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Learn to optimize machine learning tasks for environmental sustainability. Discover how to use real-time electricity data and low-carbon energy sources for model training and inference, reducing the carbon footprint of your cloud operations.
TrashTech ♻️🗑️ is a Flask project powered by deep learning that classifies waste items into seven categories. It encourages recycling ♻️🌍 and promotes reuse ♻️🔨 by providing creative ideas for repurposing waste materials. TrashTalk aims to inspire individuals to take action and contribute to a cleaner, greener planet. 🌱🌎
R code & data for the analysis of the environmental impact of a "run-of-river" hydropower plant on the riverine ecosystem of the Saldur stream, a glacier-fed stream located in the Italian Central-Eastern Alps.
This is the code for the matchmaking and environmental impact optimization done within the Fertigteil 2.0 (Precast Concrete Components 2.0) research project. The accompanying paper, "Matter as Met" was published and presented on the Design Modelling Symposium: Towards Radical Regeneration in Berlin, 2022.
Satellite-based causal attribution of coastal water clarity degradation to nickel smelting expansion at Indonesia's Morowali Industrial Park using Bayesian structural time series, multi-algorithm changepoint detection, and Sentinel-2 land cover intensity analysis.
EcoShift is a web-based application designed to help individuals, especially interns, track their sustainable habits and measure real-time CO₂ savings. It promotes environmental awareness through habit tracking, data visualization, and community engagement.
A real-time Streamlit dashboard that analyzes and forecasts carbon intensity in the UK using machine learning models like Random Forest and SGD. Includes dynamic anomaly detection, environmental impact scoring, and actionable green recommendations to promote sustainable energy practices.