ARTIFICIAL INTELLIGENCE IN ENHANCING BIODEGRADATION PROCESSES: A DATA-DRIVEN APPROACH TO ENVIRONMENTAL SUSTAINABILITY

Objective: The purpose of this study is therefore to assess how AI can be used to advance biodegradation processes and thus support environmental sustainability by increasing the biodegradation rate, decreasing the likelihood of human mistakes, and anticipating the right conditions. Methodology: A quantitative research method was used and 250 professionals from different sectors including environmental scientists, molecular biologists, bio-technologists, and professionals in the field of Artificial Intelligence were included in the sample. The research applied a structured questionnaire that left a great impression on the participant’s opinions on the efficiency of AI in biodegradation, which was a blend of Likert-scale and multiple-choice questions. Using descriptive statistics, Cronbach’s Alpha for reliability, and principal component analysis (PCA) for dimensionality the data were analyzed. Results: Based on the results, it emerged that AI is considered to be useful in increasing biodegradation efficiency and estimating the best conditions. However, some questions were made about the effectiveness of using AI in determining the original human errors. Cronbach’s Alpha yielded a negative value of -0. 397 hence pointing out the low internal consistency of the data gathered The PCA result also indicated that perceiving AI was influenced by more than a single dimension, and the first two principal components accounted for 17 percent only. 5% and 17. 0% of the variance. Conclusion: Although there are various benefits to using AI in biodegradation there are also some limitations, especially on the reliability and consistency of the process. It was also observed from the results of the study that refinement of the AI tools and research focusing on the aims of AI to the actual biodegradation requirements are required. Filling these gaps will be critical to realizing AI’s potential for supporting environmental sustainability.

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Publication Details

Journal
Journal of medical & health sciences review.
Published
2025-04-09
DOI
https://doi.org/10.62019/p2cm6s55
Primary Topic
Machine Learning and Data Classification
Type
article
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article

ARTIFICIAL INTELLIGENCE IN ENHANCING BIODEGRADATION PROCESSES: A DATA-DRIVEN APPROACH TO ENVIRONMENTAL SUSTAINABILITY

Muhammad Usman, Muhammad Zafar Rafique, Abdul Rauf
Journal of medical & health sciences review.
Machine Learning and Data Classification
article

ARTIFICIAL INTELLIGENCE IN ENHANCING BIODEGRADATION PROCESSES: A DATA-DRIVEN APPROACH TO ENVIRONMENTAL SUSTAINABILITY

Muhammad Usman, Muhammad Zafar Rafique, Abdul Rauf
article en

Abstract

Objective: The purpose of this study is therefore to assess how AI can be used to advance biodegradation processes and thus support environmental sustainability by increasing the biodegradation rate, decreasing the likelihood of human mistakes, and anticipating the right conditions. Methodology: A quantitative research method was used and 250 professionals from different sectors including environmental scientists, molecular biologists, bio-technologists, and professionals in the field of Artificial Intelligence were included in the sample. The research applied a structured questionnaire that left a great impression on the participant’s opinions on the efficiency of AI in biodegradation, which was a blend of Likert-scale and multiple-choice questions. Using descriptive statistics, Cronbach’s Alpha for reliability, and principal component analysis (PCA) for dimensionality the data were analyzed. Results: Based on the results, it emerged that AI is considered to be useful in increasing biodegradation efficiency and estimating the best conditions. However, some questions were made about the effectiveness of using AI in determining the original human errors. Cronbach’s Alpha yielded a negative value of -0. 397 hence pointing out the low internal consistency of the data gathered The PCA result also indicated that perceiving AI was influenced by more than a single dimension, and the first two principal components accounted for 17 percent only. 5% and 17. 0% of the variance. Conclusion: Although there are various benefits to using AI in biodegradation there are also some limitations, especially on the reliability and consistency of the process. It was also observed from the results of the study that refinement of the AI tools and research focusing on the aims of AI to the actual biodegradation requirements are required. Filling these gaps will be critical to realizing AI’s potential for supporting environmental sustainability.

Journal of medical & health sciences review.Vol. 2(2)
North China University of Technology (CN), University of Azad Jammu and Kashmir (PK), NED University of Engineering and Technology (PK)
Openalex Percentile: Top 97%
Machine Learning and Data Classification
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