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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 712 条记录，以下是281-290 订阅

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29th international computing and Combinatorics conference, COCOON 2023

29th International Computing and Combinatorics Conference, C...

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29th international computing and Combinatorics conference, COCOON 2023

ISBN: (纸本)9783031491924

The proceedings contain 60 papers. The special focus in this conference is on computing and Combinatorics. The topics include: Sink Location Problems in Dynamic Flow Grid Networks;list 3-Coloring on Comb-Convex and Caterpillar-Convex Bipartite Graphs;parameterized Algorithms for Cluster Vertex Deletion on Degree-4 Graphs and General Graphs;sum-of-Local-Effects data Structures for Separable Graphs;variants of Euclidean k-Center Clusterings;red-Black Spanners for Mixed-Charging Vehicular Networks;self-stabilizing (Δ+ 1 ) -Coloring in Sublinear (in Δ ) Rounds via Locally-Iterative Algorithms;on Detecting Some Defective Items in Group Testing;an Efficient data Analysis Method for Big data Using Multiple-Model Linear Regression;conditional Automatic complexity and Its Metrics;multi-Load Agent Path Finding for Online Pickup and Delivery Problem;improved Sourcewise Roundtrip Spanners with Constant Stretch;randomized data Partitioning with Efficient Search, Retrieval and Privacy-Preservation;the k Edge-Vertex Domination Problem;resource-Adaptive Newton’s Method for Distributed Learning;DR-Submodular Function Maximization with Adaptive Stepsize;on the Routing Problems in Graphs with Ordered Forbidden Transitions;delaying Decisions and Reservation Costs;A PTAS Framework for Clustering Problems in Doubling Metrics;A Physical Zero-Knowledge Proof for Sumplete, a Puzzle Generated by ChatGPT;streaming and Query Once Space complexity of Longest Increasing Subsequence;approximating Decision Trees with Priority Hypotheses;approximating the λ -low-density Value;Exponential Time complexity of the complex Weighted Boolean #CSP;hardness and Approximation for the Star β -Hub Routing Cost Problem in Δβ -Metric Graphs;Linear Time Algorithms for NP-Hard Problems Restricted to GaTEx Graphs;polynomial Turing Compressions for Some Graph Problems Parameterized by Modular-Width.

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Benchmarking the Variational Quantum Eigensolver using different quantum hardware 4

Benchmarking the Variational Quantum Eigensolver using diffe...

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4th IEEE international conference on Quantum computing and Engineering, QCE 2023

作者： Bentellis, Amine Matic-Flierl, Andrea Mendl, Christian B. Lorenz, Jeanette Miriam Fraunhofer Institute for Cognitive Systems Iks Munich Germany Technical University of Munich Cit Department of Computer Science Garching Germany Technical University of Munich Institute for Advanced Study Garching Germany

ISBN: (纸本)9798350343236

The Variational Quantum Eigensolver (VQE) is a promising quantum algorithm for applications in chemistry within the Noisy Intermediate-Scale Quantum (NISQ) era. The ability for a quantum computer to simulate electronic structures with high accuracy would have a profound impact on material and biochemical science with potential applications e.g., to the development of new drugs. However, considering the variety of quantum hardware architectures, it is still uncertain which hardware concept is most suited to execute the VQE for e.g., the simulation of molecules. Aspects to consider here are the required connectivity of the quantum circuit used, the size and the depth and thus the susceptibility to noise effects. Besides theo-retical considerations, empirical studies using available quantum hardware may help to clarify the question of which hardware technology might be better suited for a certain given application and algorithm. Going one step into this direction, within this work, we present results using the VQE for the simulation of the hydrogen molecule, comparing superconducting and ion trap quantum computers. The experiments are carried out with a standardized setup of ansatz and optimizer, selected to reduce the number of required iterations. The findings are analyzed considering different quantum processor types, calibration data as well as the depth and gate counts of the circuits required for the different hardware concepts after transpilation. © 2023 IEEE.

关键词： Electronic structure

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29th international computing and Combinatorics conference, COCOON 2023

29th International Computing and Combinatorics Conference, C...

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29th international computing and Combinatorics conference, COCOON 2023

ISBN: (纸本)9783031491894

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A Framework of cognitive Intelligence-Enabled Welding Cyber Physical System 19

A Framework of Cognitive Intelligence-Enabled Welding Cyber ...

