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Modeling EEG data into Graphs for the Prognostic of Patients in Coma Using Graph Neural Networks 34th

Modeling EEG Data into Graphs for the Prognostic of Patients...

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34th Brazilian conference on Intelligent Systems

作者： Nascimento Junior, Odelmo O. Assis, Dhara L. C. Destro Filho, Joao B. Zhao, Liang Carneiro, Murillo G. Univ Fed Uberlandia Fac Comp Uberlandia MG Brazil Univ Sao Paulo Dept Comp & Math Ribeirao Preto Brazil

ISBN: (纸本)9783031790379;9783031790386

In this work we present a modeling approach using Graph Neural Networks (GNNs) to support the prognosis of patients in coma (PPC) based on Electroencephalogram (EEG) exams. Coma is a critical medical condition characterized by an extended state of unconsciousness. EEG is an exam that captures the electrical activity of the brain through electrodes positioned on the patient's scalp. It provides valuable information about cerebral functions and can also be used for the monitoring and diagnosis of several diseases. In this study, we investigate four GNN architectures specifically designed for PPC analysis, leveraging the power of convolutional-based layers. In addition, we also develop a modeling strategy to represent EEG sequential data into a graph, named EEGraph, effectively harnessing the spatial positions of electrodes. The main contribution of representing the EEG exam in a graph structure is also to explore all the inherent spatial information the exam has. As it is a highly sensitive exam with generally noisy results on the microvolt scale, its spatial exploration may reveal the potential to improve the PPC task. Experimental results considering real data showed that our proposed architecture outperformed the LSTM model in terms of F1-score and accuracy. Statistical tests evidenced the enhanced predictive performance achieved by exploiting the spatial aspects of EEG data using GNNs. Moreover, the findings highlight the significant potential of GNNs to contribute to the PPC task from EEG data.

关键词： Graph Neural Networks Electroencephalogram Coma outcome Sequential data Graph-based learning EEGraph

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Multi-Group Tensor Canonical Correlation analysis 23

Multi-Group Tensor Canonical Correlation Analysis

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14th ACM conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Zhou, Zhuoping Tong, Boning Tarzanagh, Davoud Ataee Hou, Bojian Saykin, Andrew J. Long, Qi Shen, Li Univ Penn Philadelphia PA 19104 USA Indiana Univ Bloomington IN USA

ISBN: (纸本)9798400701269

Tensor Canonical Correlation analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.

关键词： Canonical Correlation analysis Tensor Decomposition Neuroimaging Alzheimer's Disease

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Knowledge Map of Chinese Geriatric Medical Care Research Field: Research Status and Trend—data visualization analysis Based on Cooc Software 2

Knowledge Map of Chinese Geriatric Medical Care Research Fie...

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2022 2nd International conference on Health Big data and Smart Sports, HBDSS 2022

作者： Bao, Hong-Rui Cui, Yan-Yin Yue, Hui School of Health Management Changchun University of Chinese Medicine Changchun130117 China

ISBN: (纸本)9783800760428

In the long-term context of an ageing population, geriatric medical care is an important safeguard for the Chinese response to the problem of aging. The study aimed to explore the research hotspots and process of geriatric medical care. We conducted a visual analysis of the literatures from 1986 to 2021 in CNKI, Wanfang and VIP database by Louvain algorithm and bibliometric method. We found that the research of geriatric medical care in China lags behind that in developed countries, and the development mainly presents three stages: germination, development and adjustment. The number of publications presents a steady growth trend;Medical and Health journals are the main publication journals;The core research teams are mainly composed of universities and research institutes in Shanghai and Beijing;The clustering topics focus on exploring the elderly medical security system, the feasibility of the medical service model, the management of chronic diseases, the development of the silver industry, medical disputes and health education for the elderly. Finally, we come to the conclusion that the health management of chronic diseases has become a new research hotspot and the exploration of new medical service models is an important trend. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Human resource management

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AUTOVI: Empowering Effective Tracing and visualizations with AI

AUTOVI: Empowering Effective Tracing and Visualizations with...

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International conference on Information visualisation (iv)

作者： Farida AbouWard Ali Salem Nada Sharaf German International University Cairo Egypt

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

This paper introduces AUTOVI, a data exploration platform that changes how individuals interact with and understand datasets. Autovi's contribution comes from its simplicity and focus on making the experience of the user both simple and informative. Autovi achieves its goals by preprocessing real-world data comprehensively, ensuring thorough analysis of different datasets. The modular design of Autovi empowers users, making data exploration supportive and engaging. What sets Autovi apart is its transparent Multi-Layer Perceptron (MLP) classifier and dynamic user feedback mechanisms, which prioritize clarity in training processes for better user understanding. Autovi demonstrates efficiency in data exploration tasks compared to traditional coding approaches. Autovi also automates insights, making data exploration accessible to users of all proficiency levels.

