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A Remote Health Monitoring System for the Elderly Based on Emerging Technologies 2nd

A Remote Health Monitoring System for the Elderly Based on E...

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2nd international conference on Emerging Trends in Engineering and Technology, EGTET 2022

作者： Mansoor, C.M.M. Chettri, Sarat Kumar Naleer, H.M.M. Department of Information Technology South Eastern University of Sri Lanka Oluvil Sri Lanka Department of Computer Applications Assam Don Bosco University Guwahati India Department of Computer Science South Eastern University of Sri Lanka Oluvil Sri Lanka

ISBN: (纸本)9789819943616

In the background of a rapidly aging population, the widespread deployment of smart wearable devices might help ease the societal burden created by the growing demand for healthcare and support among the elderly. Despite the fact that technology plays an important part in attaining these goals, any solution needs to be planned, implemented, and verified via domain expertise. This chapter examines the technologies that are employed in healthcare. Machine Learning (ML), Cloud computing, big data, the Internet of Things (IoT), Artificial intelligence (AI) and remote and wearable sensor network devices are among the technologies studied. Similarly, the data for a research article are gathered from a previously published study 92 papers from reputable publications. The essay also includes a systematic review technique. In addition, the study was prompted by Cloud computing and IoT technology because of its high accuracy rate, manageable interface, and effective and efficient outcomes. They will need both financial and technical aid to collaborate with healthcare specialists and provide both the technology, knowledge needed for treatments to be beneficial. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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3rd EAI international conference on IoT and big data Technologies for Health Care, IotCARE 2022

3rd EAI International Conference on IoT and Big Data Technol...

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3rd EAI international conference on IoT and big data Technologies for Health Care, IotCARE 2022

ISBN: (纸本)9783031335440

The proceedings contain 23 papers. The special focus in this conference is on IoT and big data Technologies for Health Care. The topics include: Personalized Recommendation Method of Maternal and Child Health Education Resources Based on Association Rule Mining Algorithm;methods of Integrating Ideological and Political Education into Health Management in Colleges and Universities Based on Internet of Things Technology;data Mining of Psychological Tendency and Health of Ideological and Political Students in Higher Vocational Tourism English Courses;intelligent Imaging Method of Nuclear Magnetic Resonance Medical Devices Based on Compression Sensing;evaluation of Post Fitness of Employees in Health Care Enterprises Based on big data;design of Telemedicine and Health Care System Based on Embedded Technology;anomaly Detection Method of Healthcare Internet of Things Gateway Supporting Edge computing;research on Fast Encryption of Electronic Health Record data Based on Privacy Protection;research on Secure Storage of Healthcare data in the Environment of Internet of Things;research on Cloud Health Privacy Information Protection Algorithm Based on data Mining;architecture of Wide Area Health Monitoring System;internet of Things Technologies in Healthcare for People with Hearing Impairments;artificial intelligence-Based Early Warning Method for Abnormal Operation and Maintenance data of Medical and Health Equipment;abnormal Signal Recognition Method of Wearable Sensor Based on Machine Learning;data Security Mining Method for Social Media Users’ Mental Health Status Test Based on Machine Learning Algorithm;early Warning Method of College Students Mental Subhealth Based on Internet of Things.

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Research on Compression of Teacher Guidance Network Use Global Differential computing Neural Architecture Search 5

Research on Compression of Teacher Guidance Network Use Glob...

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5th IEEE international conference on Artificial intelligence and big data, ICAIBD 2022

作者： Shen, Haozhe Li, Wenjun Li, Yilin Yue, Keqiang Li, Ruixue Li, Yuhang Hangzhou Dianzi University School of Electronic Information Hangzhou China

ISBN: (纸本)9781665499132

Large scale deep neural network has achieved unprecedented performances in many fields, such as computer vision, computer auditory, medical monitor and the others. However, it faces severe challenge for application scenarios with limited hardware resources and high real-time requirements. In our recent work, a globally differentiable deep neural architecture search method is adopted. At the same time, the knowledge of the teacher network is introduced into the network search, and the student network that is more sensitive to the tea-cher network is automatically searched. The student network is used to replace the teacher network for the original task in series resource-constrained devices. Compared with teacher network, student network has tiny size and remarkable performance close to the teacher network with knowledge distillation, so that the neural architecture search method is skillfully turned into a model compression method. We have verified the proposal in cifar10, cifar100 and engineering datasets. Experiments show that this method can greatly reduce the scale of the task network and maintain excellent performance similar to the original network. Additional experimental results on Jetson TX2 and raspberry show that the reasoning time of compressed network decreases significantly. © 2022 IEEE.

