Accepted Papers

Signsynth: Audio, Text, Video to Indian Sign Language Converter

Ch. Kalyani¹, K. V. K. Saibhaskar², K. Lavanya Lakshmi², K. E. S. R. K. Raju², Rehana Tabassum², V. Sai Gowtham², ¹Assistant Professor, Computer Science and Engineering, ANITS, Visakhapatnam, Andhra Pradesh, India, ²Computer Science and Engineering, ANITS, Visakhapatnam, Andhra Pradesh, India

ABSTRACT

Sign language, which allows for effective expression through gestures and movements, is one of the most crucial forms of communication for those who are deaf or hard of hearing. However, tools for translating spoken or written language into Indian Sign Language (ISL) are limited. This paper introduces SignSynth, a novel system designed to convert audio, text, and video content into ISL videos. By leveraging speech-to-text conversion, natural language processing, and tokenization, the system processes meaningful words while omitting redundant terms. These words are mapped to a curated database of ISL gestures represented as videos, which are then merged into a seamless output using advanced video processing techniques. With over 63 million individuals in India experiencing hearing disabilities, SignSynth offers a scalable and cost-effective solution to bridge communication gaps. This system has significant potential to enhance accessibility and facilitate interaction between hearing and non-hearing individuals, paving the way for advancements in real-time sign language translation

Keywords

Indian Sign Language, Speech-to-Text, Gesture Recognition, Neural Networks, Sign Language Translation.

Trade Forecasting with AI: Integrating Machine Learning, Deep Learning, and Explainability for Crude Oil Price Prediction

Balasubramanian S¹ and Natarajan M², ¹Research Scholar, Department of Computer and Information Science, Annamalai University, Tamil Nadu, India,²Assistant Professor/Programmer, Department of Computer and Information Science, Annamalai University, Tamil Nadu, India

ABSTRACT

Due to ever-changing market conditions, predicting crude oil prices is complex and challenging. Traditional models struggle to adapt, while advanced AI models like LSTMs can find hidden patterns but often lack transparency. This study compares Machine Learning (Random Forest) and Deep Learning (LSTM) to assess both accuracy and interpretability. It also explores Explainable AI (XAI) methods like SHAP and LIME to make AI predictions more understandable for policymakers. The results show that combining machine learning, deep learning, and XAI techniques improves both the accuracy and trustworthiness of crude oil price forecasts.

Keywords

Explainable AI, Trade Forecasting, Random Forest, LSTM, SHAP, LIME, Machine Learning, Deep Learning, xAI, Crude Oil Trade.