www.isi.ac

ISI Journals

[International Scientific Indexing]

[Institute for Scientific Information]

[P-ISSN: 2413-5100] & [E-ISSN: 2413-5119]

Transformative Synergy: Machine Learning and Neural Networks in Environmental Science

Open PDF in Browser
Asian Journal of Basic and Applied Sciences, 2024

Autour(s)

  • Betania Rahayu

Abstract

This article explores the symbiotic relationship between machine learning, neural networks, and environmental science, aiming to harness the power of advanced technologies to address pressing environmental challenges. The research investigates how machine learning algorithms and neural networks can be applied to analyze complex environmental data, model intricate ecological systems, and contribute to sustainable solutions. Through an extensive literature review, a robust research methodology, and a presentation of results, this study delves into the transformative potential of integrating machine learning and neural networks in the realm of environmental science.

About ISI Journals: ISI Journals are devoted to the rapid worldwide dissemination of research and is composed of a number of specialized research networks.

Special thanks to:

[Science Direct, Elsevier, Springer, SAGE Publications, EBSCOHost, Oxford University Press, CRC Press, Cambridge University Press, Pearson Education, Wolters Kluwer, Cengage, McGraw Hill, Hodder & Stoughton, Macmillan Learning, Scholastic, IEEE Standards Association, Association for Computing Machinery, American National Standards Institute, American Society of Mechanical Engineers, NFPA, American Society of Civil Engineers, ASTM International, Brazilian National Standards Organization, Emerald, Taylor & Francis, Wiley, ProQuest, JSTOR, Springer Nature]

Powered by ISI Journals (International Scientific Indexing & Institute for Scientific Information)