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Farhan Khan's avatar
Farhan Khan
Rawalpindi, Pakistan
AI Specilist, AI Automation, Chatbots, Business, Workflow
New to Contra
AI Specilist, AI Automation, Chatbots, Business, Workflow
Cover image for Automated Video Dehazing & Atmospheric
Automated Video Dehazing & Atmospheric Haze Simulation System 🚀 Project Overview This advanced Computer Vision project is designed to address visibility challenges in adverse weather conditions. The system features a dual-module architecture: it can synthetically inject realistic atmospheric fog/haze into crystal-clear video streams for dataset generation, and conversely, restore heavily degraded, foggy videos into crisp, high-visibility outputs in real-time. 🛠️ Core Functionality & Modules Module 1: Atmospheric Haze Simulation Purpose: Generates synthetic datasets to train and benchmark object detection models (like YOLO) for bad weather conditions. How it works: Implements mathematical scattering models to calculate depth maps and overlay a realistic layer of dense fog or smoke over clean video frames. Module 2: Real-Time Video Dehazing Purpose: Restores clarity and vivid color to video streams captured in low-visibility environments. How it works: Leverages physics-based Computer Vision algorithms (such as Dark Channel Prior - DCP) or Deep Learning frameworks to estimate atmospheric light, eliminate transmission noise, and reconstruct the scene's original contrast. 🎯 Use Cases & Applications Autonomous Vehicles: Enhances the sight and reliability of self-driving car sensors in dense fog. Smart Surveillance (CCTV): Improves security monitoring and facial recognition accuracy under harsh outdoor weather. Drone Navigation: Aids aerial drones in safely navigating through smoke, dust storms, or low-lying clouds. 💻 Tech Stack Used Language: Python Libraries: OpenCV, NumPy, Matplotlib, PyTorch / TensorFlow (if deep learning was applied) Concepts: Image Processing, Atmospheric Scattering Models, Feature Restoration, Video Pipeline Optimization
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Cover image for E-Assistant is an autonomous AI
E-Assistant is an autonomous AI shopping agent designed to streamline the consumer decision-making process. By simultaneously querying multiple e-commerce platforms, it utilizes a proprietary value-ranking algorithm to provide real-time product comparisons based on price, rating, and review volume.
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Cover image for Automated Multi-Agent AI Support &
Automated Multi-Agent AI Support & Lead Triage Pipeline Are your high-ticket clients waiting hours for an email response? This intelligent multi-agent n8n workflow instantly screens, analyzes, and responds to customer emails in real-time, utilizing advanced RAG (Retrieval-Augmented Generation) to deliver human-like support instantly. Project Overview: This is an enterprise-grade AI automation system designed to eliminate manual customer support queues. Instead of simple auto-replies, it uses a multi-agent routing structure combined with a dynamic knowledge base to handle complex inquiries autonomously. How It Works (Under the Hood): Instant Inbound Triage: A Gmail Trigger catches incoming emails instantly, extracting raw content for processing. AI Intent Classification: An initial OpenAI model acts as a gatekeeper, analyzing the email to determine if it is a valid customer support request or irrelevant noise. Conditional Routing: An advanced router splits the path: non-support emails receive a polite automated Telegram update, while actual support tickets are routed to the main AI engine. Context-Aware AI Agent: The core Customer Support Agent is equipped with an OpenAI Chat Model, conversational memory, and a custom Vector Store Tool. Pinecone RAG Integration: The agent queries a Pinecone Vector Database (powered by OpenAI Text Embeddings) to fetch real-time, accurate company documentation and context, eliminating hallucinations. Automated Action & Response: Once the resolution is drafted, the system automatically creates a draft in Gmail for review and sends an instant internal notification via Telegram. Why This Wins Clients (The Value Pitch): Zero Hallucinations: Connected to a live vector database (Pinecone) so the AI only speaks from approved company data. Reduced Overhead: Cuts down customer support response times from hours to under 60 seconds. Production-Ready Architecture: Designed with modern n8n AI sub-nodes, structured tools, and modular scaling capabilities.
