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检索条件"任意字段=2024 Conference on Visualization in Data Science-VDS-Annual"

共 115 条记录，以下是21-30 订阅

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Design and Evaluation of data visualizations in a Smart Agriculture Monitoring System (SAMS) 4th

Design and Evaluation of Data Visualizations in a Smart Agri...

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4th International conference on Advanced Research in Technologies Information Innovation and Sustainability-ARTIIS-annual

作者： Brenes, Jose A. Murillo Quintana, Maria Isabel Sanchez Castillo, Jose Daniel Lopez, Gustavo Marin-Raventos, Gabriela Univ Costa Rica Res Ctr Informat & Commun Technol CITIC San Jose 11501 Costa Rica Univ Costa Rica Grad Program Comp & Informat PPCI San Jose 11501 Costa Rica

ISBN: (纸本)9783031840777;9783031840784

Technological solutions for smart agriculture are essential for enhancing decision-making, especially for small-scale farmers with limited technological proficiency. This study evaluates a data visualization system designed for smart agriculture, focusing on its usability, utility, and user experience. The evaluation involved diverse participants, including farmers and students from the Agronomy, Physics, and Computing disciplines, using standardized questionnaires like the System Usability Scale (SUS) and the User Experience Questionnaire (UEQ). The results indicate strong acceptance and interest in the system, with users appreciating its innovative elements, such as speedometer-style charts and colour-coded thresholds. These visualizations address the limitations of traditional agricultural tools by providing clearer decision-making guidance. This research offers practical insights into designing user-friendly data visualization tools for small-scale farmers. It underscores the importance of intuitive, user-centred visualizations in improving agricultural productivity and provides a foundation for future studies in precision agriculture. The findings highlight the potential of such systems to bridge the gap between complex data and actionable insights, particularly for users with varying levels of technological familiarity.

关键词： data visualization User experience and usability evaluation Smart agriculture UEQ SUS

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Immersive 3D Medical visualization in Virtual Reality using Stereoscopic Volumetric Path Tracing 31

Immersive 3D Medical Visualization in Virtual Reality using ...

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IEEE conference on Virtual Reality and 3D User Interfaces (VR)

作者： Taibo, Javier Iglesias-Guitian, Jose A. Univ A Coruna CITIC Ctr ICT Res La Coruna Spain

ISBN: (纸本)9798350374025;9798350374032

Scientific visualizations using physically-based lighting models play a crucial role in enhancing both image quality and realism. In the domain of medical visualization, this trend has gained significant traction under the term cinematic rendering (CR). It enables the creation of 3D photorealistic reconstructions from medical data, offering great potential for aiding healthcare professionals in the analysis and study of volumetric datasets. However, the adoption of such advanced rendering for immersive virtual reality (VR) faces two main limitations related to their high computational demands. First, these techniques are frequently used to produce pre-recorded videos and offline content, thereby restricting interactivity to pre-defined volume appearance and lighting settings. Second, when deployed in head-tracked VR environments they can induce cyber-sickness symptoms due to the disturbing flicker caused by noisy Monte Carlo renderings. Consequently, the scope for meaningful interactive operations is constrained in this modality, in contrast with the versatile capabilities of classical direct volume rendering (DVR). In this work, we introduce an immersive 3D medical visualization system capable of producing photorealistic and fully interactive stereoscopic visualizations on head-mounted display (HMD) devices. Our approach extends previous linear regression denoising to enable real-time stereoscopic cinematic rendering within AR/VR settings. We demonstrate the capabilities of the resulting VR system, like its interactive rendering, appearance and transfer function editing.

关键词： Computing methodologies-Virtual reality Computing methodologies-Ray tracing- Human-centered computing-Graphical user interfaces

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CARDIOVASCULAR DISEASE PREDICTION USING MACHINE LEARNING FOR DECISION science AND ANALYSIS 45

CARDIOVASCULAR DISEASE PREDICTION USING MACHINE LEARNING FOR...

