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共 3243 条记录，以下是131-140 订阅

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Advances on smart and Soft computing 1

引用

丛书名： Advances in intelligent Systems and computing

1000年

作者： Faisal Saeed Tawfik Al-Hadhrami Fathey Mohammed Errais Mohammed

ISBN: (数字)9789811560484

ISBN: (纸本)9789811560477

This book gathers high-quality papers presented at the First international conference of Advanced computing and Informatics (ICACIn 2020), held in Casablanca, Morocco, on April 12–13, 2020. It covers a range of topics, including artificial intelligence technologies and applications, big data analytics, smart computing, smart cities, Internet of things (IoT), data communication, cloud computing, machine learning algorithms, data stream management and analytics, deep learning, data mining applications, information retrieval, cloud computing platforms, parallel processing, natural language processing, predictive analytics, knowledge management approaches, information security, security in IoT, big data and cloud computing, high-performance computing and computational informatics.

关键词： Computational Intelligence Big data Artificial Intelligence Communications Engineering, Networks

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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...

引用

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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Middle School Student's intelligent Uniform Design with Wearable Devices Based on Sustainability 8

Middle School Student's Intelligent Uniform Design with Wear...

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8th international conference on Cloud computing and Big data analytics (ICCCBDA)

作者： Cao Yeqing Liang Xiaoning Dalian Polytech Univ Sch Fash Dalian Peoples R China

ISBN: (纸本)9781665455336

In order to solve the problem of middle school student's rapid growth and excessive purchase of school uniforms caused by the waste of social resources, from the perspective of sustainable development, the characteristics of intelligent wearable school uniforms for middle school students are combed and analyzed, summed up its design principles and design methods, and do sustainable design practice. The research results show that the embedded position of the intelligent component should be hidden in the clothing structure, and meet the change of the vertical and horizontal growth value of the human body at the same time. The concept of sustainable development provides ideas for the innovative design of wearable school uniforms, balances the relationship between intelligent elements and school uniform design, and has certain guiding significance for the school uniform design of middle school students.

关键词： sustainability school uniform intelligent clothing wearable devices intelligent elements

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smart Contracts for Multiagent Plan Execution in Untrusted Cyber-physical Systems 25

Smart Contracts for Multiagent Plan Execution in Untrusted C...

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25th IEEE international conference on High Performance computing, data and analytics (HiPC)

作者： Shukla, Anshu Mohalik, Swarup Kumar Badrinath, Ramamurthy Ericsson Res Bangalore Karnataka India

ISBN: (纸本)9781728101149

intelligent Cyber-physical systems can be modeled as multi-agent systems with planning capability to impart adaptivity for changing contexts. In such multi-agent systems, the protocol for plan execution must result in the proper completion and ordering of actions in spite of their distributed execution. However, in untrusted scenarios, there is a possibility of agents not respecting the protocol either due to faults or due to malicious reasons thereby resulting in plan failure. In order to prevent such situations, we propose to implement the execution of agents through smart contracts. This points to a generic architecture seamlessly integrating intelligent planning-based CPS and smart-contracts.

关键词： Artificial Intelligence Planning Multi-agent Blockchain smart Contract Cyber-physical systems

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An Overview of Digital Twins Application in smart Energy Grids 18

An Overview of Digital Twins Application in Smart Energy Gri...

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IEEE 18th international conference on intelligent Computer Communication and Processing (ICCP)

作者： Cioara, Tudor Anghel, Ionut Antal, Marcel Antal, Claudia Arcas, Gabriel Ioan Croce, Vincenzo Tech Univ Cluj Napoca Dept Comp Sci Cluj Napoca Romania Engn Powertrain Syst Bosch Engn Ctr Cluj Napoca Romania Engn Ingn Informat Spa Rome Italy

