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检索条件"任意字段=2024 Conference on Visualization in Data Science-VDS-Annual"

共 115 条记录，以下是41-50 订阅

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Smart Solar Forecasting: Machine Learning Approaches for Predicting Solar Power

Smart Solar Forecasting: Machine Learning Approaches for Pre...

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2024 IEEE Region 10 conference, TENCON 2024

作者： Kumar, Madhav Borgohain, Santanu Panda, Kaibalya Prasad Thokchom, Surmila Panda, Gayadhar National Institute of Technology Department of Electrical Engineering Meghalaya India Pandit Deendayal Energy University Department of Electrical Engineering Gujarat Gandhinagar India National Institute of Technology Department of Computer Science and Engineering Meghalaya India Nitttr Department of Electrical Engineering Kolkata India

ISBN: (纸本)9798350350821

Solar energy is one of the most popular renewable energy sources in recent years. These solar systems not only reduce the need for electricity but also greatly improve the environment. Due to our excessive dependence on fossil fuels and their limited sources and the pollution caused by them, most people are turning to solar energy to power their homes and industries. It's critical to understand the location of the solar panel and the local weather since solar power efficiency is highly dependent on environmental factors. With the inherent unpredictability of photovoltaic power output, machine learning is a useful tool for predicting how weather conditions affect solar power plants, and there are various other viable prediction methods available. This research evaluates several meteorological characteristics for PV forecasting using numerous machine learning algorithms. Using the collected historical data set, a machine learning model is created. Necessary preprocessing techniques such as univariate and bivariate analysis are used. Machine learning algorithms benefit from data visualization as it makes it easier to understand features and create a robust classification model. Accuracy-related performance metrics such as MAE, MSE, and RMSE are used to compare the methods. We evaluate and contrast the various models' performances. This evaluation approach helps to choose the algorithm that best suits the task at hand. © 2024 IEEE.

关键词： Mean square error

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A Framework to Transform Metadata and Document-Level Tabular Spatial Information and Measurements to Marine Geology Gazetteer 10th

A Framework to Transform Metadata and Document-Level Tabular...

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10th annual International conference on Information Management and Big data (SIMBig)

作者： Suryani, Muhammad Asif Beth, Christian Wallmann, Klaus Renz, Matthias Christian Albrechts Univ Kiel Inst Informat Kiel Germany GEOMAR Helmholtz Ctr Ocean Res Kiel Kiel Germany

ISBN: (纸本)9783031636158;9783031636165

In many natural science domains, scientific documents provide a very important source for fundamental measurements that have been recorded at various geo-spatial locations. Manually extracting these reported measurements and their location information from these documents is extremely tedious and expensive, necessitating an automatic approach. These documents, primarily accessible in PDF format, are designed for platform-independent viewing and printing rather than presenting content in a conveniently accessible structure, which poses a significant challenge for automatic extraction. Relevant records of measurements and location information are often provided in tables that are structured quite diversely regarding the number of (nested) rows and columns, table orientation and depend on the article template and the author's style of presentation. In this paper, we propose a framework that, to the best of our knowledge, is the first generic approach that focuses on automatic location information and measurement extraction from tables in PDF documents. Based on various publications of arbitrary format from Marine Geology, we showcase how to segregate both spatial and quantitative measurement information, which alongside respective metadata could answer numerous pertinent questions about values of SR (Sedimentation Rate) and MAR (Mass Accumulation Rate) at specific locations, who measured these values, and referenced information (identifier) to these locations. Our proposed framework also validates the extracted information from scientific publications. Finally, all the aggregated information constitutes a novel Marine Geology Gazetteer and offers an interactive visualization.

关键词： Information Extraction Tabular data Spatial Information Marine Geology Gazetteer

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Question Answering for Decision-making in Green Building Design A Multimodal data Reasoning Method Driven by Large Language Models 44

Question Answering for Decision-making in Green Building Des...

