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2023 ieee International Conference on Big data, Bigdata 2023

作者： Sarma, Himangshu Shaik, Mahaboob Tangirala, Surya Teja Singh, Satyam Kumar IIIT Sri City Department of Computer Science & Engineering Andhra Pradesh India

ISBN: (纸本)9798350324457

Teaching with virtual reality (VR) has transformed the traditional educational environment by providing immersive and engaging learning experiences. Students may explore realistic simulations, control items, and study hands-on in a virtual reality classroom. Using virtual reality technology Mathematics education may be taken to the next level by utilizing this immersive technology, which improves how students learn and engage with mathematical topics. 3D geometry and trigonometry issues are typically difficult for students to grasp when presented in 2D planes. Using VR Geometry students, in particular, may enter a three-dimensional mathematical environment where abstract concepts come to life. They are able to visualize geometric forms, manipulate things, and investigate mathematical concepts in ways that typical schools do not allow. In this article, a Geometrical classroom was created utilizing a virtual reality environment. There are two sections to the classroom. In the first section, students may study about various 3D geometrical forms such as cubes, cuboids, cones, cylinders, spheres, and pyramids. In the second section, a game-based quiz app was designed in which students could engage and earn reward points and also able to understand the answer through 3D visualization. For the scientific evaluation of the VR platform, a three-phase research study was conducted. A total of 24 students took part in the study, and they were separated into two groups based on their understanding of the themes presented on the platform. In the last step, technical evaluation of the system as well as learning improvement were measured using a questionnaire after going through the VR game-based platform. The study yields excellent findings in that students' understanding increases, and 83% of students found that they can readily perceive diverse geometrical forms using the VR game-based platform. © 2023 ieee.

关键词： component formatting insert style styling

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Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network

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ieee TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2023年第1期70卷 25-40页

作者： Gu, Wenting Li, Boyi Luo, Jianwen Yan, Zhuangzhi Ta, Dean Liu, Xin Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China Tsinghua Univ Sch Med Dept Biomed Engn Beijing 100084 Peoples R China Fudan Univ Ctr Biomed Engn Shanghai 200433 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China Fudan Univ State Key Lab Med Neurobiol Shanghai 200433 Peoples R China

localization microscopy (ULM) overcomes the acoustic diffraction limit and enables the visualization of microvasculature at subwavelength resolution. However, challenges remain in ultrafast ULM implementation, where short data acquisition time, efficient data processing speed, and high imaging resolution need to be considered simultaneously. Recently, deep learning (DL) -based methods have exhibited potential in speeding up ULM imaging. Nevertheless, a certain number of ultrasound (US) data (L frames) are still required to accumulate enough localized microbubble (MB) events, leading to an acquisition time within a time span of tens of seconds. To further speed up ULM imaging, in this article, we present a new DL-based method, termed as ULM-GAN. By using a modified conditional generative adversarial network (cGAN) framework, ULM-GAN is able to recon-struct a superresolution image directly from a temporal mean low-resolution (LR) image generated by averaging l- frame raw US images with l being significantly smaller than L. To evaluate the performance of ULM-GAN, a series of numerical simulations and phantom experiments are both implemented. The results of the numerical simulations demonstrate that when performing ULM imaging, ULM -GAN allows similar to 40-fold reduction in data acquisition time and similar to 61-fold reduction in computational time compared with the conventional Gaussian fitting method, without com-promising spatial resolution according to the resolution scaled error (RSE). For the phantom experiments, ULM -GAN offers an implementation of ULM with ultrafast data acquisition time (similar to 0.33 s) and ultrafast data processing speed (similar to 0.60 s) that makes it promising to observe rapid biological activities in vivo.

关键词： Generators Imaging Spatial resolution Image reconstruction Location awareness Superresolution data acquisition Deep learning (DL) generative adversarial networks superresolution (SR) ultrasound (US) imaging ultrasound localization microscopy (ULM)

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Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT’s Potential to Apply Graph Layout Algorithms

Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT...

