International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 6 Issue 6
November-December 2024
Indexing Partners
A Comparative Study on How AI is Partnering with Nature for a Sustainable Future.
Author(s) | Rehmah Ahmed Batki, Hasan Phudinawala |
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Country | India |
Abstract | Mounting ecological and socio-economic challenges demand a paradigm shift to sustainability. Artificial intelligence (AI) becomes a powerful means to pursue this objective; it establishes a “Symbiotic Synergy” with nature’s complex networks. This study attempts to investigate the SDGs using AI as a transformative tool, the sustainability of AI and its socio-economic impact. The research applies a multimethod approach, using surveys, case studies and categorizations to determine how AI is helping achieve the SGDs in harmony with nature. Key applications of AI in important domains are covered including agriculture and sanitation, renewable energy sector, hydrogen energy sector and the impact that AI is having leading to the future sustainable cities. Case studies and real-world examples demonstrate the effectiveness of AI technologies in forecasting energy demand, improving power dispatchment, and boosting performance within renewable resources. This study is a guide to the harnessing of AI potential for meeting SDGs, which leads us into an era where humans will attain great heights with cooperation between technology and nature. |
Keywords | Sustainable AI, Symbiotic Synergy, AI for SDGs, Sector Applications |
Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
Published In | Volume 6, Issue 3, May-June 2024 |
Published On | 2024-05-07 |
Cite This | A Comparative Study on How AI is Partnering with Nature for a Sustainable Future. - Rehmah Ahmed Batki, Hasan Phudinawala - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.19193 |
DOI | https://doi.org/10.36948/ijfmr.2024.v06i03.19193 |
Short DOI | https://doi.org/gttbht |
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