关键词： Training data visualization Collaboration User experience Real-time systems Encoding Iterative methods Artificial intelligence Faces Testing

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Integration and visual analysis of Biomolecular Networks Through UNIPred-Web 2nd

Integration and Visual Analysis of Biomolecular Networks Thr...

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22nd International conference on Web Engineering (ICWE)

作者： Perlasca, Paolo Frasca, Marco Ba, Cheick Tidiane Gliozzo, Jessica Notaro, Marco Pennacchioni, Mario Valentini, Giorgio Mesiti, Marco Univ Milan Dept Comp Sci Anacleto Lab Milan Italy

ISBN: (纸本)9783031253799;9783031253805

The integration of heterogeneous biological data into a common network representation is of paramount importance in different areas of biology and medicine. The size of the generated network in many cases prevents the possibility of its graphical visualization, inspection, and identification of characteristics. In this paper, we present the main features of UNIPred-Web (UNIPred-Web is available at https://***/), a web application realized to support the user in the integration of several biomolecular networks and for their visualization and navigation at different resolution levels. In particular, the identification and effective visualization of hierarchical communities allow an easy exploration of the entire network, while protein function prediction functionalities support the user in the interpretation process.

关键词： visualization of biomolecular networks Web services for protein function Network integration Community hierarchy detection

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Level of Detail visual analysis of Structures in Solid-State Materials

Level of Detail Visual Analysis of Structures in Solid-State...

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25th Eurographics conference on visualization, EuroVis 2023

作者： Thygesen, Signe Sidwall Abrikosov, Alexei I. Steneteg, Peter Masood, Talha Bin Hotz, Ingrid Scientific Visualization Group Linköping University Sweden

ISBN: (纸本)9783038682196

We propose a visual analysis method for the comparison and evaluation of structures in solid-state materials based on the electron density field using topological analysis. The work has been motivated by a material science application, specifically looking for new so-called layered materials whose physical properties are required in many modern technological developments. Due to the incredibly large search space, this is a slow and tedious process, requiring efficient data analysis to characterize and understand the material properties. The core of our proposed analysis pipeline is an abstract bar representation that serves as a concise signature of the material, supporting direct comparison and also an exploration of different material candidates. © 2023 The Authors.

关键词： Topology

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visual Attention for Malware Classification 4

Visual Attention for Malware Classification

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conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications iv

作者： Alsadi, Naseem Hilal, Waleed Surucu, Onur Giuliano, Alessandro Gadsden, Stephen A. Yawney, John McMaster Univ 1280 Main St W Hamilton ON L8S 4L8 Canada Adastra Corp 8500 Leslie St 600 Thornhill ON L3T 7M8 Canada

ISBN: (纸本)9781510651036;9781510651029

Amidst the extensive global integration of computer systems and augmented connectivity, there have been numerous difficulties within ensuring confidentiality, integrity and availability across all systems. Malware is an ever-present and persistent challenge for security systems of all sorts. Numerous malware detection methods have been proposed, with traditional approaches no longer providing the necessary protection against evolving attack methodologies and strategies. In recent years, machine learning for malware detection has been investigated with great success. In addition, the analysis of application operation code, or opcode, due to its unavoidable nature, can reveal necessary information about software intention. visualization of opcode data allows for simple data augmentation and texture analysis. The proposed approach utilizes a simple visual attention module to perform a binary classification task on program data, focusing on visualized application opcode data. The proposed model is tested with an ARM-based Internet of Things (IoT) application opcode dataset. In addition, a comparative analysis, using numerous metrics, is conducted on the proposed model's performance along with several other algorithms. The results indicate that the proposed method outperformed all other tested techniques in accuracy, recall, precision, and F-score.

关键词： Malware Detection Artificial Intelligence Deep Learning

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Automated Extraction of Prosodic Structure from Unannotated Sign Language Video 30

Automated Extraction of Prosodic Structure from Unannotated ...

