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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

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Investigation of Auto Emotional Detection of Health Professionals Based on Bio Information data Analytics 2

Investigation of Auto Emotional Detection of Health Professi...

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2nd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Kumar, Ashish Lourens, Melanie Elizabeth Tiwari, Nitin Dass, Pranav Kumar, M v Suresh Abdullah, Khairul Hafezad Computer Science and Engineering Its Engineering College Greater Noida India Durban University of Technology Faculty of Management Sciences South Africa Community Medicine Affiliation- Asmc Uttar Pradesh India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Koneru Lakshmaiah Education Foundation Guntur India Universiti Teknologi Mara Science Laboratory Unit Department of Academic Affairs Malaysia

ISBN: (纸本)9781665437899

Emotion detection is an important aspect in healthcare industries. Effective analysis of emotion detection helps in analyses patient's mental state, psychological state, disease progression rate etcetera. Emotion detection is also required for healthcare professionals (doctors and nurses). Automatic emotion detection is usually done with different technologies such as AI technology, multimodal system, pattern recognition, signal analysis, audio-visual analysis etcetera. The present research analyses the most effective technology for auto-emotion detection among all the technologies. The survey-based statistical analysis has been done in this research with 53 participants from different healthcare sectors of the United Kingdom. The data shows that AI-based multimodal system and Pattern recognition using Electrocardiogram and Electroencephalogram are the most effective technologies for automatic-emotion detection. The analysis also showed that emotion-detection is necessary for healthcare professionals and this analysis helps in enhancing patient's recovery rate by analysing their mental state. © 2022 IEEE.

关键词： Electrocardiograms

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LAMM: Language-Assisted Multi-Modal Instruction-Tuning dataset, Framework, and Benchmark 37

LAMM: Language-Assisted Multi-Modal Instruction-Tuning Datas...

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37th conference on Neural Information Processing Systems (NeurIPS)

作者： Yin, Zhenfei Wang, Jiong Cao, Jianjian Shi, Zhelun Liu, Dingning Li, Mukai Huang, Xiaoshui Wang, Zhiyong Sheng, Lu Bai, Lei Shao, Jing Ouyang, Wanli Shanghai Artificial Intelligence Lab Shanghai Peoples R China Beihang Univ Beijing Peoples R China Univ Sydney Sydney NSW Australia Fudan Univ Shanghai Peoples R China Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781713899921

Large language models have emerged as a promising approach towards achieving general-purpose AI agents. The thriving open-source LLM community has greatly accelerated the development of agents that support human-machine dialogue interaction through natural language processing. However, human interaction with the world extends beyond only text as a modality, and other modalities such as vision are also crucial. Recent works on multi-modal large language models, such as GPT-4v and Bard, have demonstrated their effectiveness in handling visual modalities. However, the transparency of these works is limited and insufficient to support academic research. To the best of our knowledge, we present one of the very first open-source endeavors in the field, LAMM, encompassing a Language-Assisted Multi-Modal instruction tuning dataset, framework, and benchmark. Our aim is to establish LAMM as a growing ecosystem for training and evaluating MLLMs, with a specific focus on facilitating AI agents capable of bridging the gap between ideas and execution, thereby enabling seamless human-AI interaction. Our main contribution is three-fold: 1) We present a comprehensive dataset and benchmark, which cover a wide range of vision tasks for 2D and 3D vision. Extensive experiments validate the effectiveness of our dataset and benchmark. 2) We outline the detailed methodology of constructing multi-modal instruction tuning datasets and benchmarks for MLLMs, enabling rapid scaling and extension of MLLM research to diverse domains, tasks, and modalities. 3) We provide a primary but potential MLLM training framework optimized for modality extension. We also provide baseline models, comprehensive experimental observations, and analysis to accelerate future research. Our baseline model is trained within 24 A100 GPU hours, framework supports training with v100 and RTX3090 is available thanks to the open-source society. Codes and data are now available at https://***/.

