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

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14th Arabic conference of the Arab Union for Astronomy and Space Sciences, AUASS-CONF 2023

14th Arabic Conference of the Arab Union for Astronomy and S...

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14th Arabic conference of the Arab Union for Astronomy and Space Sciences, AUASS-CONF 2023

ISBN: (纸本)9789819632756

The proceedings contain 21 papers. The special focus in this conference is on Arab Union for Astronomy and Space Sciences. The topics include: Foreword;Transiting Exoplanets from Sharjah Astronomical Observatory (SAO-M47): The Exoplanet HAT-P-25 b Using L & v Filters;the Jewel in the Crown: Archiving and Analyzing Astronomical Spectro-visual Binaries Big data;new Insight Concerning Primordial Lithium Production;Neutrinos from Type-II Supernovae;estimation of the Transit Time of Multiple Coronal Mass Ejection Events;A Comprehensive Study of the Physical and Geometrical Characteristics of the Close visual Binary System HIP 45571;The United Nations Group of Experts on the Standardization of Geographical Names (UNGEGN) and Its Relationship with the International Astronomical Union (IAU) on the Naming/Designation of Extraterrestrial Objects’;Assessment of Active Space Debris Removal Methods Using the Weighted Sum Model (WSM);thermal Control and analysis of a 3U Nanosatellite with Deployed Panels;advancements in Astronomy and Space Sciences in Jordan: Contributions from Experts and Astrophysical Institutions;the Central Distance Distribution of Hα and γ-ray Burst Solar Flares;update of the Solar Radiation Atlas for the Arab Republic of Egypt 2023;jurisprudential Reliance on Astronomical Calculations in Determining the Beginnings of the Hijri month;sun vertical Depressions and Their Effects on the Morning Twilight Phases in Egypt;thermal analysis of an Educational Nano Satellite;Twenty-Five years of the Foundation of the Arab Union for Astronomy and Space Sciences (AUASS);legal Challenges in Establishing Human Settlements in Space;a City-Wide Planetarium;controlling Robot Through Satellite: Telesurgery or Remote Surgery.

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Evaluating the Effectiveness of Random Forest versus Decision Tree Algorithms in Predictive Analytics for Enhancing Sustainable Development Strategies in Real Estate, Infrastructure, and Construction: A Machine Learning Perspective

Evaluating the Effectiveness of Random Forest versus Decisio...

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2024 IEEE International conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Gala, Deepali Mayur Pawar, Bhakti Band, Gayathri Dalal, Arvind Chakraborty, Arnab Das, Utpala Patil, Bhaskar v. Bharati Vidyapeeth Deemed to be University Institute of Management Kolhapur India Manav Rachna Educational Institutions MRIIRS School of Leadership and Management Haryana Faridabad India Ramdeobaba University School of Management Nagpur India Manav Rachna Educational Institutions MRIIRS Haryana Faridabad India

ISBN: (纸本)9798331539948

This study explores the application of machine learning algorithms, specifically decision tree and random forest models, to predict house prices within the Indian real estate market. Utilizing a comprehensive dataset obtained from Kaggle, this research encompasses extensive data preprocessing, feature engineering, and exploratory data analysis to identify key predictors of house prices. The comparative analysis aims to assess the accuracy, efficiency, and applicability of both models in the context of predictive analytics for sustainable development goals (SDGs) in real estate. The findings reveal that the random forest model outperforms the decision tree model, demonstrating superior predictive accuracy with a lower Root Mean Squared Error (RMSE). Key predictors identified include living area, number of bathrooms, and the condition of the property, with the random forest model providing deeper insights into feature interactions and their collective impact on house prices. This research highlights the potential of advanced machine learning techniques in enhancing real estate market analysis and strategic decision-making. The superior performance of the random forest model underscores its value in predictive analytics, offering stakeholders in the real estate sector a robust tool for achieving greater accuracy in property valuation and market analysis. Future research directions include the integration of additional data sources and the exploration of other machine learning algorithms to further refine predictive models and extend their applicability to sustainable development within the real estate, infrastructure, and construction industries. © 2024 IEEE.

