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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | INTELLIGENT CHILD SAFETY SYSTEM USING MACHINE LEARNING IN IoT DEVICES |
| Authors Name: | E. PRABHAKAR , Jajala Naveena , Gottupelli Vahinitha , Govindu Sai Teja |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_192117
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| Published Paper Id: | IJNRD2304468 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Due of their vulnerability, tracking and protecting children is of highest importance. A sophisticated smart security system is now required due to the rise in crimes including child abduction, child trafficking, child abuse, and so forth. In order to help parents, watch and track their children in real time instead of having to constantly be nearby, a self-alerting "INTELLIGENT CHILD SAFETY SYSTEM USING MACHINE LEARNING IN IOT DEVICES" was developed. This system is meant to be worn by the child as a wrist band, hand glove, arm band, or belt on a daily basis. The technology is made to watch over children's whereabouts and physical health in real time. An Arduino controller, a Raspberry Pi, and sensors that track changes in parameters like temperature, BVP (blood volume pulse), and GSR (galvanic skin response) are all included in this electronic system. GPS and GSM modules are also utilized by the system. The Decision Tree Classifier algorithm uses sensor value inputs to identify any distress scenario. A text message containing the victim's location is sent to the registered contact numbers using a GSM module and tracked using a GPS module. The innovative aspect of this work is the greater precision of the autonomous decision-making process. |
| Keywords: | Child safety, GPS, GSM, sensors, Arduino, Raspberry-Pi, decision tree classifier, autonomous decision, and intelligent child safety system employing machine learning in IoT devices. |
| Cite Article: | "INTELLIGENT CHILD SAFETY SYSTEM USING MACHINE LEARNING IN IoT DEVICES ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.e491-e496, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304468.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2304468 Registration ID: 192117 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: e491-e496 Country: -, -, - Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304468 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304468 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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