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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Jeya, R. Singh, Dimpy Jha, Anuj Mallick, Soumik SRM Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350353778

Agriconnect, a web-based platform, empowers farmers with data-driven crop production insights through a two-model machine learning approach. The Crop Insights Model leverages a fine-tuned large language model (LLM) called Gemini. Gemini is first enriched with dealer information from a user-created, state-categorized CSV file. To ensure location accuracy, SerpApi retrieves data from Google Maps based on user searches, which is then fed into a vector database alongside the dealer information. This comprehensive dataset fine-tunes Gemini for location-specific insights. The Crop Recommender Model then utilizes the enhanced Gemini to analyze user-provided climate and soil data (type and moisture). Based on this analysis, the fine-tuned LLM recommends crops best suited for specific conditions, aiming to optimize crop selection and mitigate climate and market fluctuations risks. Finally, an interactive visualization dashboard presents this information, empowering farmers to make informed decisions and implement effective practices. © 2024 ieee.

关键词： data accuracy

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Traversing Through Terabytes to Decode the Tapestry of Tangible Technology Trends 4

Traversing Through Terabytes to Decode the Tapestry of Tangi...

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4th ieee International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dhingra, Lovish Ganga, S. Reddy, B. Kumar, R. Thanga Borade, Monali Ravindra Kannimuthu, S. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of It Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - UG Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam College of Engineering Department of Information Technology Coimbatore641032 India

ISBN: (纸本)9798331518981

Understanding the intricate network of visible technology trends is crucial in today's fast-changing technological environment. We'll enter a terabyte-sized digital jungle as we strive to understand the complicated network of new technologies that are transforming our world. The exponential increase in digital data demands sophisticated techniques for making sense of changing tech landscapes. In 'Traversing Through Terabytes to Decode the Tapestry of Tangible Technology Trends,' the authors provide an innovative method that brings together Natural Language Processing (NLP), Machine Learning (ML), and Network analysis. Using natural language processing, the technique extracts relevant phrases and themes from large amounts of raw textual data. Machine learning algorithms interpret speech and forecast trends. In contrast, network analysis uncovers the deep relationships between numerous technical fields. Our solution beats standard methods after rigorous testing. This would help stakeholders grasp technology trends and make better strategic decisions. © 2024 ieee.

关键词： Emerging Technologies Machine Learning Natural Language Processing Network analysis Sentiment analysis Text Preprocessing Trend Prediction Unstructured data visualization

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Auto-GO: Reproducible, Robust and High Quality Ontology Enrichment visualizations

Auto-GO: Reproducible, Robust and High Quality Ontology Enri...

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2023 ieee International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Grassucci, Isabella Massacci, Alice Sperandio, Eleonora Baldinelli, Sara Pallocca, Matteo Ticconi, Fabio Betti, Martina National Cancer Institute Uosd Biostatistics Bioinformatics and Clinical Trial Center Ircss Regina Elena Rome Italy National Research Council Institute of Experimental Endocrinology and Oncology Naples Italy

ISBN: (纸本)9798350337488

Bioinformatics and data Science are routinely challenged to take out intelligible results from huge amounts of data. These results, in turn, are conveyed through plots and visualizations that should be easily reproducible for scientific soundness and ethical reasons. This process is of critical importance in Genomics, when dealing with large multi-omics datasets. One of the final steps of these analyses is Gene Set enrichment, where web tools represent a valuable resource but not a reliable surrogate for standardized, high-quality visualizations. Here we present Auto-GO, a software framework that proposes a standardization of the Gene Functional Enrichment process along with an R package able to produce high-quality visualization in an automated manner, improving the reproducibility of the whole analytical process. We present three use cases in Cancer Transcriptomics and Epigenomics datasets as a proof-of-concept to visualize Multiple Differential Expression and Single Sample Gene Set Enrichment analysis. Finally, we show how it can be employed to shear light on publicly available datasets, even in small casuistry of Rare Cancers. © 2023 ieee.

关键词： data visualization Differential Enrichment Gene Ontology Reproducibility Transcriptomics visualization Automatization

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Multi-Criteria Decision analysis for Aiding Glyph Design

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ieee Transactions on visualization and Computer Graphics 2025年 PP卷 PP页

作者： Hsieh, Hong-Po Zavatsky, Amy Chen, Min University of Oxford United Kingdom

Glyph-based visualization is one of the main techniques for visualizing complex multivariate data. With small glyphs, data variables are typically encoded with relatively low visual and perceptual precision. Glyph designers have to contemplate the trade-offs in allocating visual channels when there is a large number of data variables. While there are many successful glyph designs in the literature, there is not yet a systematic method for assisting visualization designers to evaluate different design options that feature different types of tradeoffs. In this paper, we present an evaluation scheme based on the multi-criteria decision analysis (MCDA) methodology. The scheme provides designers with a structured way to consider their glyph designs from a range of perspectives, while rendering a semi-quantitative template for evaluating different design options. In addition, this work provides guideposts for future empirical research to obtain more quantitative measurements that can be used in MCDA-aided glyph design processes. © 2025 ieee.

