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North Karnataka Subsection Flagship International Conference (NKCon), ieee

作者： P Nandieswar Reddy Sai Aswath S Rithvika Alapati Radha D Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Twitter's exponential increase in data has transformed it into a rich source for machine learning research, unveiling patterns of opinions and behaviors. This research introduces an automated pipeline built on Kafka, adept at handling large volumes of real-time Twitter data. Utilizing the Twitter API, tweets bearing the hashtag #justice are retrieved and efficiently processed via Kafka. PySpark facilitates data analysis by enabling aggregation, Jaccard similarity computation, and k-means clustering. Preprocessing with Python libraries and TF-IDF enhances analysis accuracy. Jaccard similarity aids in comparing tweets across socio-economic categories. Aggregated data is stored in MongoDB for further examination. Additionally, Streamlit offers visualization tools for effortless exploration of Twitter discourse trends. Thus, this project exemplifies the potential of big data and its impact on investigating social justice discourse in the digital age.

关键词： Social networking (online) Blogs Pipelines data visualization Machine learning Market research Information age Real-time systems Libraries Socioeconomics

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Research on Operation and Maintenance Optimization of Substation Area Based on data visualization 6

Research on Operation and Maintenance Optimization of Substa...

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6th International symposium on Computer and Information Processing Technology, ISCIPT 2021

作者： Xie, Zhiwen Wu, Hui Electric Power Research Institute of Guangdong Power Grid Corporation Guangzhou510030 China China Southern Power Grid Technology Co. LTD Guangzhou510030 China

ISBN: (纸本)9781665441377

At present, there are many researches on optimization planning and operation and maintenance reliability improvement of distribution network, but most of them focus on power balance and optimal investment, and lack of in-depth research on power supply unit division, model division and target network. However, in engineering practice, the workload and decision space of these links are relatively large, which requires relevant decision support. Most of the research on station operation and maintenance focuses on station fault and its treatment, but lacks practical data analysis for field operation and maintenance. In this paper, the visualization and clustering concept is used to build a target network optimization model suitable for urban distribution network. Based on the visualization target optimization method, the paper investigates the investment strategy decision model considering the current situation of network, load demand and target network construction, also the paper makes research on data operation and maintenance management improvement based on the concept of data visualization. © 2021 ieee.

关键词： data visualization

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GNEA: A Novel GCN-based Network Embedding Algorithm for Semantic Social Network 10

GNEA: A Novel GCN-based Network Embedding Algorithm for Sema...

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10th International symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Zhao, Jinbo He, Jinling Du, Yuanhan Tang, Ming Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

With the development of semantic social networks, social networks become more complex and their size expands rapidly, which brings significant challenges to social network analysis. Network Embedding can transform the network from a high-dimensional adjacency matrix to a low-dimensional one, which has excellent foreground in link prediction, vertex classification, and graph visualization. Nowadays, most algorithms are proposed for static networks with little consideration of the dynamic changes in the network. However, most real-world networks change over time. Directly applying static network embedding algorithms to dynamic networks will result in poor stability, flexibility, and efficiency. Because of these shortcomings, we propose a graph convolution-based dynamic network representation learning algorithm (GNEA), which combines Graph Convolutional Network (GCN) with Long Short-Term Memory (LSTM) to train the time series networks. GCN is applied to extract features in the network, and LSTM is utilized to capture temporal information. GNEA can not only perform characterization learning on the network but also obtain more abundant information during the dynamic evolution of the network. GNEA is compared with several typical algorithms on the datasets Stochastic Block Model (SBM), Autonomous Systems (AS), and Digital Bibliography and Library Project (DBLP). The results prove that GNEA has good performance in MAP and precision@k. © 2024 ieee.

关键词： Complex networks

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Enhanced Diagnosis of Pneumothorax with an Improved Real-Time Augmentation for Imbalanced Chest X-rays data Based on DCNN

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ieee-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第3期18卷 951-962页

作者： Wang, Yaqi Sun, Lingling Jin, Qun Hangzhou Dianzi Univ Microelect CAD Ctr Key Lab RF Circuits & Syst Minist Educ Hangzhou 310018 Peoples R China Waseda Univ Fac Human Sci Dept Human Informat & Cognit Sci Tokorozawa Saitama 3591192 Japan

