Design, develop, and implement machine learning algorithms tailored to healthcare data, such as medical records, patient demographics, diagnostic images, etc.Train machine learning models using various techniques such as supervised learning (diseases detection and classification)Using Yolo Algorithms . Evaluate the performance of these models using appropriate metrics and fine-tune them for optimal results.preprocess, and transform healthcare data to make it suitable for machine learning tasks. This might involve dealing with missing values, normalization, feature extraction, etc.Create an efficient ML API using the FastAPI framework in Python.Deploy machine learning models to an Ubuntu virtual machine using Docker.Document machine learning models, methodologies, and findings in a clear and concise manner. Generate reports to communicate insights and outcomes to stakeholders within the organization.Stay abreast of the latest advancements in machine learning, Computer Vision, healthcare technology, and telemedicine practices. Continuously upgrade your skills and knowledge to remain competitive in the field.

Proficient in IoT and Electrical Engineering.Skilled in programming ESP32 boards for IoT applications.Proficiency in using simulation software like ISIS Proteus and Wokwi.Dedicated to the study and analysis of Photovoltaic (PV) systems.Strong troubleshooting skills in electrical and IoT systems.Committed to developing innovative solutions for IoT and electrical challenges.

Subject: Intelligent and energy-autonomous floating robotic system aimed at managing pollution in water bodies.Dataset Creation with RoboFlow:To train our robotic system effectively, a diverse and comprehensive dataset is crucial. We utilize RoboFlow, a versatile platform for creating, annotating, and augmenting datasets for machine learning applications. The dataset comprises images and corresponding annotations of various pollutants commonly found in water bodies, including plastics, oil spills, algae blooms, and debris. These annotations provide ground truth labels necessary for training our machine learning model.Computer Vision and Machine Learning with YOLO Framework:We leverage the YOLO (You Only Look Once) framework for its efficiency and accuracy in real-time object detection tasks. YOLO facilitates the detection of pollutants in water bodies from images captured by our floating robotic system's onboard cameras. Through a combination of convolutional neural networks (CNNs) and deep learning techniques, our model learns to recognize and classify different types of pollutants with high precision.Deployment in Web Application using Flask:To make our pollution management system accessible and user-friendly, we deploy the trained model in a web application using Flask, a lightweight web framework in Python. The web application serves as an interface for users to interact with the floating robotic system remotely. Users can input commands, receive real-time updates on pollution levels, and visualize detected pollutants through an intuitive graphical interface.

Successfully delivered NDVI-based vegetation health assessment tools, contributing to data-driven decision-making in environmental and agricultural projects.Implemented automated geospatial analysis pipelines, reducing processing time by 30% and enhancing project efficiency.Developed user-friendly visualization dashboards, improving data accessibility and interpretation for non-technical stakeholders

Manufacture of Solar Dryers and Ovens:Engaging in brainstorming sessions to develop design solutions.Generating detailed manufacturing drawings and Bills of Materials (B.O.M.).Evaluating the usability, environmental impact, and safety aspects of designs.Utilizing computer-aided design (CAD) and computer-assisted engineering (CAE) software to develop viable solutions.Establishing inspection and test schedules.Ensuring compliance with established procedures and processes within the quality system while contributing to its continuous improvement.Drafting technical reports on failure modes and mechanisms, drawing insights from literature, test data, post-test analysis (PTA) reports, CAE analyses, and materials analyses.