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共 3255 条记录，以下是1711-1720 订阅

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Gait-Based Authentication for smart Locks Using Accelerometers in Two Devices 22nd

Gait-Based Authentication for Smart Locks Using Acceleromete...

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11th international conference on intelligent Networking and Collaborative Systems (INCoS) / 22nd international conference on Network-Based Information Systems (NBiS)

作者： Watanabe, Kazuki Nagatomo, Makoto Aburada, Kentaro Okazaki, Naonobu Park, Mirang Kanagawa Inst Technol 1030 Shimo Ogino Atsugi Kanagawa 2430292 Japan Univ Miyazaki 1-1 Gakuen Kibanadai Nishi Miyazaki Miyazaki 8892192 Japan

ISBN: (纸本)9783030290290;9783030290283

smart locks can be opened and closed electronically. Fingerprint or face authentication is inconvenient for smart locks because it requires the user to stop for several seconds in front of the door and remove certain accessories (e.g., gloves, sunglasses). This study proposes a user authentication method based on gait features. Conventional gait-based authentication methods have low identification accuracy. The proposed gait-based authentication method uses accelerometers in a smartphone and a wearable device (i.e., smartwatch). We extracted 31 features from the acquired acceleration data and calculated identification accuracy for various machine-learning algorithms. The highest accuracy was 95.3%, obtained using random forest. We found that the maximum interval, minimum interval, and minimum value had the highest contributions to identification accuracy, and variance, median, and standard deviation had the lowest contributions.

关键词： Locks (fasteners)

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Slow Speed Alert for Speed Breakers and Potholes Using IoT and analytics in the Context of smart Cities 2nd

Slow Speed Alert for Speed Breakers and Potholes Using IoT a...

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2nd international conference on smart IoT Systems: Innovations and computing, SSIC 2019

作者： Soundararaj, Sathish Kumar Bagavathi Sivakumar, P. Ananthanarayanan, V. Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789811384059

An Android mobile application is developed as a prototype to detect speed breakers/potholes and voice alert the driver prior to reaching the speed breaker/pothole using IoT and analytics. Through mobile application, accelerometer sensor and GPS sensor data are collected. The collected sensor data is further processed in order to detect speed breakers/potholes. Further processing is done based on the GPS coordinates collected to voice alert the user a few seconds prior to reaching the speed breaker or the pothole which in turn helps the driver in avoiding the accidents. The information collected from the registered user is shared and then stored, processed, and analyzed in a Cloud. The alert message from the system warns the users of the smart city. This also helps as a tool for authorities to immediately to attend to any repairs. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Accelerometers

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Analysis of Text Mining Tools in Disease Prediction 20th

Analysis of Text Mining Tools in Disease Prediction

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20th international conference on Hybrid intelligent Systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Kumari, Shabnam Vani, V. Malik, Shaveta Tyagi, Amit Kumar Reddy, Sravanti SRMIST University ChennaiTamilnadu603203 India School of Computer Science and Engineering Vellore Institute of Technology ChennaiTamilnadu600127 India Terna Engineering College MumbaiMaharshtra India Center for Advanced Data Science Vellore Institute of Technology ChennaiTamilnadu600127 India VJIET HyderabadTelangana India

ISBN: (纸本)9783030730499

Due to rapid creation of digital data by Internet of Things devices or smart devices, many new modern mining strategies/techniques require to handle/analyse this large amount of data. Note that more than 90% of today’s data is in present (generated) unstructured or semi-structured data format (most of part of this data is being generated only in the past decade). The discovery of appropriate patterns and trends to analyse the text documents from this large big data (i.e., large volume of data) is a big issue. Text mining is a process of extracting interesting and non- trivial patterns from huge amount of text documents. There exist different techniques and tools to mine the text (also other data format) and discover valuable information for future prediction and decision making process. Basically, there are two terms used in making or extracting some relevant information from a data-set, i.e., prediction modelling, and text mining. Predictive models are often used to detect crimes and identify suspects, after the crime has taken place/to detect an email, how likely that it is spam. Similarly, text mining used in applications like digital libraries, academic research field, life science, social media, business intelligence, etc. Today’s different text mining techniques are available for analysing the text patterns and their mining process, some of them are included here as: document classification (text classification, document standardization), information retrieval (keyword search/querying and indexing), document clustering (phrase clustering), natural language processing (spelling correction, lemmatization, grammatical parsing, and word sense disambiguation), information extraction (relationship extraction/link analysis), and web mining (web link analysis), etc. This article discusses and analyse the text mining techniques and their applications in diverse fields of life. This work discusses about several use-cases, efficient algorithms like apriori algorithm, ass

关键词： Classification (of information)

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Machine Learning Application to Priority Scheduling in smart Microgrids 16

Machine Learning Application to Priority Scheduling in Smart...