关键词： visual languages

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Haemodynamic Parameters analysis through Audio Signal Processing analysis of Doppler Audio Signals for Carotid Artery Assessment 24

Haemodynamic Parameters Analysis through Audio Signal Proces...

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Proceedings of the 11th International conference on Bioinformatics Research and Applications

作者： vidya Gopal T v Inayathullah Ghori Avinash Eranki Renu John Department of Biomedical Engineering Indian Institute of Technology Hyderabad Hyderabad Telangana India Department of Cardiology Kamineni Hospital Hyderabad Hyderabad Telangana India

ISBN: (纸本)9798400717536

Limitations of traditional auscultation for vascular assessment prompt the exploration of alternative methods. This study investigates the potential of Doppler ultrasound signal analysis as a non-invasive and potentially cost-effective tool for screening carotid artery health. We analyzed Doppler data from 216 individuals (18-59 years) and identified distinct frequency ranges associated with blood flow. Statistical analysis revealed significant correlations between these frequencies and hemodynamic parameters, suggesting a link to vascular health. These findings hold promise for developing a novel screening method for early detection of carotid artery stenosis.

关键词： Carotid artery

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analysis of the impact of the multiple waves on the middle devonian part of geological section of the Orenburg Region 23

Analysis of the impact of the multiple waves on the middle d...

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23th conference on Oil and Gas Geological exploration and Development, Geomodel 2021

作者： Ekimenko, A.v. Filichev, A.v. Tatianin, N.v. Martynov, A.v. Gazpromneft STC Gazpromneft-GEO TNG-Group

ISBN: (纸本)9789462823839

The relevance of the issue under study is determined by several reasons. On the one hand, the Carbon-Fran interval is the most resource-intensive. This Last Devonian-Early Carboniferous interval is second only to the Eifelian - Frasnian one in terms of undiscovered resources. On the other hand, the interpretation of the seismic data for this interval is associated with a number of difficulties: uncertain tracking of the seismic horizons, large errors in the depth predictiob and rock properties prediction. This makes it necessary to assess the reliability of the interpretation of seismic data. In this paper, the study of the background of multiple waves is carried out on the basis of wave modeling and analysis of synthetic seismograms. © Geomodel 2021 - 23th Conf. on Oil and Gas Geo. Expl. and Dev. All rights reserved.

关键词： Seismic waves

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data Interface Matching and Information Security Measurement of Scientific and Technological Innovation Measurement analysis and Multi-Agent Economic MIS

Data Interface Matching and Information Security Measurement...

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2022 International conference on Edge Computing and Applications, ICECAA 2022

作者： Ma, Ying Zhou, Tingting Yingkou Institute of Technology Liaoning Yingkou115014 China

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

This paper establishes a vector autoregressive model based on the current development status of the digital economy and studies the correlation between the digital economy and economic growth MIS from a dynamic perspective, and found that the digital economy has a strong supporting role in the growth of the total economic volume. The coordination degree model calculates the scientific and technological innovation capabilities of China's 30 provinces (except Tibet) from 2018 to 2022, and the coordination, green, open, and shared level of high-quality economic development. The basic principles of the composition of the security measurement are expounded, and the measurement information model can be used as a logic model. The analysis of security measure composition summarizes the selection principle and selection process of security measurement, and analyzes and compares the measure composition methods in several typical security measurement methods. © 2022 IEEE.

关键词： Security of data

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Explorative visual analysis of Spatio-temporal Regions to Detect Hemodynamic Biomarker Candidates

Explorative Visual Analysis of Spatio-temporal Regions to De...

