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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

共 1353 条记录，以下是81-90 订阅

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Research analysis on the application of big data and artificial intelligence in Chinese medicine diagnosis

Research analysis on the application of big data and artific...

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2022 international conference on Cloud computing, big data and Internet of Things, 3CBIT 2022

作者： Zhuang, Yanping Zhang, Xuan Luo, Simin Lin, Guiling Tian, Xiaoyu Chen, Yi Lai, Mingyin Gong, Aimin Hainan Medical University Department of Traditional Chinese Medicine Haikou China

ISBN: (纸本)9781665492256

The application of modern technologies such as big data and artificial intelligence in life and work has inspired scholars to apply them to Chinese medicine diagnosis. And the application of big data is just the right way to digitise and organise and classify millennia of knowledge, experience and literature on Chinese medicine diagnosis. Based on this, the robot will be made intelligent through artificial intelligence-related technology, thus promoting the transmission, development and innovation of Chinese medicine diagnosis. In this paper, the literature is obtained by searching the title 'big data TCM' or 'Artificial intelligence TCM' on CNKI. The obtained literature was read, analyzed and summarized in order to provide theoretical help for further realizing the objectification of TCM diagnosis. © 2022 IEEE.

关键词： big data

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Determining Important Features in Multidimensional Health data for Individualized Precision Healthcare

Determining Important Features in Multidimensional Health Da...

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive intelligence and computing, 2023 international conference on Cloud and big data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Cong, Ruichen Wu, Jianlun Nishimura, Shoji Ogihara, Atsushi Jin, Qun Graduate School of Human Sciences Waseda University Tokorozawa Japan School of Public Health Zhejiang Chinese Medical University Hangzhou China Waseda University Faculty of Human Sciences Tokorozawa Japan

ISBN: (纸本)9798350304602

In recent years, there has been a growing need for individuals' health management by using sensors and wearable devices to record daily activity and monitor health indicators. A large amount of health data needs to be analyzed to investigate the essential impact factors related to individuals' health and help individuals manage their health. In this paper, we investigate the important features influencing personal health from health data obtained from wearable devices and health data based on Traditional Chinese Medicine (TCM). We focus on investigating and selecting more influential health features and then performing machine learning algorithms for modeling. The results show that daily activity consumption is of a greater influence on wearable device data, and the pulse position that represents the kidney is identified as having the most significant impact on TCM health status among all pulse positions. Moreover, we selected the most influential features to perform the regression model and compared them with all the features. The results show that after feature selection, the Mean Squared Error (MSE) is smaller, and the R-square Score (R2) is greater than before. © 2023 IEEE.

关键词： Mean square error

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Overview of the application of agriculture big data research 3

Overview of the application of agriculture big data research

引用

3rd international conference on Artificial intelligence, Information Processing and Cloud computing, AIIPCC 2022

作者： Hu, Dahui Du, Zhiguo Business College of Southwest University Chongqing China

ISBN: (纸本)9783800759330

The processing and analysis results based on big data provide reliable decision support for decision makers and users. With the further development of agricultural informatization, big data technology has been gradually applied to agricultural planning, production, management and marketing of agricultural products. Firstly, the problems to be solved in the application of big data in agriculture are put forward. Then, the main contents and difficulties of data acquisition, data analysis and processing, data application and service in the application of big data in agriculture are analysed. Finally, the problems in the application of big data in agriculture are proposed and the application of big data in agriculture is prospected. © VDE VERLAG GMBH.

关键词： big data

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National Telecom Volunteer: Utilizing A Machine Learning Model To Predict Cellphone Network Coverage Using big data Analysis From data Collected Through Crowdsourcing

National Telecom Volunteer: Utilizing A Machine Learning Mod...

