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

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15th International Symposium on visual Information Communication and Interaction, VINCI2022

15th International Symposium on Visual Information Communica...

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15th International Symposium on visual Information Communication and Interaction, VINCI2022

ISBN: (纸本)9781450398077

The proceedings contain 22 papers. The topics discussed include: abstract and concrete materials: what to use for visualization onboarding for a treemap visualization?;a comparative study of color between abstract paintings, oil paintings and Chinese ink paintings;gaze-guided class activation mapping: leverage human visual attention for network attention in chest X-rays classification;conditions of preserving mental images by contour deformation;accelerating GPU rendering of 2D visualizations using resolution scaling and temporal reconstruction;animated transitions for small-scale visualizations;LegisLatio: a visualization tool for legislative roll-call vote data;ICEVis: interactive clustering exploration for tumor sub-region analysis in multiparametric cancer imaging;visual analysis of spatio-temporal earthquake events;investigation of two complexity sources for statistical graphs;and toward in-situ authoring of situated visualization with chorded keyboards.

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Oil and Gas Generation, Migration, Production Prediction, and Reservoir Characterization of Northern Denver Basin: Implication from the Total Petroleum Systems

Oil and Gas Generation, Migration, Production Prediction, an...

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2023 SPE/AAPG/SEG Unconventional Resources Technology conference, URTC 2023

作者： Rahman, Mohammad Fox, Mathew Kramer, Darrell Mullen, Chris Impac Exploration Services Inc. HoustonTX United States Ossidiana Energy DenverCO United States

The Denver-Julesburg (Dj) basin has multiple oils and gas producing unconventional reservoirs but the oil-source-reservoir correlation of hydrocarbon from these reservoirs are not well fingerprinted through detailed geochemistry dataset. It is important to determine the origin of hydrocarbons to estimate the hydrocarbon phase, GOR and production prediction. Many of these reservoir parameters vary based on the type of source rock and nature of its expulsion in varying PVT conditions. This study focuses on the detailed geochemistry from source rock, extracted oil, mud gas, and production gas and oil to determine the origin of the hydrocarbon stored in different Cretaceous intervals from Denver basin and their production equivalent phases. Geochemistry data were generated from cored rocks, cuttings, mud gas, extracted oils and compared with the produced gas and oils from the Denver basin. This article includes source rock analysis through Rock-Eval pyrolysis on cored and cuttings rocks, Leco-TOC, gas composition and compound specific isotopes via GC-IRMS, thermal extract gas chromatography (TEGC), high resolution gas chromatography, Gas Chromatography-Mass Spectrometry (GCMS) biomarker analysis on MPLC (medium pressure liquid chromatography) separated saturates and aromatics, bulk carbon isotope analysis on extracted oil and produced oil (Peters et al., 2005;Rahman et al., 2016;Rahman et al., 2017). Clayton and Swetland (1980) concluded that all the Cretaceous oils are compositionally similar throughout the basin. But the extracted oils from cored rock and cuttings and associated gas and oil data from several intervals from this study clearly depict there are significant differences in oils found in these Cretaceous reservoirs. Geochemistry data from source rock suggests that most of the organic matter in different Cretaceous source rocks are of Type ii kerogen. However, the source rock differs in chemistry because of depositional environment associated with marine sha

关键词： Gas chromatography

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exploration and Practice of data visualization 24

Exploration and Practice of Data Visualization

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Proceedings of the 2024 5th International conference on Big data Economy and Information Management

作者： Gongzheng Yu Shaowu Yang College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9798400711862

Currently, data visualization is widely used in industries such as smart city display, rail transit monitoring and network traffic analysis, public opinion analysis. The data sources are relatively stable and the data formats are relatively regular. However, in some scenarios, the data is relatively scattered and of low quality, and there is limited introduction to the entire process of collecting, circulating, and aggregating this data into visual applications. Based on this, a comprehensive implementation method for metadata collection, raw data aggregation, and data visualization was explored and practiced, and a clear and scalable solution was proposed, from the backend to the frontend, the ideas and methods of data collection and aggregation, as well as the methods of building websites using the Django framework and implementing typical visualization applications using Echarts tools, were introduced.

关键词： data aggregation

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visualizing the Cosmos: A Novel Method for Text Recombination with Space News 22nd

Visualizing the Cosmos: A Novel Method for Text Recombinatio...