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Cover image for Real Time SMS Spam Detection/Classification
Real Time SMS Spam Detection/Classification System
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MATLAB(1)
Mohammed Alsamdani's avatar
Mohammed Alsamdani
Denver, USA
Data Scientist | Renewable Energy Analyst | Prompt Engineer.
Data Scientist | Renewable Energy Analyst | Prompt Engineer.
Cover image for Power Systems Optimization using Data Science
Power Systems Optimization using Data Science
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Cover image for Analyzing Customer Energy Consumption Patterns
Analyzing Customer Energy Consumption Patterns
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Cover image for Renewable Energy Data Analysis
Renewable Energy Data Analysis
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MATLAB(1)
Matthew Reniva's avatar
Matthew Reniva
Las Pinas, Philippines
AI & Backend Engineer building scalable automation
New to Contra
AI & Backend Engineer building scalable automation
Cover image for Our project focused on making
Our project focused on making a computer program better at telling the difference between real photos and fake ones made by AI. We took an existing program and changed it to make it much more accurate. Now, it can correctly identify fake images about 95% of the time, which is a big improvement from its original 70% accuracy. With AI now able to create very convincing fake images quickly (Khadatkar, 2024), our work is important for spotting these fakes. 1 Our research paper explains how we made these improvements using a special kind of computer learning. This work can help address the challenges that come with the rise of AI-generated images.
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Cover image for 🚀 Build AI Automation Agents
🚀 Build AI Automation Agents for Your Business (What I’m Actually Doing Right Now) Most businesses don’t need “more AI”… They need less manual work. Lately, I’ve been building AI automation agents that: → Read and respond to emails automatically → Extract data from documents (invoices, reports, forms) → Connect APIs between tools (CRM, databases, dashboards) → Turn messy workflows into clean, automated systems And the goal is simple: Save hours of manual work every single week. 💡 Example: One system I built reduced manual document processing by ~70% using AI + backend automation. What I’ve noticed in 2026: Businesses are overwhelmed with tools… But what they really need is integration + automation that actually works. That’s where I focus. If you’re: ✔ Running repetitive operations ✔ Manually handling data or emails ✔ Using tools that don’t talk to each other You’re leaving time (and money) on the table. I’m currently looking to: 🔧 Build real-world AI automation systems 📊 Solve messy backend/data problems 🤝 Work with founders & teams who want efficiency If this sounds like what you need, Let’s connect or message me. Or just follow — I’ll be sharing real builds (not theory). #AI #Automation #Backend #Startups #SaaS #Productivity
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Cover image for Title: Automated Defective Product Detection
Title: Automated Defective Product Detection System on a Production Line Developed a deep learning model for real-time image analysis to identify and flag defective products on a production line. Automated quality control processes to minimize manual inspection.
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Cover image for Personalized Recipe Recommendation App Base
Personalized Recipe Recommendation App Base on Dietary Restrictions and Preferences Designed an application providing tailored recipe suggestions based on userdefined allergies, dietary restrictions (vegetarian, vegam, gluten-free), and cuisine preferences.
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MATLAB(1)
Cory Hisey's avatar
Cory Hisey
Cologne, Germany
Expert in Embedded Systems & 3D Design Solutions
Expert in Embedded Systems & 3D Design Solutions
Cover image for Creating a gear manufacturing process using Matlab - YouTube
Creating a gear manufacturing process using Matlab - YouTube
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Cover image for Resume Parser With Python - YouTube
Resume Parser With Python - YouTube
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Cover image for 3D Printed Radio Switch Mount - YouTube
3D Printed Radio Switch Mount - YouTube
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MATLAB(1)
Amitai Assayag's avatar
Amitai Assayag
Jerusalem, Israel
Senior Medical Imaging & Spatial AI Research Engineer
New to Contra
Senior Medical Imaging & Spatial AI Research Engineer
Cover image for MAFAT Radar Classification Challenge
MAFAT Radar Classification Challenge
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Cover image for Precision Medical Imaging and Printing Pipeline
Precision Medical Imaging and Printing Pipeline
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Cover image for Spatial AI Research for AR/VR
Spatial AI Research for AR/VR
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Cover image for TrackEverything: Enhanced Object Detection and Tracking
TrackEverything: Enhanced Object Detection and Tracking
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MATLAB(1)