AI Specialist with hands-on experience in computer vision, data analysis, and smart control systems. Skilled in YOLO-based object detection, video streaming with Raspberry Pi, and robotics (drones and underwater vehicles). Proficient in Python, C++, and Java, with creative expertise in Adobe tools and Microsoft Office.
build intelligent systems that solve real-world problems through cameras, sensors, and automation. From drone navigation to inventory monitoring in supermarkets, I combine machine learning with smart engineering to make daily operations smarter and faster. I also create tools that connect devices like Raspberry Pi with remote systems, bringing AI closer to the edge.
In the field of AI development, my work focuses on creating advanced systems that use artificial intelligence to solve complex problems.
Read MoreComputer vision is one of my primary areas of focus, where I use cutting-edge technologies to enable machines to interpret and understand visual data. For example, I work with object detection algorithms like YOLO (You Only Look Once) to analyze video feeds in real-time.
Read MoreData is at the core of most modern businesses, and my role in data analysis is to help turn that data into valuable insights.
Read MoreDetection & Tracking Master is an interactive web application that leverages the YOLOv8 model to perform object detection and tracking in images and videos. Built with Streamlit, it allows users to upload files or use YouTube links and view results instantly in an intuitive visual interface.
Lung Diseases Classifier is a deep learning project designed to detect and classify various lung diseases from chest X-ray images using the NIH ChestX-ray14 dataset. It employs advanced models like Convolutional Neural Networks (CNNs) and Capsule Networks (CapsNets) to enhance diagnostic accuracy. The project includes comprehensive data preprocessing, exploratory analysis, and model training on both sample and full datasets. It supports multi-label classification across 14 disease categories, including pneumonia, fibrosis, and emphysema. Implemented in Jupyter Notebooks with Keras, it offers a practical approach to medical image analysis.
Acoustic Data Analysis is an interactive application designed for analyzing and visualizing acoustic emission (AE) signals. Using PyQt5 for its graphical interface, the tool allows users to explore time, frequency, and spatial data through various visualizations such as heatmaps, time-domain plots, and frequency distributions. It supports filtering data based on different criteria and provides an intuitive user experience for detailed acoustic data analysis.
Oryx J022201 is an AI-driven inventory management system that uses YOLOv10 for real-time shelf gap detection in retail stores. The system is powered by a Raspberry Pi, which processes data in real time and connects to surveillance cameras. It also integrates a fully autonomous robot that navigates the shelves, identifies gaps, and assists with restocking, enhancing store operations and product placement.