ISBN: (纸本)9781665464376

The Digital Twins (DTs) offer promising solutions for smart grid challenges related to the optimal operation, management, and control of energy assets, for safe and reliable distribution of energy. These challenges are more pressing nowadays than ever due to the large-scale adoption of distributed renewable resources at the edge of the grid. DTs are leveraging on technologies such as Internet of Things (IoT), big data analytics, machine learning, and cloud computing, to analyze data from different energy sensors, view and verify the status of physical energy assets and extract useful information to predict and optimize the asset's performance. In this paper, we will provide an overview of the DTs application domains in the smart grid while analyzing existing the state-of-the-art literature. We have focused on the following application domains: energy asset modeling, fault and security diagnosis, operational optimization, and business models. Most of the relevant literature approaches found are published in the last 3 years showing that the domain of DTs application in smart grid is hot and gradually developing. Anyway, there is no unified view on the DTs implementation and integration with energy management processes, thus, much work still needs to be done to understand and automatize the smart grid management.

关键词： Digital twins smart grid energy assets modeling business models energy services

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Barriers to SMEs Adoption of Big data analytics for Competitive Advantage 4

Barriers to SMEs Adoption of Big Data Analytics for Competit...

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4th international conference on intelligent computing in data Sciences (ICDS)

作者： Willetts, Matthew Atkins, Anthony S. Stanier, Clare Staffordshire Univ Sch Digital Technol & Arts Stoke On Trent Staffs England

ISBN: (纸本)9781728180847

Small and medium-sized enterprises (SMEs) (which includes micro companies employing <;10) make a significant contribution to the UK economy accounting for 99.9% of all businesses, employing 60% of the work force and generates 2,168 pound billion;this represents 52% of the turnover of all businesses in the UK [1]. Big data analytics is rapidly being utilised by large companies on a global scale to gain competitive advantage which is well documented in the literature. However, the evidence from the literature indicates that SMEs are underutilising this technology for a variety of reasons, for example lack for expertise and cost implications. The intention of this paper is to identify barriers to the adoption of Big data analytics by SMEs to help them overcome the challenges and to exploit the benefits of Big data analytics to improve their competitive advantage which will benefit the wealth of the country particularly in the aftermath of Covid-19.

关键词： Big data Big data analytics SMEs Barriers to Big data analytics Adoption Competitive Advantage Covid-19

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Collaboration technologies for secure road traffic congestion control system in Egypt

Collaboration technologies for secure road traffic congestio...

引用

2017 international conference on Energy, Communication, data analytics and Soft computing, ICECDS 2017

作者： El Zouka, Heshem A. Department of Computer Engineering College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

ISBN: (纸本)9781538618868

This work presents a distributed system that is designed for road traffic management in smart cities using cloud computing. This distributed design responds to the particular limitations of an emergency situation, specially the fixed infrastructures that are out of service because no energy supply is available. Many developed nations now use intelligent transportation systems to solve traffic related problems and to provide smooth and safe ride on roads. Poor infrastructure and the attitude of road users are the main reasons behind traffic congestion in non-smart cities. Vehicular ad hoc networks are considered a less expensive, distributive and collaborative traffic congestion system. It needs inexpensive devices to be incorporated in vehicles and communicate with the cloud service providers to accumulate the data and fetch into the system. data to be disseminated is buffered at the road side parked vehicle, which continuously provides data dissemination services for the passing by vehicles and vehicular Ad-Hoc networks provide a smart solution for traffic density detection and better parking management of traffic over Non-lane based roads of smart cities. This paper highlights the findings of a parking-based opinion survey conducted at Alexandria city in Egypt and proposes a smart solution for parking management and traffic detection through a data dissemination scheme for Vehicular ad hoc networks and cloud computing technologies in Egypt. © 2017 IEEE.

关键词： Cloud computing

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Hybrid Machine Learning-Based intelligent Technique for Improved Big data analytics 6

Hybrid Machine Learning-Based Intelligent Technique for Impr...

