A Study On The Use Of Sequential Data Mining To Predict Aeroallergen Concentrations.

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Ms Salma Mohammad Shafi
Dr. Uruj Jaleel

Abstract

This paper introduces a predictive model that forecasts aeroallergen concentrations by utilizing sequential data mining techniques. The study identifies distinct and repetitive patterns in historical aeroallergen concentration data, employing Generalized Sequential Pattern Mining (GSP) to extract frequent patterns. These findings enable the creation of predictive models that aid allergy management and assist policymakers in preparing timely interventions. The results demonstrate the capability of the model to forecast trends in pollen, spore, and other airborne allergen concentrations, thus improving public health outcomes.

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Author Biographies

Ms Salma Mohammad Shafi

(Research Scholar), Department Of Computer Science And Engineering, Kalinga University, Naya Raipur C.G.

Dr. Uruj Jaleel

(Research Supervisor ), Associate Professor, Department Of Computer Science And Engineering, Kalinga University, Naya Raipur C.G.