引用

2024 international conference on Computational intelligence and computing Applications, ICCICA 2024

作者： Sharma, Himanshu Joshi, Atul Government Of India Department Of Telecommunications Ministry Of Communications India

ISBN: (纸本)9798350306446

In India remarkable developments have been taking place in the field of 5G technology and associated use cases after the launch of 5G in Oct 2022. However, no tool is available for real-time monitoring of the presence of telecom technology (2G/3G/4G/5G) and QoS (Quality of Service) within a region or to an individual. Thus, this paper proposes a methodology and related tools to measure ground-level QoS, results of which can be utilised by DoT (Department of Telecommunications), Government of India for taking proactive steps to provide high-quality telecom coverage to all citizens, focusing on strategic areas achieving the goals of the National Digital Communication Policy 2018. To achieve this, a smartphone app was developed to collect data from citizens who can share their locations and cell phone signal data as volunteers. Considering the country's population of almost ~1.4 billion, a 1% volunteer rate would generate significant data for the server. A highly available server architecture design has been proposed to handle this massive data. Mobile BTS (Base Transceiver Station), topographical and meteorological data from the government will be used along with collected signal data. Machine learning will forecast data for the missing locations since there will be few nationwide volunteers. © 2024 IEEE.

关键词： Deep learning

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LTCS: Lightweight Tree-Chain Scheme for ADS-B data Authentication

LTCS: Lightweight Tree-Chain Scheme for ADS-B Data Authenti...

引用

international conference on big data intelligence and computing, datacom 2022

作者： Cui, Haoran Zhu, Shibo Yue, Meng College of Electronic Information and Automation Civil Aviation University of China Tianjin300300 China College of Safety Science and Engineering Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9789819922321

As a widely used air traffic monitoring and information transmission technology, the Automatic Dependent Surveillance - Broadcast (ADS-B) is gradually deployed to aircraft around the world. However, due to the openness of its communication protocol, it is vulnerable to malicious attacks. Aiming at the security threats of ADS-B system, this paper proposes an ADS-B data security scheme on the basis of tree-chain structure to realize the lightweight guarantee of ADS-B message reliability. Firstly, we used the idea of symmetric key delay disclosure in classical data authentication scheme to construct a tree-chain authentication model, which reduces the computation amount in authentication process and the length of key chain. Next, we used SM2 digital signature, which is more secure and computationally efficient, to authenticate the initial information in the signature phase. Compared with the original authentication scheme, our scheme improves the computation speed by about 4 times and reduces the number of keys in the authentication process, and is more applicable for ADS-B use cases with high real-time demand and tight computing resources. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Development Trends of big data and Artificial intelligence in Virtual Simulation Technology 14th

Development Trends of Big Data and Artificial Intelligence i...

引用

14th international conference on Frontier computing, FC 2024

作者： Yang, Yidong Wang, Lanfeng Wei, Yuhua Guangzhou Huali Science and Technology Vocational College Guangzhou China

ISBN: (纸本)9789819627974

With the development of information technology, big data technology and artificial intelligence have become important directions for informationization in the world today. big data technology can help people better process, manage, and utilize massive amounts of data, while artificial intelligence can make computers more intelligent, achieving automated and intelligent processing and decision-making. Combining the two can achieve more powerful information processing and application capabilities, with broad application prospects. The application of artificial intelligence in virtual simulation technology has also played a crucial role. Artificial intelligence technology can simulate human intelligent behavior and provide more intelligent support for virtual simulation technology. Through artificial intelligence algorithms, virtual simulation can be more automated and intelligent for simulation and prediction, improving the efficiency and accuracy of virtual simulation. In addition, artificial intelligence can also help virtual simulation technology achieve autonomous learning and optimization, continuously improving its performance and functionality. Based on the above issues, this article explores a virtual gesture interaction perception simulation model based on CNN (Convolutional Neural Networks) algorithm, extracts gesture datasets using artificial intelligence algorithms, and estimates gesture coordinates. The experimental results indicate that the sample sizes are 100, 200, and 300. The time for virtual gesture interaction perception based on CNN algorithm is 7.39ms, 10.87ms, and 14.67ms, which are 4.37ms, 5.61ms, and 7.07ms less than RNN (Recurrent Neural Network) algorithm, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Literature Representation using Character Networks based on Sentiment Analysis

Literature Representation using Character Networks based on ...