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22nd Mexican International conference on Artificial Intelligence (MICAI)

作者： Zhiyenbekov, Zhalgas Omirbekova, Zhanar Mutanov, Galymkair Tasbolatov, Madiyar Kazakh British Tech Univ Sch Informat Technol & Engn Alma Ata 050000 Kazakhstan Al Farabi Kazakh Natl Univ Affiliat Dept Comp Sci Alma Ata 050000 Kazakhstan Inst Informat & Computat Technol Alma Ata 050000 Kazakhstan

ISBN: (纸本)9783031476396;9783031476402

As the volume of data continues to surge, researchers are confronted with the challenge of extracting meaningful insights from this wealth of information. Despite rapid advancements in Natural Language Processing (NLP) techniques in the AI industry, there remain gaps and opportunities for further exploration, particularly in the realm of data recombination techniques and methods. This paper proposes a novel text recombination method to facilitate the generation of recombined words from a given text. The process commences with a 'stanza' model, which identifies and compiles Named Entity Recognitions (NERs) into a list. These NERs are then cross-referenced with Wikipedia pages to retrieve relevant information, thereby enhancing entity understanding and analysis. The ensuing step involves preprocessing the output text from the previous stage, generating a list of unique words while eliminating stop words. This preprocessing stage serves to remove noise and focus on meaningful words, laying the groundwork for more effective clustering. To enable clustering, we employ vector embeddings, representing words in a 2-dimensional space, rendering them suitable for clustering techniques. Notably, the proposed method further enhances results by re-clustering words after applying K-Means, thereby identifying the most fitting candidate words for recombination. Comparatively, this method outperforms large language models (LLMs) due to its incorporation of NER information, utilization of Wikipedia pages, and effective preprocessing techniques. Unlike LLMs, which operate as resource-intensive black boxes on static data, this method benefits from real-time information access and knowledge base updates. Furthermore, each stage of the process is visualized to control the progress correctly. Thus, due to the plots of word clusterization, the proposed text recombination approach showed positive results.

关键词： Named Entity Recognition word embeddings Principal Component analysis Natural Language Processing

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Breast Cancer Classification: In-depth exploration of Different Paradigms and Ensemble Technique 18

Breast Cancer Classification: In-depth Exploration of Differ...

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18th INDIAcom;11th International conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Adem, Abdulahi Mahammed Kant, Ravi Sonia Kumari, Sudesh Swaroopa, C. Naga Gupta, Gaurav Shoolini University Yogananda School of AI Computer and Data Sciences Solan India Ece Department Ambala Mullana India Bpsmv Sonipat Khanpur Kalan India Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India

ISBN: (纸本)9789380544519

Many lives are lost to breast cancer every year. Prognosis and timely identification of cancer types are vital components of cancer research, as early diagnosis is critical to effective treatment. This study presents a comprehensive investigation into breast cancer classification, employing an extensive suite of datasets from Wisconsin Breast Cancer for machine learning models. Rigorous preprocessing techniques were applied to refine the dataset, segmented into groups of testing and training. The ensemble of models, comprising Logistic Regression, Decision Tree, Random Forest, XGBoost, Support Vector Machine (SVM), Gaussian Naive Bayes, K-Nearest Neighbors (KNN), AdaBoost, together with Deep Neural Network (DNN), underwent meticulous evaluation. For a more in-depth analysis, performance metrics like ROC-AUC scores, F1 Score, accuracy, precision, and recall were used. The ROC curve, or receiver operating characteristic provided visual insights, highlighting the trade-offs involving specificity and sensitivity. The ensemble model emerged as a promising approach, showcasing collaborative strengths across diverse algorithms, and achieves an accuracy of 98.2%. Confusion matrices and running time comparisons further elucidated model intricacies and computational efficiencies. This study will contribute valuable insights into breast cancer diagnostics, emphasizing the importance of tailored model selection and collaboration in clinical decision-making. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cancer classification deep learning Ensemble machine learning

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A Survey of visualization and analysis in High-Resolution Connectomics

A Survey of Visualization and Analysis in High-Resolution Co...

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24th Eurographics/IEEE VGTC conference on visualization (EuroVis)

作者： Beyer, Johanna Troidl, Jakob Boorboor, Saeed Hadwiger, Markus Kaufman, Arie Pfister, Hanspeter Harvard Univ Visual Comp Grp Cambridge MA 02138 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA King Abdullah Univ Sci & Technol Visual Comp Ctr Thuwal Saudi Arabia

The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high-resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state-of-the-art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open-source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.

关键词： Pipelines

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Seismic Response Characteristics of Tight Oil Sweet Spots in Fuyu Reservoir Under the Source in the Northern Songliao Basin 13th

Seismic Response Characteristics of Tight Oil Sweet Spots in...

