AI Based Eco Lifestyle Advisor

Sejal, Thakre and Vidhi, Meshram and Krutika, Tikhat and Rushikesh, Tajne and Prof. Anupam, Chaube (2024) AI Based Eco Lifestyle Advisor. International Journal of Trend in Scientific Research and Development, 8 (5). pp. 1015-1022. ISSN 2456-6470

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Abstract

Sustainable living is a lifestyle that tries to lower an individual's or community's impact on the environment in every day-to-day eco-friendly choice. There includes aspects of waste reduction, environmental protection, carbon emission, and ecological and renewable energy. Critical elements include sustainable diet selection, home efficiency renovations, responsible consumption, and responsible traveling. People may not advocate for international efforts to respond to climate change, preserve biodiversity, and advance the health of the planet for generations yet to come but by doing small achievable things.Using advanced machine learning algorithms and data analytics, the AI-Based Eco-Friendly Lifestyle Advisor encourages changes in lifestyle with regard to sustainability that are tailored to each user. This type of adviser gives the users balanced recommendations on how to reduce carbon footprints, conserve resources, and make eco-friendly choices according to their habits, interests, and the state of their environment. Such an advanced adviser built on artificial intelligence algorithms for the following purposes: understand behavior and preferences while considering the living conditions of each local area to render personally tailored recommendations that suit personal lifestyles. In other words, it is a translation of good ecological choices into action. Be it suggesting plant-based recipes, pointing out energy-saving home improvements, or recommending some green brand- -the adviser accompanies you in all those day-to-day decisions.Integration of large streams of sensor data and satellite images for real-time predictions with an AI model helps make predictions possible for the analysis of CO2 levels, along with actionable insights in emission reduction.

Item Type:	Article
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	26 Oct 2024 09:17
Last Modified:	26 Oct 2024 09:17
URI:	http://eprints.umsida.ac.id/id/eprint/14427

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