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visual analysis of Spatio-temporal Phenomena with 1D Projections

COMPUTER GRAPHICS FORUM 2021年第3期40卷 335-347页

作者： Franke, M. Martin, H. Koch, S. Kurzhals, K. Univ Stuttgart Stuttgart Germany Swiss Fed Inst Technol Zurich Switzerland Inst Adv Res Artificial Intelligence Vienna Austria

It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio-temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio-temporal data, based on a set of neighborhood-preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real-world application scenarios and discuss the feedback we received from domain and visualization experts.

关键词： CCS Concepts • Human-centered computing → visual analytics Geographic visualization Information visualization visualization systems and tools

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A Hybrid Machine Learning Approach for Sleep and Awake States Detection Using EEG data

A Hybrid Machine Learning Approach for Sleep and Awake State...

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2024 IEEE International conference on Innovation and Novelty in Engineering and Technology, INNOvA 2024

作者： Adhyapak, Shrinivas Ghuli, Anand Patil, Shashidhar Hiremath, Shivamurti Department of MCA BLDEA’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Affiliated to Visvesvaraya Technological University Belagavi Karnataka Vijayapur India

ISBN: (纸本)9798331505134

Brain wave analysis significantly contributes in neurological diagnostics and research by clinically offering critical insights into neural activity and different patterns of sleep. This work aims in accurately detecting sleep stages using Electroencephalography (EEG) data using Exploratory data analysis (EDA) and advanced modelling techniques, with a focus on precisely identifying and categorizing sleep stages. The dataset for this study is extracted from Kaggle that includes complete EEG recordings of individuals during sleep is processed and visualized using EDA. During the modelling stage, predictive models for sleep stage classification are constructed using Machine Learning (ML) algorithms like Random Forest, SvM, KNN, and Logistic Regression. Using ensemble techniques, hyperparameter optimization, and cross-validation, these models go through rigorous training, validation, and testing. The most accurate model was a hybrid one that combined Random Forests, SvM, KNN, and Logistic Regression as a meta-learner. The polysomnographic recordings that make up the dataset provide rich brain wave data from various stages of sleep. The methodology was validated using benchmark datasets, which confirmed its high accuracy in identifying different stages of sleep. These results demonstrate the importance of rigorous data exploration and reliable modelling approaches for the advancement of neuroinformatics research. © 2024 IEEE.

关键词： Sleep research

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Analyzing Difficulties of visual Questions Using Entropy values of Answer Predictions 8th

Analyzing Difficulties of Visual Questions Using Entropy Val...

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8th International conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Geetha Yadav, M. Nennuri, Rajasekhar Deepthi, N. Siri Harshitha, K. Naga vinay, A. CS & IOT MALLA REDDY UNIVERSITY Hyderabad India Department of CSE Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9789819786657

Our approach introduces a novel method for assessing the difficulty of visual questions in visual Question Answering (vQA) that does not depend on human annotations. Rather than using human-provided responses to real-world situations, as previous studies have done, we evaluate the performance of various vQA models to determine the level of question difficulty. We combine the predicted response distributions of three models: a baseline model that considers both images and questions, and two modified models that incorporate image and question inputs. We group visual questions in the vQA v2 validation set into clusters using k-means clustering and use advanced techniques to calculate accuracy and entropy values for each cluster. Each cluster corresponds to a specific level of visual question complexity, eliminating the need for explicit labeling of difficulty. Our approach effectively identifies clusters of challenging visual questions that even state-of-the-art tools struggle to answer. We conducted a thorough analysis of the vQA v2 dataset and found that all methods performed poorly on the most challenging cluster (with around 10% accuracy), and the accuracy of different models’ predictions diverged as the question difficulty increased. We also observed a strong correlation between entropy and accuracy values for each cluster. Our method can evaluate visual question complexity without depending on ground truth data. By clustering visual questions in the test set, we can inspire the development of new research approaches and techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： visual analytics

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Intelligentization of Art Design System Based on Multidimensional visual Image Reconstruction Algorithm 3

Intelligentization of Art Design System Based on Multidimens...

