Paper Title
Supervised Teachable Machine
Article Identifiers
Authors
Ayush Sachin Walekar , Lalit Padma Kartik Saridevi , Bhavik Prakash Bafna , Ninad Pravin Deshmukh
Keywords
Machine Learning, HDFS, Graphical User Interface ,User Interface, HyperText Markup Language, Cascading, Deep Learning, Search Engine Optimization
Abstract
Our system is designed to empower developers in creating their own models through a systematic process. Initially, it focuses on data preprocessing, a crucial step that lays the foundation for model performance. By meticulously cleaning, transforming, and organizing data, developers can ensure the highest quality input for their models. The real innovation comes with the ability to experiment with a diverse range of models. Our platform offers a selection of cutting-edge algorithms, each with its own unique strengths. Developers can easily switch between these models, adjusting parameters on the fly to fine-tune their performance. This dynamic model experimentation allows developers to maintain a detailed record of which models excel under specific conditions.
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How To Cite (APA)
Ayush Sachin Walekar, Lalit Padma Kartik Saridevi, Bhavik Prakash Bafna, & Ninad Pravin Deshmukh (November-2023). Supervised Teachable Machine. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), d445-d447. https://ijnrd.org/papers/IJNRD2311348.pdf
Issue
Volume 8 Issue 11, November-2023
Pages : d445-d447
Other Publication Details
Paper Reg. ID: IJNRD_209714
Published Paper Id: IJNRD2311348
Downloads: 000121979
Research Area: Engineering
Country: Nagpur, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2311348.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311348
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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Licence
This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.
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Current Issue: Volume 10 | Issue 10 | October 2025
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