关键词： data visualization

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An Analytical Study on Toy Age Grading Methods: A Comparative Study of large Language Models and Machine Learning Approaches

An Analytical Study on Toy Age Grading Methods: A Comparativ...

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2024 ieee International symposium on Product Compliance Engineering-Asia, ISPCE-AS 2024

作者： Au, Shui Lun Mak, S.L. Tang, W.F. Hong Kong College of Technology Hong Kong Hong Kong Vocational Training Council Hong Kong Hong Kong School of Science & Technology Hong Kong Metropolitan University Hong Kong Hong Kong

ISBN: (纸本)9798331523008

This research delves into the utilization of large Language Models (LLMs) and Machine Learning (ML) in age grading assessments within the toy industry. It compares the efficacy of these methodologies in establishing suitable age ranges for toys, a critical aspect for ensuring child safety and compliance with regulations. LLMs, harnessing pretrained models, enable swift evaluations through visual and textual inputs, yielding instant results with minimal human intervention. Conversely, ML models necessitate training on labeled datasets to gauge age appropriateness, presenting opportunities for ongoing improvement with updated data. A comparative analysis between a trained ML model and GPT 3.5 Turbo unveiled differ accuracies and readiness in age grading assessments. While LLMs offer rapid insights, ML models shine in scalability and the refinement of accuracy over time. The study's findings highlight the strengths and limitations of each approach, proposing customized applications for stakeholders in the toy safety and development domain based on their distinct requirements and resources. © 2024 ieee.

关键词： Contrastive Learning

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An Exploratory data analysis and visualizations of Underprivileged Communities Diabetes dataset for Public Good 22

An Exploratory Data Analysis and Visualizations of Underpriv...

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22nd ieee/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Owda, Majdi Owda, Amani Yousef Fasli, Maria Arab Amer Univ Fac Data Sci UNESCO Chair Data Sci Sustainable Dev Ramallah West Bank Palestine Arab Amer Univ Fac Grad Studies Dept Nat Engn & Technol Sci Ramallah West Bank Palestine Univ Essex Sch Comp Sci & Elect Engn Inst Analyt & Data Sci Colchester Essex England

ISBN: (纸本)9798350309188

Exploratory data analysis and visualization are essential tools for discovering patterns, highlighting anomalies, and discovering insights from datasets. In addition, visualizations tend to provide information in a way that is more accessible for the audience to understand and comprehend. Underprivileged communities will need an easier way to comprehend issues, challenges, and threats such as avoiding diseases. The use of exploratory data analysis and visualizations of diabetes datasets can be used as a means to make this topic easier to understand for underprivileged communities. A methodology has been developed to explore a dataset collection of diabetes patients. The dataset was obtained from the Palestinian community medical center (first-hand dataset), then the dataset was prepared and cleaned, and all rows with missing values or wrong values were dropped and removed, then an exploratory data analysis with a large number of data visualizations was produced, and last, a sample of the produced visualizations have been used to conduct a social acceptance experiment to educate an underprivileged population in the Palestinian community about the risks of diabetes. The result of the experiment was excellent and very promising in which participants understood the contents in a much easier way and provided very positive feedback on utilizing visualizations of diabetes datasets for public awareness and good.

关键词： Exploratory data analysis data visualizations Diabetes dataset Underprivileged Communities Public Good

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Development of Intelligent analysis and visualization Software Tools for Environmental Monitoring data

Development of Intelligent Analysis and Visualization Softwa...

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2024 International Conference on Power, Electrical Engineering, Electronics and Control, PEEEC 2024

作者： Bao, Jingyue Li, Jiajie Wuhan Technology and Business University Hubei Wuhan430065 China ZheJiang University of Technology Engineering Design Group Co. Ltd Zhejiang Hangzhou317699 China

ISBN: (纸本)9798350378917

With the increasingly severe environmental pollution problem, the widespread deployment of environmental monitoring stations has generated massive and complex environmental data. Traditional data processing methods seem inadequate in the face of such a large amount of data, while the introduction of big data technology has brought revolutionary changes to the processing and analysis of environmental monitoring data. Through big data analysis, not only can the accuracy and completeness of monitoring data be significantly improved, but also the efficiency and quality of environmental monitoring can be greatly improved, providing solid data support and scientific decision-making basis for environmental management departments. This article focuses on developing an efficient and intelligent software tool for analyzing and visualizing environmental monitoring data, aiming to meet the high requirements for data processing and display in current environmental protection work. This software deeply integrates big data analysis and deep learning (DL) technology, and achieves intelligent mining and deep analysis of environmental monitoring data by constructing advanced algorithm models. The experimental results show that the software can accurately identify environmental change trends, predict potential pollution events, evaluate environmental quality levels, and present the analysis results to users in an intuitive and understandable visual form. © 2024 ieee.

关键词： Environmental monitoring

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Towards Advancing Reservoir Model analysis with Virtual Reality 21

Towards Advancing Reservoir Model Analysis with Virtual Real...

