ML Intern @AIW Works , Open Source Contributor

github

Komal Gupta  • Peerlist

In a world heading towards automation by leaps and bounds transaction authentication should not be the first thing to stop. This project optimizes the process of entering your credit card information by using Optical Character Recognition (OCR) making it much easier than typing in everything by hand. OCR is a mechanical or electronic conversion of images of typed, handwritten, or printed text into machine-encoded text[1]. This system will read the text from credit and debit cards, extract the exacts and auto-fill this information in forms which can significantly reduce relieved amount of human work. This is further coupled and padded with a mix of classical and quantum machine learning methods to improve fraud detection, enhanced customer service at lower costs by using large language model based smart AI-driven bots. Such an innovative approach does not just make user experience better than others but also increases security of financial transactions more efficiently[2]. This initiative stitches together OCR, advanced machine learning algorithms and generative AI to create a complete solution for secure data processing, meaningful fraud detection and automated customer discovery.Demo link - <a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://drive.google.com/file/d/1wKG2qHI9fB1jQW2eVVZCO6TzvfDr95pP/view?usp=sharing">https://drive.google.com/file/d/1wKG2qHI9fB1jQW2eVVZCO6TzvfDr95pP/view?usp=sharing</a>

Credit Card Processing Using AI

Developed a python application which can use your hands as a mouse to operation function like right click , scroll, left click , drag etc. via hand recognition.

Hand Gesture Recognition Mouse

This document describes the technical design of the Machine Learning Model - Using ML and Python library known as pyqt5 which is used for GUI programming and designing. The primary goal of this document is to describe the technical vision for how machine learning can be realized in various ways, as well as for predictive maintenance to save a device from sudden and unexpected wear and tear. This document depicts various aspects of the device through an architectural overview of the system. It can also be used as a starting point for developers.

GUI ML Model

Detecting skin cancer was earlier a critical task for successful treatment and recovery. Unfortunately, traditional methods of diagnosis rely heavily on the expertise of dermatologists, whose availability can be limited. This paper proposed a novel approach using weight classification to balance the various skin cancer groupings and adjust for data anomalies. On the Skin cancer MNIST dataset to identify seven different types of skin lesions as skin cancer. A Convolutional neural network was designed to predict the type of cancerous and proposed model is further trained using a range of hyperparameter combinations to improve its accuracy. In summary, this research presents an innovative approach to skin cancer detection using CNN. After performing all the necessary preprocessing and training steps the proposed framework offers a promising solution to early skin cancer detection with an accuracy of 91.75% results.