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19th IEEE international conference on Automation Science and Engineering, CASE 2023

作者： Liu, Tianyuan Jiang, Yanan Zheng, Pai Bao, Jinsong The Hong Kong Polytechnic University Department of Industrial and Systems Engineering 999077 Hong Kong Aerospace System Engineering Shanghai Shanghai201111 China College of Mechanical Engineering Donghua University Shanghai201620 China

ISBN: (纸本)9798350320695

Building a welding cyber physical system (WCPS) is an important way to realize intelligent welding. However, the interaction between the current welding cyber world and the physical world is insufficient. On the one hand, it is reflected in the lack of cognition of the welding process that contains quality information. On the other hand, the interaction efficiency based on manual experience is low. A cognitive intelligence-enabled WCPS is proposed. Firstly, the welding knowledge graph is constructed to model and manage the multi-source data, so as to improve the interaction efficiency between welding cyber space and physical space. Secondly, the cognitive capability of the WCPS is improved through perceptual and cognitive computing. Finally, the welding action formed by each knowledge node is optimized based on reinforcement learning to improve the interactive capability of the WCPS. © 2023 IEEE.

关键词： Cyber Physical System

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Prediction of Infant cognitive Development with Cortical Surface-Based Multimodal Learning 26th

Prediction of Infant Cognitive Development with Cortical Sur...

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26th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Cheng, Jiale Zhang, Xin Zhao, Fenqiang Wu, Zhengwang Yuan, Xinrui Wang, Li Lin, Weili Li, Gang South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China Univ N Carolina Dept Radiol Chapel Hill NC 27515 USA Univ N Carolina Biomed Res Imaging Ctr Chapel Hill NC 27515 USA Pazhou Lab Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Exploring the relationship between the cognitive ability and infant cortical structural and functional development is critically important to advance our understanding of early brain development, which, however, is very challenging due to the complex and dynamic brain development in early postnatal stages. Conventional approaches typically use either the structural MRI or resting-state functional MRI and rely on the region-level features or inter-region connectivity features after cortical parcellation for predicting cognitive scores. However, these methods have two major issues: 1) spatial information loss, which discards the critical fine-grained spatial patterns containing rich information related to cognitive development;2) modality information loss, which ignores the complementary information and the interaction between the structural and functional images. To address these issues, we unprecedentedly invent a novel framework, namely cortical surface-based multimodal learning framework (CSML), to leverage fine-grained multimodal features for cognition development prediction. First, we introduce the fine-grained surface-based data representation to capture spatially detailed structural and functional information. Then, a dual-branch network is proposed to extract the discriminative features for each modality respectively and further captures the modality-shared and complementary information with a disentanglement strategy. Finally, an age-guided cognition prediction module is developed based on the prior that the cognition develops along with age. We validate our method on an infant multimodal MRI dataset with 318 scans. Compared to state-of-the-art methods, our method consistently achieves superior performances, and for the first time suggests crucial regions and features for cognition development hidden in the fine-grained spatial details of cortical structure and function.

关键词： Cognition Prediction Multimodality rs-fMRI sMRI

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Traumatic Brain Injury Classification Based on Slice Feature Selection and Transformer 8

Traumatic Brain Injury Classification Based on Slice Feature...

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8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Liu, Jinyu Ye, Tianhui Huang, Lili Li, Chuanfu Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China The First Affiliated Hospital of Anhui University of Chinese Medicine Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350302455

Traumatic brain injury (TBI) usually originates from primary injury, has a high rate of death and disability, and can lead to temporary or permanent neurological deficit in severe cases. Therefore, it is very important to make an accurate imaging diagnosis as early as possible. Deep learning is widely used in medical image analysis, and the use of convolutional neural networks for initial diagnosis of traumatic brain trauma has been proven effective. Most of the traumatic brain injury recognition work in recent years is to classify a single image. We propose a new approach for diagnosing traumatic brain injuries from CT images that takes advantage of the sequence relationship between slices. Unlike natural images, medical images require doctors to synthesize information from multiple slices to make an accurate diagnosis. To address this, we combine convolutional neural networks (CNN) and Transformers to extract complex features and classify brain CT images by leveraging the correlation between slices. We also introduce an embeddable slice feature selection module that further improves accuracy. We evaluated our approach on a hospital-provided dataset and achieved superior performance compared to existing methods. © 2023 IEEE.

关键词： Convolutional neural networks

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cognitive Stress Recognition During Mathematical Task and EEG Changes Following Audio-Visual Stimuli for Relaxation

Cognitive Stress Recognition During Mathematical Task and EE...

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2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Rashmi, C.R. Shantala, C.P. Channabasaveshwara Institute of Technology Department of Computer Science & Engineering Tumkur Karnataka Gubbi India

ISBN: (纸本)9798350313987

Solving mathematical tasks or puzzles is one of the most common learning processes in many learning activities. This task can be either simple or complex depending on the level of study. Many individuals feel the fear of math tasks which will hamper the learning process due to stress. Hence, recognition of stress plays a crucial role in many learning activities. In this work, machine learning-based automatic recognition of mental stress during math puzzle solving is developed using EEG signals. EEG signals are recorded using Enobio 8 device in two categories, one during math puzzle solving and another in happy state with audio-visual stimuli with funny video clips. The raw signals are preprocessed to remove the artifacts using 2-40Hz band pass filter. Filtered data is used to extract EEG bands. EEG relative band power is analyzed to observe the changes during math task and relaxation. The two mental states are classified using Long Short Term Memory (LSTM) deep learning model. The classification accuracy of 86% is achieved in beta band. © 2023 IEEE.

