Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{180611,
        author = {Mr. SANKET MOHAN MANE and Mr. KAUSTUBH ASHOK KUMBHARKAR and Mr. SAMARTH SUBHASH MAHIMKAR and Dr. SARIKA S PATIL and Prof. NILESH G BHOSKAR},
        title = {Monitoring and predicting appliance emission using Machine Learning Approaches},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1583-1592},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180611},
        abstract = {With the growing concerns about 
environmental 
pollution 
and energy efficiency, 
monitoring and predicting appliance emissions have 
become critical in ensuring sustainable resource 
utilization. Traditional emission tracking methods rely 
on periodic assessments and manual monitoring, which 
are often inefficient, time-consuming, and lack real-time 
adaptability. To address these challenges, this study 
explores the potential of machine learning (ML) 
approaches to enhance the accuracy and efficiency of 
appliance emission monitoring and prediction. 
The proposed system leverages data from sensors and 
appliance usage records to analyze emission patterns. 
Various machine learning models, including regression 
techniques, 
decision trees, and deep learning 
frameworks, are implemented to predict future emission 
levels. Feature engineering and data preprocessing 
techniques are employed to improve model accuracy. 
Performance evaluation is conducted using key metrics 
such as Mean Absolute Error (MAE), Root Mean 
Square Error (RMSE), and R-squared (R²) scores to 
compare the effectiveness of different models. 
Experimental results demonstrate that machine 
learning-based approaches significantly outperform 
conventional methods in predicting emissions with 
higher accuracy and adaptability. The study also 
highlights the challenges associated with data 
inconsistencies, sensor calibration, and real-time 
processing. The findings contribute to the development 
of intelligent, automated, and data-driven solutions for 
emission control, aiding policymakers, industries, and 
researchers in reducing environmental impact and 
promoting sustainable energy practices.},
        keywords = {Emission Monitoring, Machine Learning,  Prediction Models, Environmental Sustainability, Smart  Systems.},
        month = {June},
        }