Akshat Tripathi - Data Analyst | ContraWork by Akshat Tripathi
Akshat Tripathi

Akshat Tripathi

AI & ML engineer building production-ready impact models

Ready for work

Akshat is ready for their next project!

Followed by GALLERY L and Bezawit A

Karera, India

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Karera, India

Cover image for RidgeRecon — AI Fingerprint Reconstruction RidgeRecon
RidgeRecon — AI Fingerprint Reconstruction RidgeRecon is an AI-powered fingerprint reconstruction project designed to restore and enhance damaged, incomplete, or low-quality fingerprint images. The system uses deep learning and image processing techniques to reconstruct missing fingerprint ridge patterns while preserving important structural details. The goal is to transform degraded fingerprint inputs into clearer, more usable representations for further analysis. Key Features 🧠 Deep learning-based fingerprint reconstruction 🔬 Restoration of damaged and incomplete ridge patterns 🖼️ Image preprocessing and enhancement 📐 Ridge structure preservation ⚡ Automated reconstruction pipeline 📊 Support for large-scale fingerprint datasets Tech Stack Python • TensorFlow/Keras • OpenCV • NumPy • Pandas • Deep Learning • Image Processing RidgeRecon combines computer vision and deep learning to tackle a challenging image restoration problem and demonstrates practical applications of AI in biometric image processing. #AI #MachineLearning #DeepLearning #ComputerVision #FingerprintRecognition #ImageProcessing #TensorFlow #Python #Biometrics #ArtificialIntelligence
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Cover image for Aegis — AI-Powered Drone Detection Aegis
Aegis — AI-Powered Drone Detection Aegis is a computer vision system built with YOLO and deep learning to automatically detect drones in images and video streams. The system identifies drones in real time and provides precise bounding boxes with confidence scores for each detection. The project focuses on developing a fast and reliable object detection pipeline that can be integrated into security monitoring, surveillance, restricted-area protection, and automated aerial monitoring systems. Key Features Real-time drone detection using YOLO Accurate bounding-box localization Confidence score for every detection Image and video inference Optimized computer vision pipeline Scalable for real-time surveillance applications Tech Stack Python · YOLO · PyTorch · Ultralytics · OpenCV · Computer Vision Aegis demonstrates how modern deep learning and object detection can be applied to build practical AI-powered surveillance solutions. #ArtificialIntelligence #MachineLearning #ComputerVision #DeepLearning #YOLO #ObjectDetection #Python #PyTorch #OpenCV #Ultralytics #DroneDetection #AISurveillance #RealTimeDetection #ImageProcessing #VideoAnalytics
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Cover image for AutoML Forge is an end-to-end
AutoML Forge is an end-to-end AutoML platform designed to simplify the machine learning workflow by automating key stages from data preparation to model evaluation. The platform allows users to upload their datasets and build machine learning models without manually handling every step of the traditional ML pipeline. Key Features Automated data preprocessing and cleaning Automatic feature handling and transformation Classification and regression workflows Anomaly detection Model comparison and evaluation Automated model selection Hyperparameter optimization Performance metrics and visualizations Model prediction and inference Easy-to-use interactive interface Tech Stack Python · PyCaret · FastAI · Scikit-learn · Pandas · NumPy · Streamlit The project focuses on making machine learning more accessible while reducing repetitive experimentation and development time. It demonstrates practical experience in machine learning automation, model evaluation, data preprocessing, and building AI-powered applications. Role: AI/ML Developer Project Type: Machine Learning / AutoML / Data Science
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Cover image for Developed a deep learning-based brain
Developed a deep learning-based brain tumor classification system using MRI images. The project applies transfer learning with pretrained CNN architectures to automatically classify brain MRI images into different tumor categories. What I worked on MRI image preprocessing and augmentation Transfer learning and CNN-based classification Model training and validation Performance evaluation and comparison Prediction on new MRI images Visualization of model performance and results Technologies Python, TensorFlow, Keras, OpenCV, NumPy, Pandas, Matplotlib, EfficientNet, DenseNet, MobileNetV2, InceptionV3, and Xception. This project demonstrates the application of computer vision and deep learning to medical image classification.
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