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44th annual conference of the Association for Computer Aided Design in Architecture: Designing Change, ACADIA 2024

作者： Li, Yihui Yan, Xiaoyue Zhou, Hao Lin, Borong School of Architecture Tsinghua University China Tsinghua University Institute for Urban Governance and Sustainable Development IUGSD China

ISBN: (纸本)9798989176472

In recent years, the critical role of green buildings in addressing energy consumption and environmental issues has become widely acknowledged. Research indicates that over 40% of potential energy savings can be achieved during the early design stage. Therefore, decision-making in green building design (DGBD), which is based on modeling and performance simulation, is crucial for reducing building energy costs. However, the field of green building encompasses a broad range of specialized knowledge, which involves significant learning costs and results in low decision-making efficiency. Many studies have already applied artificial intelligence (AI) methods to this field. Based on previous research, this study innovatively integrates large language models with DGBD, creating GreenQA, a question answering framework for multimodal data reasoning. Utilizing Retrieval Augmented Generation, Chain of Thought, and Function Call methods, GreenQA enables multimodal question answering, including weather data analysis and visualization, retrieval of green building cases, and knowledge query. Additionally, this study conducted a user survey using the GreenQA web platform. The results showed that 96% of users believed the platform helped improve design efficiency. This study not only effectively supports DGBD but also provides inspiration for AI-assisted design. © 2024 Association for Computer Aided Design in Architecture. All rights reserved.

关键词： Question answering

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Tackling Front-End Challenges: Strategies for Time-Series data Retrieval and visualization

Tackling Front-End Challenges: Strategies for Time-Series Da...

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IEEE annual International Computer Software and Applications conference (COMPSAC)

作者： Naiyana Sahavechaphan Manot Rattananen Jukkrapong Ponharn Tanapon Yotanak Krit Punpreuk Kamron Aroonrua National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Developing front-end applications involving large datasets with concurrent usage poses significant challenges. These challenges encompass efficient data retrieval and visualization. This paper addresses these issues within the domain of time-series data and explores methods such as progressive data retrieval and data aggregation strategies to effectively manage and present large datasets.

关键词： data visualization data aggregation Software

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Deep Multi-time visualization and Analytics for E-commerce Platform 4th

Deep Multi-time Visualization and Analytics for E-commerce P...

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4th International conference on Advanced Network Technologies and Intelligent Computing-ANTIC-annual

作者： Bhatnagar, Jay R. Iyer, Ravi Prakash Rawat, Krishna Tripathi, Ankit Bhatnagar, Akhil Goyal, Dhruv GEU Univ Dehra Dun 248002 Uttarakhand India Krea Univ Sricity 517646 Andhra Pradesh India IT FUZONe Syst Noida 110096 Uttar Pradesh India

ISBN: (纸本)9783031837821;9783031837838

The Online Rating visualization (ORV) is a widely used tool along with text reviews that offer business intelligence about product performance to prospective e-buyers. The rapid growth in e-business environment has led to an increasing trend to host products and related analytics for prospective consumers in a manner that is informative, credible, and fair. Surprisingly, there is not much prior research done in making the ORV productive and informative. In this paper, we formulate a deep-time resolved view of buyer sentiment analysis called as DBRV (Deep-Bin Rating-Vote visualization) based on equi-length time slices of ratings-votes. We also propose a novel performance measure called Popularity index (P) which combines ratings-votes meaningfully in time differential manner to depict discriminant power of the DBRV. Simulations performed using real data of ratings-votes for iPhone 12 and Earbuds with 4139 and 8497 reviews respectively, shows that DBRV offers more informative insights about consumer experience than average ratings-votes. The proposed DBRV-1 depicts time-binning of ratings-votes distribution thereby illustrating its information resolved into timebins. Likewise, DBRV-2 powered by P conveys higher intelligence about perceived product performance than existing ORV. Finally, we also formulate a more effective and novel user-interactive dashboard, shown for iPhone 12 and Earbuds, that offers a promising frontend analytics for prospective customers.

