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Syed Affan Hussain

I build things for the AI

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Syed Affan Hussain

About Me

Artificial Intelligence Engineer

Hello! I'm Syed Affan Hussain, an AI Engineer with a strong foundation in machine learning, computer vision, and natural language processing. With a Bachelor's degree in Computer Science from Sir Syed University of Engineering and Technology, I have 3 years of experience in AI. Currently, I work at HnH Soft Tech Solutions Pvt. Ltd., a German-based company in Karachi, Pakistan.

My professional journey includes developing high-accuracy disease diagnosis systems and personalized medical information retrieval systems. I have a proven track record in delivering computer vision and NLP projects, optimizing models, and collaborating on full-stack AI applications.

I am passionate about public speaking, continuous learning, and applying AI innovations to solve real-world problems. I hold certifications from IBM, Stanford University, and Microsoft, and have been recognized with awards such as the Valedictorian Award at Sir Syed University and the Best Public Speaker Award.

My Journey

Experience

March 2023 - Present

Associate AI Engineer - HnH Soft Tech Solutions Pvt (Ltd)

Karachi, Pakistan

I have been pivotal in developing innovative AI solutions. My notable project includes building a disease diagnosis system with a 98% accuracy rate, which predicts diseases and recommends tests based on input symptoms, thus eliminating the need for initial doctor consultations. Additionally, I developed a personalized medical information retrieval system using vector databases, enabling efficient querying of relevant data from medical PDFs. My role involves rigorously testing, optimizing, and deploying AI models, and staying updated with the latest AI advancements to explore innovative solutions for ongoing projects.

Nov 2021 - Feb 2023

Junior AI Engineer - Digital Landscape

Karachi, Pakistan

I delivered various Computer Vision and NLP projects for global clients, focusing on optimizing model performance and building tailored datasets per the Software Requirements Specifications (SRS). I collaborated closely with front-end developers and UX designers to build comprehensive full-stack AI applications. My tenure at Digital Landscape honed my skills in AI and allowed me to contribute to cutting-edge AI solutions, ensuring seamless integration and functionality across multiple projects.

Education

2020-2024

Bachelor of Science in Computer Science

Sir Syed University of Engineering and Technology

My Skills

AI Skills

Machine Learning90%

Deep Learning85%

Data Modeling90%

Python95%

Computer Vision Skills

Image Processing85%

Object Detection80%

Image Segmentation85%

OpenCV70%

NLP Skills

Text Analysis85%

Sentiment Analysis80%

NLP Libraries (NLTK, Spacy)75%

Generative AI Skills

GPT Models80%

Text Generation75%

Generative Adversarial Networks70%

My project

Brain Tumor Detection

This is a web-based brain tumor detection system that utilizes Convolutional Neural Networks (CNN) to achieve approximately 92% accuracy. Developed using Python and advanced machine learning techniques, this system analyzes MRI scans to accurately identify the presence of brain tumors. By leveraging the power of CNN, the detection process is both efficient and reliable, providing critical support in medical diagnostics.

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Early Disease Detection

This project focuses on early disease detection using the Multinomial Naive Bayes algorithm. The model is trained to classify diseases based on symptoms extracted from a provided dataset, encompassing more than 150 symptoms.

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Diet Recommendation System

This project uses a pre-trained machine learning model to recommend a diet based on user input such as weight, height, and gender. It utilizes k-means clustering for grouping food items into clusters and recommends a diet from the cluster predicted by the model.

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AI Gym Trainer

This project will guide you through three different exercises including Curl, Deadlift, and Squat using Mediapipe Pose Estimation and OpenCV. By leveraging these advanced technologies, you'll receive real-time feedback and guidance, allowing you to improve your form and perform these exercises more effectively on your own.

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Particle Swarm Optimization

In this project, Particle Swarm Optimization (PSO) was used on a dataset with approximately 17,000 data points to enhance feature selection for machine learning models. For Random Forest, the accuracy increased from 88% with all features to 93% using 1,093 PSO-selected features. Similarly, for Support Vector Machine (SVM), accuracy dramatically improved from 38% to 98% with the same PSO-selected features.

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Image Generation Using DALL-E

This application allows authenticated users to generate images using OpenAI's DALL-E model based on the provided prompt. The app includes an admin panel to view request logs and search functionality.

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