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IEEE international conference on big data and Smart computing (bigComp)

作者： Park, Myeonggeon Park, Sunghong Shin, Hyunjung Ajou Univ Dept Artificial Intelligence Suwon South Korea Ajou Univ Dept Artificial Intelligence Dept Ind Engn Suwon South Korea

ISBN: (纸本)9781665421973

In this study, we propose a method for expressing literary works using machine learning. The proposed method expresses novels by composing a network of characters appearing in the novel and identifying the flow of sentiment scores between characters who agree or oppose the main character. Characters, represented as nodes on the constructed network, are extracted via the name entity recognizer, whereas edges are constructed based on the emotional words described in the novel. Protagonist and antagonist groups on the character network are classified via signed graph clustering. The novel proceeds through the interaction between the characters, and the groups are segmented to emphasize this through character grouping and network construction. A novel is classified into four acts, and the emotions of each group are emphasized and expressed in each act. In this study, 20 novels are clustered using the proposed method;subsequently, they are compared and tested using other expression methods.

关键词： Text Mining Text Embedding Text Representation Novel Representation Character Networks Name Entity Recognizer

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Graph Neural Networks with Stability and Discernability

Graph Neural Networks with Stability and Discernability

引用

IEEE international conference on big data and Smart computing (bigComp)

作者： Thee, Jong Ho Shin, Hyunjung Ajou Univ Dept Artificial Intelligence Suwon 16499 South Korea Ajou Univ Dept Artificial Intelligence Dept Ind Engn Suwon 16499 South Korea

ISBN: (纸本)9781665421973

Graph neural networks (GNNs) are popular for solving graph-related tasks in fields ranging from bioinformatics to social networks. The essential mechanism of a GNN model is local-neighborhood aggregation. However, it is innately difficult to aggregate the global information of a graph even though such information plays a key role in graph-classification tasks such as molecular-function prediction and community detection in social networks. Moreover, the local-aggregation mechanism suffers from limited expressive power, making the model unstable and difficult to distinguish non-isomorphic graphs. To overcome these limitations, we propose a GNN equipped with heat kernel convolution for gathering global information while ensuring stability and heat kernel trace normalization for improving the expressive power. We validated our model against previous methods using various benchmark graph datasets, and the experimental results demonstrate that the proposed GNN's performance is comparable to those of state-of-the-art methods.

关键词： Graph representation learning graph neural networks heat kernel graph classification

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Learning Neural Networks without Lazy Weights

Learning Neural Networks without Lazy Weights

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IEEE international conference on big data and Smart computing (bigComp)

作者： Lee, Dong-gi Cho, Junhee Kim, Myungjun Park, Sunghong Shin, Hyunjung Ajou Univ Sch Med Dept Psychiat Suwon South Korea Kakao Corp Sungnam South Korea Ajou Univ Dept Artificial Intelligence Suwon South Korea Ajou Univ Dept Artificial Intelligence Dept Ind Engn Suwon South Korea

ISBN: (纸本)9781665421973

Various approaches have been suggested for the regularization of neural networks, including the well-known Dropout and Dropconnect, which are simple and efficient to implement and therefore have been widely used. However, there is a risk of loss of well-trained weights when dropping nodes or weights randomly. In this paper, we propose a regularization method that preserves well-trained weights and removes poorly trained weights. This was motivated by the observation that the trained weights become further trained. We define these as eager weights whereas the opposite as lazy weights. On every weight update, the distribution of the changes in weight values is examined, and the lazy weights are removed layer-wise. The results demonstrate that the proposed method has a faster convergence rate, avoids overfitting, and outperforms competing methods on the classification of benchmark datasets.

关键词： Neural networks Regularization Overfitting Deep learning

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Internet of things (IoT) in Agriculture 7

Internet of things (IoT) in Agriculture

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7th international conference on computing, Communication and Security, ICCCS 2022 and 2022 4th international conference on big data and Computational intelligence, ICBDCI 2022

作者： Sharma, Devanshi Chaudhary, Alka Kumar, Anil Amity University Amity Institute of Information Technology Uttar Pradesh Noida India DIT University School of Computing Dehradun India

ISBN: (纸本)9798350309904

A vital challenge in agriculture is to fertilize manufacture within the farm and transport it to the tip shoppers with the simplest doable worth and best possible grade. Presently way and wide, it is found that around 50% for the agricultural produce never reach the end consumer due to wastage and insignificant costs. © 2022 IEEE.

关键词： Internet of things

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