Heat Gen — Heatmap & Deep-Learning Analysis Application

Technologies Used

Python

Flask

QT

Heat Gen — Heatmap & Deep-Learning Analysis Application

Overview

Heat Gen is a desktop application built to generate heatmaps from datasets and perform deep-learning–based analysis and visualization, helping users turn raw data into insightful visual patterns automatically.

Problem

Users dealing with complex, high-dimensional data lacked a simple, intuitive tool to visualize patterns, spot correlations or anomalies, and apply advanced analysis — doing so manually (or via code/scripts) was time-consuming and prone to error. They needed a straightforward, desktop-based solution to generate heatmaps and run deeper data analysis without extensive technical overhead.

Solution

Heat Gen offers:

• Automated heatmap generation — transforms raw datasets into 2D color-coded visualizations where values are represented by color gradients.

• Deep-learning–based data analysis — integrates a model-driven analysis pipeline to detect patterns, anomalies, or clusters based on the data.

• User-friendly interface — suitable even for users without deep technical background; no need to write code for visualization or analysis.

• Flexible input and output — supports typical dataset formats, produces clear visual outputs and summary results for easy interpretation.

Result

With Heat Gen, users can quickly convert raw datasets into clear, interpretable heatmaps, and run deep-learning analyses to reveal hidden patterns — saving time and reducing manual errors, while making data-driven insights more accessible.

Video Demo

Published Research

Heat Gen is supported by research that was officially published in the Journal of Engineering Science and Technology (JESTEC), Vol. 18, Issue 2, April 2023.