Zuha Junaid
AI Engineer & Creative Strategist create Immersive Solutions
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Zuha Junaid - 3D AI & Software Engineer Portfolio
A modern, immersive 3D portfolio website showcasing AI/ML projects, full-stack development expertise, and technical skills. Built with cutting-edge web technologies and designed with glassmorphism aesthetics.
🌐 Live Portfolio: https://zuha3dport-s3994me9.manus.space
✨ Features
🎨 Design & Aesthetics
Dark Glassmorphism: Frosted glass cards with backdrop blur effects
3D Animations: Animated particle background with mouse interaction
Responsive Design: Fully optimized for desktop, tablet, and mobile devices
Smooth Transitions: Elegant page transitions and hover effects
Premium Color Palette: Dark blue (#0a0e27) with teal (#1dd1a1) and purple (#a29bfe) accents
📄 4 Complete Pages
Home - Hero section with animated 3D background and quick stats
About - Technical skills showcase, education, and interests
Projects - 4 major projects with detailed descriptions and technologies
Contact - Contact form and social media links
🎯 Interactive Elements
Animated particle background system
Glass card hover effects
Staggered entrance animations
Smooth page navigation
Responsive contact form
Social media integration
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Executive Summary
The primary objective of this project was to identify and mitigate security vulnerabilities within a custom-built academic portal. By implementing a defense-in-depth strategy, the assessment utilized multiple security testing methodologies to ensure a robust security posture. The evaluation revealed moderate security risks, primarily involving server misconfigurations and missing security headers.
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Al-Insaan Care: A Supportive App for Illiterate Needy People
Al-Insaan Care is a mobile application specifically designed to bridge the accessibility gap for illiterate and needy individuals. By removing traditional text-based barriers, the platform empowers users to request and receive essential financial aid and material donations—including food, clothing, electronics, and blood—with dignity and ease.
Project Overview
The primary goal of Al-Insaan Care is to provide an inclusive, user-friendly experience for populations that struggle with literacy. The application leverages multimedia tools to ensure that navigating the path to assistance is as intuitive as possible.
Key Features
Accessibility-First Design: Information is presented through visuals, audio instructions, and videos rather than text.
Voice-Activated Requests: Users can submit vital information (CNIC, address, house details) via voice messages or direct calls.
Thorough Verification: An administrative department validates all requests to ensure aid reaches those truly in need.
Streamlined Distribution: Integrated delivery services handle the distribution of material goods, while money and blood are managed through direct channels.
Social & Community Engagement: A social feed allows for community interaction and updates on humanitarian efforts.
UI & User Experience
The interface is designed to be clean and minimalist, focusing on high-contrast iconography and simple workflows.
Main Components
Section AND Description
Donation Portal: Uses icons (cutlery for food, shirt for clothing, etc.) to help users identify categories without reading.
Checkout Flow: A simple process with a 100% discount model for needy recipients, resulting in a total cost of Rs.0.
Dashboard: Provides administrators and donors with a visual overview of contributions and user growth through line graphs.
Chat Interface: Includes a microphone option for instant voice commands and communication with support staff.
Activity Feed: Tracks real-time user interactions, likes, and comments to foster a supportive community.
How It Works
Request: A user in need accesses the app and submits their details using voice commands or multimedia tools.
Verify: The administrative team reviews the voice-submitted data and validates the request.
Fulfill: Donors or the organization provide the requested items.
Deliver: The delivery department ensures the items reach the verified address via courier or direct distribution.
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Diabetes Prediction System
Developed a prediction system using Pima Indians dataset, achieving 84% accuracy with Random Forest. Performed comprehensive data cleaning and median imputation for missing values. Implemented feature scaling and model evaluation with cross-validation.
84% Accuracy Data Cleaning Feature Scaling
Technologies
Python Scikit-learn Pandas Random Forest
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Facial Emotion Recognition AI
Designed and trained a custom CNN to classify human facial expressions into 7 categories using the FER-2013 dataset. Built an interactive Streamlit dashboard for real-time emotion prediction from images. Implemented class weighting to address dataset imbalance and improved model performance.
Custom CNN Architecture Real-time Dashboard 84% Accuracy
Technologies
Python TensorFlow Keras CNN Streamlit
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