关键词： Distillation

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Application of big Digital Media data Compression Recognition Algorithm in Modern Art Design Training intelligence

Application of Big Digital Media Data Compression Recognitio...

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2022 international conference on Edge computing and Applications, ICECAA 2022

作者： Liu, Youquan Huang, Wenhua School of Design Xianyang Normal University Shaan'xi Xianyang712000 China

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Application of big digital media data compression recognition algorithm in modern art design training intelligence is studied in the paper. There is an aggregation effect on the information represented by the same kind of data, and a distancing effect on the information represented by different types of data, which confirms the point of this paper, that is, the essence of classification is to "identify the differences between species and the similarities within the species", while the data amount is large enough. Further, the big data compression algorithm and cultural image recognition algorithm are designed. MFS is a dedicated file system for virtualized clusters, while neither traditional NTFS nor ext3 are dedicated file systems for virtualized clusters. With these enhanced ideas, the performance of the model is then tested. © 2022 IEEE.

关键词： Image recognition

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An Approach for Traffic Flow Prediction Combining 3D Convolution and Attention Mechanism 19

An Approach for Traffic Flow Prediction Combining 3D Convolu...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Li, Lecheng Dai, Fei Huang, Bi Liu, Guozhi Wang, Shuai Zuo, Yuxuan Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350346558

Highly reliable traffic flow forecasting is essential to improve the management efficiency of intelligent transportation systems. However, traffic flow data contains complex spatio-temporal relationships, while this complex relationship is an organic whole. Therefore, improving traffic flow prediction performance is a challenging task. Some existing works have demonstrated the existence of spatio-temporal correlation in traffic flow data, but not all the features in the data are fully mined, mainly because the spatio-temporal diversity of traffic flow data is not taken into account, and the modeling approach of multi-network structure is used for separate modeling, which destroys the overall principle of traffic flow and eventually makes it difficult to achieve the expected prediction effect of the model. Within this paper, we propose a deep learning model ST-3DANet with a single network structure to model the spatio-temporal raster data features of traffic flow to improve the prediction ***-3DANet combines 3D convolution and residual structure, and introduces an attention mechanism to automatically capture the spatio-temporal correlation of traffic flow data. Specifically, the spatio-temporal data for traffic flow prediction is taken as a whole, and the spatio-temporal diversity in traffic data is automatically mined by 3D convolution, and the model's ability to discover spatiotemporal diversity and long-range spatial correlation is enhanced by using the attention mechanism. Experiments were conducted on a real dataset in New York, and the results showed that our model outperformed other benchmark models in terms of predictive effectiveness. © 2022 IEEE.

关键词： Convolution

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The design of a mechanical force feedback VR data glove 21

The design of a mechanical force feedback VR data glove

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21st IEEE international conference on Ubiquitous computing and Communications / 5th IEEE international conference on data Science and Computational intelligence / 21st IEEE international conference on Computer and Information Technology

作者： Lin, Jiliang Hu, Bin Pang, Jiahao Liao, Jingjie Xiamen Univ Technol Sch Int Educ Xiamen 361000 Peoples R China Soochow Univ Sch Arts Suzhou Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China Wuzhou Univ Sch Big Data & Software Engn Wuzhou 543002 Peoples R China

ISBN: (纸本)9781665477260

With the development of virtual reality technology, data gloves began to widely popularize various fields. Some of the force feedback data gloves at this stage still have problems such as high cost and complex structure. The mechanical force feedback data glove proposed in this paper uses the principle of mechanical rotation, using a potentiometer as the rotating shaft and a nylon rope to drive the potentiometer to rotate, so that it records the relevant data instead of sensors, and uses a servo to bring force feedback experience. Experiments indicate that the force feedback glove in this paper has both the force feedback effect of the existing force feedback data gloves and is lower in cost than the existing data gloves and has good generalizability.

关键词： data gloves virtual reality machine vision mechanical design force feedback gloves

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How Do Medical Professionals Perceive Artificial intelligence: An Analysis of Reddit data 17

How Do Medical Professionals Perceive Artificial Intelligenc...