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25th Eurographics Conference on visualization, EuroVis 2023

作者： Di Bartolomeo, Sara Severi, Giorgio Schetinger, Victor Dunne, Cody Northeastern University United States TU Wien Austria

ISBN: (纸本)9783038682196

Large language models (LLMs) have recently taken the world by storm. They can generate coherent text, hold meaningful conversations, and be taught concepts and basic sets of instructions—such as the steps of an algorithm. In this context, we are interested in exploring the application of LLMs to graph drawing algorithms by performing experiments on ChatGPT, one of the most recent cutting-edge LLMs made available to the public. These algorithms are used to create readable graph visualizations. The probabilistic nature of LLMs presents challenges to implementing algorithms correctly, but we believe that LLMs’ ability to learn from vast amounts of data and apply complex operations may lead to interesting graph drawing results. For example, we could enable users with limited coding backgrounds to use simple natural language to create effective graph visualizations. Natural language specification would make data visualization more accessible and user-friendly for a wider range of users. Exploring LLMs’ capabilities for graph drawing can also help us better understand how to formulate complex algorithms for LLMs;a type of knowledge that could transfer to other areas of computer science. Overall, our goal is to shed light on the exciting possibilities of using LLMs for graph drawing—using the Sugiyama algorithm as a sample case—while providing a balanced assessment of the challenges and opportunities they present. A free copy of this paper with all supplemental materials to reproduce our results is available on *** . © 2023 The Authors.

关键词： data visualization

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The COVID'19 Dashboard with Implementation of Face Mask Detection and Social Distancing Detection Algorithm

The COVID'19 Dashboard with Implementation of Face Mask Dete...

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Nair, Meghna Manoj Sai, Gadiparthy Harika Pattabiraman, V. Sriraman, Harini School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350399387

With the outbreak of the global pandemic, India seemed to reach its peak with regard to the number of confirmed positive cases in the months of April and May. Hence, the decision was made to develop a data visualization project with one of the efficient visualization tools Tableau to help people analyze the scenario of the cases across the country. To contribute to state-wise and country-wise analysis of COVID cases in India, 2 dashboards have been developed. The first dashboard consists of the analysis of cases across the country giving a holistic and overall view of the number of deaths, positive cases, and density of cases in each state which is done through color variation. On the other hand, the second dashboard gives a detailed state-wise analysis of cases with the necessary parameters and details catering to every individual state as per the preference of the user. On merging these components, users can get an all-inclusive analysis based on different parameters on the COVID'19 cases across India at a glance. In order to prevent a further spike in cases, implementing a face mask detection system will also take place after conducting a thorough analysis of the possible machine learning algorithms. Two major object detection algorithms were taken into consideration and based on the conclusion drawn, the best algorithm - RCNN was used to implement the face mask detection system. This project is solely motivated by the current extreme situation in the world and as an attempt to provide a solution to combat the same. © 2023 ieee.

关键词： COVID-19

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Long Short-Term Memory Auto-Encoder-Based Position Prediction Model for Fixed-Wing UAV during Communication Failure

IEEE Transactions on Artificial Intelligence

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ieee Transactions on Artificial Intelligence 2023年第1期4卷 173-181页

作者： Kant, Ravi Saini, Poonam Kumari, Julee Defence Electronics Application Laboratory Defence Research and Development Organisation Dehradun248001 India Department of Computer Science and Engineering Punjab Engineering College Chandigarh148023 India Centre for Development of Advanced Computing Mohali160071 India

Unmanned aerial vehicles (UAV's) safe flight is one of the most important tasks of ground control stations (GCS). However, sometimes due to communication failure between ground data terminal (GDT) and UAV, GCS is not able to ensure a safe flight, which could be hazardous. In such conditions, the accurate UAV position finding prediction becomes essential to serve as sustenance for UAVs. Hence, this article proposes a UAV's position finding prediction model using a deep neural network application for successfully pointing the GDT toward UAV in real time. The proposed work consists of three components, namely data preprocessing, long short-term memory autoencoder (LSTM-AE)-based deep learning model, and mathematical formulation to calculate the ground radar look angle. First, we use different preprocessing techniques on a historical raw dataset for better visualization and generate patterns. To achieve this, Tensor flow along with Keras packages have been used. Next, an LSTM autoencoder deep learning model is applied to the same dataset in order to predict an accurate 4-D position of the UAV. Finally, the output from the LSTM autoencoder model is used in a mathematical model to calculate the GDT look-angles called Azimuth and Elevation. Both the parameters have been used to point the GDT toward the predicted point on the Airspace. We evaluate our proposed model against famous evaluation matrices, namely mean absolute error, mean square error, and root-mean-square error to validate the experimental results. © 2022 ieee.

