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@article{179940,
        author = {Dr. Somu. K and Deepanraj.S and Dhayanithi.R and Naveen.K and Raguman.J},
        title = {Developing A Zigbee-Based Long-Range System for Recovering Children from Bore Wells Using Graphical Analysis Methods},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8858-8863},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179940},
        abstract = {[Nowadays, the challenge is the rising 
number of unsealed bore wells that pose risks to 
children. Early detection and continuous monitoring of 
children's activities using Zigbee are unreliable. To 
tackle the issues related to the Graphical Analysis 
Method, early detection and automation of children's 
activities yield more accurate results, and document 
collection is more secure. Moreover, Min-Max 
Normalization eliminates duplicate data, minimizes 
unknown data, and maximizes valuable data during the 
preprocessing phase. Additionally, the K-Nearest 
Neighbors (KNN) algorithm assesses classified images 
from cameras that identify boreholes in various 
locations, accounting for differences in borehole sizes 
while calculating overall behavior to improve the 
analysis process. Ultimately, the proposed method 
suggests that early childhood intervention can reduce 
exposure to harmful elements. It evaluates indoor and 
outdoor borewell activities about these factors. It 
calculates comprehensive monitoring of children's 
activities 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 a high performance while maintaining 
standard scalability. These techniques reduce time 
complexity, and performance remains within an 
accurate range of 95%.},
        keywords = {ZigBee,  graphical  data  preprocessing,  KNN,  method, data normalization, data  classification.},
        month = {May},
        }