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3rd International conference on Smart data Intelligence, ICSMDI 2023

作者： Wang, Yuejuan School of Computer and Art Anhui Technical College of Industry and Economy Anhui Hefei430071 China

ISBN: (纸本)9781665464871

Among the mainstream methods of image aesthetic quality evaluation, it can be divided into traditional aesthetic evaluation methods based on the artificial design features and current popular aesthetic evaluation methods based on deep learning. Hence, this paper studies the intelligentization of art design system based on the multidimensional visual image reconstruction algorithm. In the proposed study, the modelling process contains the 2 essential aspects, namely, the multidimensional image analysis and image perception, respectively. The framework is modelled separately and then combined for the comprehensive analysis of image aesthetic quality evaluation for art design system. After testing on large sets of data, the performance is shown to be efficient. © 2023 IEEE.

关键词： Image analysis

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Research on visual analysis Method of Software Function Testing Process

Research on Visual Analysis Method of Software Function Test...

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2021 International conference on Computer Graphics, Artificial Intelligence, and data Processing, ICCAID 2021

作者： Zuo, Zihuan Wu, Yadong Chen, Yuansong Zhang, Xiaorong Qin, Renchao School of Computer Science and Technology Southwest University of Science and Technology Mianyang621000 China School of Computer Science and Engineering Sichuan University of Science and Engineering Zigong643002 China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

The existing research on software testing visualization is mostly at the code and developer level, and the visualization content is mostly at the code line and document level, and the target user group is very limited. In addition, the thirdparty software evaluation agency involves a large number of paper documents in the testing and review, which is inconvenient to manage, difficult to consult and analyze, and work efficiency is low. In order to solve these problems, we propose a visual analysis framework for software functional testing process. Starting from the functional testing process and the key information of each process, a set of visual analysis process is constructed, the data extraction method of the test document and the visual analysis method of the test process are studied, and the visual analysis prototype system is constructed to support the software testing process. And the key information of each process for quick analysis and further exploration. Finally, we demonstrated the application of the system in actual test projects through case analysis. © 2022 SPIE.

关键词： Software testing

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ANOvIZ: A visual Inspection Tool of Anomalies in Multivariate Time Series 37

ANOVIZ: A Visual Inspection Tool of Anomalies in Multivariat...

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37th AAAI conference on Artificial Intelligence, AAAI 2023

作者： Trirat, Patara Nam, Youngeun Kim, Taeyoon Lee, Jae-Gil School of Computing KAIST Korea Republic of

ISBN: (纸本)9781577358800

This paper presents ANOvIZ, a novel visualization tool of anomalies in multivariate time series, to support domain experts and data scientists in understanding anomalous instances in their systems. ANOvIZ provides an overall summary of time series as well as detailed visualizations of relevant detected anomalies in both query and stream modes, rendering near real-time visual analysis available. Here, we show that ANOvIZ streamlines the process of finding a potential cause of an anomaly with a deeper analysis of anomalous instances, giving explainability to any anomaly detector. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Time series

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LineFormer: Line Chart data Extraction Using Instance Segmentation 17th

LineFormer: Line Chart Data Extraction Using Instance Segmen...

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17th International conference on Document analysis and Recognition (ICDAR)

作者： Lal, Jay Mitkari, Aditya Bhosale, Mahesh Doermann, David Univ Buffalo Dept Comp Sci & Engn Artificial Intelligence Innovat Lab A2IL Buffalo NY 14068 USA

ISBN: (纸本)9783031417337;9783031417344

data extraction from line-chart images is an essential component of the automated document understanding process, as line charts are a ubiquitous data visualization format. However, the amount of visual and structural variations in multi-line graphs makes them particularly challenging for automated parsing. Existing works, however, are not robust to all these variations, either taking an all-chart unified approach or relying on auxiliary information such as legends for line data extraction. In this work, we propose LineFormer, a robust approach to line data extraction using instance segmentation. We achieve state-of-the-art performance on several benchmark synthetic and real chart datasets. Our implementation is available at https://***/TheJaeLal/LineFormer.

关键词： Line Charts Chart data Extraction Chart OCR Instance Segmentation

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Fire Detection and Autonomous Evacuation Guidance System 3

Fire Detection and Autonomous Evacuation Guidance System

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3rd International conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： vadlamudi, Sandyarani Roja Ramani, D. Preetham, S. Dagadi, Ganesh Goyal, Nipun Mantri, Harshavardhan v. Department of AIML New Horizon College of Engineering Bengaluru India Department of CSE New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331527549