关键词： Commerce

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Mechanism and Pressure Behavior of Fracturing Flooding Wells in Tight Reservoir

Mechanism and Pressure Behavior of Fracturing Flooding Wells...

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2024 SPE Gas and Oil Technology conference, GOTECH 2024

作者： Wang, Yang Weng, Zhoutian Kadet, valery v. Feng, Naichao National Key Laboratory of Petroleum Resources and Engineering China University of Petroleum Beijing Beijing China Gubkin Russian State University of Oil and Gas Moscow Russia Research Institute of Petroleum Exploration and Development PetroChina Beijing China

ISBN: (纸本)9781959025405

Fracturing flooding is a new energy-supplement technique emerging in recent years, which requires delivering high-efficiency oil agents to the sites with enriched remaining oil through fracturing. In this paper, we introduce the main characteristics of fracturing flooding technique. The pressure responses of induced horizontal fracture of injection well is obtained by modelling the fluid flow in the fractures with variable-mass-radial flow by point source method. The fracture-closure induced flow-rate change is considered by Duhamel's principle. Compared with conventional fractured well, the fracture properties in fracturing flooding wells vary with time during the shut-in testing period since they are not strengthened by proppants. Linear-flow analysis, fracture-storage coefficient analysis, and fracturing flooding interference analysis methods are comprehensively discussed and compared to estimate the fracture parameters based on the measured bottom-hole pressure data. Copyright © 2024, Society of Petroleum Engineers.

关键词： Fracture

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analysis of Users’ Demand for visual Images of Chinese Medicine Time-Honored Brands Based on the Kano Model 2nd

Analysis of Users’ Demand for Visual Images of Chinese Medi...

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2nd International conference on Image, vision and Intelligent Systems, ICIvIS 2022

作者： Yu, Mengyao He, Changhua Ma, Yue Yunnan University of Business Management Yunnan Kunming650106 China

ISBN: (纸本)9789819909223

Objective-based on the relevant research methods of the Kano model, this paper makes a specific exploration of the principles and design ideas of Chinese medicine’s time-honored brand image according to users’ needs. Methods the relevant data of the user needs of the time-honored brand image of Chinese medicine were obtained through the questionnaire survey method. And then, through the Kano model, the user needs were divided into basic needs (m), charm needs (a), no special difference needs (I), reverse needs (R), and expectation needs (o), to explore the internal needs of users for the visual image of the Chinese medicine brand. Moreover, integrate the sensory experience of users for the brand image. The perception process of interactive experience and emotional experience and the most original needs of users are transformed into reliable data. The needs are calculated through the calculation method of better worse coefficient. Results it is proposed that the brand image of time-honored pharmaceutical brands needs to unify the visual images and create the IP images, to make the brand image of time-honored Chinese pharmaceutical brands have the characteristics of the times and interest. Conclusion according to the needs of users, summarizing the methods and strategies suitable for optimizing the visual image of Chinese medicine time-honored brands is not only the inheritance and tolerance of Chinese traditional medicine culture but also provides reference and reference for the continuous growth of Chinese medicine time-honored brands. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Behavioral research

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EXTENDING vISUOSPATIAL analysis IN DESIGN COMPUTING: An exploration with a Novel GPU-Based Algorithm and Form-Based Codes 28th

EXTENDING VISUOSPATIAL ANALYSIS IN DESIGN COMPUTING: An Expl...

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28th International conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2023

作者： Kimm, Geoff White, Marcus Burry, Mark Swinburne University of Technology Australia

ISBN: (纸本)9789887891796

This paper responds to a gap observed between the contemporary capacity for calculation and analysis of visibility of built environment features, such as buildings, in digital urban and architectural computational research models and the functionality of off-the-shelf software tools available to professionals. The research investigates the potential of visibility analysis to be embedded and extended within computational-based workflows of software tools to better meet urban design and planning industry needs. We introduce a novel method for visibility calculation that exposes output data for further analysis within a computational workflow and implement it in a game development engine used by software tool providers. Based in our engagement with a local government authority, we then use that method to demonstrate a workflow in the context of form-based building codes in which the visual impact of a building is considered rather than prescriptive limits on dimensions and use. Our results indicate the novel method has substantial performance improvements compared to an alternative mode of visibility calculation and that software providers could more thoroughly integrate and extend visibility analysis to meet industry needs. © 2023 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.

