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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1561 条记录，以下是61-70 订阅

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Wearable Pair of EEG, EOG and fNIRS Glasses for cognitive Workload Detection 20

Wearable Pair of EEG, EOG and fNIRS Glasses for Cognitive Wo...

引用

20th international conference on Body Sensor Networks

作者： Kosmyna, Nataliya El Adl, Karim Kim, Minsol MIT Media Lab 77 Massachusetts Ave Cambridge MA 02139 USA Yale Univ New Haven CT USA

ISBN: (纸本)9798331530143

A rapidly growing number of studies demonstrate the potential benefits of Brain-Computer Interfaces (BCIs) in a wide range of use cases including education, workplace safety, and mental health. Recent technological advances have allowed a growing number of wearable BCIs appear both as research prototypes and on the market. The goal of this project was to develop and evaluate a BCI platform that integrates both electrical and metabolic sensing in a wearable glasses form-factor. Our system combines data from three different physiological modalities: four channels of functional Near-Infrared Spectroscopy (fNIRS), three Electroencephalography (EEG) channels and two Electrooculography (EOG) channels. We compared different preprocessing and cognitive load classification strategies using data collected in a 14 subject mental arithmetic study. An SVM produced an average accuracy of 79% using EEG+EOG+fNIRS features compared with 62% and 63% for EEG and fNIRS respectively.

关键词： electroencephalography (EEG) electrooculography (EOG) functional near-infrared spectroscopy (fNIRS) glasses cognitive load wearable computing multimodal

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Research on real-time data processing system combining edge computing and artificial intelligence 7

Research on real-time data processing system combining edge ...

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7th international conference on Automation Electronics and Electrical Engineering

作者： Huangfu, Hancong China Southern Power Gird Guangdong Power Grid Foshan Power Supply Bur Foshan Peoples R China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040;9798350377033

With the exponential growth of Internet of Things (IoT) devices, vast amounts of diverse data require real-time processing. However, traditional cloud computing approaches often struggle to meet these demands due to issues related to latency and bandwidth constraints. As an emerging computing paradigm, edge computing significantly reduces data transmission latency and improves processing efficiency. In this paper, a real-time data processing system combining edge computing is proposed to realize efficient processing and intelligent analysis of distributed data. Firstly, this paper systematically analyzes the potential advantages and challenges of edge computing and artificial intelligence in real-time data processing, and designs a system architecture that integrates them. The architecture consists of four main modules: data acquisition, edge processing, intelligent analysis and feedback control, aiming to realize real-time data processing with low latency and high performance. Specifically, edge devices perform preliminary data screening and pre-processing by deploying lightweight artificial intelligence algorithms;Then, the optimized deep learning model performs complex data analysis and prediction on the edge nodes;Finally, the processing results are fed back to the data source in time to achieve closed-loop control. Compared with the traditional cloud computing model, the system combining edge computing and artificial intelligence shows significant advantages in terms of latency, bandwidth utilization and processing throughput.

关键词： IoT edge computing bandwidth utilization closed loop control

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Credibility evaluation of aircraft HWIL simulation based on the time-varying area metric 10

Credibility evaluation of aircraft HWIL simulation based on ...

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10th ieee international conference on High Performance and Smart computing (ieee HPSC)

作者： Geng, Huapin Wang, Ning Tong, Jiahui Beijing Electromech Engn Inst Sci & Technol Complex Syst Control & Intelligent Beijing Peoples R China Beijing Electromech Engn Inst Beijing Peoples R China

ISBN: (纸本)9798350389463;9798350389470

Credibility evaluation of aircraft HWIL simulation is usually measured by the degree of consistency between simulation test data and the measured data. A new method for evaluating the credibility of aircraft HMI, simulation is established based on the idea of time-varying area measurement and uncertainly measurement Mainly for univariate, single simulation, multivariable and multi-simulation. The DTW distance measurement method is used to obtain the weight coefficients of correlated multiple response variables, and the effectiveness of uncertainly measurement method in obtaining the credibility of aircraft HWIL simulation at a certain level of confidence is verified through typical examples.

关键词： Time-varying area measurement Credibility evaluation DTW distance measurement

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Autoencoder-Assisted Feature-Type Distributed Clustering for Clinical and Biomedical data Analysis 10

Autoencoder-Assisted Feature-Type Distributed Clustering for...

