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

共 718 条记录，以下是291-300 订阅

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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 ...

引用

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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Multimodal Deep Fusion in Hyperbolic Space for Mild cognitive Impairment Study 26th

Multimodal Deep Fusion in Hyperbolic Space for Mild Cognitiv...

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

作者： Zhang, Lu Na, Saiyang Liu, Tianming Zhu, Dajiang Huang, Junzhou Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (纸本)9783031439032;9783031439049

Multimodal fusion of different types of neural image data offers an invaluable opportunity to leverage complementary cross-modal information and has greatly advanced our understanding of mild cognitive impairment (MCI), a precursor to Alzheimer's disease (AD). Current multi-modal fusion methods assume that both brain's natural geometry and the related feature embeddings are in Euclidean space. However, recent studies have suggested that non-Euclidean hyperbolic space may provide a more accurate interpretation of brain connectomes than Euclidean space. In light of these findings, we propose a novel graph-based hyperbolic deep model with a learnable topology to integrate the individual structural network with functional information in hyperbolic space for the MCI/NC (normal control) classification task. We comprehensively compared the classification performance of the proposed model with state-of-the-art methods and analyzed the feature representation in hyperbolic space and its Euclidean counterparts. The results demonstrate the superiority of the proposed model in both feature representation and classification performance, highlighting the advantages of using hyperbolic space for multimodal fusion in the study of brain diseases. (Code is available here (https://***/nasyxx/MDF-HS).)

关键词： Hyperbolic Space Multimodal Fusion MCI

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Few-Shot Custom Speech Synthesis with Multi-Angle Fusion 8

Few-Shot Custom Speech Synthesis with Multi-Angle Fusion

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

作者： Zhao, Haifeng Liu, Hao Cao, Mingwei Liu, Yi Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui Hefei230601 China PKU-HKUST Imsl Shenzhen Key Lab Shenzhen China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350302455

In this paper, we propose the TDNN-VITS model: an efficient custom speech synthesis system that can synthesize the speech of arbitrary target speakers, well-improving speech quality as well as reducing the amount of adaptive data. Our model consists of a speaker encoding module, which aims to extract speaker timbre information, and a TTS module, which is based on the fully end-to-end VITS model, but improved according to the problems of custom speech. To better improve the speech quality and speaker similarity, we propose a multi-angle fusion speaker embedding approach. With only 10 speech data and about one minute, good results can be achieved by just fine-tuning for half an hour. The experimental results show that our model is improved compared to the previous classical model, and good speech naturalness and speaker similarity are obtained. The ablation experiments also show the effectiveness of our multi-angle fusion. © 2023 IEEE.

关键词： Embeddings

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VERY-RL: An Adaptive Image Steganography Algorithm Based on Reinforcement Learning 2

VERY-RL: An Adaptive Image Steganography Algorithm Based on ...

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2nd international Symposium on computing and Artificial Intelligence, ISCAI 2023

作者： Long, Yong Qian, Qing Cui, Yunhe Information School Guizhou University of Finance and Economics Guiyang China Engineering Research Center of Text Computing and Cognitive Intelligence Guizhou University Guiyang China

ISBN: (纸本)9798400708954

Adaptive image steganalysis algorithm is one of the important research direction in the field of information security. Nowadays, digital image steganography based on deep learning has been widely used to protect sensitive data during transmission or storage. However, it is a great challenge to achieve the accurate location of the best steganography position in the cover image and improve the model training accuracy is a great challenge. To solve the above problems, a novel digital image steganography algorithm based on reinforcement learning - VERY-RL, is proposed to obtain the optimal embedding strategy. Meanwhile, according to the designed distortion function, the parameters of VERY-RL are updated by gradient backpropagation to minimize the embedding loss. Experimental results show that, compared with other steganographic methods based on reinforcement learning, the detection error rate of VERY-RL is improved by 21.94% when resisting steganalysis detection. In addition, the FLOPs of the model are reduced by 24.8%. It can be seen that VERY-RL improves the localization accuracy of pixel modification bits, and the steganography network has a stronger representation ability, indicating that VERY-RL has better steganographic security. © 2023 ACM.

关键词： Steganography

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Efficient Spatio-Temporal Out-of-Distribution Detection for Autonomous Systems 4

Efficient Spatio-Temporal Out-of-Distribution Detection for ...

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4th international conference on Intelligent Technology and Embedded Systems, ICITES 2024

作者： Ren, Jiankang Li, Simeng Li, Yong Yang, Xin Liu, Lin Li, Shengyu Dalian University of Technology School of Computer Science and Technology Dalian China Dalian University of Technology Key Laboratory of Social Computing and Cognitive Intelligence Dalian China Xinjiang Normal University School of Computer Science and Technology Xinjiang China

ISBN: (纸本)9798331506292

Learning-enabled components (LECs) are increasingly deployed in autonomous systems to provide adaptive and intelligent decision making in a complex dynamic environment. However, most LECs are typically data-driven, and they may exhibit erroneous predictions impacting system safety when there are some distribution shifts between real testing scenarios and training scenarios. In this paper, we introduce an efficient spatio-temporal Out-of-Distribution (OOD) detection scheme for time-series data to improve the detection performance and robustness over the state-of-the-art. Specifically, the optical flow method is employed to extract the spatio-temporal features in the high-dimensional time series data, and the extracted spatio-temporal optical flow features are encoded with the variational autoencoder to obtain input manifold for the OOD detection. Moreover, we proposed a martingale-based inductive conformal anomaly detection method to achieve lower bounded false alarms owing to the statistical foundation and robustness characteristic of martingale. The experimental evaluation demonstrates that our proposed approach significantly outperforms the existing methods in terms of detection capability and robustness. © 2024 IEEE.

关键词： autonomous systems satety Out-of-Distribution detection time-series data

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