关键词： Graphics processing unit

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KeywordScape: visual Document exploration using Contextualized Keyword Embeddings

KeywordScape: Visual Document Exploration using Contextualiz...

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2022 conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： voigt, Henrik Meuschke, Monique Zarrieb, Sina Lawonn, Kai University of Jena Germany University of Bielefeld Germany University of Magdeburg Germany

ISBN: (纸本)9781959429418

Although contextualized word embeddings have led to great improvements in automatic language understanding, their potential for practical applications in document exploration and visualization has been little explored. Common visualization techniques used for, e.g., model analysis usually provide simple scatter plots of token-level embeddings that do not provide insight into their contextual use. In this work, we propose KeywordScape, a visual exploration tool that allows users to overview, summarize, and explore the semantic content of documents based on their keywords. While existing keyword-based exploration tools assume that keywords have static meanings, our tool represents keywords in terms of their contextualized embeddings. Our application visualizes these embeddings in a semantic landscape that represents keywords as islands on a spherical map. This keeps keywords with similar context close to each other, allowing for a more precise search and comparison of documents. © 2022 Association for Computational Linguistics.

关键词： Embeddings

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Leveraging Python in Decline Curve analysis: Automation, Integration, and Advancements in Reservoir Management

Leveraging Python in Decline Curve Analysis: Automation, Int...

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2024 SPE Gas and Oil Technology conference, GOTECH 2024

作者： Nukala, S.T. Kumar, A. Rajput, S. Lopes, v.S. SLB Mumbai India

ISBN: (纸本)9781959025405

Decline curve analysis (DCA) serves as a fundamental technique in the realm of oil and gas production, particularly for assessing the diminishing production rates of wells and projecting their future performance. This method entails fitting standard curves to historical production data, enabling the identification of trends that reflect the decline in production over time. By extrapolating these established curves, DCA facilitates the prediction of future well performance. It stands as a pivotal tool in estimating recoverable reserves, especially applicable in scenarios where the production history spans a sufficiently long duration to discern discernible decline patterns. An integral technology employed in this process involves an automated Decline Curve analysis (DCA) methodology, capable of autonomously executing on a machine with minimal human intervention and reduced error rates. The traditional DCA, rooted in Arp's equation, distinguishes three primary types of decline curves: exponential, hyperbolic, and harmonic. This research endeavor encompasses the development of a Python-based framework by the authors and comparison of different methods, aimed at applying Decline Curve analysis (DCA) methods to wells in a manner characterized by impartiality, systematic execution, and automation. various methodologies for automating decline curve analysis were explored, and their outcomes were meticulously compared. This innovative approach stands in stark contrast to the conventional practice of manual DCA, which has historically dominated the industry. As the oil and gas sector transitions into the digital transformation era, the fusion of digital capabilities with traditional expertise in DCA becomes increasingly imperative. Embracing this synergy facilitates not only enhanced efficiency and accuracy but also fosters a deeper understanding of reservoir behavior and production dynamics. © 2024, Society of Petroleum Engineers.

关键词： Python

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Deep Learning: Exploring the Depths of Artificial Neural Networks for Advanced data analysis and Pattern Recognition 5

Deep Learning: Exploring the Depths of Artificial Neural Net...

