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3rd international conference on intelligent Communication, Control and Devices, ICICCD 2018

作者： Ambigavathi, M. Sridharan, D. Department of Electronics and Communication Engineering Anna University CEG Campus Chennai India

ISBN: (纸本)9789811386176

The large volume of healthcare data continues to mount every second, making it harder and very difficult to find any form of useful information. Recently, big data is changing the traditional way of the data delivery system into valuable insights, especially in the healthcare industry. It provides a lot of benefits in the healthcare sector to detect critical diseases at the initial stage and deliver better healthcare services to the right patient at the right time. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured vital data rapidly produced by the various healthcare information storage systems. However, there are several issues to be addressed in the current health data analytics platforms that offer technical methods such as vital data collection, aggregation, process, analysis, visualization, and sharing. Due to lack of detailed analysis in the existing research works, this paper examines the most significant characteristics of big data analytics in health care, various data sources and its data types, five R’s of big data analytics, and then briefly discusses the recent open research challenges with future directions. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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Effect Monitoring of Physical Education Curriculum Based on Wearable Sensor in intelligent Environment 2

Effect Monitoring of Physical Education Curriculum Based on ...

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2nd international conference on computing and data Science, CONF-CDS 2021

作者： Liang, Feng Shan, Zhouxiang Wang, Dongle School of Leisure and Health Guilin Tourism University Guilin China Sport and Military Teaching Department Anhui University Hefei China

Reasonable amount of exercise is the premise of physical education teaching. On this premise, in the process of students' physical education teaching, the appropriate degree of actual exercise amount will affect the effect of motor skill learning and physical exercise to a great extent. Through monitoring the amount of exercise and then evaluating the quality of physical education teaching is the focus of the implementation of physical education. Looking for simple and effective monitoring equipment has important practical significance. In this paper, through the analysis of the current situation of wearable sensor in the development of physical education teaching, put forward the effect monitoring of sports curriculum based on wearable sensor in intelligent environment. In this paper, 258 freshmen and sophomores who use wearable devices are selected for monitoring and analyzing, and the pertinence, accuracy and data of wearable sensor physical education course and traditional physical education course are compared and analyzed, and the results are discussed and analyzed. It is concluded that wearable sensor can promote physical education curriculum, not only can improve the teaching pertinence, strengthen the accuracy, but also can In order to improve the scientificity of physical education curriculum effect monitoring. The research in this paper has important practical significance for monitoring the effect of wearable sensors in physical education curriculum. © Published under licence by IOP Publishing Ltd.

关键词： Curricula

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Constraint-Driven IoT-Based smart Agriculture for Better e-Governance 1

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4th international conference on ICT for Sustainable Development, ICT4SD 2019

作者： Dalela, Pankaj Kumar Basu, Saurabh Majumdar, Sabyasachi Sachdev, Smriti Kushwaha, Niraj Kant Yadav, Arun Tyagi, Vipin Centre for Development of Telematics Mehrauli New Delhi India

ISBN: (数字)9789811509360

ISBN: (纸本)9789811509353

Agriculture, with its allied sectors, is one of the largest livelihood providers in India as well as other developing countries. It also contributes a significant figure to the Gross Domestic Product (GDP), 23% in India in 2016. Several research works have used IoT to automate and revolutionize different aspects of agriculture but have not been able to achieve a significant level of automation in India. These solutions are not able to have ground-level impact due to challenges faced in deploying wireless sensor networks, developing of application and incorporating diversity of crop, soil type, and climate parameters in a single solution. Other practical limitations include absence of funds with farmers to deploy such sophisticated solutions, lack of technical knowledge, and erratic power supply in remote areas. In this paper, we propose a solution to the above challenges faced in field deployment in the form of a Geo-intelligent Platform with inherent monitoring of soil and crop irrigation requirements using IoT nodes. Resource-based analytics are developed by integrating data from the field nodes, weather forecasts, and Agromet Advisory from Agriculture department. The proposed solution is aimed to provide a direct knowledge sharing channel between farmer and government agencies to facilitate farmers in proper crop management and government agencies in policy formation, subsidy finalization and issuing of advisories as part of e-Governance initiatives adopted worldwide. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Developing countries

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Mining and analyzing behavior patterns of smart home users based on cloud computing 2nd

Mining and analyzing behavior patterns of smart home users b...

