A hybrid machine learning model as a combination of natural language processing and time series forecasting for stock market prediction using two different types of datasets: numerical and textual data.

Identify fake news and real news using algorithms like bert,Roberta,Xlnet,Explainable AI with Shap and Nlp techniques on tested data.

Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.

This project builds a deep-learning-based heartbeat sound classification system using MFCC features and multiple models including CNN, BiLSTM, and a Hybrid CNN–BiLSTM architecture. The system detects and classifies heart sounds into normal, murmur, and artifact categories, supporting early cardiac abnormality detection.

Publication Title - Novel Cyber Attack Detection Using Hybrid Deep Learning Model. Published on - International Journal of Science and Innovative Engineering & Technology (IJSIET) Vol.1, Sep 2022

This project implements a hybrid deep learning model capable of recognizing emotions in human speech by analyzing acoustic characteristics of audio signals. Manual classification of emotions in voice recordings is time-consuming, inconsistent, and lacks scalability. This system automates this process by detecting emotions .

Interactive simulation platform for thyroid nodule classification using ML, DL, and hybrid models. Built for education, visualization, and model evaluation on real ultrasound datasets.

BullBearAI: Stock market prediction built for clarity. Uses a hybrid LSTM-CNN model to deliver actionable insights from complex financial data.