关键词： Real time systems

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San Francisco World: Leveraging Structural Regularities of Slope for 3-DoF Visual Compass

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ieee ROBOTICS AND AUTOMATION LETTERS 2025年第1期10卷 382-389页

作者： Ham, Jungil Kim, Minji Kang, Suyoung Joo, Kyungdon Li, Haoang Kim, Pyojin Gwangju Inst Sci & Technol Sch Mech & Robot Engn Gwangju 61005 South Korea Univ Massachusetts Mannig Coll Informat & Comp Sci Amherst MA 01002 USA Ulsan Natl Inst Sci & Technol Artificial Intelligence Grad Sch Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Dept Comp Sci & Engn Ulsan 44919 South Korea Hong Kong Univ Scienceand Technol Guangzhou Thrust Robot & Autonomous Syst Guangzhou 511458 Peoples R China Hong Kong Univ Scienceand Technol Guangzhou Thrust Intelligent Transportat Guangzhou Peoples R China

We propose the San Francisco world (SFW) model, a novel structural model inspired by San Francisco's hilly terrain, enabling 3D inter-floor navigation in urban areas rather than being limited to 2D intra-floor navigation of various robotics platforms. Our SFW consists of a single vertical dominant direction (VDD), two horizontal dominant directions (HDDs), and four sloping dominant directions (SDDs) sharing a common inclination angle. Although SFW is a more general model than the Manhattan world (MW), it is a more compact model than the mixture of Manhattan world (MMW). Leveraging the structural regularities of SFW, such as uniform inclination angle and geometric patterns of the four SDDs, we design an efficient and robust DD/vanishing point estimation method by aggregating sloping line normals on the Gaussian sphere. We further utilize the structural patterns of SFW for the 3-DoF visual compass, the rotational motion tracking from a single line and plane, which corresponds to the theoretical minimal sampling for 3-DoF rotation estimation. Our method demonstrates enhanced adaptability in more challenging inter-floor scenes in urban areas and the highest rotational tracking accuracy compared to state-of-the-art methods. We release the first dataset of sequential RGB-D images captured in San Francisco world (SFW) and open source codes at: https://***/.

关键词： Vectors Estimation Accuracy Tracking Three-dimensional displays Navigation 3-DOF visualization Urban areas Gravity Vision-based navigation mapping SLAM data sets for SLAM RGB-D perception

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A Novel Perspective on Brain Tumor Classification Using Hybrid Algorithm

A Novel Perspective on Brain Tumor Classification Using Hybr...

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2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

作者： Jayanthi, R. Christinal, A. Hepzibah Hephzibah, R. Shekinah, T. Bajaj, Chandrajit Chandy, D. Abraham Karunya University Department of Mathematics Coimbatore India Loyola - Icam College of Engineering and Technology Department of Computer Science and Engineering Chennai India University of Texas Computational Applied Mathematics Chair in Visualization Austin United States Karunya University Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350338003

Brain tumor classification is a critical task in medical imaging, where the early and accurate detection of brain tumors is crucial for patient treatment and survival. In recent years, Machine Learning techniques have emerged as a promising approach for automated brain tumor classification. In this paper, we investigate the performance of three ML classifiers - Support Vector Machine, Naïve Bayes, and an ensemble of SVM and Naïve Bayes - for the two-class classification of brain tumor data into a tumor and no tumor classes. Our experiments demonstrate that the ensemble of SVM and Naïve Bayes classifier achieved an accuracy of 97% outperforming the other classifiers. These results indicate that our ensemble classifier can be a powerful tool for the accurate and efficient classification of brain tumor data, potentially leading to improved patient outcomes. © 2023 ieee.