Pneumothorax is a common pulmonary disease that can lead to dyspnea and can be life-threatening. X-ray examination is the main means to diagnose this disease. Computer-aided diagnosis of pneumothorax on chest X-ray, as a prerequisite for a timely cure, has been widely studied, but it is still not satisfactory to achieve highly accurate results. In this paper, an image classification algorithm based on the deep convolutional neural network (DCNN) is proposed for high-resolution medical image analysis of pneumothorax X-rays, which features a Network In Network (NIN) for cleaning the data, random histogram equalization data augmentation processing, and a DCNN. The experimental results indicate that the proposed method can effectively increase the correct diagnosis rate of pneumothorax, and the Area under Curve (AUC) of the test verified in the experiment is 0.9844 on ZJU-2 test data and 0.9906 on the ChestX-ray14, respectively. In addition, a large number of atmospheric pleura samples are visualized and analyzed based on the experimental results and in-depth learning characteristics of the algorithm. The analysis results verify the validity of feature extraction for the network. Combined with the results of these two aspects, the proposed X-ray image processing algorithm can effectively improve the classification accuracy of pneumothorax photographs.

关键词： X-rays Diseases Lung Medical diagnostic imaging data visualization Histograms Deep convolutional neural network (DCNN) pneumothorax chest X-rays imbalanced data visualization

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Interactive visualization of BioMedical data

Interactive Visualization of BioMedical Data

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ieee International Conference on Big data

作者： Mohammad Shafiqul Islam Rahat Ibn Rafiq Guenter Tusch College of Computing Grand Valley State University Michigan USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Public databases such as the NCBI Gene Expression Omnibus (GEO) house millions of experimental gene expression datasets invaluable for transcriptome meta-analysis, enabling researchers to identify genes, pathways, and biomarkers with enhanced accuracy. However, while these datasets offer great potential, finding relevant biological pathways in disease-related contexts remains challenging. To address this, we introduce a web-based visualization tool that utilizes PubMed abstracts to extract key biomedical entities, such as genes, pathways, diseases, species, and chemicals, and represent their relationships through an interactive graph. By leveraging PubMed’s annotated MeSH terms, our tool allows users to explore the literature more intuitively, bypassing traditional dataset search limitations and focusing on published results. The tool transforms complex query outputs into a graph-based network, providing a visual interface for navigating connections between biological entities. This interactive approach helps researchers to analyze relationships within large-scale biomedical data and uncover potential pathways for drug repurposing and disease understanding. We discuss the tool’s design using ***, its application in biomedical research, and the challenges faced when visualizing large datasets, offering new insights for improved data interpretation and exploration.

关键词： Drugs data visualization Biomarkers Biology Gene expression data mining Bioinformatics Chemicals Diseases Biomedical imaging

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Towards Classifying Histopathological Microscope Images as Time Series data

Towards Classifying Histopathological Microscope Images as T...

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ieee International symposium on Biomedical Imaging

作者： Sungrae Hong Hyeongmin Park Youngsin Ko Sol Lee Bryan Wong Mun Yi Korea Advanced Institute of Science and Technology Daejeon South Korea Seegene Medical Foundation Seoul South Korea

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

As the frontline data for cancer diagnosis, microscopic pathology images are fundamental for providing patients with rapid and accurate treatment. However, despite their practical value, the deep learning community has largely overlooked their usage. This paper proposes a novel approach to classifying microscopy images as time series data, addressing the unique challenges posed by their manual acquisition and weakly labeled nature. The proposed method fits image sequences of varying lengths to a fixed-length target by leveraging Dynamic Time-series Warping (DTW). Attention-based pooling is employed to predict the class of the case simultaneously. We demonstrate the effectiveness of our approach by comparing performance with various baselines and showcasing the benefits of using various inference strategies in achieving stable and reliable results. Ablation studies further validate the contribution of each component. Our approach contributes to medical image analysis by not only embracing microscopic images but also lifting them to a trustworthy level of performance.

关键词： visualization Image analysis Histopathology Microscopy Time series analysis Focusing Image sequences Reliability Medical diagnostic imaging Cancer

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An Automated Pipeline to Create an Atlas of in Situ Hybridization Gene Expression data in the Adult Marmoset Brain

An Automated Pipeline to Create an Atlas of in Situ Hybridiz...