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17th international conference on Wirtschaftsinformatik, WI 2022

作者： Weber, Sebastian Knop, Michael Niehaves, Björn University of Siegen Information Systems Siegen Germany

With the advent of big data and the ever-increasing computing power, technologies that use artificial intelligence (AI) are becoming continuously widespread. In several applications, AI has already reached or surpassed human-like capabilities, paving its way in many fields, including medicine. However, when AI takes over tasks from medical professionals (MPs) in making high-stake health-determining decisions, skepticism seems to prevail on the one hand and overconfidence in AI on the other. This research-in-progress aims to investigate these mechanisms to contribute designing a successful human-AI collaboration. To do so, we will examine what MPs understand by the broad concept of AI, how opinions about AI arose, and what MPs’ emotions are. Therefore, we will analyze AI-related data of medical subreddits using natural language processing (NLP) techniques and qualitative analysis. © 2022 17th international conference on Wirtschaftsinformatik, WI 2022. All rights reserved.

关键词： Artificial intelligence

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Computational intelligence Techniques for big data Analytics: A Contemplative Perspective 4th

Computational Intelligence Techniques for Big Data Analytics...

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4th international conference on Recent Innovations in computing, ICRIC 2021

作者： Divya Singh, Vikram Dahiya, Naveen Department of Computer Science and Engineering Chaudhary Devi Lal University Sirsa India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India

ISBN: (纸本)9789811682476

big data analytics has become an area of great research potential with the emergence of enabling technologies of Web 3.0 and Web 4.0. The proliferation of mobile devices, sensor devices, cloud computing and cyber-physical systems has generated such huge amount of data that if analysed properly are an asset to any organisation or institution. Traditional methods for data analysis are no longer sufficient to handle this data. So, newer and more efficient tools and technologies need to be developed to tap this potential. Computational intelligence techniques like artificial neural networks, evolutionary intelligence, genetic algorithms, federated machine learning, etc., are some of the methods used for big data analytics. In this regard, this paper aims to address the details of different computational intelligence techniques used for big data analytics and provide an in-depth detail of each technique. This study will provide insights and knowledge to the research community to have a one-stop solution to identify the pros and cons of various computational techniques. Further researchers will be able to identify which technique will suit their particular analytics problem at hand. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data Analytics

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Dynamic big data Drift Visualization of CPU and Memory Resource Usage in Cloud computing 1

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18th international conference on Artificial intelligence Applications and Innovations (AIAI)

作者： Mehmood, Tajwar Latif, Seemab Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Islamabad Pakistan

ISBN: (数字)9783031083334

ISBN: (纸本)9783031083334;9783031083327

Drift Visualization gives better insight into the nature of changes in the data distribution. Cloud trace is dynamic and generated at a very high pace, it's a serious problem that needs more deep discussions. Drift in cloud resource usage can cause low resource efficiency. In order to achieve optimal resource utilization, cloud provider needs a prediction model based on the data insights. Efficient drift detection optimizes the model prediction. Changes in data can cause these models to reduce their accuracy over a period of time. The focus of this research is to visualize the drift in the cloud at the cluster level. These visualizations will help cloud providers in understanding major factors contributing to the drift. In this paper, Cluster-based visualization using k-means is used to show the drift in the cloud.

关键词： Concept drift Drift detection CPU usage Memory usage Drift visualization

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Multi-Vision Services Acceleration Framework for IoT Devices

Multi-Vision Services Acceleration Framework for IoT Devices

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IEEE international conference on Web Services (IEEE ICWS)

作者： Xie, Jianhang Ding, Chuntao Ren, Shenyuan Li, Yidong Key Lab Big Data Artificial Intelligence Transpor Minist Educ Beijing Peoples R China Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China

ISBN: (纸本)9798350368567;9798350368550

Deploying a single deep neural network (DNN) model on ubiquitous Internet of Things (IoT) devices to provide multiple vision services (e.g., semantic segmentation service, facial attribute recognition service) has attracted significant interest from industry and academia. However, the most relevant studies have two limitations: (i) the multiple services DNN commonly has low performance due to the gradient interference between different vision services;(ii) the multiple services DNN has low inference speed on IoT devices. To this end, this paper introduces a multiple vision services acceleration framework for IoT devices. The proposed MCBNet contains a novel multi-controllable branching structure to control different services explicitly for improving multiple services performance and a service balance layer with the per-channel trainable masks in specific branches for mitigating gradient interference. In deployment, the proposed framework converts the multi-branch topology MCBNet model into the plain topology Rep-MCBNet model by re-parameterization technique for accelerating services inference speed. Experimental results on the CelebA and Cityscapes datasets show that our proposed framework outperforms the state-of-the-art approaches in terms of performance and inference speed. In particular, we deploy the Rep-MCBNet on the laptop with Intel mobile processors demonstrating that the proposed framework achieves 1.9x faster processing frames per second than ETR-NLP, with 1.5x fewer parameters and 1.3x fewer floating-point operations.

关键词： Service computing multiple vision services DNN IoT devices re-parameterization

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