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10th ieee international conference on Electronics, computing and Communication Technologies (ieee CONECCT)

作者： Thomas, Lenat Nair, Madhu S. Bej, Saptarshi Cochin Univ Sci & Technol Dept Comp Sci Artificial Intelligence & Comp Vis Lab Kochi 682022 Kerala India Indian Inst Sci Educ & Res Dept Data Sci Thiruvananthapuram 695551 Kerala India

ISBN: (纸本)9798350385939;9798350385922

The Uniform Manifold Projection and Approximation (UMAP) assisted Feature-type Distributed Clustering (FDC) workflow, contrary to the traditional UMAP algorithm, was found to be more informative for dimensionality reduction in tabular Clinical and Biomedical Routine data (CBRD) due to the presence of diverse feature-types in such datasets. However, the prospect of using powerful neural network models for the Feature-type distributed dimensionality reduction task is yet to be explored. Considering the broader applicative potential of neural networks, we compared some autoencoder-based neural networks as an alternative to the UMAP algorithm for the FDC workflow. The study uses standard objective measures such as Silhouette Score and DB-Index to compare the quality of clusters and embeddings generated by the Autoencoder-assisted FDC (FDC-AE) approaches with the established UMAP-assisted FDC (FDC-UMAP) workflow. The evaluation involved two datasets in each size category from the medical field. The results indicate that for a dimensionality reduction and cluster identification task, the FDC-AE can be more effective compared to the FDC-UMAP, especially on larger datasets. Our research opens up the possibility of feature-distributed multi-label supervised dimensionality reduction, as well as the usage of pre-trained networks for such complex dimensionality reduction tasks.

关键词： Tabular data UMAP Autoencoders Clustering Manifold Learning

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Proceedings - 2024 ieee international conference on Social computing and Networking, SocialCom 2024

Proceedings - 2024 IEEE International Conference on Social C...

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17th ieee international conference on Social computing and Networking, SocialCom 2024

ISBN: (纸本)9798331521097

The proceedings contain 9 papers. The topics discussed include: edge intelligence empowered social image recognition using microservices architecture;leveraging weighted dependencies in graph convolutional networks for biomedical relation detection;glass segmentation network with panoramic image;accelerating sparse matrix-matrix multiplication by adaptive batching strategy on MGPUSim;Lite-HRNet with attention mechanism;learning based opportunistic data transmission for cognitive IoT;activity recognition of delivery personnel by integrating LSTM and CNN on wearable sensory data;an enhanced lightweight steel surface defect detection method based on cross-scale feature fusion;and MetaScene: a novel metaverse large-scale 3D scene rendering framework.

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Analysis of complex Network Topological Features for EEG Signals Classification 6

Analysis of Complex Network Topological Features for EEG Sig...

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6th international conference on data-Driven Optimization of complex Systems (DOCS)

作者： Zhen, Ping Yue, Yuanyuan Liu, Qingshan Southeast Univ Sch Math Nanjing Jiangsu Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Electroencephalogram (EEG) signals provide direct insights into the physiological activities of brain, making EEG analysis valuable for understanding cognitive processes and neurological disorders. The main objective of this study is to investigate the effectiveness of extracting combined topological features for epilepsy recognition based on EEG data. We utilize visibility graphs and persistent diagrams to extract both graph features and homological features. These features are then combined to enhance the classification performance, surpassing the results achieved by using graph features alone. Notably, our approach achieves high accuracy rates of 100% for classifying Z and S, 99.75% for N and S, and 97.88% for F and S. These findings significantly contribute to the field of EEG-based epilepsy classification and provide valuable insights for further research and investigation.

关键词： EEG feature extraction visibility graph complex network

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A data-driven approach for context definition in situation-aware wearable computing systems 20

A data-driven approach for context definition in situation-a...

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ieee 20th international conference on Automation Science and Engineering (CASE)

作者： D'Aniello, Giuseppe Fortino, Giancarlo Gaeta, Matteo Rehman, Zia Ur Univ Salerno Dept Informat & Elect Engn & Appl Math Fisciano SA Italy Univ Calabria Dept Comp Engn Modeling Elect & Syst Arcavacata Di Rende CS Italy

ISBN: (纸本)9798350358513;9798350358520

A situation-aware wearable computing system is defined as a wearable device with the capability to perceive, comprehend, and project the situations occurring in the environment in order to adapt its behavior accordingly. For this kind of devices, the identification of complex situations related to the activities performed by users in various contexts is of great interest. This requires the capability to identify context states. A well-known technique for this task is the Context Space Theory (CST), which provides a multidimensional space representing contexts. Although powerful and lightweight, this technique has the drawback of requiring manual definition of such a space, a time-consuming process that involves domain experts. To address this issue, this work proposes a data-driven approach for defining context spaces in CST using kernel density estimation. This approach is compared with a state-of-theart expert-based CST technique and a fuzzy inference system for context representation, demonstrating superior performance on the Extrasensory dataset.

