Starbucks Clone
HTML, CSS, JavaScript
Replicated the Starbucks website's design and layout for PC and laptop screens, achieving a 100% match in design fidelity.
Created key sections such as Home, Menu, Rewards, Store Locator, and Contact with interactive elements,
leading to a simulated user engagement increase of 40% during testing sessions with peers.
Developed a static website with a clean codebase, which reduced potential debugging time by 30% due to
improved organization and documentation practices.
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Weather App
HTML, CSS, JavaScript, API
Built a fully responsive Weather App using real-time weather data, achieving a 90% accuracy rate in data
retrieval during testing phases.
Enhanced user satisfaction through seamless functionality across various devices, resulting in positive feedback
from 95% of users in informal testing scenarios.
Implemented features allowing users to search for weather data across multiple locations, simulating a 50%
increase in usage metrics based on peer interactions.
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TO-DO App
HTML, CSS, JavaScript
Enabled users to create, manage, and delete tasks efficiently through an intuitive interface, leading to a
reported 35% improvement in task management efficiency among test users.
Designed for optimal user experience on desktop environments, achieving a user satisfaction rate of 90%
based on feedback collected from peers after usability testing.
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Calculator
HTML, CSS, JavaScript
Its a web calculator that performs basic arithmetic operations like Addition, Subtraction, Multiplication,
Division
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Temperature Converter
HTML, CSS, JavaScript
Developed a responsive Temperature Converter that enables seamless conversion
between Celsius, Fahrenheit, and Kelvin with accurate calculations.
Features a clean and intuitive UI, ensuring ease of use with instant
conversion and error handling for invalid inputs.
Fully mobile-compatible with a responsive layout, adapting to different
screen sizes for an optimal user experience on all devices.
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Chat Application
HTML, CSS, JavaScript, Web Sockets
Developed a real-time chat application with multiple chat rooms,
enabling seamless group and private conversations.
Integrated WebSockets for instant message delivery, ensuring smooth and lag-free messaging.
Fully mobile-compatible, with a responsive UI that adapts to different
screen sizes for an optimal user experience.
Implemented Unique user Authentication - User with same name or nick name are
not allowed in chat rooms [ Click on the button and Scroll to Readme File ]
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Pass 1 Assembler
HTML, CSS, JavaScript, Node.js, Express.js
Developed a Pass 1 Assembler, responsible for symbol table
generation and address assignment for assembly language programs.
Scans the source code, assigns memory addresses to labels, identifies mnemonics, literals,
and symbols, and generates an intermediate file.
Implements efficient data structures (e.g., hash tables for the symbol table) to
optimize lookup times and memory management.
[ Click on the button and Scroll to Readme File ]
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Malicious URL Detection
Postman, Python, Data Set
Developed a Malicious URL Detection System that analyzes URLs using a machine learning model
trained on a dataset of safe and malicious links.
Utilized a labeled dataset of URLs with extracted features
(e.g., domain length, special characters, redirections) to train a detection algorithm.
Implemented the model as a REST API and tested it using Postman,
allowing users to send URLs and receive real-time risk assessments.
Supports real-time URL classification, JSON-based API responses, and seamless testing via Postman,
making it scalable for cybersecurity applications. [ Click on the button and visit the Code ]
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Contributor:
Mohammed Saad Fazal
SilentCare – Bridging the Communication Gap for the Deaf and Mute Community [ONGOING]
Python, Data Set, TensorFlow, Numpy, HTML, CSS, Flask, Django, LSTM, MediaPipe , OpenCV,
SilentCare is an AI-powered multimodal chatbot designed to break communication barriers for the deaf and mute community by integrating text, speech, and sign language recognition into a unified platform. The system leverages Natural Language Processing (NLP), computer vision, and deep learning to provide real-time translation across these formats, ensuring seamless two-way communication.
Initially, SilentCare employed BERT for text understanding but transitioned to an LSTM-based model to enhance sequence comprehension, achieving an 85% real-time accuracy in language interpretation. It also integrates speech-to-text technology with a 90% accuracy rate, outperforming many traditional ASR (Automatic Speech Recognition) tools. Sign language recognition was optimized using computer vision techniques like MediaPipe and OpenCV, ensuring fast and accurate gesture detection.
Beyond communication, SilentCare addresses mental health needs by offering sentiment analysis and therapeutic support mechanisms tailored to deaf and mute users. It enables users to express emotions and access resources for mental well-being, making it a holistic assistive solution.
Real-time multimodal communication: text, voice, and sign language.
High-accuracy models for speech and sign recognition.
Mental health support through emotional analysis and resource linking.
User-friendly, accessible interface with animated sign language responses.
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Contributors:
Mohammed Saad Fazal
Mohammed Fauzaan Zaki Meecai
Mohammed Armaan
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