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检索条件"任意字段=5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019"

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Development of data Acquisition System for Real-Time Monitoring Using a Smartphone 5

Development of Data Acquisition System for Real-Time Monitor...

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5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Seretloa, Semakaleng Alatise, Mary Tshwane Univ Technol Dept Comp Syst Engn Pretoria South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

the main aim of this study is to develop and deploy a distributed data acquisition system to acquire different environmental parameters using sensors. the available literature has neglected to account for the role that the concept of real-time monitoring plays, a shortcoming this paper addresses. We explored to what extent, and in what ways can real-time monitoring improve the concept of data acquisition. We do so to better enable access to data from anywhere in the world using smartphones. In this paper, a distributed data acquisition system application for real-time monitoring using smartphone is presented. the system has two sensors, namely, DHT22 to measure both temperature and humidity, and BMP180 Barometer sensor which measures temperature, pressure, and altitude. For data acquisition, a Raspberry Pi is used, which acquires data from the sensors that are connected to it the Raspberry Pi has in-built Wireless Fidelity (Wi-Fi) which enables it to communicate to the outside world. A webserver, in the form of thingspeak is created and runs on the Raspberry Pi to allow real-time monitoring from a smartphone or any smart mobile device. the user can log into thingspeak and monitor the temperature, humidity, air pressure and altitude of the environment. this paper gives an overview over existing literature, presenting the evolution of the real-time data acquisition in a full spectrum of different industries, and focusing on a particular subset that deals with digital-based data acquisition. In this study we explored people's conceptions of sensing and analysis capabilities of different data acquisition systems, their accuracy and reliability in big and small industries.

关键词： data acquisition raspberry pi real-time monitoring thingspeak sensors

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SpatialHD: Spatial Transformer Fused with Hyperdimensional computing for AI Applications 5

SpatialHD: Spatial Transformer Fused with Hyperdimensional C...

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5th ieee international conference on Artificial intelligence Circuits and Systems, AICAS 2023

作者： Bettayeb, Meriem Hassan, Eman Mohammad, Baker Saleh, Hani Khalifa University of Science and Technology System on Chip Lab Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350332674

Brain-inspired computing methods have shown remarkable efficiency and robustness compared to deep neural networks (DNN). In particular, HyperDimensional computing (HDC) and Vision Transformer (ViT) have demonstrated promising achievements in facilitating effective and reliable cognitive learning. this paper proposes SpatialHD, the first framework that combines spatial transformer networks (STN) and HDC. First, SpatialHD exploits the STN, which explicitly allows the spatial manipulation of data within the network. then, it employs HDC to operate over STN output by mapping feature maps into high-dimensional space, learning abstracted information, and classifying data. In addition, the STN output is resized to generate a smaller input feature map. this further reduces computing complexity and memory storage compared to HDC alone. Finally, to test the model's functionality, we applied spatial HD for image classification, utilizing the MNIST and Fashion-MNIST datasets, using only 25% of the dataset for training. Our results show that SpatialHD improves accuracy by ≈ 8% and enhances efficiency by approximately 2.5x compared to base-HDC. © 2023 ieee.

关键词： Image classification

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big data intelligence Solution for Health Analytics of COVID-19 data with Spatial Hierarchy 7

Big Data Intelligence Solution for Health Analytics of COVID...

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7th ieee international conference on big data intelligence and computing, datacom 2021

作者： Leung, Carson K. Zhao, Chenru University of Manitoba WinnipegMB Canada

ISBN: (纸本)9781665420716

In the current era of big data, technological advancements have made it easy and quick to generate and collect huge volumes of varieties of data from wide ranges of rich data sources. these big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by big data intelligence and computing. In this paper, we propose a big data intelligence solution for health analytics with spatial hierarchy. In particular, we focus on analyzing coronavirus disease 2019 (COVID-19) epidemiological data at different spatial granularity levels. Since its outbreak, there have been cumulatively millions of COVID-19 cases observed in various spatial locations around the world. Our solution focuses on analyzing and mining valuable information and useful knowledge (e.g., distribution, frequency, patterns) of health-related states and characteristics in populations in various spatial locations in a top-down fashion along the spatial hierarchy. To reduce redundancy, our solution discovers and returns to users (e.g., researcher, civilian) new information and knowledge not found at previous spatial hierarchical levels. the discovered information and knowledge helps the users to understand the disease better, and thus take an active role to fight, control, and/or combat the disease. Evaluation of our big data intelligence solution on real-life COVID-19 data demonstrates its practicality in health analytics of the data with spatial hierarchy and in revealing new knowledge about COVID-19 cases at different spatial granularity levels. the solution is expected to be adaptable to health analytics of other diseases. © 2021 ieee.