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16th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Dridi, Aicha Moungla, Hassine Afifi, Hossam Badosa, Jordi Ossart, Florence Kamal, A. E. Inst Polytech Paris Telecom SudParis F-91120 Palaiseau France Sorbonne Univ PSL Univ CNRS Inst Polytech ParisLMD IPSLEcole PolytechENS Palaiseau France Centrale Supelec GEEPS Gif Sur Yvette France Iowa State Univ Iowa City IA USA

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131290

The need to integrate flexible and intelligent mechanisms for energy management becomes a necessity. In this paper, we are considering a microgrid with infrastructures having production capacities and consumption needs. Several data and constraints related to the microgrid consumption have been collected, in addition to data concerning the production of renewable energy from Photovoltaic panels (PV). data history is used as input to a neural network to predict one day ahead of consumption and production. Then, a prioritized scheduling family of algorithms is presented. First, we introduce a mathematical formulation to our problem. Then, we propose various scenarios that go from an exact solution to heuristic-based use cases, including scheduling of several energy classes with a maximum scheduling time lapse. Results show that prioritized scheduling, including time lapse based on predictions, can give more reliable results than scheduling based on bin packing.

关键词： Artificial Intelligence (AI) Deep Learning (DL) Long Short-Term Memory (LSTM) Bin Packing (BP) smart Microgrid Optimization

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A novel IoT-based approach towards diabetes prediction using big data 4th

A novel IoT-based approach towards diabetes prediction using...

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4th international conference on Research in intelligent and computing in Engineering, RICE 2019

作者： Biswas, Riya Pal, Souvik Cuong, Nguyen Ha Huy Chakrabarty, Arindam Department of Computer Science & Engineering JIS College of Engineering Kalyani India Department of Computer Science & Engineering Brainware University Kolkata India College of Information Technology The University of Da Nang Da Nang Viet Nam Department of Management Rajiv Gandhi University Itanagar India

ISBN: (纸本)9789811527791

Big data is a modern teamster of today’s economical world. data are being digitalized in today’s world as imperative judgment is taken by Big data analytics. In our manuscript, we have discussed about Big data analytics in IoT ecosystems and its implications in healthcare. Healthcare is concerned now a days and big data is holding all the supportive hands in IoT-based healthcare systems. In healthcare, we have discussed about Diabetes Mellitus which is a non-communicable disease. This paper deals with the proposed system of diagnosis of diabetes. Hence it is assertive that we do some surveys on how we can manage to handle large data files, technologies are defined and also predictions of diabetes through IOT sensor and management have been discussed. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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Machine Learning Techniques and computing Technologies for IoT based smart Healthcare (COVID-19 Case Study)

Machine Learning Techniques and Computing Technologies for I...

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Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Mahmood Hussain Mir Sanjay Jamwal Shahidul Islam Qamar Rayees Khan Baba Ghulam Shah Badshah University Rajouri Jammu and Kashmir India

ISBN: (纸本)9781665438124

The exponential growth of smart devices and gadgets connected to the Internet of Things (IoT) produces massive amounts of data. The number of internet-connected devices is predicted to outnumber people by 25 to 50 billion by 2025. The new concept IoT refers to the combination of wired and wireless embedded communication technologies, sensors, actuators (transducers), and internet-connected items. The data generated by these IoT devices is in high velocity, variety, and varsity in accordance with location and time. IoT is generating data so it one of the primary sources of big data. intelligent analysis and processing need an hour to develop smart IoT applications to tackle this huge volume of data. This article accesses various computing frameworks such as Cloud computing, Fog computing and Edge computing environments for smarter IoT applications. This article also presents how machine learning algorithms can be incorporated in IoT data to get better insights. Various machine learning algorithms are explained and how these algorithms can be applied to data to get a higher level of information. This studies key contribution is how computing frameworks can be integrated with machine learning techniques in the healthcare sector to get better insights from the data. Mostly in this article, IoT and machine learning algorithms are discussed with respect to COVID-19. Moreover, the potential application areas and open issues of IoT data analytics and Machine Learning are discussed.

