LANGUAGE DETECTION USING MACHINE LEARNING

LANGUAGE DETECTION USING MACHINE LEARNING

P. Harshita Krishna Sri [1], R. Tagore [2], P. Anil Kumar [3], M. Sowmya Sri [4], G. Sumanth [5],K. Lakshmi Narayana [6]

[1],[2],[3],[4],[5] Department of Computer Science and Artificial Intelligence (CAI), Sri Vasavi Engineering College, Tadepalligudem, Andhra Pradesh, India.

[6] Professor, Department of Computer Science and Engineering (CSE), Sri Vasavi Engineering College, Tadepalligudem, Andhra Pradesh, India.

ABSTRACT

This paper presents an innovative Machine Learning (ML) model for language detection that combines the power of logistic regression with a multimodal approach. The proposed model is designed to handle three types of inputs: sequential text data, files, and image representations. The proposed model offers a versatile and accurate solution for identifying languages across diverse data modalities. The model architecture employs logistic regression to enhance interpretability and feature extraction from each input modality. Trained on a comprehensive multilingual dataset, the model exhibits robust performance, showcasing its applicability to real-world scenarios. The model’s ability to process text, files, and images makes it well-suited for applications in content filtering, cross-modal information retrieval, and multilingual sentiment analysis. This research contributes to the advancement of language detection models by offering a unified solution for handling diverse input types.