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2024 International annual conference of the American Society for Engineering Management and 45th annual Meeting: Engineering Management Riding the Waves of Smart Systems, ASEM 2024

作者： Luthra, Raghav Singh, Vyomika Ranjit, Manish Northeastern University United States Indian Institute of Technology Roorkee India

ISBN: (纸本)9798985333480

Machine learning (ML) paradigms, principally, feature-engineered ML classifiers, in the field of data science and engineering management are paving way for data-driven predictions, decision engineering, optimized management & overall remodelling of modern engineering management practices. The agility and flexibility of machine learning classifiers especially in healthcare engineering management, aids in predictive medical analysis & engineering, automated quality control, and improved healthcare decision support systems. In this paper, we are proposing an in-depth and systematic review of ML-based prediction models for cardiovascular disease prediction (CVD) based on data visualization, feature engineering, ML-based algorithms implementation and post-classification metrics assessment. The heart is a crucial organ of the human body, and in contemporary lifestyles characterized by reduced physical activity and suboptimal nutrition, Cvds pose significant health risks. The proficient union of ML with engineering management in predicting Cvds on the basis of clinical data (including demographic information, medical history and previous lab results, if any), medical imaging and genetic information on the lines of feature extraction and pattern recognition can assist in leveraging data-driven insights to improve patient outcomes, streamline healthcare treatment operations and overall resource utilisation. Using the CVD dataset extracted from Kaggle, we have experimented with feature-based supervised learning techniques such as Decision-tree classifiers (DT), k-nearest neighbor (KNN), Support Vector Machines (SVM), logistic regression and random forest classifier (RF). The Random Forest Classifier, with its 90.75% accuracy, is the most efficient algorithm, out of all, for predicting Cvds, according to the comprehensive study's findings, followed by SVM, KNN, and DT Classifier. Copyright © American Society for Engineering Management, 2024.

关键词： Patient treatment

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Out-Bore MRI-Guided Breast Biopsy Employing Machine Vision and Augmented Reality Techniques: Preliminary Study 46

Out-Bore MRI-Guided Breast Biopsy Employing Machine Vision a...

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46th annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Jeon, Sangseo Kim, Yeonhyeong Lim, Sunghwan Korea Institute of Science and Technology Center for Healthcare Robotics Artificial Intelligence and Robotics Institute Seoul02792 Korea Republic of

ISBN: (纸本)9798350371499

Magnetic resonance imaging (MRI) of the breast demonstrates superior sensitivity and specificity in distinguishing cancerous tissue. Despite the availability of in-bore MRI-guided breast biopsy, its application is restricted to a limited subset of individuals. We present a novel methodology developed for out-bore MRI-guided breast biopsy. Our approach integrates three primary functionalities: real-time pose tracking of the biopsy needle and a head-mounted display (HMD), real-time shape sensing of the breast with deformable registration, and augmented reality (AR)-assisted visualization of the MRI target and navigational information. Comprehensive experiments were performed to investigate the core performance metrics of the method, including targeting accuracy, update rate of the deformable registration, and data refresh rate and latency of the AR visualization. The mean targeting accuracy was 4.55 ± 1.11mm. The average update rate for deformable registration was 42.76 ± 13.96ms. Furthermore, the data refresh rate and latency associated with AR visualization were recorded as 59 ± 23f ps and 192 ± 73ms, respectively. The experimental findings underscore the system's promising potential in facilitating successful outcomes for out-bore MRI-guided breast *** relevance - The proposed system facilitates out-bore MRI-guided breast biopsy, enhancing accessibility to MRI-guided breast biopsy for patients. This expansion is anticipated to significantly elevate the detection rate of breast cancer © 2024 IEEE.

关键词： Biopsy

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A Targeted Approach to Improving Spatial visualization Skills of First-Year Engineering Students

A Targeted Approach to Improving Spatial Visualization Skill...

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2024 ASEE annual conference and Exposition

作者： Dunsworth, Qi Lewis, Dean Q. Pennsylvania State University United States

Decades of studies on spatial visualization skills have provided solid evidence that improving such skills will benefit learning in STEM *** some colleges and universities have spatial thinking training built into the curriculum, it is not always efficient to require all students to take the training considering the economic and administrative *** paper documents an exploratory study conducted in a small college within a public university in *** than 200 first-year college students from three science and engineering classes participated and the effectiveness of the 6-week training program was *** goal of the study is to answer the following questions: Is there a relationship between spatial visualization skills and final course grades in first-year science and engineering classes? Students at which level of spatial skills benefit the most from the training program? Both performance and qualitative survey data were collected before and after the training ***' spatial visualization skills were measured on both the 30-point and 78-point scale, the latter representing the four-tier system of item complexity per Maeda, Yoon, Kim-Kang, and Imbrie [9].The results on the 30-point scale did not reveal a correlation between pre-test performance and the course final *** the 78-point scale, small positive correlations were found between the ability to solve more complex mental rotation problems and the final grades in computer *** high-scoring students in the pre-test did not show more advancement in the post-test after the ***, the post-test revealed a strong gain among those with low-to-medium spatial ability aptitude at the beginning of the *** finding is consistent with a previous ***, students' confidence in solving mental rotation problems grew *** paper shared detailed results, implications, as well as curricular plans. © American Society for Engineering

关键词： Students

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data Systems Education: Curriculum Recommendations, Course Syllabi, and Industry Needs 2024

Data Systems Education: Curriculum Recommendations, Course S...

