This is Aryan Verma,
a student.

DataSightPro empowers users to effortlessly transform raw data into actionable insights through its intuitive interface and robust analytical capabilities. By simply uploading a CSV or Excel file, users gain access to a suite of features including comprehensive data overviews, statistical analyses, interactive visualizations, and predictive modeling. This platform streamlines the data analysis workflow, enabling users to quickly identify key trends, outliers, and correlations, and generate detailed PDF reports, making data-driven decision-making accessible to a wider audience.

This project focuses on improving security and transparency in healthcare data management by using blockchain technology and smart contracts. Patient records, prescriptions, and medical transactions are stored on a decentralized blockchain network, ensuring data integrity and preventing unauthorized modifications. Smart contracts automate processes such as record sharing, patient consent, and insurance verification. The system demonstrates how blockchain can create a secure, tamper-proof healthcare ecosystem while maintaining patient privacy and improving trust among hospitals, insurers, and patients.{tested using ETH testnets(sepolia) and Postgre}

This project involved developing a real-time dog detection system utilizing OpenCV, PyTorch, torchvision, and Python. The system processes live camera footage to identify dogs within a restricted area. If a dog is detected continuously for a user-defined time period, an alert is triggered and sent to the user via the Telegram Bot API. It implement computer vision and deep learning techniques for practical applications, integrating real-time processing with notification systems for effective monitoring and security.

Healthy Spoons is a nutrition-focused application designed to help people make healthier food choices. The platform analyzes ingredients in recipes and suggests healthier alternatives while maintaining taste and nutritional balance. Users can explore recipes, check nutritional values, and receive recommendations based on dietary preferences(protein-rich, iron-rich or fiber-rich) or health goals. The project highlights how technology can promote better eating habits and support overall well-being through smarter recipe planning.

This project demonstrates a practical application of IoT principles by implementing a Bluetooth-controlled garage door opening system. Utilizing a breadboard for creating an electronic circuit, an Arduino microcontroller and a stepper motor, the system allows users to remotely lock and unlock their garage door via a smartphone application. The project showcases the integration of embedded systems, wireless communication, and electromechanical components to create a convenient and secure access solution.

Clockwork is a dynamic web application that empowers users to create and personalize their own online clocks. Users can tailor their clocks with a variety of features, including customizable designs, stopwatch and timer functionalities, and a global clock display with selectable timelines. The application offers an intuitive and user-friendly interface for seamless customization and navigation.

It is a dynamic, 3D interactive experience built using Three.js, React, HTML, CSS, and JavaScript. The .dlb file was downloaded from an open-source library and subsequently customized. This responsive design demonstrates the ability to seamlessly integrate creative visual elements with contemporary web technologies. Users can navigate through a unique 3D environment, exploring my projects and skills in an engaging and immersive way.

This project proposes a process scheduling system that considers the environmental impact of computing tasks. Instead of scheduling tasks solely based on performance or priority, the system also evaluates carbon intensity and energy consumption. By prioritizing processes during periods of lower carbon emissions or more efficient energy usage, the scheduler aims to reduce the overall carbon footprint of computing systems. The project demonstrates how sustainable computing practices can be integrated into operating system scheduling strategies.

This project explores password security by building a tool using Hashcat and JtR that evaluates password strength and demonstrates how weak passwords can be cracked. The evaluator analyzes factors such as length, character diversity, and predictability to estimate password strength. The offline cracking module simulates common attack techniques like dictionary attacks and brute-force attempts to highlight potential vulnerabilities. The project aims to raise awareness about secure password practices and the importance of strong authentication.

This project involved developing a real-time colour detection system using an Arduino Uno, TCS3200 colour sensor, LCD display, and embedded programming in the Arduino IDE. The system captures colour information from objects placed under the sensor and processes the RGB values to identify the corresponding colour. Once detected, the colour name and values are displayed on the LCD screen for immediate feedback. The project demonstrates the use of embedded systems, sensor interfacing, and basic automation techniques for practical applications such as automated sorting, quality inspection, and smart monitoring in industrial and IoT-based environments.

This project involved developing an intelligent optimization system for managing civil aviation cargo operations and airport traffic using Python, Pandas, NumPy, and optimization algorithms. The system processes aviation datasets including passenger flow, flight schedules, routes, and ticket information to analyze airport traffic patterns and cargo demand. A Simulated Annealing (SA) algorithm is applied to optimize flight scheduling, cargo allocation, and airport resource utilization while minimizing delays and congestion. It implements data analysis, forecasting, and heuristic optimization techniques to improve operational efficiency, demonstrating the application of artificial intelligence and operations research in modern aviation management systems.

This project aims to identify fraudulent health insurance claims using machine learning techniques. The models were trained on the data released by Kaggle. By analyzing historical claim data, the system learns patterns associated with suspicious activities such as abnormal billing amounts, repeated claims, or unusual treatment combinations. Various classification models are trained to detect potentially fraudulent claims. The solution helps insurance companies reduce financial losses, improve claim verification efficiency, and strengthen fraud prevention mechanisms.