Data-driven insights are essential for modern agriculture. This research
paper introduces a machine learning framework designed to improve how we
educate and reach out to people in the field of horticulture. The framework
relies on data from the Horticulture Online Help Desk (HOHD), which is like a
big collection of questions from people who love gardening and are part of the
Extension Master Gardener Program (EMGP). This framework has two main parts.
First, it uses special computer programs (machine learning models) to sort
questions into categories. This helps us quickly send each question to the
right expert, so we can answer it faster. Second, it looks at when questions
are asked and uses that information to guess how many questions we might get in
the future and what they will be about. This helps us plan on topics that will
be really important. It's like knowing what questions will be popular in the
coming months. We also take into account where the questions come from by
looking at the Zip Code. This helps us make research that fits the challenges
faced by gardeners in different places. In this paper, we demonstrate the
potential of machine learning techniques to predict trends in horticulture by
analyzing textual queries from homeowners. We show that NLP, classification,
and time series analysis can be used to identify patterns in homeowners'
queries and predict future trends in horticulture. Our results suggest that
machine learning could be used to predict trends in other agricultural sectors
as well. If large-scale agriculture industries curate and maintain a comparable
repository of textual data, the potential for trend prediction and strategic
agricultural planning could be revolutionized. This convergence of technology
and agriculture offers a promising pathway for the future of sustainable
farming and data-informed agricultural practices

通过分析园艺业从业人员的文本查询，我们展示了机器学习技术预测园艺业趋势的潜力。我们利用自然语言处理、分类和时间序列分析等方法，识别了从业者查询中的模式，并预测了园艺业的未来趋势。结果表明，机器学习技术还可用于预测其他农业领域的趋势，如果大规模农业企业能建立和维护一个类似的文本数据库，趋势预测和战略农业规划的潜力将会革新。技术与农业的融合为未来可持续农业和数据驱动型农业实践提供了一个有前景的发展路径。

从文本到趋势：现代农业未来的独特园艺分析视角

From Text to Trends: A Unique Garden Analytics Perspective on the Future  of Modern Agriculture

With machine learning conferences growing ever larger, and reviewing
processes becoming increasingly elaborate, more data-driven insights into their
workings are required. In this report, we present the results of a survey
accompanying the first "Learning on Graphs" (LoG) Conference. The survey was
directed to evaluate the submission and review process from different
perspectives, including authors, reviewers, and area chairs alike.

本文对首届 “Learning on Graphs”(LoG) 学术会议进行了调查评估，探究会议的论文投稿和审查过程，从不同的角度，包括作者、审稿人和领域主席等，取得了基于数据的见解。

评估 “学习图论” 会议体验

Evaluating the "Learning on Graphs" Conference Experience

The widespread adoption of electronic health records and digital healthcare
data has created a demand for data-driven insights to enhance patient outcomes,
diagnostics, and treatments. However, using real patient data presents privacy
and regulatory challenges, including compliance with HIPAA and GDPR. Synthetic
data generation, using generative AI models like GANs and VAEs offers a
promising solution to balance valuable data access and patient privacy
protection. In this paper, we examine generative AI models for creating
realistic, anonymized patient data for research and training, explore synthetic
data applications in healthcare, and discuss its benefits, challenges, and
future research directions. Synthetic data has the potential to revolutionize
healthcare by providing anonymized patient data while preserving privacy and
enabling versatile applications.

本文探讨使用生成式 AI 模型，如 GAN 和 VAE，为研究和培训创建逼真的匿名患者数据，以及在医疗保健中应用合成数据的益处、挑战和未来研究方向。