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29th annual conference on Innovation and Technology in Computer science Education (ITiCSE)

作者： Miedema, Daphne Taipalus, Toni Ajanovski, Vangel V. Alawini, Abdussalam Goodfellow, Martin Liut, Michael Peltsverger, Svetlana Young, Tiffany Univ Amsterdam Amsterdam Netherlands Eindhoven Univ Technol Eindhoven Netherlands Tampere Univ Tampere Finland Ss Cyril & Methodius Univ Skopje North Macedonia Univ Illinois Urbana IL USA Univ Strathclyde Glasgow Scotland Univ Toronto Mississauga Mississauga ON Canada Kennesaw State Univ Marietta GA USA Robert Gordon Univ Aberdeen Scotland

ISBN: (纸本)9798400712081

data systems have been an important part of computing curricula for decades, and an integral part of data-focused industry roles such as software developers, data engineers, and data scientists. However, the field of data systems encompasses a large number of topics ranging from data manipulation and database distribution to creating data pipelines and data analytics solutions. Due to the slow nature of curriculum development, it remains unclear (i) which data systems topics are recommended across diverse higher education curriculum guidelines, (ii) which topics are taught in higher education data systems courses, and (iii) which data systems topics are actually valued in data-focused industry roles. In this study, we analyzed computing curriculum guidelines, course contents, and industry needs regarding data systems to uncover discrepancies between them. Our results show, for example, that topics such as data visualization, data warehousing, and semi-structured data models are valued in industry, yet seldom taught in courses. This work allows professionals to further align curriculum guidelines, higher education, and data systems industry to better prepare students for their working life by focusing on relevant skills in data systems education.

关键词： data systems education database curriculum industry knowledge gap skill set data engineering

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visualization and Interpretation of Lithium Iron Phosphate Cell Variable Characteristics by Adapting Python data science Packages

Visualization and Interpretation of Lithium Iron Phosphate C...

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2024 IEEE Silchar Subsection annual conference, SILCON 2024

作者： Persis, P.Keren Geetha, R. Saveetha School of Engineering Simats Department of Coding Linguistics Tamil Nadu Chennai India Saveetha School of Engineering Simats Department of Cloud Computing Tamil Nadu Chennai India

ISBN: (纸本)9798331540821

datascience describes itself as an exploration of how to analyze unstructured data and draw conclusions from it. The batteries are used in a variety of applications to supply or absorb power, including vehicle electrification, stationary energy storage, and smart grid, where appropriate management systems are required to guarantee their effectiveness, safety, and sustainability. Numerous cutting-edge data science techniques have been developed in this field to manage battery operation effectively. Simple statistical methods, more complex and advanced machine learning methods, and so forth can all be used to generate insights Lithium ion cell data sheets must be interpreted in order to comprehend how the cells behave when exposed to different temperatures. It is aimed to visualize and identify correlations between voltage, current, and temperature of LFP data as it is used in Electric Vehicles. The present investigation will examine and emphasize the correlations between the output voltage and current of lithium iron phosphate cells under different conditions. From the Analysis, it is found that a weak negative correlation between voltage and temperature, meaning that a higher voltage corresponds to a slight decrease in temperature, but the effect may not be very strong due to the variability in the data. The cell voltage trend indicates a peak in temperature around 4.0V, followed by a decline, reflecting a weak negative correlation between voltage and temperature that varies with cycle count segments. © 2024 IEEE.

关键词： Lithium-ion batteries

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Visual Representation of Tabular Electronic Health Records for Predicting Sudden Cardiac Arrest 46

Visual Representation of Tabular Electronic Health Records f...

