Python Code for Graphical Deep Learning in Agriculture, Finance and Medical

Description

Exploring Graph Convolutional Networks Across Diverse Domains

Dive into the transformative world of Graph Convolutional Networks (GCNs) with this comprehensive resource tailored for enthusiasts and researchers. This package includes a meticulously designed Jupyter Notebook that demonstrates practical GCN applications across diverse fields, including intrusion detection, medical diagnostics, agricultural prediction, and financial forecasting.

Accompanying the notebook are two seminal research papers: one focused on cutting-edge applications of GCNs in the medical field, and the other, the foundational paper that introduces the core concepts and theoretical framework of GCNs. Together, these resources provide a blend of theory and practice, making it a valuable toolkit for anyone keen on exploring the capabilities of GCNs in solving real-world problems.

The notebook contains the following codes:
Classification of the type of attack on a network using the BOTIOT dataset
Depression Detection using EEG signals from Openneuro website
Crop yield prediction using GCN for feature extraction and LSTM for prediction
Stock forecasting using Microsoft data from yfinance package

Whether you’re a student, researcher, or practitioner, this product serves as a gateway to harnessing the potential of GCNs in multidisciplinary contexts.

abhishek gupta

ScholarsColab.com is an innovative and first of its kind platform created by Vidhilekha Soft Solutions Pvt Ltd, a Startup recognized by the Department For Promotion Of Industry And Internal Trade, Ministry of Commerce and Industry, Government of India recognised innovative research startup.