TEJAS Journal of Technologies and Humanitarian Science

Paper 1: Reducing FastText's Limits in Romanized Language Detection

Authors : Yashi Bajpai, Aditi Joshi and Mr. Amit Srivastava

Doi: https://doi.org/10.63920/tjths.44001

Abstract

To identify the language of a given text, language identification models such as FastText are used often. However, these models frequently have trouble accurately categorizing text that is written in the Roman (Latin) nature but have historically used non-Latin scripts like Hindi, Japanese and Chinese. In our research, we analyze FastText's performance on romanized inputs and find a pattern of misinterpretation into unrelated languages and lower confidence scores. We solve this by implementing a score-based thresholding method, which hides the input's anticipated language label and classifies it as romanized if the confidence score that FastText returns is less than the set threshold (0.5). This threshold-based method increases classification reliability through testing on several languages and romanized inputs. This study identifies a significant weakness in existing language identification systems and suggests a simple, adjustable modification to improve their effectiveness in multilingual, real-world situations.

Paper 2:RetentionPro-AI Powered Customer Retention & Churn Prediction System

Authors : Ravindra Chaurasia, Vaishnavi Srivastava, Sumit Chaurasiya, Shubham Singh, Anurag Singh

Doi: https://doi.org/10.63920/tjths.44002

Abstract

Customer churn—the loss of existing clients—poses a major challenge for business growth. This study predicts churn using structured datasets containing demographics, transaction history, and engagement metrics. Multiple machine learning models, including XGBoost, Random Forest, and LightGBM, were trained and evaluated using accuracy, precision, recall, and F1-score. XGBoost achieved the highest predictive performance, effectively identifying at-risk customers while minimizing false positives. The research provides a practical framework for integrating machine learning into customer relationship management systems, enabling timely interventions and data-driven strategies to improve retention, reduce churn, and enhance long-term revenue stability

Paper 3:AI Driven Health Diagnostic & Disease Prediction System

Authors : Saumya Rai, Dr. R K Singh

Doi: https://doi.org/10.63920/tjths.44003

Abstract

Proposed paper on an AI-Driven Health Diagnostic and Disease Prediction System that analyzes patient symptoms, medical history, and clinical metrics. Multiple machine learning models, including XGBoost, Random Forest, and LightGBM, were trained and evaluated using accuracy, precision, recall, and F1-score. XGBoost delivered the best predictive performance, accurately identifying high-risk patients while reducing false diagnoses. The system provides a scalable framework for integrating machine learning into healthcare platforms, enabling early detection, faster diagnosis, and data-driven clinical decision support. This approach improves patient outcomes, reduces diagnostic delays, and strengthens overall healthcare efficiency.