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5th IEEE Global conference for Advancement in Technology, GCAT 2024

作者： Ravikumar, Rajesh Jagadeesan Ammupriya, A. Devi, P.P. Pandi, v.Samuthira Akula, Akhila Sankar, K. Evernorth Health Services ChattanoogaTN37402 United States St Joseph's College of Engineering Department of Mba Chennai India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Avadi Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Rajalakshmi Engineering College Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350376685

This research provides a comprehensive exploration of LSTMs, CNNs, and RNNs, with a focus on their application in advanced data analysis and pattern recognition. We begin by examining the theoretical underpinnings of each architecture, highlighting their unique features and the mechanisms by which they process and learn from data. In the process of collecting long-term relationships in sequential data, LSTMs have been demonstrated to perform exceptionally well, making them an excellent choice for applications such as language modeling and time series prediction. CNNs are discussed in the context of their ability to extract hierarchical features from spatial data, rendering them particularly effective for image and video analysis. RNNs, with their inherent ability to process sequences of varying lengths, are analyzed for their applications in speech recognition and machine translation. Following this, the research begins to conduct a comparative examination of different architectures, examining how well they perform on benchmark datasets as well as challenges that are encountered in the real world. We assess their strengths and limitations, considering factors such as accuracy, computational efficiency, and scalability. Additionally, we explore the challenges associated with training deep neural networks, including the need for extensive data preprocessing, the risk of overfitting, and the importance of hyperparameter tuning. Finally, we discuss the implications of these findings for future research and practical applications in data analysis and pattern recognition. The study concludes that while each architecture has its niche, the combination of LSTMs, CNNs, and RNNs offers a powerful toolkit for tackling complex problems across diverse domains. The research underscores the potential of deep learning to unlock insights from data at unprecedented scales and complexities, paving the way for innovative solutions in fields ranging from healthcare to finance. © 2024 IE

关键词： Deep neural networks

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Development of A vision-based Ship Behavior Characteristics Extraction Approach and Comparison with K-means Clustering 32nd

Development of A Vision-based Ship Behavior Characteristics ...

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32nd International Ocean and Polar Engineering conference, ISOPE 2022

作者： Cai, Zhiyuan Yu, Long Fan, Qidong Li, Lecheng Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration State Key Laboratory of Ocean Engineering School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781880653814

The poor quality of real ship data and the lack of navigation status marks make it different from the experimental data recorded with experimental conditions, which is difficult to be effectively used in the study of ship performance. In this paper, a visual analysis system of navigation logic is developed to assist in abnormal data processing. At the same time, the visual analysis is used to divide the real ship data sub units, and the small sample navigation state determination set is obtained. The results of K-means clustering are compared with the determined set, and the navigation state class is obtained by semantic classification. Factor analysis is used to analyze the characteristics of navigation state data, so as to reduce the dimension of data and make the classification result better. Taking the inland navigation mark maintenance work boat data as an example, this method is realized to label the ship navigation status for the unknown large sample data set. © 2022 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： K-means clustering

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Towards Efficient visual-Language Alignment of the Q-Former for visual Reasoning Tasks

Towards Efficient Visual-Language Alignment of the Q-Former ...

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2024 conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kim, Sungkyung Lee, Adam Park, Junyoung Chung, Andrew Oh, Jusang Lee, Jay-Yoon Seoul National University Korea Republic of UC Berkeley United States Weavel Inc. Korea Republic of Graduate School of Data Science Seoul National University Korea Republic of

ISBN: (纸本)9798891761681

Recent advancements in large language models have demonstrated enhanced capabilities in visual reasoning tasks by employing additional encoders for aligning different modalities. While the Q-Former has been widely used as a general encoder for aligning several modalities including image, video, audio, and 3D with large language models, previous works on its efficient training and the analysis of its individual components have been limited. In this work, we investigate the effectiveness of parameter efficient fine-tuning (PEFT) the Q-Former using InstructBLIP with visual reasoning benchmarks ScienceQA and IconQA. We observe that applying PEFT to the Q-Former achieves comparable performance to full fine-tuning using under 2% of the trainable parameters. Additionally, we employ AdaLoRA for dynamic parameter budget reallocation to examine the relative importance of the Q-Former's sublayers with 4 different benchmarks. Our findings reveal that the self-attention layers are noticeably more important in perceptual visual-language reasoning tasks, and relative importance of FFN layers depends on the complexity of visual-language patterns involved in tasks. The code is available at https://***/AttentionX/InstructBLIP_PEFT. © 2024 Association for Computational Linguistics.

关键词： Budget control

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