关键词： COVID-19

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Competitive Swarm Optimizer Based on Individual Learning Mechanism 5

Competitive Swarm Optimizer Based on Individual Learning Mec...

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5th international conference on High Performance big data and Intelligent Systems, HDIS 2023

作者： Tang, JingQi Li, Wei Wang, Lei School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9798350394160

In view of the disadvantages of the traditional competitive swarm optimizer (CSO), such as falling into local minimization or poor convergence accuracy, this paper proposed an enhanced CSO algorithm called competitive swarm optimizer based on individual learning mechanism (ILCSO). Firstly, the individual selection rate and the learning rate are designed to dynamically select the winner and loser. the losers are updated by the precise competitive learning strategy to improve the algorithm exploitation ability. Secondly, the mutation strategy based on performance improvement is introduced, which improves the search ability of algorithm. the proposed algorithm effectively balances the local search and global exploration with the individual learning mechanism, and improves the probability of finding the global optimal solution. Finally, ILCSO is compared with six classical meta-heuristic algorithms on CEC2014 benchmark functions. the Wilcoxon rank-sum test is used to demonstrate that the ILCSO is effective. Experimental results and statistical analysis show that ILCSO has higher convergence speed and convergence accuracy. ©2023 ieee.

关键词： Swarm intelligence

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Research on university ranking based on literature big data and industry thesaurus - Taking double first-class universities as an example 5

Research on university ranking based on literature big data ...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Fan, Yuhang Wang, Xiaoxia Chen, Miao Zhu, Yue Xu, Pengjing Shanghai Technology Development Co. Ltd. Shanghai200062 China Data Science and Artificial Intelligence Institute of Shanghai Science and Technology Innovation Resources Center Shanghai200062 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Most of the current university rankings base on the academic system and lack the detailed classification. It is not that suitable for industry field to screen the universities for the potential corporation on research and development. In this paper, we adopt big data processing technology to form a structured process of university rankings based on the industrial system, with support on the massive literature data and the built industry thesaurus from our previsions study. It consists of four modules: resource download, text segmentation, data processing and result presentation. Results show that our new ranking can reach the precise divisions on subdivided industry, also in accord with other authoritative rankings in the similar divisions from both classifications. Our ranking can possibly help understand the research priorities of different universities in perspective, improve the efficiency of technology transfer from universities to industry and sketch the map of industry-university-research system. © 2022 ieee.

关键词： big data

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5th ieee international conference on Computer and Communication Engineering Technology, CCET 2022

5th IEEE International Conference on Computer and Communicat...

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5th ieee international conference on Computer and Communication Engineering Technology, CCET 2022

ISBN: (纸本)9781665467353

the proceedings contain 58 papers. the topics discussed include: an improved lion swarm optimization algorithm based on tent-map and differential evolution;a self-attentive interest retrieval recommender;marine predators algorithm based on trigonometric function;research on hybrid intelligence wargame method;automatic current transformer verification technology for high-speed railway power based on edge-cloud collaborative computing;grasshopper optimization algorithm based on adaptive curve and reverse learning;research on a user context model based on data mining;large-scale distance matrix calculation method based on contraction hierarchies;epidemic data analysis based on data visualization technology;improving aerospace big data infrastructure and applications with distributed file system and massive parallel calculation;arbitrary-shaped text detection with gaussian probability distance distribution;similarity computation of heterogeneous ontology based on graph attention network;and a small target detection method based on feature enhancement and positioning optimization.

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Towards data-Driven Pediatrics in Zimbabwe 5

Towards Data-Driven Pediatrics in Zimbabwe

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5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Batani, John Maharaj, Manoj Sewak Univ KwaZulu Natal Sch Management IT & Governance Durban South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Efficient and effective healthcare systems utilize the available data at every level to provide evidence-based care and improve procedures and practice in order to meet the three goals of healthcare institutions - access, quality and efficiency. Regardless of the changing child health needs and often failure by traditional healthcare models to cope, most of the public health institutions in Sub-Saharan Africa are not providing data-driven pediatrics to meet the ever-changing child health needs. there is a lack of utilization of the data collected by the routine health information systems to provide evidence-based and personalized pediatrics. this study employed an exploratory research design to explore the opportunities, and potential challenges of data-driven pediatrics based on the lessons learnt from the introduction of the electronic health record and Neotree (a digital health system deployed in Zimbabwe and Malawi to help health workers manage neonates' health) in Makonde District. Twenty public health workers participated in interviews and focus groups and reports from the district health information system provided further insights. the study revealed that data-driven pediatrics could improve access, efficiency and quality of pediatric care, regardless of such potential challenges as fear of medico-legal hazards, centralization of decision-making, resistance by healthcare workers, network challenges and computer illiteracy. To increase the chances of success, the following lessons learnt from electronic health record and neotree introduction could help: start small, sensitize communities first, involve line healthcare workers from the beginning, do not train in a haste and demystify technology's purpose. the study revealed that there are pediatricians and nurses willing to shift to data-driven pediatrics if the technologies are available.