关键词： COVID-19 Cloud computing Wireless sensor networks Machine learning algorithms Transducers Machine learning Medical services

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Port logistics efficiency evaluation based on DEA model

Port logistics efficiency evaluation based on DEA model

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

作者： Lu, Wendi Department of Economics and Management Dalian University DalianLiaoning China

ISBN: (纸本)9789811525674

First, this paper uses data envelopment analysis (DEA) to analyze the logistics efficiency of four major ports in Liaoning Province. Then based on the results of data envelopment analysis (DEA) analysis, the relationship between logistics efficiency and its influencing factors is studied. The results show that the overall pure technical efficiency of port logistics in Liaoning Province has been maintained at a high level and the state is stable. The change in overall technical efficiency is mainly due to changes in scale efficiency and needs to be further strengthened. The economies of scale need to be further improved, and the port logistics efficiency in Liaoning Province still has great potential for development. The Tobit model is used to empirically study the influencing factors of port logistics efficiency on the results of data envelopment analysis (DEA). The results show that the economic development level of the port city and the development level of the secondary industry have a positive impact on the development of port logistics, and the effect is remarkable. © Springer Nature Singapore Pte Ltd. 2020.

关键词： data envelopment analysis

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Structural optimization of service trade under the background of big data

Structural optimization of service trade under the backgroun...

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

作者： Li, Guiping School of Economics Shanghai University Shanghai200072 China

ISBN: (纸本)9789811525674

With the advent of big data, the traditional way of service trade has not meet the needs of the development of international trade. Therefore, we need to use means of big data when you trade and analyze factors affecting the development of service trade in one country to optimize the structure of service trade. Moreover, the efficiency and quality of service trade can also be improved. This article selects the Shandong province service trade structure optimization as the research object and analyze the impact factors that affects the structural optimization of service trade in Shandong province under the era of big data. The grey correlation analysis comprehensively analyze various factors and the connection degree of relative index of the service trade structure optimization. Grey correlation analysis concluded that the level of science and technology, human capital has the highest correlation with the relative index of service trade structure optimization. Therefore, we should promote the optimization and upgrading of service trade structure and continuously improve the service trade competitiveness in Shandong province by increasing the investment of scientific research and education funds to improve the market openness. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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A GPU Unified Platform to Secure Big data Transportation Using an Error-Prone Elliptic Curve Cryptography 4th

A GPU Unified Platform to Secure Big Data Transportation Usi...

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4th international conference on data Management, analytics and Innovation, ICDMAI 2020

作者： Bhattacharjee, Shiladitya Chakkaravarhty, Divya Midhun Chakkaravarty, Midhun Rahim, Lukman Bin Ab. Ramadhani, Ade Wahyu Universiti Teknologi PETRONAS Seri IskandarPerak Darul Ridzuan Malaysia Faculty of Computer Science and Multimedia Wisma Lincoln Petaling JayaSelangor Darul Ehsan Malaysia

ISBN: (纸本)9789811556159

The time and space optimization, protecting data loss and safeguarding privacy as well as integrity are the priorities in any large file transmission system. However, the current literature is lacking to provide such a system which can take care of each of these aspects very strongly in a unified way. Hence, this research proposes a Graphics Processing Unit (GPU)-based error-prone elliptic curve cryptography with an advanced memory management mechanism. It comprises the elliptic curve public-key cryptography (ECC) to offer higher defiance against the various privacy and integrity attacks. It further includes an advanced dual round of error regulator which can sense and spot any numbers of discrete or continuous faults. This error control technique can regenerate the data if there is any accidental loss during transportation. The usages of advanced parallel execution with a GPU and a unique memory management mechanism make it enable to handle required memory and time during the execution very efficiently. The achieved result shows that the projected unified approach offers higher Throughput on every incident which signifies that it is time-efficient. Additionally, it offers higher SNR, Avalanche Effect and a curtailed proportion of data Loss. It specifies that the suggested mingled technique is proficient to protect privacy, integrity and mishap of data in big data hauling with the efficient management of available internal memory. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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Adaptive Window Based Sampling on The Edge for Internet of Things data Streams 11

Adaptive Window Based Sampling on The Edge for Internet of T...

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11th international conference on Networks of the Future

作者： Hafeez, Taimur McArdle, Gavin Xu, Lina Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9781728180557

The Internet of Things (IoT) generates a massive amount of data. To relieve the pressure placed on cloud and network services, sampling techniques have been applied to remove the redundant data. Thanks to the recently emerged Edge computing (EC) paradigm, sampling on the edge rather than on the IoT end devices can be more intelligent. Most existing sampling algorithms, applicable to edge, do not consider the complex use cases such as predictive analytics. In this paper, we propose a real-time Adaptive Window Based Sampling (AWBS) algorithm to dynamically sample IoT time-series data on the edge. We have verified AWBS on an IoT machine health dataset from NASA using Local Outlier Factor, a well-known unsupervised abnormality detection method. The results were compared with two state-of-the-art sampling algorithms. The experimental results show that AWBS outperforms the other two algorithms by 1) effectively reducing the data to 6.91% and 2) accurately identifying the abnormal behaviour of the machine.

关键词： Time series data sampling edge computing predictive maintenance

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