关键词： E-commerce Ratings Consumer Analytics Deep-Time Binning Popularity index

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GHG Web Portal Dashboard: A Scalable and Flexible Digital Platform for Efficient Environmental Monitoring 4th

GHG Web Portal Dashboard: A Scalable and Flexible Digital Pl...

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4th International conference on Advanced Research in Technologies Information Innovation and Sustainability-ARTIIS-annual

作者： Bastos, Goncalo Martinho, Ricardo Ferrao, Silvia Martinho, Rita Matias, Hugo Polytech Univ Leiria ESTG Leiria Portugal Polytech Univ Leiria INESCC DL ESTG Leiria Portugal CoLAB NET4CO2 Network Sustainable CO2 Econ Porto Portugal

ISBN: (纸本)9783031832062;9783031832079

The importance of efficient digital platforms for monitoring greenhouse gas (GHG) indicators cannot be overstated in contemporary environmental management and sustainability efforts. Such platforms are crucial for accurate, real-time data collection and integration, facilitating regulatory compliance, and enabling strategic decision-making. However, developing a usable digital platform for GHG monitoring is challenging due to the complexity of integrating diverse data sources, ensuring data accuracy and reliability, and providing user-friendly interfaces for varied stakeholders. Despite the availability of multiple official data sources on GHG emissions, no specific digital platform exists to comprehensively aggregate and manage this data. Existing systems often lack the flexibility to incorporate new indicators and adapt to evolving regulatory requirements. This paper presents a digital platform capable of seamlessly integrating data from various sources and offering robust usability, to enhance the effectiveness of GHG emission monitoring. The platform can be valuable to stakeholders, including regulatory bodies, industry leaders, environmental organizations, and the general public to address the complexities of environmental data and advancing global sustainability efforts.

关键词： data visualization Greenhouse gas (GHG) emissions Interactive Dashboard Usability

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DeepDSMRI: Deep Domain Shift Analyzer for MRI 28th

DeepDSMRI: Deep Domain Shift Analyzer for MRI

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28th annual conference on Medical Image Understanding and Analysis, MIUA 2024

作者： Kushol, Rafsanjany Kalra, Sanjay Yang, Yee-Hong Department of Computing Science University of Alberta Edmonton Canada Division of Neurology Department of Medicine University of Alberta Edmonton Canada Neuroscience and Mental Health Institute University of Alberta Edmonton Canada

ISBN: (纸本)9783031669545

The use of MR images in medical image analysis from different centers in clinical applications and medical research has grown in popularity. However, challenges arise due to inherent variability between centers, leading to domain shift issues that reduce the reliability and robustness of the analysis results. Furthermore, the lack of suitable tools for analyzing domain shift hampers the progress of developing and validating domain adaptation and harmonization techniques. Utilizing pre-trained deep models as feature extractors, we introduce a novel framework called Deep Domain Shift analyzer for MRI (DeepDSMRI), designed explicitly to comprehend the extent of domain shift in MRI datasets. DeepDSMRI provides adequate insights into the existence of domain shift for diverse MRI modalities, including structural, functional, and diffusion-weighted images. The proposed framework incorporates visualization tools (e.g., t-SNE and UMAP) to illustrate grouping similar data and isolating dissimilar data into distinct clusters. Moreover, the quantitative analysis measures the classification accuracy between domains and the domain shift distance. The efficacy of the proposed DeepDSMRI is demonstrated through experimental assessments conducted on seven extensive multi-center neuroimaging databases. The source code is available at (https://***/rkushol/DeepDSMRI). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Magnetic resonance imaging

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System Construction of Artificial Intelligence Dependent Cloud Service Mode based on Deep Learning Core Algorithms

System Construction of Artificial Intelligence Dependent Clo...

