http://hdl.handle.net/123456789/13175 Thu, 16 Jul 2026 09:02:38 GMT 2026-07-16T09:02:38Z http://hdl.handle.net/123456789/20653 Smart Living Furqan Ahmed, 01-134211-022; Abaid-Ur-Rehman, 01-134202-068 The integration of Internet of Things (IoT) technology with modern home infrastructure has revolutionized the concept of living spaces, enabling smart environments that prioritize convenience, efficiency, and security. This project, titled "Smart Living: IoT-Based Home Automation System with Fingerprint Authentication", presents a comprehensive and secure solution to automate household appliances while ensuring robust user authentication through biometrics. The system architecture leverages the ESP32 microcontroller as the central processing and communication unit, connected to a suite of electrical appliances including fans, lights, a water motor, and a door locking mechanism. The mobile application—developed in Java for Android—serves as the primary user interface, allowing authenticated users to remotely control and monitor devices in real time. Integration with Firebase provides reliable cloudbased communication, enabling seamless command transmission and feedback between the mobile client and the ESP32 unit. To enhance security, the system incorporates fingerprint-based authentication both at the application login level and at the physical access point of the main door. This dual-layer biometric approach ensures that only authorized individuals can gain access to the system and the household premises. The door lock mechanism is operated using a micro servo motor, triggered only after a successful fingerprint match. The project demonstrates a working prototype that controls one fan, three lights across different rooms, a water motor, and a main door lock. The hardware setup includes relay modules for appliance switching, a 12V-to-5V converter for stable microcontroller power, a servo motor for the locking system, and battery backup for uninterrupted operation. In addition to security and automation, the system is designed to be scalable. Future versions will include a QR code-based pairing module to allow users to add new IoT devices easily. This design consideration ensures the system remains adaptable to evolving user needs and expanding device ecosystems. Through iterative testing and validation, the system has shown reliable performance in controlling appliances remotely, maintaining secure access, and handling real-time communication over Firebase. It serves as a practical example of how IoT and biometric technologies can be integrated to create responsive, intelligent, and secure home environments. This project not only contributes to the growing field of home automation but also addresses critical aspects such as user authentication, system reliability, and expandability. Its implementation demonstrates how academic knowledge in embedded systems, mobile application development, and cloud communication can be translated into a viable realworld solution. Supervised by Mr. Abdul Rahman Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/123456789/20653 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/20648 Urdu Lenz Syed Mujadil Ahmad Hazqeel, 01-134212-177; Esha Pervaiz, 01-134212-040 Urdulenz is a web-based platform designed to provide seamless, bidirectional translation of PDF documents between English and Urdu. It addresses the growing need for accurate bilingual document processing in academia, business, and government sectors. The system leverages state-of-the-art Natural Language Processing (NLP) models, advanced PDF parsing tools, and a user-friendly interface to deliver fast, reliable translations. At the core of Urdulenz is the MarianMT model, a neural machine translation system based on the Transformer architecture. Fine-tuned on the Opus-100 dataset containing English–Urdu parallel texts, MarianMT captures the unique syntactic and contextual nuances of both languages. Integrated via Hugging Face’s API, it enables fast and scalable translation services while preserving meaning and handling complex language features. Urdulenz supports one-column, text-based PDFs and handles scanned PDFs using OCR. For text extraction, it uses PyMuPDF and PDFPlumber, while Tesseract.js manages OCR for image-based content. This versatility allows the platform to process a wide range of documents efficiently. Developed in React.js, the responsive UI enables users to upload PDFs, monitor translation progress, and download results across devices. Security is built in through JWT-based authentication and session management. The platform maintains a translation history for user convenience and includes fallback mechanisms to ensure continuity during API disruptions. Urdulenz has strong market potential in Urdu-speaking regions like Pakistan and parts of India, enabling broader access to academic, business, and government content. It benefits students, professionals, and officials by bridging language barriers in research papers, reports, legal documents, and more. Future developments include adding support for other regional languages (e.g., Arabic, Punjabi, Sindhi), improving OCR through advanced models like LayoutLM or Donut, and enabling translation of non-textual content using vision-language models. Mobile app versions and real-time translation features are also planned. In summary, Urdulenz is a scalable, impactful tool that combines cutting-edge translation and document processing technologies to enhance access to information and enable smoother communication between English and Urdu speakers. Supervised by Ms. Aima Zahoor Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/123456789/20648 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/20652 RINXO: A Web-based Crypto Exchange Platform Muhammad Zeeshan Khaliq, 01-134212-144; Tehfeez Sadik, 01-134212-186 This research has presented Rinxo, a new web-based cryptocurrency exchange platform focused on improving the digital asset trading experience through accessibility, security, and user-oriented design. As cryptocurrencies continue to grow in popularity, existing trading platforms are expensive, confusing, have outdated UI, are limited in responsiveness across devices, and feature low options for data graphical visualization. Rinxo will provide an experience that addresses these gaps using the latest web technology to produce an efficient, responsive, and intuitive user experience catered for both novice and intermediate users. Rinxo incorporates live market data through third-party APIs giving our users instant access to accurate price movements, volume and market sentiment. Rinxo features a clean interactive UI, with customizable dashboards and dynamic charts, providing consistent performance on desktops, tablets and smartphones. Built with a modular and scalable architecture for optimal performance, Rinxo offers, secure authentication, and hundred percent, zero downtime, always on service - even when overloaded. The platform has some core features: user registration and authentication, live trading simulations, portfolio management, transaction history and record keeping, as well as real time notifications. The platform has been designed in the key concepts of accessibility, usability, and responsive design. Unlike typical platforms such as Binance, Kraken or KuCoin, Rinxo is designed to be inclusive, compliant, and future integration of AI trading tools, advanced analytics, and various compliance features. Facilitating an agile development process, while enabling ongoing feedback loops allows Rinxo to adapt to a variable and evolving landscape, meeting market expectations, and user behavior. This project illustrates how RInxo will set the tone for crypto trading platforms by putting performance, simplicity, and enabling user empowerment first. Supervised by Mr. Qazi Mohsin Ijaz Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/123456789/20652 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/20654 A Career Lens Website for Job Market Trend Analysis and Job Application Listings Shayan Hassan Abbasi, 01-134212-167; Zeeshan Ali, 01-134212-197 The dynamic nature of the job market presents significant challenges for students, recent graduates, and job seekers in making informed career decisions. Despite the abundance of employment platforms, there remains a notable gap in accessing comprehensive, real- time job market trend analysis combined with direct application capabilities. This project introduces CareerLens, a web-based platform designed to bridge this gap by providing data- driven job market insights alongside streamlined job application functionalities. The system leverages web scraping technologies to extract and analyze real-time job posting data from LinkedIn, presenting users with visualization of emerging industry trends, in-demand skills, and salary insights. CareerLens features a dual-component architecture comprising a Python backend utilizing BeautifulSoup and Selenium for automated data extraction, and a React frontend with Node.js backend for user interface and data management. The platform offers personalized job filtering capabilities, bookmarking functionality, and trend analysis visualizations that assist users in making strategic career decisions. By utilizing MongoDB for data persistence and Firebase for real-time user interaction, CareerLens delivers a comprehensive solution for three primary target audiences: students seeking elective guidance, individuals exploring short-term skill development opportunities, and active job seekers. This project addresses the critical need for an integrated platform that not only facilitates job discovery but also provides contextual market intelligence to inform career pathway decisions in an increasingly competitive employment landscape Supervised by Mr. Mehroz Sadiq Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/123456789/20654 2025-01-01T00:00:00Z