关键词： Brain Computer Interface Deep learning Electroencephalogram Long Short Term Memory Mental Arithmetic Stress

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EEG-Based Real-Time Prediction of cognitive State on Smartphone 1

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3rd international conference on "Emerging Trends and Technologies on Intelligent Systems" (ETTIS-2023)

作者： Jain, Priyanka Sharma, Greeshma Kirode, Piyush Koria, Harsh Deep, Aakash Jain, Naveen Kumar Center for Development of Advanced Computing New Delhi India

ISBN: (数字)9789819939633

ISBN: (纸本)9789819939626

With the pace of high cutting-edge technical solutions, the ease of availability and affordability are the parameters which affect the accessibility of it to the general mass. This motivated us for a handy real-time analysis of human cognitive state using smartphones. In a technology-driven society, it is very important to measure and predict meaningful brain signals in real time. As electroencephalograph (EEG) represents a solution that has high temporal resolution, it becomes a useful tool for society. One such application is to understand that attention is useful for learning and relaxation is useful for health. This research paper provides a framework that gives insight into mobile-based real-time mental state visualization along with levels of attention and relaxation. The framework is built with an Indian-made, eight-channel EEG device (neuphony). This is the first study to show a brain-mobile interface for omnipresent, prevalent computing. We eventually construct a sophisticated mobile-based EEG monitoring system by combining signal processing algorithm with an Android mobile application and we verify its effectiveness using real-time EEG data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smartphones

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Development of a Method for Assessing the Impact of Factors on the Feasibility of Information and Management Systems Creation Projects Using Fuzzy cognitive Models 16th

Development of a Method for Assessing the Impact of Factors ...

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16th international conference on Application of Fuzzy Systems, Soft computing and Artificial Intelligence Tools, ICAFS 2023

作者： Bayramov, I.Y. Gurbanov, A.N. Sardarova, I.Z. Abbasova, S.V. Mammadova, G.G. Azerbaijan State Oil and Industry University Baku Azerbaijan

ISBN: (纸本)9783031725050

The novelty of the methodological basis for the analysis of feasibility of implementing projects to create information management systems (IMS) in 7the pre-project research stage is in the comprehensive integration of general system principles, the use of management and modeling principles, cognitive models, and decision-making support methods. This makes it possible to scientifically prove the expediency of realizing the studied project, as well as to minimize resource costs when evaluating the feasibility of developing projects in the case of uncertain initial data. The proposed method involves the use of fuzzy cognitive modeling (FCM) during the evaluation of given projects when creating IMS. This method makes it possible to determine which of the factors have a greater influence on the whole system, as well as which factors are most influenced by the system in the case of fuzzy values (triangular and trapezoidal fuzzy numbers, intervals) as well as in the case of verbal descriptions of relationships between factors. Apart from these, it also allows to evaluate the degree of influence of factors to increase the reliability of management decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Project management

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Machine Learning-Based Channel Power Analysis inside Aircraft Cabin

Machine Learning-Based Channel Power Analysis inside Aircraf...

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2023 international conference on Intelligent computing and Control, IC and C 2023

作者： Ye, Xijuan Mao, Kai Ma, Jian Song, Lixin Liu, Yuxin Zhu, Qiuming Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing China Aeronautical Key Laboratory of Aeronautical Science and Technology Nanjing Mechanical and Electrical Hydraulic Engineering Research Center of Aviation Industry Nanjing China

ISBN: (纸本)9781665457095

Communication inside aircraft cabin has gained more and more attentions to provide in-flight connectivity for high-quality user experience and enable the wireless communication of electromechanical equipment. In this paper, a power prediction method considering the coordinates of transceiver, propagation distance and carrier frequency based on the parameterized channel model and machine learning (ML) framework is proposed for wireless communication inside the aircraft cabin. In the proposed channel model, a random forest (RF) network is trained for channel characteristics based on ray tracing (RT) simulation data and applied to estimate the power parameters. The proposed method is verified inside a typical aircraft cabin at 1GHz and compared with traditional model and background propagation neural network (BPNN). The simulation results show that RF can accurately predict the power parameter and reproduce the channel environment. The prediction results of power distribution can provide valuable reference for the layout and optimization of communication nodes inside the aircraft cabin. © 2023 IEEE.

关键词： Forecasting

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