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21st ieee International symposium on Mixed and Augmented Reality (ISMAR)

作者： Aktar, Farzana Aminbeidokhti, Amir Cartwright, Stephen Chen, Zhangxin Daeijavad, Parisa Dalton, Rachel Dawar, Seher Hu, Die Lawton, Bryson Maurer, Frank Samoil, Steven Wang, Nanjia Xie, Siqi Univ Calgary Calgary AB Canada

ISBN: (数字)9781665453653

ISBN: (纸本)9781665453653

Traditional software tools for visualizing and analyzing reservoir model data utilize 2D displays, and thus can only offer capabilities subject to the restrictions of these technologies. We describe ways to advance reservoir model analysis workflows beyond conventional methods by leveraging the strengths of virtual reality technologies to improve how one understands, analyzes, and interacts with reservoir models. Specifically, we discuss a reservoir model analytics tool exploring how virtual reality can be used to improve the spatial perception and navigation of data, support more intuitive and natural data interactions, enable larger workspaces, and promote more effective collaboration over reservoir models.

关键词： Immersive Analytics Virtual Reality Reservoir Engineering Human-Computer Interaction Information Workflow visualization VR

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Discriminative Self-Paced Group-Metric Adaptation for Online Visual Identification

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2023年第4期45卷 4368-4383页

作者： Zhou, Jiahuan Su, Bing Wu, Ying Peking Univ Wangxuan Inst Comp Technol Beijing 100080 Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Key Lab Big Data Management & Anal Methods Beijing 100872 Peoples R China Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA

Existing solutions to instance-level visual identification usually aim to learn faithful and discriminative feature extractors from offline training data and directly use them for the unseen online testing data. However, their performance is largely limited due to the severe distribution shifting issue between training and testing samples. Therefore, we propose a novel online group-metric adaptation model to adapt the offline learned identification models for the online data by learning a series of metrics for all sharing-subsets. Each sharing-subset is obtained from the proposed novel frequent sharing-subset mining module and contains a group of testing samples that share strong visual similarity relationships to each other. Furthermore, to handle potentially large-scale testing samples, we introduce self-paced learning (SPL) to gradually include samples into adaptation from easy to difficult which elaborately simulates the learning principle of humans. Unlike existing online visual identification methods, our model simultaneously takes both the sample-specific discriminant and the set-based visual similarity among testing samples into consideration. Our method is generally suitable to any off-the-shelf offline learned visual identification baselines for online performance improvement which can be verified by extensive experiments on several widely-used visual identification benchmarks.

关键词： visualization Testing Adaptation models Measurement Training data models Feature extraction Learning from sharing frequent pattern mining online adaptation person re-identification self-paced learning

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AN ADVANCED OPEN-SOURCE PLATFORM FOR AIR QUALITY analysis, visualization, AND PREDICTION

AN ADVANCED OPEN-SOURCE PLATFORM FOR AIR QUALITY ANALYSIS, V...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Huang, Thomas Chung, Nga Dunn, Alex Hovland, Erik Kang, Jason Loubrieu, Thomas Neu, Jessica Roberts, Joe Hasheminassab, Sina Marlis, Kevin Bindle, Liam Estrada, Lucas Jacob, Daniel Martin, Randall Holm, Jeanne Pourhomayoun, Mohammad Henze, Daven Nawaz, Muhammad Omar Yang, Chaowei Liu, Qian CALTECH NASA Jet Prop Lab Pasadena CA 91125 USA CALTECH Pasadena CA 91125 USA Washington Univ St Louis MO 63110 USA Harvard Univ Cambridge MA 02138 USA City Los Angeles Los Angeles CA USA Calif State Univ Los Angeles Los Angeles CA 90032 USA Univ Colorado Boulder CO 80309 USA George Mason Univ Fairfax VA 22030 USA

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Ambient air pollution is the largest environmental health risk factor, leading to several million premature deaths globally per year. The challenge of combating poor air quality is exacerbated by growing urban populations, changing emissions, and a warming climate. While there have been many advances monitoring and modeling of atmospheric composition, reflected in the dramatic increase in archived Earth Observations, there is no single measurement or method that alone can provide an accurate depiction of the entire atmosphere. The rapidly growing collections of observational and modeling data require us to be smarter about what data to include, and how such data is used. In recent years, NASA has invested significantly in advancing the concepts for Analytics Collaborative Framework (ACF) [5] and New Observing Strategies (NOS) [4] to tackle our software infrastructure need for harmonized data management and dynamic acquisition of diverse measurements for on-demand, interactive, multivariate analysis, and access [3]. It is not enough to have a big data, standalone analytics solution;it is critical that we start integrating data from remote sensing, modeling, and in-situ networks in a harmonized manner that enables timely and data-driven decision-making for air quality management. This work presents the design and development of an Air Quality Analytics Collaborative Framework (AQ ACF), as part of NASA's Advanced Information Systems Technology (AIST) effort, to establish a data, machine-learning, and numerically driven platform for air quality analysis, visualization, and prediction.

关键词： air quality atmosphere remote sensing machine learning numerical model earth observation

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