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AI models forecast oncology demand and vineyard disease risk

By PulseAugur Editorial · [3 sources] ·

Two new research papers explore advanced time-series forecasting methods for distinct domains. One paper introduces an event-based approach for predicting vineyard disease risk, utilizing environmental data and comparing machine learning models like XGBoost and LSTMs. The other paper proposes a Bayesian framework with a boosting mechanism to forecast oncology demand trends, outperforming traditional methods like ARIMA and LSTMs on real-world data. AI

IMPACT These papers highlight advancements in applying machine learning to specialized time-series prediction tasks, potentially improving accuracy in agriculture and healthcare.

RANK_REASON Two distinct academic papers published on arXiv detailing novel machine learning approaches for time-series forecasting.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

AI models forecast oncology demand and vineyard disease risk

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Two distinct academic papers published on arXiv detailing novel machine learning approaches for time-series forecasting.
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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Ademir Batista dos Santos Neto, Tiago Alessandro Espinola Ferreira, Paulo Renato Alves Firmino ·

    Forecasting Oncology Demand Trends with Boosting-Based Bayesian Conjugate Models

    arXiv:2605.05270v1 Announce Type: cross Abstract: Accurate trend forecasting in healthcare time series is essential for planning and resource allocation. This paper proposes a Bayesian framework for predicting oncology demand trends, modeling weekly appointments as a Poisson proc…

  2. arXiv cs.LG TIER_1 English(EN) · Ivica Dimitrovski, Ivan Kitanovski, Danco Davcev, Slobodan Kalajdziski, Kosta Mitreski ·

    Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series

    arXiv:2605.04548v1 Announce Type: new Abstract: Accurate early warning of vineyard disease risk from environmental observations is essential for timely intervention and more sustainable crop protection. However, many existing studies formulate disease prediction as daily presence…

  3. arXiv stat.ML TIER_1 English(EN) · Paulo Renato Alves Firmino ·

    Forecasting Oncology Demand Trends with Boosting-Based Bayesian Conjugate Models

    Accurate trend forecasting in healthcare time series is essential for planning and resource allocation. This paper proposes a Bayesian framework for predicting oncology demand trends, modeling weekly appointments as a Poisson process with a Gamma prior to the demand rate. To enha…

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