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Schedule Interview NowMy name is Naeem A. and I have over 1 years of experience in the tech industry. I specialize in the following technologies: C++, Python, Arduino, ESP32, Raspberry Pi, etc.. I hold a degree in Master of Computer Science (MSCS). Some of the notable projects I’ve worked on include: IoT Home Automation System:, OCR-Based Document Processing Application. I am based in Malibu, United States. I've successfully completed 2 projects while developing at Softaims.
I possess comprehensive technical expertise across the entire solution lifecycle, from user interfaces and information management to system architecture and deployment pipelines. This end-to-end perspective allows me to build solutions that are harmonious and efficient across all functional layers.
I excel at managing technical health and ensuring that every component of the system adheres to the highest standards of performance and security. Working at Softaims, I ensure that integration is seamless and the overall architecture is sound and well-defined.
My commitment is to taking full ownership of project delivery, moving quickly and decisively to resolve issues and deliver high-quality features that meet or exceed the client's commercial objectives.
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Intel
Developed an embedded system for smart home devices, enabling remote control and monitoring via a desktop and Mobile application. Implemented secure communication protocols to ensure data integrity and confidentiality.
I developed a cross-platform desktop application that leverages OCR (Optical Character Recognition) technology to extract text from scanned documents and performs text classification using convolutional neural networks. The application was designed to improve document processing efficiency and accuracy, providing users with a powerful tool for handling large volumes of documents. Key Features: OCR Integration: Utilized Tesseract OCR to accurately extract text from scanned documents and images. Text Classification: Implemented convolutional neural networks using TensorFlow to classify extracted text into predefined categories. User-Friendly Interface: Designed an intuitive GUI using PyQt that allows users to easily upload documents, view OCR results, and see classification outputs. Cross-Platform Compatibility: Ensured the application runs smoothly on both Windows and MacOS. Challenges and Solutions: OCR Accuracy: Fine-tuned Tesseract OCR settings and preprocessed images to improve text extraction accuracy. Performance Optimization: Optimized neural network inference times to ensure the application provided real-time feedback. User Experience: Conducted usability testing to refine the GUI and enhance overall user experience. Outcome: The application significantly improved the client's document processing workflow, reducing manual effort and increasing accuracy. Positive feedback highlighted the application's efficiency, ease of use, and reliable performance.
Master of Computer Science (MSCS) in Computer science
2014-01-01-2018-01-01