关键词： Tumors

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Visualizing Sunlight Radiation in the Arctic Ocean

Visualizing Sunlight Radiation in the Arctic Ocean

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25th Eurographics Conference on visualization, EuroVis 2023

作者： Sørensen, E.B. Attard, K. Gattuso, J.-P. Kusnick, J. Jänicke, S. Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Department of Biology University of Southern Denmark Odense Denmark Danish Institute for Advanced Study University of Southern Denmark Odense Denmark CNRS-Sorbonne Université Laboratoire d’Océanographie Villefranche-sur-Mer France Institute for Sustainable Development and International Relations Paris France

ISBN: (纸本)9783038682202

The Arctic is experiencing dramatic environmental transformation due to rising temperatures and melting ice, which are affecting its environment, wildlife, and human communities. Remote sensing technologies (e.g. satellites) are increasingly being used to understand environmental change in the remote and understudied Arctic Ocean across broad spatial and temporal scales, generating vast data sets that require interactive visualization to be dynamically explored. We present a prototype visualization that uses aggregation means on different zoom levels to allow exploring a 200GB data set on sunlight in the Arctic Ocean, which consists of monthly time series of coastal pixels, photosynthetically available radiation (PAR), light attenuation coefficient (KPAR), and PAR estimated at the seafloor (PARBOTTOM) from 1998 to 2018. Our main example—the analysis of trends in sunlight radiation levels along the west coast of Greenland—exemplifies our tool’s value for marine biologists to getting a concise and interactive overview of sunlight radiation levels, which allows studying potential impacts on the Arctic ecosystem. © 2023 The Authors.

关键词： visualization

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Fine-grained parallel social modelling for analyzing COVID-19 propagation 31

Fine-grained parallel social modelling for analyzing COVID-1...

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31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023

作者： Martinez, Aymar Cublier Isabel, Alejandro Alvarez Carretero, Jesus Singh, David E. Universidad Carlos Iii de Madrid Department of Computer Science Leganés Spain

ISBN: (纸本)9798350337631

Agent-based epidemiological simulators have been proven to be one of the most successful tools for the analysis of the COVID-19 propagation. The ability of these tools to reproduce the behavior and interactions of each single individual leads to accurate and detailed results, that can be used to model fine-grained health-related policies like selective vaccination campaigns or immunity waning. One characteristic of these tools is the large amount of input data and computational resources and that they require. This relies on the development of parallel algorithms and methodologies for generating, accessing and processing large volumes of data from multiple data sources. This work presents a parallel workflow for extending the social modelling of EpiGraph, an agent-based simulator. We have included two novel parallel social generation stages -that provide detailed and realistic social model- and one new visualization stage. The work presents a description of the algorithms used in each stage and a practical evaluation on a real platform. Results show that this contribution can be efficiently executed in parallel architectures and increases the simulation detail level, representing a significant advance in the simulator scenario modelling. © 2023 ieee.

关键词： visualization

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Explainable AI for Medical Imaging: Advancing Transparency and Trust in Diagnostic Decision-Making

Explainable AI for Medical Imaging: Advancing Transparency a...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Rajaraman, P.V. Shanmugam, Udhayakumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350325188

Osteoarthritis (OA) is an existing degenerative joint disease with a potential societal impact, requiring accurate and early diagnosis for effective treatment. In this work, we have deployed deep learning techniques, specifically ResNet50 and VGG16, to predict osteoarthritis from medical imaging data. Remarkably, our models produced an impressive accuracies of 91.0% and 89.5% respectively, showing the efficacy of CNN in OA classification. To further better the interpretability in the predictions of our model,we used GradCam++ visualization, a technique that enabled us in generating heat maps that highlights the regions crucial for model decision-making. Optimizing GradCam++, we noticed an improvement in accuracy to 93.0%, reinforcing its value in refining model interpretability. The generated heat maps are used not only in understanding the model validation but also in providing clinicians with useful information regarding the areas of concern within the medical images. Our outcomes shows the ability of combining deep learning techniques with visualization tools like GradCam++ to enhance predictive accuracy and to offer trustable and interpretable results. The areas of concern in the heat maps throw light on the specific features influencing the model's decision. This combined method promises for advancing the field of medical image analysis, fostering more accurate and clinically applicable diagnostic tools for osteoarthritis and potentially other medical conditions. © 2023 ieee.

关键词： Artificial Intelligence(AI) Deep Learning Diagnostic decision Explainable AI (XAI) Explainable model Learning Process

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