A Smart Caffeine Level Predicting and Analysis Solution with Sequential Machine Learning Model

A Smart Caffeine Level Predicting and Analysis Solution with Sequential Machine Learning Model using Artificial Intelligence and Computer Vision

Tianrui Zhang (Northwestern University, USA) and Ang Li (California State University, USA)

Abstract

Caffeine is the most widely consumed stimulant globally, yet its overconsumption poses significant health risks. Traditional methods for measuring caffeine content, such as weighing coffee, can be impractical in everyday settings. This paper proposes an innovative solution that leverages artificial intelligence (AI) and machine learning, specifically utilizing a Sequential Convolutional Neural Network (CNN), to predict caffeine levels based on image analysis of coffee. The system processes images to determine brightness, correlating this data with caffeine concentration on a defined scale. Challenges such as dataset selection, prediction accuracy variability, and training epoch limitations were addressed through data cleaning and iterative model training. Experiments revealed that the model achieves a high accuracy rate, indicating its potential as a practical tool for consumers aiming to monitor their caffeine intake. This application not only enhances user convenience but also promotes healthier consumption practices by providing a reliable method for estimating caffeine levels visually

Keywords

Caffeine Level, Artificial Intelligence, Biology

#artificialintelligence #machinelearning #computervision #caffeine #biology #convolutionalneuralnetworks