AI Trained Object Recognition using Google Teachable Machine

AUTHOR(S)

Harshini R., Somesh R.S., Sushanthi Raj, Vignesh, Dr. Pavithra G., Dr. Sindhu Sree M., Dr. T.C.Manjunath, Rajashekher Koyyeda, Aditya T.G.

DOI: https://doi.org/10.46647/ijetms.2023.v07i03.080

ABSTRACT
AI Trained Object Recognition using Google Teachable Machine is presented in this paper. Computer vision and artificial intelligence heavily rely on object recognition, a critical task for many applications such as robotics, autonomous vehicles, and surveillance systems. This paper provides an overview of object recognition using AI Trained models through Google Teachable Machine, a web-based platform that requires no coding or programming skills to train machine learning models. The steps involved in training an object recognition model using Google Teachable Machine are explored, and the model's performance is evaluated on a real-world dataset. The study finds that Google Teachable Machine is a user-friendly and powerful tool for training object recognition models with high accuracy. In conclusion, our study has demonstrated the effectiveness and ease of use of Google Teachable Machine for training object recognition models using AI. The web-based platform allows users to train models without any coding or programming skills, making it accessible to a wide range of users. Our evaluation of the model's performance on a real-world dataset showed high accuracy, indicating the potential of Google Teachable Machine for use in various applications such as autonomous vehicles, surveillance systems, and robotics. With the continuous advancements in AI and computer vision technologies, we believe that Google Teachable Machine will continue to be an important tool for object recognition in the future. The work done & presented in this paper is the result of the mini-project work that has been done by the first sem engineering students of the college and as such there is little novelty in it and the references are being taken from various sources from the internet, the paper is being written by the students to test their writing skills in the starting of their engineering career and also to test the presentation skills during their mini-project presentation. The work done & presented in this paper is the report of the assignment / alternate assessment tool as a part and parcel of the academic assignment of the first year subject on nanotechnology & IoT.
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References:

Pavithra G., Dr. T.C.Manjunath, Kesar T.N., “Development of a GUI to detect glaucomatic diseases using Very Deep CNNs”, Springer’s Book Series Lectures Notes in Electrical Engineering (LNEE) series of Springer, Indexing : ISI Proceedings, EI-Compendex, SCOPUS, MetaPress, Web of Science and Springer link, 2018.

Pavithra G., Dr. T.C Manjunath, Shazia Anjum, “Efficient segmentation of ultrasound image using smoothing algorithm”, Int. Jour. of Inno. Res. in Comp. Engg., IJIRCCE, IF 4.447 (2014), Vol. 3, Issue 4, pp. 3512-3517, April-May 2015.

Pavithra G., Rahimunnisa Nagma, Dr. T.C.Manjunath, “Novel Development Coding using SVC Concepts in IP Scenario”, SSRG’s Int. Jour. of Engg. of Video Trends & Tech. (IJETT), UGC Approved Journal, IF 2.88, Vol. 22, No. 9, pp. 405-410, Apr. 2015.

Pavithra G., Rahimunnisa Nagma, Dr. T.C.Manjunath, “Effects of scalability in video coding with H.264/SVCH.264/SVC quantizations”, Int. Jour. of Adv. Res. in Comp. Sci. & Soft. Engg., IJARCSSE, IF 2.5, Vol. 5, Issue 4, pp. 1-6, Apr. 2015.

Pavithra G., Chaitra J. Gowda, Dr. T.C.Manjunath, “A new methodology of multi-object tracking in the image processing world” , Int. Jour. of Emerg. Tech. & Res. (IJETR), IF 0.997, Vol. 2, Issue 3, pp. 48-49, May-Jun 2015.

Pavithra G., Chaitra J. Gowda, Dr. T.C.Manjunath, “Investigation of Multi-object Tracking With Data Organization and Path Controlling in images”, Int. Jour. of Scientific Res. in Comp. Sci. Appls. & Management Studies (IJSRCSAMS), UGC approved Journal, IF 0.342, Global Impact Factor: 0.465, Vol. 4, Issue 3, May 2015

Pavithra G., Dr. Arunkumar G., Dr. T.C.Manjunath, “Uncertainty in mechanical systems – A conceptual Design”, Journal of Innov. Res. & Solutions (JIRAS)-Inter Disciplinary Int. Journal, IF 3.546, Sr. No. 40, Volume No.1, Issue No. 1, pp. 286 - 292, Jan-Jun 2015.

Pavithra G., Dr. T.C. Manjunath, Dr. Dharmanna Lamani, “Automated Diagnose of Neo- vascular Glaucoma Disease using advance Image Analysis Technique”, Int. Jour. of App. Info.

Dr. T.C. Manjunath, Subiya Yaseen, Pavithra G., Syst. (IJAIS), Vol. 9, Issue No. 6, pp. 1-6, No. 6, Sept. 2015. “A review on evolution in Telecommunication Infrastructure in the modern day world”, Int. Jour. of App. Engg. Res. (IJAER), SCOPUS (2010-2017), UGC Approved Journal - 2017 (No. - 64529), listed in Anna

Univ. Chennai Annex. II - 2014 (Sl.No.8565), Vol. 10, No. 55, Spl. Issue, pp. 224-229, 2015.

How to Cite This Article:
Harshini R., Somesh R.S., Sushanthi Raj, Vignesh, Dr. Pavithra G., Dr. Sindhu Sree M., Dr. T.C.Manjunath, Rajashekher Koyyeda, Aditya T.G. .AI Trained Object Recognition using Google Teachable Machine . ijetms;7(3):558-561. DOI: 10.46647/ijetms.2023.v07i03.080