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24th Eurographics conference on visualization, Eurovis 2022

作者： Derstroff, Adrian Leistikow, Simon Nahardani, Ali Ebrahimi, Mahyasadat Hoerr, verena Linsen, Lars University of Münster Germany Medical Physics Group Institute of Diagnostic and Interventional Radiology Jena University Hospital Friedrich Schiller University Jena Jena Germany Heart Center Bonn Department of Internal Medicine II University Hospital Bonn Bonn Germany Clinic for Radiology University Hospital Münster Münster Germany Leipniz-Institute of Photonic Technology Jena Germany Friedrich Schiller University Jena Germany Institute of Physical Chemistry Abbe Center of Photonics Jena Germany

ISBN: (纸本)9783038681854

Biomarkers are measurable biological properties that allow for distinguishing subjects of different cohorts such as healthy vs. diseased. In the context of diagnosing diseases of the cardiovascular system, researchers aim - among others - at detecting biomarkers in the form of spatio-temporal regions of blood flow obtained by medical imaging or of derived hemodynamical parameters. As the search space for such biomarkers in time-varying volumetric multi-field data is extremely large, we present an interactive visual exploration system to support the analysis of the potential of spatio-temporal regions to discriminate cohorts. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： Diagnosis

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A Novel Deep Learning based Stress analysis and Detection Scheme using Characteristic data 8

A Novel Deep Learning based Stress Analysis and Detection Sc...

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8th IEEE International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Geetha, R. Krishnamoorthy, N.v. Shakthi Murugan, K.H. Gnanaprakasam, C. Swarna, M. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Kuniyamuthur Coimbatore641008 India Narayana Engineering College Department of Electronics and Communication Engineering AndhraPradesh Gudur524101 India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Poonamallee Chennai600123 India

ISBN: (纸本)9798350347791

One of the most time-consuming and complex study subjects in the present day is trying to anticipate the amount of stress experienced by working professions. Although short-term stress might help us perform under pressure, chronic or severe stress compromises our well-being and causes disruptions in our daily life. Many stress-related health issues are avoidable if identified and treated early. A person's stress level may be determined by monitoring changes in a variety of biosignals, including temperature, electromagnetic, resistive, acoustical, visual and so on. There are a plethora of studies that may help you figure out what causes your stress and what kinds of things help you deal with it. Previous studies had a number of drawbacks, including more design complexity, more mistakes, worse efficiency, and a higher rate of misclassification. This research aims to apply an enhanced deep learning technique called Learning based Novel Stress Analyzer (LNSA) to forecast the level of stress of industry professionals in an effort to address these problems. Included in the methods proposed by this study are information preparation, extraction of features, classification and LNSA-based identification. Professionals' logical characteristic data is gathered via sensors and used as a dataset for processing the complete system. The sensors known as 3-Axis Accelerometer, Electrocardiogram, Pulse Sensor, Body Temperature and Respiration Sensor collect the defining data. Specifically, it eliminates redundant characteristics and completes incomplete data in the relevant dataset through a process called pre-processing. This paper provides the final classification findings, in which it show high accuracy in results and the technique of LNSA suggested in this paper accurately estimate the level of stress in a given situation. © 2023 IEEE.

关键词： Classification (of information)

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vMambaDA: visual State Space Model with Unsupervised Domain Adaptation in Cervical Cancer 6

VMambaDA: Visual State Space Model with Unsupervised Domain ...

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6th IEEE International conference on Image Processing, Applications and Systems, IPAS 2025

作者： vu, Hoang-Nguyen Nguyen, Thi-Ngoc-Truc Bui, Minh-Duc Huynh, Kim-Ngan Ngoc Minh Nguyen, Thanh Minh, Hoang Tran Nguyen v, Duy AI Lab - AI Vietnam Ho Chi Minh City Viet Nam Ho Chi Minh City University of Science Ho Chi Minh City Viet Nam Ho Chi Minh City Medicine and Pharmacy University Ho Chi Minh City Viet Nam Trung Vuong Hospital Ho Chi Minh City Viet Nam Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331506520

