Welcome to the repository for my conference paper on stock market analysis and predictive models. In this paper, I explore various models to analyze and predict stock market trends. I have employed a combination of traditional time series models and modern machine learning techniques to provide insights into stock price movements.
Deep Learning project October 2023
A project intended to bring context to embodied computational agents. Layered brains from hybrid models. Contextual Neurodevelopmental Dynamics & GSoC 2020 assoc content.
A machine learning model is trained to determine the word in an audio file
Sentiment Analysis of Tweets using Neural Networks with Pytorch
Automation of tests using selenium 4 , core java, testNG, Maven
My Hybrid Model (Deep Learning and Machine Learning) Projects
This is my academic thesis work (individual). Submitted in partial fulfilment of the requirements for Degree of Bachelor of Science in Computer Science & Engineering
Time series regression modeling on a dataset of supermarket sales across years, with the Darts library in Python.
Methodology and code to use social data for forecasting shortage of essential commodities (gasoline/PPE/toilet paper) during disasters like hurricanes and pandemics
Meetei Mayek Character Recognition: Hybrid CNN+LSTM model for Meetei Mayek script recognition.
To develop an advance forecasting model that adeptly incorporates solar irradiance data, leveraging its predictive capabilities to elevate forecasting performance and reliability.
Website pages for Model Deployment of ICH Detection using DL
A comparative study of a classic CNN model and a CNN-SVM hybrid where the feature matrix learnt by a CNN's convolutional layers are used to train a multi-class SVM classifier.
A Julia implementation of three different recommender systems based on the concept of Neural Collaborative Filtering.
Forward dynamic hybrid FE-MB model of the lumbosacral spine based on the Male Visible Human Project built in ArtiSynth.
An end-to-end Hybrid Learning Model built using CNN+LSTM layers to detect covid-19 from Chest X-ray images. Comparative study has been performed along with modified CNN architectures of transfer learning models : Xception, MobileNet and VGG19. An end-trend web based application was developed using flask framework and was hosted using Heroku.
Predictive Modeling of Neurological State with Multidimensional Time Series Data in Parkinson Disease Patients
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