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2nd EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2020

作者： Lu, Xing-hua Mo, Chang-shen Wang, Xiao-qi Ma, Qing-qing Huali College Guangdong University of Technology Guangzhou511325 China

ISBN: (纸本)9783030510992

Aiming at the problem of poor robustness of traditional user behavior pattern mining analysis method, a cloud computing-based intelligent home user behavior pattern mining analysis method is designed. intelligent household IoT from using cloud computing method and data mining the user behavior patterns, establish a two-layer neural network level of data is divided into 2 kinds, the user behavior mode by setting the input weight vector calculation after classifying data correlation between user behavior model, using Apriori algorithm, input minimum support and minimum confidence, on the basis of analyzing the correlation between data, and establish the user behavior mode decision tree, on the basis of complete analysis of cloud computing smart home user behavior patterns mining method design. Through the comparison experiment with the traditional method, it is concluded that the designed mining analysis method based on cloud computing has higher robustness, the proposed cloud computing-based intelligent home user behavior pattern mining method has good application space. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.

关键词： Cloud computing

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Advances in metagenomics based on high-throughput sequencing and big data mining

Advances in metagenomics based on high-throughput sequencing...

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

作者： Wang, Na Li, Kun Song, Yangyang Wang, Jinguo Department of Anesthesiology The First Hospital of Jilin University Changchun China Department of Urology Tengzhou Central People’s Hospital Tengzhou China Department of Urology The First Hospital of Jilin University Changchun China

ISBN: (纸本)9789811525674

The study of life science and the development of biotechnology have produced new requirements for DNA sequencing technology. Next-generation sequencing which is developed on the basis of traditional Sanger sequencing technology is widely applied in many fields for its advantages of high-throughput and low-cost. Next-generation sequencing is called high-throughput sequencing, because it is characterized by the large amount of sequence data produced in a single operation. With the improvement of sequencing technology and the development of genomics, the determination of the DNA sequence of a single species has been unable to meet the development of science, so a new research, metagenomics has been put forward. © Springer Nature Singapore Pte Ltd. 2020.

关键词： data mining

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Edge-Centric Queries’ Stream Management Based on an Ensemble Model 8th

Edge-Centric Queries’ Stream Management Based on an Ensembl...

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8th international Workshop on Combinations of intelligent Methods and Applications, CIMA 2018 held in conjunction with the 30th IEEE international conference on Tools with Artificial Intelligence, IEEE ICTAI 2018

作者： Kolomvatsos, Kostas Anagnostopoulos, Christos School of Computing Science University of Glasgow GlasgowG12 8RZ United Kingdom

ISBN: (纸本)9789811519178

The Internet of things (IoT) involves numerous devices that can interact with each other or with their environment to collect and process data. The collected data streams are guided to the cloud for further processing and the production of analytics. However, any processing in the cloud, even if it is supported by improved computational resources, suffers from an increased latency. The data should travel to the cloud infrastructure as well as the provided analytics back to end users or devices. For minimizing the latency, we can perform data processing at the edge of the network, i.e., at the edge nodes. The aim is to deliver analytics and build knowledge close to end users and devices minimizing the required time for realizing responses. Edge nodes are transformed into distributed processing points where analytics queries can be served. In this paper, we deal with the problem of allocating queries, defined for producing knowledge, to a number of edge nodes. The aim is to further reduce the latency by allocating queries to nodes that exhibit low load (the current and the estimated);thus, they can provide the final response in the minimum time. However, before the allocation, we should decide the computational burden that a query will cause. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query. The complexity class, thus, can be matched against the current load of every edge node. We discuss our scheme, and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Web-Based Electricity Cost Modeling for Bangladesh Power Sector to Improve Capacity and Transparency

Web-Based Electricity Cost Modeling for Bangladesh Power Sec...

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Future Technologies conference, FTC 2020

作者： Shamsul Alam, M. Zaman, Marzia Razzaque Rupom, M.A. Mondal, Alam Hossain Department of Electrical and Electronics Engineering Daffodil International University Dhaka Bangladesh e-POWER Walter Light Hall Room 108 19 Union Street KingstonONK7L 3N6 Canada OS CLiCKS 160 Lake Circus Kalabagan Dhaka1205 Bangladesh

ISBN: (纸本)9783030631277

A study project undertaken for developing a web-based electricity cost model (WECM) is presented in this paper. The project targets to complete by five years that includes development of a prototype application, followed by a pilot phase with various cost analysis of real-life data and finally a nation-wide implementation of the model in the power sector of Bangladesh. The overall objectives of this study project are: (i) design and develop a well-structured, scalable database and a web application (ii) capacity building in Web and mobile app development and (iii) integrate machine learning based analytics using real-life data. This paper discusses the detailed methodology used for the web application development and capacity building. In addition, the first prototype application developed, and future scope are also presented. Finally, the expected outcome of this ongoing study and benefit of WECM are discussed. The proposed model for migrating the operation of one of the vital sectors i.e. the energy sector in Bangladesh to cloud is a bold step towards "Digital Bangladesh". Upon successful completion of this study project, it is expected that about 90% of the energy sector’s data can be automatically captured and reported for management and operation using WECM. © 2021, Springer Nature Switzerland AG.

关键词： Machine learning

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6g access network for intelligent internet of healthcare things: Opportunity, challenges, and research directions 2nd

6g access network for intelligent internet of healthcare thi...

