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@article{179941,
        author = {Dr. Somu. K and Dhevika.P and Jayalakshmi.R and Sneha.M and Subashini.S},
        title = {Egg Incubation Automated Hatching System Naïve Bayesian},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8852-8857},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179941},
        abstract = {In recent times, efficient egg incubation has 
become crucial to ensuring the success of poultry 
breeding operations. Early detection and continuous 
monitoring of egg automatic detection using Artificial 
Intelligence (AI) can be costly. To address the 
challenges related to the Naïve Bayes Classification 
Method, early detection and automation of egg 
incubation yield more accurate results, and the 
collection of documents is more secure. Moreover, Min
Max Normalization eliminates duplicate data, 
minimizes unknown data, and maximizes valuable data 
during the preprocessing phase. Additionally, the 
Estimation of dew-point temperature measures 
humidity and vapor and tests the ratio between the 
processes. Ultimately, the proposed method classifies 
data, tests it, and validates the predicted data for 
performance. Calculation is a training process, while 
the testing data is multi-level in the classification 
process. Each type of data is given for the individual 
network connection of the classification data. It 
calculates comprehensive monitoring based on input 
data from video testing of the training data. Initially, it 
identifies nearby objects, children, and other elements 
to assess the effectiveness of these activities. The process 
offers greater reliability and achieves high performance 
while 
maintaining standard scalability. These 
techniques reduce time complexity, and performance 
remains within an accurate range of 91%.},
        keywords = {Naïve Bayesian, data preprocessing, data  normalization, Estimation of dew-point temperature,  data classification.},
        month = {May},
        }