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46th annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Afrin, Rubya Mohammed, Emad A. Far, Behrouz University of Calgary Dept. of Ese Calgary Canada Wilfrid Laurier University Dept. of Physics and Computer Science Waterloo Canada

ISBN: (纸本)9798350371499

Computer-aided diagnosis in healthcare involves collecting and modeling patients' comprehensive health-related information from their Electronic Health Records (EHRs). Recently, the extensive acquisition of EHRs and scalable architectures of deep learning methods such as convolutional neural networks (CNNs) are generating robust healthcare assistance. Yet, the laborious pre-processing steps on EHR data and domain-specific feature engineering hinder the results of the model's interpretability and transparency. Moreover, conducting obscure feature engineering tasks without domain knowledge may lead to the loss of relevant features, limiting the model's ability to make reliable predictions. To alleviate these challenges, we proposes a method to represent the tabular EHR data in 2D images without leveraging any pre-processing or data cleaning tasks, which results in a generalized and more interpretable visualization of EHR tabular data. In addition, we evaluate the proposed method by predicting a cardiovascular disease, Sudden Cardiac Arrest (SCA), using pre-trained deep CNN models. The results demonstrate that the proposed method can perform highly in the SCA prediction without any missing value imputation and allow more transparency with less human expert intervention. © 2024 IEEE.

关键词： Electronic health record

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A Head-to-Head Evaluation of a Novel Universal Influenza Vaccine Against Current Formulation: Implications for Future Immunization Strategies

A Head-to-Head Evaluation of a Novel Universal Influenza Vac...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Di Salvatore, Valentina Crispino, Elena Maleki, Avisa Russo, Giulia Pappalardo, Francesco University of Catania Department of Health and Drug Sciences Catania Italy University of Catania Department of Biomedical and Biotecnological Sciences Catania Italy University of Catania Department of Mathematics and Computer Science Catania Italy

ISBN: (纸本)9798350386226

Influenza remains a significant public health concern, with annual vaccine formulations traditionally developed based on the most prevalent virus strains from the previous year. This process often results in mismatches between the vaccine and circulating strains, limiting efficacy. In this study, we utilize the UISS-FLU simulator to compare the effects of a novel influenza vaccine formulation, developed in our previous research, against this year's standard vaccine. We aim to evaluate the potential advantages of our formulation in terms of immunogenic response and protective efficacy. By leveraging in silico methodologies, we can enhance vaccine design and optimization, allowing for a more adaptive and responsive approach to influenza immunization. Our findings will provide insights into the future of vaccine development, showcasing how computational tools can improve public health outcomes by potentially yielding a universal vaccine solution. © 2024 IEEE.

关键词： Immunization

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Application of data Analysis and visualization Tools for U.S. Renewable Solar Energy Generation, Its Sustainability Benefits, and Teaching In Engineering Curriculum

Application of Data Analysis and Visualization Tools for U.S...

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2024 ASEE annual conference and Exposition

作者： Radhakrishnan, Ben D. Altamirano, Nelson Jaurez, James J. Department of Engineering Data and Computer Sciences College of Business Engineering and Technology National University San DiegoCA United States Department of Finance Economics Marketing and Accounting College of Business Engineering and Technology National University San DiegoCA United States Department of Computer Science & Cyber Security College of Business Engineering and Technology National University San DiegoCA United States

The advancement of data Analysis technologies with visualization has gained significant ground in the industries and they are also gaining ground in higher education curriculum. This research will focus on the application of these techniques to the energy industry - in particular, solar renewable energy generation in all the States in the United States (US). One strong opposing action against the progression of climate change is the use of renewable energy. The objective of the research is to develop a case study on renewable solar energy and its impact on abating or preventing CO2 emissions to help reduce the severe impacts of climate change in the United States and demonstrate how it supports the 3Es of sustainability at the same time. This research paper will specifically explore the past production of solar energy in all the states in the US, and with the use of data analysis tools will predict the production to the year 2030. The reduction of CO2 emissions with the use of renewable solar energy is in direct support of the three elements of sustainability, namely the 3Es: Environment, Economics, and Equity (or social justice). This research will quantify the past benefits already realized in all these three areas for solar energy, and project them up to 2030. Cluster analysis technique will be applied to solar generation across all US States to identify group(s) at distinct levels of production. This can help States to follow the leading State(s) policy and process to increase their solar generation and thus help manage climate change. Solar energy generation challenges and recycling issues of solar equipment will also be addressed. This case study approach will fill a gap that currently exists in engineering education when it comes to exploring renewable energy and its sustainability benefits with modern data analysis tools. This research will use publicly available data sources (e.g., NREL, EIA), ubiquitous Excel, and open-source data analytics and prediction

关键词： Curricula

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