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Role of AI Technique for Controlling Micro Plastic on Marine Eco System
Summary
This paper developed a machine learning system using Support Vector Machine (SVM) algorithms to classify microplastic density in ocean water based on oceanographic sensor data, achieving 93% accuracy. The system is proposed as a scalable, automated alternative to labor-intensive manual microplastic sampling in marine environments. AI-driven monitoring tools like this could make it practical to track plastic pollution across vast ocean areas where manual surveys are infeasible.
Microplastics in marine environments are a growing ecological concern due to their small size, wide distribution, and harmful effects on aquatic life. Traditional methods for identifying these particles are labor-intensive and not suited for large-scale monitoring. This paper presents a smart detection framework using machine learning, specifically Support Vector Machine (SVM), to classify microplastic density based on oceanographic data. A publicly accessible dataset was used for training and testing, with preprocessing steps including normalization and encoding. The SVM model demonstrated superior classification accuracy of 93% compared to other algorithms like Logistic Regression and Random Forest. These results suggest that AI-driven approaches, particularly those using SVM, can significantly enhance marine pollution surveillance.
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