International Journal on Science and Technology

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InboxG - Smart Mail Classifier

Author(s) Deepkumar Ketanbhai Hirpara, Dr. Vishal Polara
Country India
Abstract InboxG is a machine learning-driven system developed to enhance email management and prioritization within a gear manufacturing company (Elecon Engineering Company Limited). The system intelligently streamlines email workflows by detecting urgency, classifying emails into predefined categories, and assisting in generating appropriate responses. Key features include Mail Categorization using a Decision Tree classifier, which accurately organizes emails for improved accessibility and management. Urgency Detection leverages NLP-based sentiment analysis techniques to identify and prioritize critical emails, ensuring timely attention to important communications. To further enhance productivity, the Response Generation feature employs the Gemma 1.1 2B instruction-tuned model, providing contextually relevant and efficient automated replies. An intuitive User Dashboard offers a centralized platform for monitoring, managing, and prioritizing emails in real-time. Designed for seamless integration with existing corporate email systems, the project architecture utilizes Python, TensorFlow, and Flask for the backend, and HTML, CSS and Javascript for the frontend. The system is optimized through careful model selection and hyperparameter tuning, offering a robust and scalable solution for modern email management challenges.
Field Engineering
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-05-20
DOI https://doi.org/10.71097/IJSAT.v16.i2.4548
Short DOI https://doi.org/g9mpbj
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