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ieee International symposium on Biomedical Imaging

作者： Charissa Poon Muhammad Febrian Rachmadi Michal Byra Matthias Schlachter Binbin Xu Tomomi Shimogori Henrik Skibbe Brain Image Analysis Unit RIKEN Center for Brain Science Wako Japan Institute of Fundamental Technological Research Polish Academy of Sciences Warsaw Poland EuroMov Digital Health in Motion Univ Montpellier Alès France Lab for Molecular Mechanisms of Brain Development RIKEN Center for Brain Science Wako Japan

We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists of segmentation of gene expression from microscopy images and registration of images to a standard space. Automation of this pipeline is necessary to analyze the large volume of data in the genome-wide whole-brain dataset, and to process images that have varying intensity profiles and expression patterns with minimal human bias. To reduce the number of labelled images required for training, we develop a semi-supervised segmentation model. We further develop an iterative algorithm to register images to a standard space, enabling comparative analysis between genes and concurrent visualization with other datasets, thereby facilitating a more holistic understanding of primate brain structure and function.

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Big data and data visualization Challenges

Big Data and Data Visualization Challenges

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ieee International Conference on Big data

作者： Manoj Muniswamaiah Tilak Agerwala Charles C. Tappert Seidenberg School of CSIS Pace University White Plains New York

Big data has gained attention due to its significant impact on society. With the growth of IoT, data has grown exponentially. Big data has played an essential role in decision-making. After collecting a large volume of data, one of the critical tasks is performing analysis. As a result, there is a rise in the adoption of data visualization, where the raw data is converted into useful information through research and the generation of patterns. The representation of the natural numbers offers an easier way to interpret the collected data, and thus, it becomes easier to comprehend the significance of the data collected. While data visualization is significant, it faces numerous challenges; therefore, this paper aims to provide the different challenges of Big data visualization.

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Trends & Opportunities in visualization for Physiology: A Multiscale Overview

Trends & Opportunities in Visualization for Physiology: A Mu...

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24th Eurographics/ieee VGTC Conference on visualization (EuroVis)

作者： Garrison, Laura A. Kolesar, Ivan Viola, Ivan Hauser, Helwig Bruckner, Stefan Univ Bergen Norway Bergen Norway Haukeland Hosp Mohn Med Imaging & Visualizat Ctr Bergen Norway Rainfall AS Bergen Norway King Abdullah Univ Sci & Technol Thuwal Saudi Arabia TU Wien Vienna Austria

Combining elements of biology, chemistry, physics, and medicine, the science of human physiology is complex and multifaceted. In this report, we offer a broad and multiscale perspective on key developments and challenges in visualization for physiology. Our literature search process combined standard methods with a state-of-the-art visual analysis search tool to identify surveys and representative individual approaches for physiology. Our resulting taxonomy sorts literature on two levels. The first level categorizes literature according to organizational complexity and ranges from molecule to organ. A second level identifies any of three high-level visualization tasks within a given work: exploration, analysis, and communication. The findings of this report may be used by visualization researchers to understand the overarching trends, challenges, and opportunities in visualization for physiology and to provide a foundation for discussion and future research directions in this area.

关键词： visualization

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Classification and analysis of Techniques and Tools for data visualization Teaching

Classification and Analysis of Techniques and Tools for Data...

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ieee Global Engineering Education Conference (ieee EDUCON)

作者： Cuadrado-Gallego, Juan J. Demchenko, Yuri Losada, Miguel A. Ormandjieva, Olga Univ Alcala Dept Comp Sci Madrid Spain Concordia Univ Dept Comp Sci Montreal PQ Canada Univ Amsterdam Informat Inst Amsterdam Netherlands

ISBN: (纸本)9781728184784

data visualization addresses the use of graphics with the purpose to obtain or transmit the knowledge in a easier and faster way, this is it main, and in many cases unique purpose. Since their invention data graphics has evolved and many techniques has been developed, and in the last decades, with the definition and evolution of the data Science, data visualization has become to be used profusely, in that manner that, by one side, the data Science Body of Knowledge, DS-BoK, define five knowledge area groups that should be taught when learning data Science, in all of them data visualization is taken a main role for different reasons applying each knowledge area;and by other side all the data Science development environments, open source or proprietary, include tools for performing data visualizations. This paper presents the results of a research carried out with the main objective of improving the teaching of data visualization using two ways: propose a new system to classify the large amount of different graphical techniques for presenting data that can be found in the literature;and analyze using different attributes quite all the most important different tools, open source and private, that are available to develop data graphics mainly form a data visualization teaching point of view.

关键词： data Science data Visualizaion data Science Education data visualization Education Engineering Education Explanatory Graphs Explorative Graphs Open Software visualization Tools Propietary Software visualization Tools

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