Effective fire emergency responses are critical to ensuring safety and minimizing damage. This research exhibits an innovative fire detection and evacuation system that utilizes deep-learning and real-time video analysis to improve response times and efficiency. Utilizing convolution neural nets (CNNs), the system is able to detect fires with high precision and minimal delay, while dynamically generating evacuation plans depending on the fire's location. The system integrates advanced machine learning techniques to analyse building layouts and determine the safest, shortest evacuation routes. This information is conveyed to occupants via a public address system, enabling a streamlined and well-coordinated evacuation process. By combining real-time fire detection with intelligent route optimization, the system offers a significant advancement over traditional fire safety technologies. The paper also addresses key challenges such as processing latency and the system's ability to operate effectively in real-world conditions. Experimental results demonstrate the reliability of the CNN model—Inception v3 for fire recognition and Dijkstra algorithm for route planning—in both accuracy and speed aspects. The system’s ability to handle complex visual data while maintaining operational efficiency is a highlight of this research. Along with technical details, the research explores the practical implications of implementing this system in various environments. Key challenges include maintaining low latency in video streaming and fire detection, as well as ensuring the effectiveness of the escape route analysis in complex environments. Future improvements focus on scalability, with plans to expand the system’s capabilities to support multi-camera setups and larger buildings, making it adaptable for widespread use. © 2025 IEEE.

关键词： video streaming

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A Meticulous analysis of Diverse Human Traits in Avid Readers and Non-Readers Through Advanced data Analytics and Machine Learning Approach

A Meticulous Analysis of Diverse Human Traits in Avid Reader...

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2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Aziz, Farook Abdul Sowmya, v.J. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350382693

In the contemporary digital landscape, the enduring practice of reading books encounters formidable challenges amidst the pervasive allure of instant digital gratification. This paper embarks on a nuanced exploration, dissecting the intricate interplay between digital culture and the enduring significance of reading. While past research has predominantly scrutinized the dwindling popularity of reading and its repercussions on empathy and academic performance, our study takes a deeper dive. We focus on scrutinizing the ramifications of reading, or its absence, on pivotal aspects including stress levels, sleep quality, memory, and cognitive functions. Through rigorous comparative analysis between avid readers and non-readers, our aim is to elucidate the comprehensive impact of reading on human well-being. Furthermore, as part of our research endeavor, we intend to employ sophisticated machine learning models to predict an individual's psychological profile, particularly focusing on their position within the psychology spectrum. Leveraging cutting-edge sophisticated machine learning techniques and data visualization techniques, this research endeavors to unravel the subtle connections between reading habits and diverse dimensions of well-being, underscoring the paramount importance of nurturing a steadfast reading habit. © 2024 IEEE.

关键词： Emotional intelligence

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Deep Learning Based Classification of the Big Four Snake Species Using visual Features 14

Deep Learning Based Classification of the Big Four Snake Spe...

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14th International conference on Cloud Computing, data Science and Engineering, Confluence 2024

作者： Parida, Nishikanta Narendra, Aditya Kolimi, Pooja Reddy Panda, Priyansu Misra, Ipsit Center of Excellence-Artificial Intelligence Odisha University of Technology and Research Bhubaneswar India Amrita Vishwa Vidyapeetham Department of Artificial Intelligence Coimbatore India

ISBN: (纸本)9798350344837

According to a recent study conducted by the Centre for Global Health Research and published by the World Health Organization (WHO)., between 81.,000 and 138,000 deaths occur from snakebite envenomation annually. Approximately 45,900 snakebite deaths occur in India alone each year;of these, ninety percent are due to the 'Big Four' snake species, which are the Indian cobra (Naja naja), the Sawscaled viper (Echis carinatus), the Common krait (Bungarus caeruleus), and the Russel's viper (Daboia russelii). The majority of these cases occur in rural areas, where identifying the snake responsible for the bite quickly is critical for saving victims' lives. Automation for identifying the species of snakes has significant potential to enable timely and customized medical care, ultimately decreasing the number of fatalities. This research focuses on the exploration and evaluation of various computer vision algorithms such as Xception, EfficientNet, DenseNetl21, MobileNet v2, Inception v3, vision Transformer (viT), ResNetv2 to successfully classify the 'big four' snake species. The research then presents a performance comparison based on the experimental findings, employing a K-Fold Cross-validation approach to gather performance results for each model. The results indicate that vision Transformer (viT) outperformed other models, achieving 91.1% accuracy rate with 0.9 as the macro-F1 score. The results highlight the efficacy of deep learning algorithms for snake species identification, providing a promising avenue for accelerated treatment tailored to the specific snake species involved. © 2024 IEEE.

关键词： Convolutional neural networks

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