关键词： data-driven healthcare evidence-based healthcare data-driven pediatrics neotree child health

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Context-based data Augmentation for Improved Ballet Pose Recognition 5

Context-based Data Augmentation for Improved Ballet Pose Rec...

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5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Bowditch, Margaux Van der Haar, Dustin Univ Johannesburg Acad Comp Sci & Software Engn Johannesburg South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

As computer vision technology continually advances and expands into various application fields, modern methods enable researchers to extract the most important and relevant key features from visual data. Human action recognition is one area that has gained much interest due to its potential in a variety of domains. Ballet, an art form filled with movements and poses, is a domain where the analysis of key features is particularly relevant. At a fundamental level, well-defined features used in a computer vision pipeline, generally provide more accurate predictions. this paper presents a feature engineering and pose analysis approach with OpenPose that produces pose feature templates from distance data. the calculated distance data is used to explore feature augmentation strategies for improved ballet pose recognition results. In addition to the OpenPose distance feature data, this paper investigates geometric and spatial feature approaches to determine optimal feature configurations for the ballet pose recognition task. A ballet dataset of eight distinct poses with multiple dancers was used for the study. the results demonstrate that the proposed approach provides a way to derive baseline pose skeletons from which statistical metrics are derived to aid in determining a valid ballet pose feature space. the augmentation of calculated OpenPose distance feature data yields improved results where the leading SVM classifier for the OpenPose feature space achieves an accuracy of 99.713%. the ensemble-based feature extraction approach which uses MobileNetV3 along with Augmented OpenPose features yielded an excellent Area Under the Curve (AUC) score of 99.999%. the proposed study, therefore, provides a valuable approach that results in state-of-the-art performance for the ballet pose recognition task.

关键词： Ballet OpenPose Feature Engineering data Augmentation SVM

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Artificial intelligence in Computer Network Technology in the big data Era 5

Artificial Intelligence in Computer Network Technology in th...

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5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Keer, Priyameet Kaur Al-Safi, Jehan Kadhim Shareef Babu, S B G Tilak Ramesh, G. New Horizon College of Engg. Department of Management Studies. Bangalore India University of Thi-Qar. Department of Digital Media Iraq Aditya Engineering College Dept of ECE Andhra Pradesh Surampalem India St. Martin's Engineering College Telangana Secunderabad India

ISBN: (纸本)9798350398267

While the integration of artificial intelligence and computer technology has enriched people's daily lives, it has also become an inaccessible part of people's lives. the trend of future social development is the advancement of computer network technology, which will be the tendency of future social development. Because of the artificial intelligence technology's high level of intelligence, it has emerged as one of the most promising technologies in the realm of computer networks and is dependent on computers for its development, which led to the establishment of the technology-based development. In order to hasten the incorporation of artificial intelligence into social production and living, as well as to make production and life easier, we need to make some changes. the purpose of this study was to investigate artificial intelligence, after which the model structure of intelligent anti-spam in network security management was established, and finally, the model was evaluated. According to the findings, the intelligent anti-spam model exhibited satisfactory filtering performance. © 2022 ieee.

关键词： Artificial intelligence

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Research on the construction and application of knowledge graph for process quality optimization 5

Research on the construction and application of knowledge gr...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Qiu, Quan Li, Ran Chen, Bingquan Hu, Ning Key Laboratory of Industrial Equipment Quality Big Data Reliability Data Center MIIT the 5th Electronics Research Institute MIIT Guangzhou China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Process quality optimization is a necessary means for enterprises to improve product quality. However, the current process quality optimization of enterprises is highly dependent on experienced senior technicians. If the experience of these people cannot be effectively converted into knowledge, the value of massive industrial data cannot be effectively obtained. It has become the main bottleneck faced by the intelligent production of enterprises. To this end, this paper proposes a process quality optimization method based on knowledge graph, which uses process quality optimization rules and artificial experience knowledge to construct a process quality optimization knowledge graph including process knowledge, product defect knowledge, and process optimization knowledge. Associate process defects and defect handling cases in the form of event clusters, combine artificial intelligence and graph algorithms, and assist process quality optimization decision-making in various ways such as knowledge search, knowledge question and answer, and knowledge recommendation. © 2022 ieee.

关键词： Knowledge graph

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