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Joint 30th International conference on Computational Linguistics and 14th International conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Sevilla, Antonio F.G. Lahoz-Bengoechea, José María Díaz Esteban, Alberto Universidad Complutense de Madrid José García Santesmases 9 Madrid28040 Spain Facultad de Filología Universidad Complutense de Madrid edificio D Madrid28040 Spain Knowledge Engineering Institute Facultad de Psicología Universidad Complutense de Madrid Lateral 2 Pozuelo de Alarcón28223 Spain

ISBN: (纸本)9782493814104

As in oral phonology, prosody is an important carrier of linguistic information in sign languages. One of the most prominent ways this reveals itself is in the time structure of signs: their rhythm and intensity of articulation. To be able to empirically see these effects, the velocity of the hands can be computed throughout the execution of a sign. In this article, we propose a method for extracting this information from unlabeled videos of sign language, exploiting CoTracker, a recent advancement in computer vision which can track every point in a video without the need of any calibration or fine-tuning. The dominant hand is identified via clustering of the computed point velocities, and its dynamic profile plotted to make apparent the prosodic structure of signing. We apply our method to different datasets and sign languages, and perform a preliminary visual exploration of results. This exploration supports the usefulness of our methodology for linguistic analysis, though issues to be tackled remain, such as bi-manual signs and a formal and numerical evaluation of accuracy. Nonetheless, the absence of any preprocessing requirements may make it useful for other researchers and datasets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： visual languages

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Comparative analysis of VGGish and YAMNet Models forWelding Defect Detection 1

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8th International Scientific-Technical conference (MANUFACTURING)

作者： Avila, Alejandra Pinzon, Cesar Technol Univ Panama Cent Campus Dr Victor Levi Sasso Panama City Panama Lab Computat & Appl Theoret Mech Cent Campus Dr Victor Levi SassoBldg 1 Panama City Panama

ISBN: (数字)9783031564673

ISBN: (纸本)9783031564697;9783031564673

Manufacturing, especially in welding, requires advanced tools to detect and classify sounds associated with the process that may lead to defects. This study investigates the applicability of pre-trained convolutional neural network models known as VGGish (visual Geometry Group) and YAMNet (Yet Another Mixture-of-Experts Network) to analyze and improve welding defect detection. The methodology involved data collection and processing, as well as training and evaluation of the models in terms of accuracy and inference time. To determine the evaluation parameters, songs from two rock bands with similar styles were used to extract audio information, considering the audio amplitude, rhythm (pattern), and tempo (speed) of the songs. Additionally, the behavior of the audio was visualized using a spectrogram, which illustrates the variation of sound over time. The objective is to understand the acoustic structure of the songs, which can be useful for pattern detection and analysis of audio signals. It is expected that, after applying the pre-trained models VGGish and YAMNet in predicting audios from a custom model trained with these songs, one of the two pre-trained models will demonstrate superior performance in terms of accuracy and inference time. This denotes a pre-trained model with better probabilities, which can be further used in other research for welding audio, aiding in the development of the acoustic monitoring tool in manufacturing, especially within the welding process. This contributes to the efficient identification of defects and process optimization.

关键词： Manufacturing Audio Signal Surface Discontinuities

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Sparsity exploration for Structured and Unstructured Weight Formations in CNN Architecture

Sparsity Exploration for Structured and Unstructured Weight ...

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Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and conference (APSIPA ASC)

作者： Endrawati, Devi Noor Syafalni, Infall Sutisna, Nana Adiono, Trio Bandung Inst Technol Sch Elect Engn & Informat Bandung Indonesia Bandung Inst Technol Univ Ctr Excellence Microelect Bandung Indonesia

ISBN: (纸本)9798350300673

The Convolutional Neural Network (CNN) is a frequently used algorithm used technique for processing pixel data and visual images. Nevertheless, the utilization of extensive network sizes poses significant obstacles to the underlying technology's throughput and energy efficiency. Various methods have been devised to enhance the efficiency and computing speed of Convolutional Neural Network (CNN) acceleration, improving the accuracy of the CNN process. Compression techniques have been employed in the algorithmic aspect to minimize the number of parameters in each layer, enhancing the detection process's efficiency. Pruning is a compression technique that involves the removal of neurons and synapses that do not play a substantial role in the detection process, hence minimizing their impact on the Neural Network layer. The compression method generates sparse data, wherein a significant amount of data has a value of zero. Various pruning techniques provide distinct types of sparse data, encompassing organized structured sparse and unstructured sparse data. The consequent form of sparse data structure has many advantages and downsides. Based on an analysis of various extant sparse data structures, an innovative accelerator has been developed to perform convolution operations using various types of sparse data obtained via distinct pruning procedures. The design architecture outlined in this study involves the removal of all zero-valued data weights through a pruning technique following the training process. The accelerator will process all non-zero weights by performing the convolution process promptly, utilizing the "weight stream" data. The findings from the conducted design testing indicate that the utilization of trimmed sparse data leads to improved performance in terms of faster output during the convolution process. The researchers conducted experiments utilizing diverse types of sparse weighted data, encompassing both structured and unstructured forms. These exper

关键词： Convolution

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