Cervical cancer remains a major public health concern in vietnam, ranking second only to breast cancer among women. Early detection through screening, particularly HPv testing and Pap smears, is critical in reducing cervical cancer mortality. While deep learning has shown great promise in medical image analysis, particularly for detecting cervical cancer, challenges remain, especially when models are applied to real-world datasets with limited labeled data due to privacy concerns and expensive annotations. This research offers an effective cervical cancer diagnostic approach that combines Sliced Wasserstein Distance (SWD), Maximum Classifier Discrepancy (MCD), and the vMamba model. The vMambaDA model learns domain-invariant features and adjusts to domain shifts to address the problems of accuracy, generalization, and speed in cervical cancer screening. vMambaDA has proven to be more adept at handling the intricacies of medical images than earlier models by exhibiting better classification accuracy and sensitivity through extensive testing on both public and private datasets. This automated method could enhance the early detection of cervical cancer and intervention by pushing the limits of domain adaptation in cytopathology. © 2025 IEEE.

关键词： Lung cancer

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Interpreting Autonomous Driving Corner Cases: A visual Analytics Approach

Interpreting Autonomous Driving Corner Cases: A Visual Analy...

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Pacific (formerly Asia-Pacific APvIS) visualization Symposium

作者： Yi Sun Zekai Shao Xingyu Qiu Yun Li Ting Liu Linbing Xiang Dong Sun Siming Chen Fudan University NIO Shanghai

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

With the progression of artificial intelligence, there has been substantial advancement in autonomous driving technology. However, even the most advanced systems may confront failures in certain corner cases, necessitating enhanced analytical approaches. Traditional approaches focused on the numerical analysis of isolated sensor data, are often insufficient for deriving meaningful insights in such situations. To address this inadequacy, we propose a visual analytics approach, crafted to aid domain experts in performing analyses and extracting system improvements from cases with unexpected behaviors. This approach intricately integrates extensive driving scenarios and low-level module behaviors into the autonomous driving decision-making process, utilizing rich visualizations and an interface for interactive exploration and systematic synthesis of findings. Uniquely, our system opens the "black box" of modules in the decision-making pipeline during corner cases, taking into account both the overall decision-making pipeline and the fine-grained behaviors of the modules in the pipeline, setting our approach apart from previous works. To validate our system’s effectiveness, we perform two case studies, inviting domain experts for evaluation, and the results confirm our system’s efficacy in allowing experts to obtain crucial insights into autonomous driving systems.

关键词： Systematics Numerical analysis visual analytics Pipelines Decision making System improvement Artificial intelligence

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Factor analysis: Uncovering Factor Structure in the Indonesian Teacher Engagement Index (ITEI) Survey Results data

Factor Analysis: Uncovering Factor Structure in the Indonesi...

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International conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Grace Shirley Cam Sasmoko Yasinta Indrianti Sonya Rapinta Manalu Jurike v Moniaga Data Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Primary Teacher Education Department Faculty Humanities Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Podomoro University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

This study aims to explore the latent structure of the Indonesian Teacher Engagement Index (ITEI) based on the ITEI survey results of primary school teachers in various regions of Indonesia. exploration of the ITEI structure is obtained by identifying the main factors hidden in the ITEI survey data with machine learning. The machine learning method used is Exploratory factor analysis (EFA) with Principal Component analysis (PCA). The ITEI dataset consists of 10,365 records, covering 44 items from 6 ITEI dimensions. The machine learning analysis yielded 4 main factors that became new dimensions, explaining the variation of 29 items. The four new factors found are summarized into the dimensions of values and Ethics in Education, Personal and Social Development in Education, Character and Leadership in Education, and Social Competence and Justice in Education. This exploration was conducted to show the latent structure of the ITEI that matches the real situation and provide a deeper understanding of the main components that make up the teacher engagement index in Indonesia, so that conclusions can be drawn accurately from the survey results. The implications of these findings can be used to improve understanding and development of more effective education policies in the future.

关键词： Surveys Digital control Ethics Leadership Correlation Education Machine learning Indexes Informatics Principal component analysis

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