引用

6th international conference on Soft computing and Machine Intelligence (ISCMI)

作者： Akinyelu, Andronicus A. Univ Free State Dept Comp Sci & Informat Bloemfontein South Africa

ISBN: (数字)9781728145778

ISBN: (纸本)9781728145778

The average volume of data produced daily is estimated to be over 2.5 quintillion byte. Moreover, by year 2020, it is estimated that 1.79MB of data will be created every second by each person in the world. Apparently, big datasets contain tremendous amount of valuable information that can be used for improved decision making. However, big data requires incredible amount of storage and computational resources for effective processing. Machine Learning (ML) algorithms are effective tools popularly used to analyze and extract concealed insights from datasets. However, some ML algorithms were not originally designed to handle big datasets, hence their computational complexity decreases with increase in data size. Consequently, this makes big data analytics extremely slow or unrealistic. Therefore, there is an obvious need for fast and effective techniques for big data analytics. This paper introduces an intelligent hybrid ML-based technique suitable for big data analytics (called EDISA_ML). EDISA_ML is a boundary detection and instance selection algorithm, inspired by edge detection in image processing. It was evaluated on four ML algorithms and big datasets, and the results show that it achieved a storage reduction of over 50% and simultaneously improved the training speed of the evaluated ML algorithms by over 93% (in some cases), without meaningfully affecting their prediction accuracy.

关键词： machine learning big data analytics instance selection data reduction boundary detection

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Toward Pervasive Computer Vision for intelligent Transport System

Toward Pervasive Computer Vision for Intelligent Transport S...

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20th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Leone, Giuseppe Riccardo Carboni, Andrea Nardi, Simone Moroni, Davide Natl Res Council Italy Inst Informat Sci & Technol Pisa Italy Eikontech Srl Computat Sci Dept Pisa Italy

ISBN: (纸本)9781665416474

The smart Passenger Center (SPaCe) is a fully integrated platform that aims to overcome the complexity of centralized management of public transport infrastructure and vehicles. The SPaCe artificial intelligence engine predicts threats and critical events and proposes countermeasures by examining the daily flows of people and correlating different data and events, thanks to machine learning and big data analytics. All this massive data comes from a pervasive smart camera network that constantly monitors activities in stations, trains, buses and other places of interest. In this work, we present the idea of this computer vision distributed sub-system, the state of the art of the techniques involved and the advanced functionalities that this intelligent surveillance system offers to the upper layers. Everything is developed following the privacy-by-design paradigm;namely, no real image is recorded or transmitted, but all the elaborations take place on the edge nodes of the system.

关键词： intelligent Transport System pervasive computing edge computing privacy-by-design multiple people tracking

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data analytics Platform for the Optimization of Waste Management Procedures 15

Data Analytics Platform for the Optimization of Waste Manage...

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15th Annual international conference on Distributed computing in Sensor Systems (DCOSS)

作者： Vafeiadis, Thanasis Nizamis, Alexandros Pavlopoulos, Vissarion Giugliano, Luigi Rousopoulou, Vaia Ioannidis, Dimosthenis Tzovaras, Dimitrios Ctr Res & Technol Hellas CERTH ITI Thessaloniki 57001 Greece Links Fdn MLW Turin Italy

ISBN: (纸本)9781728105703

Today, the use of IoT devices, the interconnectivity of machines and systems, and the development of novel analytic algorithms which are main characteristics of Industry 4.0 are also transferred to other domains connected to the industrial one. The advances on industrial domain have impact on waste management one as these domains are strictly connected. The connection with sensors on industrial partners premises, the big data availability and the significant advances on data analytics, enable the waste management companies to smarten their domain and automate many of their solutions and processes. In this work, we present a data analytics platform for the optimization of waste management procedures. By using this platform, a waste management company is able to monitor and analyze sensors data from industrial partners bins and optimize its planning based on different analytic tools and historical data. Besides the platform description, special focus is given on data analytics algorithms and methods.

关键词： data analytics Industry 4.0 time series smart waste management deep learning IoT platform

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