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2nd international conference on Trends in Computational and Cognitive Engineering, TCCE 2020

作者： Kaiser, M. Shamim Zenia, Nusrat Tabassum, Fariha Mamun, Shamim Al Rahman, M. Arifur Islam, Md. Shahidul Mahmud, Mufti Institute of Information Technology Jahangirngar University Savar Dhaka1342 Bangladesh Department of EECE Military Institute of Science and Technology Dhaka Bangladesh Department of Physics Jahangirngar University Savar Dhaka1342 Bangladesh Department of Computer Science Nottingham Trent University NottinghamNG11 8NS United Kingdom

ISBN: (纸本)9789813346727

The Internet of Healthcare Things (IoHT) demands massive and smart connectivity, huge bandwidth, lower latency with ultra-high data rate and better quality of healthcare experience. Unlike the 5G wireless network, the upcoming 6G communication system is expected to provide intelligent IoHT (IIoHT) services everywhere at any time to improve the quality of life of the human being. In this paper, we present the framework of 6G cellular networks, its aggregation with multidimensional communication techniques such as optical wireless communication network, cell-free communication system, backhaul network, and quantum communication, as well as distributed security paradigm in the context of IIoHT. Such low latency and ultra-high-speed communication network will provide a new paradigm for connecting homes to hospitals, healthcare people, medical devices, hospital infrastructure, etc. Also, the requirements of 6G wireless networking, other key techniques, challenges and research direction of deploying IIoHT are outlined in the article. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Machine Learning for Predictive Diagnostics at the Edge: an IIoT Practical Example

Machine Learning for Predictive Diagnostics at the Edge: an ...

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IEEE international conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Bellavista, Paolo Della Penna, Roberto Foschini, Luca Scotece, Domenico Univ Bologna Comp Sci & Engn Dept DISI Viale Risorgimento 2 I-40136 Bologna Italy

ISBN: (纸本)9781728150895

Edge computing is becoming more and more essential for the Industrial Internet of Things (IIoT) for data acquisition from shop floors. The shifting from central (cloud) to distributed (edge nodes) approaches will enhance the capabilities of handling real-time big data from IoT. Furthermore, these paradigms allow moving storage and network resources at the edge of the network closer to IoT devices, thus ensuring low latency, high bandwidth, and location-based awareness. This research aims at developing a reference architecture for data collecting, smart processing, and manufacturing control system in an IIoT environment. In particular, our architecture supports data analytics and Artificial Intelligence (AI) techniques, in particular decentralized and distributed hybrid twins, at the edge of the network. In addition, we claim the possibility to have distributed Machine Learning (ML) by enabling edge devices to learn local ML models and to store them at the edge. Furthermore, edges have the possibility of improving the global model (stored at the cloud) by sending the reinforced local models (stored in different shop floors) towards the cloud. In this paper, we describe our architectural proposal and show a predictive diagnostics case study deployed in an edge-enabled IIoT infrastructure. Reported experimental results show the potential advantages of using the proposed approach for dynamic model reinforcement by using real-time data from IoT instead of using an offline approach at the cloud infrastructure.

关键词： Edge computing Industrial IoT Machine Learning Predictive Diagnostics Apache Kafka

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A 65-nm 8-to-3-b 1.0-0.36-V 9.1-1.1-TOPS/W Hybrid-Digital-Mixed-Signal computing Platform for Accelerating Swarm Robotics

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 2020年第1期55卷 49-59页

作者： Cao, Ningyuan Chang, Muya Raychowdhury, Arijit Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

Low-power edge-intelligence is leading to spectacular advances in smart sensors, actuators, and human-machine interfaces. In particular, energy efficiency is driving key advances in robotics, where low-power computation is augmented with smart control and mechanical systems to enable small-sized and intelligent drones, unmanned aerial vehicles (UAVs), micro-sized cars, and so on with applications in surveillance, disaster relief, and reconnaissance. Furthermore, for a variety of tasks, swarms of robots are often used as opposed to the individual robots. This article presents an energy-efficient computing platform that can enable a sample class of algorithms for swarm robotics. We demonstrate that both physical-model-based algorithms as well as learning-based algorithms can be supported on the same computing platform. We also demonstrate that with changing swarm sizes, the number of bits required to compute also scales. We take advantage of this observation to propose a hybrid-digital-mixed-signal computing platform, whose energy efficiency scales with the resolution of the data path and hence the swarm size. Measurements on a 65-nm CMOS test-chip demonstrate a peak energy efficiency of 9.1 TOPS/W at a 3-b resolution, and it scales down to 1.1 TOPS/W at an 8-b resolution.

关键词： Robot kinematics Mathematical model Swarm robotics Energy resolution Hardware Sensors Machine learning mixed signal robotics swarm intelligence

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