International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | To Implement a Secure and Interactive Virtual Classroom System Using Modern Web Frameworks |
|---|---|
| ABSTRACT | Improper waste segregation is a major issue affecting environmental sustainability and recycling efficiency. Traditional manual methods are inefficient, time-consuming, and expose workers to health risks. This project proposes an automated waste classification and segregation system using Artificial Intelligence (AI) and computer vision techniques. The system captures waste images using a camera and classifies them into categories such as plastic, metal, paper, glass, and organic waste using deep learning models like Convolutional Neural Networks (CNN). Based on classification results, the system directs waste to appropriate bins using automated mechanisms. Python-based frameworks such as TensorFlow, OpenCV, and NumPy are used for implementation. The system improves accuracy, reduces human intervention, and enhances recycling efficiency. This solution supports smart city initiatives and sustainable waste management practices. |
| AUTHOR | A.R.ASHOK KUMAR, THIYAGU K, VISHNU PRIYA L, ARAVIND S, KISHORE KUMAR P, MOHAMMAD NIHAL Assistant Professor, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India UG Scholar, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404062 |
| pdf/62_To Implement a Secure and Interactive Virtual Classroom System Using Modern Web Frameworks.pdf | |
| KEYWORDS | |
| References | [1] Zhang, G., Amin, S. H., Ensafi, Y., & Shah, B. (2022). This study focuses on machine learning techniques for pattern recognition and classification. The methodologies discussed support image-based decision systems like automated waste classification. The research provides insights into model accuracy improvement and data preprocessing strategies. [2] He, K., Zhang, X., Ren, S., & Sun, J. (2016). The authors introduce deep learning architectures that improve image recognition accuracy. Their work on convolutional neural networks is relevant to waste image classification models. The concepts support feature extraction and object recognition tasks in computer vision. [3] OpenCV Documentation (2023). This documentation provides detailed guidance on image processing and computer vision techniques. It supports image acquisition, preprocessing, and feature enhancement used in the project. OpenCV serves as a core library for implementing real-time image analysis. [4] Goodfellow, I., Bengio, Y., & Courville, A. (2016). This book explains deep learning fundamentals, including neural networks and CNNs. The concepts help in understanding model training and optimization for image classification. It serves as a theoretical foundation for AI-based waste segregation systems. [5] Scikit-learn Documentation (2023). This resource explains machine learning algorithms and evaluation techniques. It aids in understanding classification performance analysis and model validation. The library supports preprocessing and data handling tasks. [6] TensorFlow Developers Guide (2023). The guide provides practical knowledge for building and deploying deep learning models. It supports CNN implementation and model optimization techniques. TensorFlow enables scalable AI-based waste classification solutions. [7] World Economic Forum – Smart Waste Management Report (2022). This report highlights challenges and solutions in modern waste management systems. It emphasizes the role of automation and AI in improving waste segregation efficiency. The insights align with the objectives of the proposed system. [8] UN Environment Programme – Waste Management Overview (2021). This resource discusses global waste challenges and sustainable management practices. It emphasizes the importance of recycling and waste segregation. The report supports the environmental motivation behind the project. [9] Python Software Foundation Documentation (2023). This documentation explains Python programming concepts and libraries. It supports system development, data handling, and integration of AI modules. Python serves as the core programming language for the proposed system. |
