Techparade’s Portfolio
Our Portfolio
FIMAGE/VIDEO FORGERY DETECTION
This project aims to assess video content trustworthiness amid concerns over deepfakes and video forgery. It utilizes steganography and deep neural networks to detect anomalies, like optical flow discrepancies, enhancing our ability to differentiate genuine from manipulated videos and preserving digital media integrity in our interconnected world.
Automated Detection of Macular Edema
Our automated system detects Macular Edema, a key indicator of diabetic retinopathy, using OCT imaging. By extracting subretinal layers and applying machine learning classifiers, it achieves 97.78% accuracy. Early detection enables timely treatment, improving patient outcomes and reducing the long-term impact of diabetic-related eye conditions.
Content-Based Video Retrieval
This system helps users search Urdu and English media content efficiently. By using Optical Character Recognition for video captions and analyzing audio, it allows keyword-based searches. Initially supporting Urdu and English, it can be extended to other languages, aiding media houses, regulators, and intelligence agencies in content analysis.
Automated Forest Fire Detection
Our deep learning-based system detects forest fires early using images and video feeds from UAVs and cameras. It accurately identifies fire presence, estimates fire size, and suggests the necessary quantity of extinguishing material. This solution enables faster responses, minimizing environmental and economic damage caused by rapidly spreading wildfires.
Urdu Optical Character Recognition (OCR)
Our OCR system is designed for Urdu Nastaliq script. It analyzes ligature structures using sliding windows to capture projection, concavity, and curvature data, achieving 98% accuracy. This innovation supports translation software, improving machine readability and enabling faster processing of Urdu text into accessible, digital formats.
Automated Cow’s Teat Detection
We introduced a robotic milking solution using a KUKA arm equipped with a 3D ToF camera for precise teat detection. This system automates the milking process, improving efficiency in dairy farms. Successfully tested in New Zealand, it offers a promising advancement for Pakistan’s local agricultural automation market as well.
Activity and Anomaly Detection
We improve surveillance by tracking object movements and detecting abnormal activities using motion trajectories. This system classifies behaviors and identifies unusual patterns efficiently. Applicable not just to CCTV but also GPS and smartphone cameras, it advances security monitoring and situational awareness across different public and private sector environments.
Clinical Diagnostic Imaging
Our system improves diagnostic imaging by addressing noise and blur issues using multi-modality image fusion. By merging different images and applying advanced techniques, we enhance the clarity and accuracy of medical diagnostics, ultimately leading to better analysis, faster identification of abnormalities, and improved healthcare outcomes for patients through more reliable imaging processes.
Emotion Recognition via Signals
Our system uses physiological signals like ECG and EEG to recognize human emotions with high accuracy. By combining deep learning feature extraction and hybrid classification methods, it achieves remarkable precision. Tested on AMIGOS and DEAP datasets, it surpasses existing techniques, contributing to improved mental health diagnosis and holistic emotional well-being.
Weapons Detection via Imaging
We developed an automated system for detecting firearms in images and video streams. Using advanced segmentation techniques, it overcomes challenges like occlusion and transformations. By identifying weapons in real-time, the system enhances public safety and reduces reliance on manual surveillance monitoring, addressing growing concerns over mass violence and security threats.
Epilepsy Prediction System
Epilepsy Predictor is an Android app designed to forecast seizures through EEG signal analysis. By preprocessing and extracting important features, the app predicts seizures and triggers alarms for timely intervention. This locally developed solution helps reduce healthcare costs, improves patient well-being, and addresses the treatment gap for epilepsy patients across Pakistan.
Face Verification Cross-Platform App
A secure, AI-powered mobile app for real-time face verification and identity authentication. Built with Flutter and deep learning-based facial recognition, it delivers high accuracy across varying lighting and devices. Designed for both Android and iOS, the app ensures reliable performance and cross-platform support, making it ideal for modern, secure identity validation in real-world scenarios.