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2024 International conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Wu, Yanhong Zhanjiang University of Science and Technology School of Accounting Zhanjiang China Ucsi University Ucsi Graduate Business School Kuala Lumpur Malaysia

ISBN: (纸本)9798350360660

In recent years, with the rapid development of computer technology, artificial intelligence, an important branch of the discipline, has been undergoing constant changes. The essence of artificial intelligence is the use of computer technology to simulate the human way of thinking and a series of intelligent behaviors, such as co-ordination, thinking, reasoning and so on. In intelligent robots, AI (artificial intelligence) language processing systems, image recognition systems and various types of expert systems, complex computer algorithms are used to simulate certain logical reasoning of the human brain, so as to achieve the intelligence of equipment. Against the background of global economic integration and accelerated informationization, artificial intelligence technology is developing rapidly. This paper presents a distributed model cloud platform based on Kubernetes architecture for data transfer, distributed concurrent multitasking and functional business requirements through virtualization and model componentization techniques. The platform adopts a three-tier structure of IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service), and combines with ArcGIS and modern Web technologies to realize efficient visualization display. We evaluate the performance of AWS, Google Cloud, Azure, and IBM Cloud in terms of deep learning model training and inference performance, and the experimental results show that Google Cloud and Azure are the best performers in terms of training time and accuracy as well as inference speed, while AWS and IBM Cloud excel in terms of scalability and security. Ultimately, this paper provides an important reference for organizations when choosing a cloud service provider to support deep learning applications. © 2024 IEEE.

关键词： Cloud platforms

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LLM-Powered Multi-Actor System for Intelligent Analysis and visualization of IEC 61499 Control Systems

LLM-Powered Multi-Actor System for Intelligent Analysis and ...

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annual conference of Industrial Electronics Society

作者： Midhun Xavier Tatiana Laikh Sandeep Patil Valeriy Vyatkin Department of Computer Science Electrical and Space Engineering Lulea Tekniska Universitet Sweden Department of Electrical Engineering and Automation Aalto University Espoo Finland

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This paper introduces an innovative multiactor framework that harnesses the potential of LLMs to augment the functionalities of ICS. By integrating conversational AI technologies, this framework significantly improves human-machine interactions, enabling sophisticated analysis and visualization of intricate data sets. The core of the system comprises specialized LLM actors that interact through a LangGraph-based multiactor framework, addressing various aspects of IEC 61499 control systems including PLC code analysis, SQL query execution, and data visualization. This integration enables operators to interact with the control system using natural language, significantly reducing technical barriers and enhancing the accessibility and usability of complex industrial systems.

关键词： Industrial electronics Human computer interaction Structured Query Language Conversational artificial intelligence Codes Natural languages data visualization Control systems IEC Standards Usability

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Assessing User Trust in Active Learning Systems: Insights from Query Policy and Uncertainty visualization 24

Assessing User Trust in Active Learning Systems: Insights fr...

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29th annual conference on Intelligent User Interfaces, IUI 2024

作者： Thomas, Ian Oh, Song Young Szafir, Danielle Albers The University of North Carolina at Chapel Hill Chapel HillNC United States Duke University DurhamNC United States

ISBN: (纸本)9798400705083

Active learning (AL) systems have become increasingly popular for various applications in machine learning (ML), including medical imaging, environmental monitoring, and geospatial analysis. These systems rely on inputs dynamically queried from people to enhance classification. Ensuring appropriate analyst trust in these systems presents a significant obstacle as analyst over- or underreliance may adversely affect a given application. Common AL strategies enhance classification models by asking analysts to provide labels for data points with the highest degree of uncertainty. However, such model-centric policies do not consider potential priming effects on the analyst and how they will affect people's trust in the system post-training. We present an empirical study assessing how AL query policies and visualizations that enhance transparency in a classifier's decisions influence trust in automated image classifiers. We found that query policy may significantly influence an analyst's perception of system capabilities, while the level of visual transparency into classifier certainty may influence an analyst's ability to perform a classification task. Our study informs the design of interactive labeling systems to help mitigate the effects of overreliance and calibrate appropriate trust in automated systems. © 2024 ACM.

关键词： Transparency

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