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Emerging Technology Integration - Artificial Intelligence (AI) and Machine Learning (ML) for Predictive Analysis for Safety and Toxicity Assessment in Environmental Toxicology

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  • Stephen Okechukwuyem Ojji
Stephen Okechukwuyem Ojji
Department of Digital Transformation - Environmental Health and Safety Nocopycats LLC, United State Of America
Published in : International Journal of Scientific Research and Management (IJSRM) , Vol. 12 No. 05 (2024)

Submission to VIJ 2024-05-11

Keywords

  • Artificial Intelligence, Machine Learning, Environmental Toxicology, Predictive Analysis, Safety Assessment, Toxicity Assessmental

Copyright (c) 2024 Stephen Okechukwuyem Ojji

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Environmental toxicology is facing unprecedented challenges due to the escalating complexity of environmental issues. Traditional methodologies for safety and toxicity assessment are struggling to keep pace with the rapid evolution of pollutants and their impacts on ecosystems and human health. In response to this pressing need, there has been a paradigm shift in the field towards the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. This research article delves into the transformative potential of AI and ML in environmental toxicology, elucidating how these advanced computational tools offer promising solutions for predictive analysis.

By leveraging vast datasets and sophisticated algorithms, AI and ML enable more efficient, accurate, and timely assessment of environmental risks. They empower researchers and policymakers to anticipate and mitigate potential hazards before they escalate into crises. This paper provides a comprehensive overview of the diverse applications of AI and ML in environmental toxicology, ranging from predictive modeling of chemical hazards to early detection of environmental threats.

Furthermore, it highlights the potential of AI and ML to revolutionize decision-making processes in environmental management and policy formulation. By synthesizing vast amounts of data and identifying complex patterns and correlations, these technologies offer invaluable insights for crafting evidence-based policies and strategies to safeguard the environment and public health.

This research article underscores the pivotal role of AI and ML in addressing the multifaceted challenges of environmental toxicology. Through their integration, we have the opportunity to enhance our understanding of environmental risks, optimize resource allocation, and ultimately, pave the way towards a more sustainable and resilient future.

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