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13th International Field exploration and Development conference, IFEDC 2023

作者： Chen, Xue-Hai Zhao, Hai-Bo Qiao, Wei Chen, Xue-Yang Hu, Jiu-Zhan Ji, Zhi Zhang, Wan-Ting Gu, Hai-Yan Li, Yan-Jie Exploration and Development Research Institute Daqing Oilfield Co. Ltd. Heilongjiang Daqing163712 China Heilongjiang Key Laboratory of Tight Oil and Shale Oil Accumulating Research Heilongjiang Daqing163712 China Oil Production Plant of Daqing Oilfield Co. Ltd. Heilongjiang Daqing163853 China

ISBN: (纸本)9789819704828

The tight oil sweet spots in the Fuyu reservoir in the northern Songliao Basin are dominated by channel sand bodies, which are controlled by physical properties, and are vertically and horizontally discontinuous. The type, distribution, and seismic response characteristics of tight oil sweet spots are unusually complex, restricting the efficient exploration and development of tight oil. The seismic response characteristics of tight oil sweet spots were studied through seismic forward modeling of different combination types of sand bodies, as well as actual drilling, logging, and seismic data. Rock physics research shows that type I sweet spots are characterized by medium to high P-wave impedance and low Vp/Vs, while type ii sweet spots are characterized by high P-wave impedance and high Vp/Vs;Through seismic forward modeling and analysis of seismic response characteristics of typical wells, the seismic reflection patterns of various sweet spots are summarized and formed. The results show that single-layer sweet spots with a thickness of more than 3 m are medium to strong amplitude seismic reflections, with obvious seismic response and easy identification;Interbedded sweet spots with a main layer thickness of more than 3 m have single waveform and composite waveform seismic characteristics, and the seismic response of the main layer is weak, making identification difficult;The thin interlayer type with a thickness of 1–3 m and the sweet spots under strong reflection shielding on the top surface of Fuyu have complex seismic response, unclear reflection characteristics, and great difficulty in identification. Based on the research results of tight oil sweet spots seismic response, a matching technology for tight oil sweet spots seismic prediction with the core of "seismic sedimentology interpretation, high-resolution thin reservoir inversion prediction, horizontal well performance tracking and adjustment while drilling, and integrated modeling of in-situ stress and roc

关键词： Seismic response

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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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Textual-visual multimodal sentiment analysis: A Review of Approaches and Challenges

Textual-Visual multimodal sentiment analysis: A Review of Ap...

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International conference on Intelligent Systems: Theories and Applications (SITA)

作者： Soukaina Fatimi Wafae Sabbar Abdelkrim Bekkhoucha LIM Laboratory Faculty of Sciences and Techniques Mohammedia University Hassan II of Casablanca Mohammedia Morocco

Multimodal analysis is the discipline that mines and analyses data from datasets where inputs comprise more than one type of data. While traditional sentiment analysis primarily uses textual data, the rise of the available visual data has caused the appearance of visual sentiment analysis. Later on, the idea of combining multiple types of data for sentiment analyses seems to be more prominent. Multimodal sentiment analysis integrates text, acoustics, and visuals to enrich insights from individual modalities and eliminate ambiguities. This paper reviews text-image multimodal sentiment analysis; it discusses the unimodal sentiment analysis of both visual and textual data separately; then introduces the multimodal sentiment analysis approach with an emphasis on textual-visual methods, examines its different stages, and fusion techniques and concludes by addressing associated challenges and perspectives.

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Progressive Frequency-Aware Network for Laparoscopic Image Desmoking 6th

Progressive Frequency-Aware Network for Laparoscopic Image D...

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6th Chinese conference on Pattern Recognition and Computer Vision (PRCV)

作者： Zhang, Jiale Huang, Wenfeng Liao, Xiangyun Wang, Qiong Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Comp Vis & Virtual Real Te Beijing Peoples R China

ISBN: (纸本)9789819984312;9789819984329

Laparoscopic surgery offers minimally invasive procedures with better patient outcomes, but smoke presence challenges visibility and safety. Existing learning-based methods demand large datasets and high computational resources. We propose the Progressive Frequency-Aware Network (PFAN), a lightweight GAN framework for laparoscopic image desmoking, combining the strengths of CNN and Transformer for progressive information extraction in the frequency domain. PFAN features CNN-based Multi-scale Bottleneck-Inverting (MBI) Blocks for capturing local high-frequency information and Locally-Enhanced Axial Attention Transformers (LAT) for efficiently handling global low-frequency information. PFAN efficiently desmokes laparoscopic images even with limited training data. Our method outperforms state-of-the-art approaches in PSNR, SSIM, CIEDE2000, and visual quality on the Cholec80 dataset and retains only 629K parameters. Our code and models are made publicly available at: https://***/jlzcode/PFAN.

关键词： Medical Image analysis Vision Transformer CNN

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