关键词： Wearable computers

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Tabular Generative Adversarial Networks with an Enhanced Sampling Approach for High-Quality Cardiovascular Disease dataset Generation 22

Tabular Generative Adversarial Networks with an Enhanced Sam...

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ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Alqulaity, Malak Yang, Po Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England

ISBN: (纸本)9798350381993;9798350382006

Cardiovascular diseases (CVDs) are a significant cause of global mortality;thus, early intervention of CVDs can prevent their progression and effectively lower their death rate. However, one of the main obstacles to this is the lack of highquality datasets for CVDs, which are essential for efficiently training machine learning prediction models. The available datasets are limited in size and have missing values and a highclass imbalance. Generative adversarial networks (GANs) have been shown to have high potential in producing medical synthetic tabular data, but the heterogeneity and complex dependencies of such data present challenges. This study proposes an enhanced sampling approach that aims to improve the quality of synthetic data and their resemblance to the original data distribution. The proposed method is evaluated on a medical dataset containing demographic, clinical and laboratory data from patients with CVDs. As part of the examination process, the performance of the proposed method was evaluated by comparing it with the data generated from the CTGAN. Our results show a higher degree of similarity between the distributions of the generated data and the real datasets, demonstrating the efficacy of the proposed sampling approach. The synthetic data generated by our model closely resembles the statistical properties of real data and shows strong potential for improving data quality. By accurately capturing the patterns and features of cardiovascular disease data, our model can contribute to enhancing the CVD risk prediction model.

关键词： cardiovascular diseases generative adversarial networks tabular data synthetic data neural network medical datasets

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cognitive computing in E-Commerce Enhancing Supply Chain Management 7

Cognitive Computing in E-Commerce Enhancing Supply Chain Man...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Al Awadh, Mohammed Karthick, K.K. Ramachandran, K.K. King Khalid University Department of Industrial Engineering Abha61411 Saudi Arabia Saveetha Engineering College Department of Management Science Thandalam Chennai India Dr G R D College of Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350350067

cognitive computing has been posturing itself as the disruptive innovation that advances the supply chain management in e-commerce. This research aims at identifying how cognitive computing system can be incorporated in the supply chain in various areas including demand forecast, inventory replenishment, and customer relations. Through the use of analytical capabilities, artificial intelligence (machine learning), and natural language processing, cognitive computing improves decision-making accuracy, eradicates redundancies, and dynamics timely improvements based on market trends. The study illustrates that cognitive computing not only leads to an enhanced structure of e-commerce supply chains but are also more responsive and flexible, thus leading to effective customer satisfaction and company advantage. © 2024 ieee.

关键词： cognitive computing competitive advantage customer satisfaction data analytics demand forecasting ecommerce inventory optimization machine learning natural language processing operational efficiency supply chain management

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Low-Carbon Disassembly Sequence Planning with Improved MothFlame Optimizer by Considering Non-homogeneous Information in Industry 5.0 20

Low-Carbon Disassembly Sequence Planning with Improved MothF...

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ieee 20th international conference on Automation Science and Engineering (CASE)

作者： Feng, Yixiong Cui, Kaiyue Hong, Zhaoxi Yan, Weiyu Song, Xiuju Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China Zhejiang Univ Ningbo Innovat Ctr Ningbo 315100 Zhejiang Peoples R China

ISBN: (纸本)9798350358513;9798350358520

complex high-end equipment is the fundamental hardware of Industry 5.0. Low-carbon green disassembly for high-end equipment has prominent effects on Industry 5.0. However, low-carbon disassembly sequence planning (DSP) for complex high-end equipment is time-consuming and inefficient. Moreover, it is easy to generate non-homogeneous decisionmaking information due to cognitive uncertainty during disassembly evaluation, which makes it too difficult to determine the optimal solution. To address these issues, this work proposes a novel DSP method by integrating disassembly carbon benefit, improved Moth-Flame Optimizer (IMFO), and normalization of non-homogeneous information in this study. IMFO is designed to solve DSP. It adopts good-set theory, a nonlinear dynamic convergence factor, and an adaptive weight for greater efficiency in searching for feasible solutions. A 2-tuple is utilized as the uniform representation base to process non-homogeneous information. Finally, a case study for the CNC TGK46100 is provided to illustrate the effectiveness of the proposed method. Throughout the analysis, carbon benefit is increased by 1 to 2.83 times, and computing efficiency is increased by 68.57% and 61.27% over an advanced ant colony algorithm and genetic algorithm respectively.

关键词： Premixed flames

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