switzerland AG.

关键词： data analytics

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Comprehensive Teaching Mode Based on Blockchain

Comprehensive Teaching Mode Based on Blockchain

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international conference on Application of intelligent Systems in Multi-modal Information analytics, MMIA 2020

作者： Huang, Qiang Mao, Xingjing Wang, Mantao Feng, Xinhui School of Information Engineering Sichuan Agricultural University YaanSichuan625000 China

ISBN: (纸本)9783030514303

This article starts from the study of traditional education models and finds that there are significant disadvantages in grade management system. In order to achieve efficient management of grade and the objectivity of performance sources, this article introduces the idea of "blockchain + education" and explores new teaching modes based on the characteristics of the blockchain. The teaching system can realize new features such as scores that cannot be tampered with, real names, and diversity, so that students’ results no longer come from a single scoring standard, and further improve the quality of education and teaching. © 2021, Springer Nature Switzerland AG.

关键词： Security of data

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smart Agriculture: A Survey on Challenges and Opportunities with Recent Advancements

Smart Agriculture: A Survey on Challenges and Opportunities ...

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international conference on Automation, Signal Processing, Instrumentation and Control, i-CASIC 2020

作者： Thangatamilan, M. Suji Prasad, S.J. Vivekanandan, S. Kongu Engineering College Perundurai Erode Tamil Nadu638060 India Vellore Institute of Technology Vellore Tamil Nadu632014 India

ISBN: (纸本)9789811582202

Agriculture is the backbone of the economy in many countries, but the introduction of the Internet of Things (IoT) is not very well-known among the farmers. Lack of technological awareness and the cost of IoT devices play a crucial role in the selection of technology for smart agriculture. In the upcoming years, these technologies will improve food productivity through smart farming, so that the system can accommodate the increasing human population around the world. IoT technologies facilitate different agricultural development. Based on the above scenario, this paper presents an extensive review of current research works and studies in agriculture for exploring the best practices which can help farmers at the field level for increasing production and quality. This study reveals the different methodologies used in modern smart agriculture and highlighting the concepts related to surveillance and monitoring in agriculture fields, big data analytics in agriculture, navigation autonomous vehicle monitoring, efficient and precision methodologies for smart agriculture. The outcomes from the review addressed the challenges to get the benefits of genuinely connected and intelligent IoT devices in agriculture. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Wireless sensor networks

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