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Real-time-Vernacular-Sign-Language-Recognition-using-MediaPipe-and-Machine-Learning
The deaf-mute community have undeniable communication problems in their daily life. Recent developments in artificial intelligence tear down this communication barrier. The main purpose of this paper is to demonstrate a methodology that simplified Sign Language Recognition using MediaPipeβs open-source framework and machine learning algorithm. The predictive model is lightweight and adaptable to smart devices. Multiple sign language datasets such as American, Indian, Italian and Turkey are used for training purpose to analyze the capability of the framework. With an average accuracy of 99%, the proposed model is efficient, precise and robust. Real-time accurate detection using Support Vector Machine (SVM) algorithm without any wearable sensors makes use of this technology more comfortable and easy.Data-Science-Project-on-Prediction-of-Bengaluru-Housing-Price
This data science project series walks through step by step process of how to build a real estate price prediction website. I will first build a model using sklearn and linear regression using bangaluru housing prices dataset from kaggle.com. Second step would be to write a python flask server that uses the saved model to serve http requests. Third component is the website built in html, css, bootstrap and javascript that allows user to enter home square ft area, bedrooms etc and it will call python flask server to retrieve the predicted price. During model building I will cover data science concepts such as data loading and cleaning, outlier detection and removal, feature engineering, dimensionality reduction, gridsearchcv for hyperparameter tunning, k fold cross validation etc.MNIST-Handwritten-Digit-Recognition-using-CNN
Machine_Learning_Introductory
Data-Science-Prediction-Models
In this repository you will find prediction model of different data sets taken from kaggle.com . I worked on mini Data science projects as a beginner. Hopefully this will be a stepping stone in my future career of persuing data science.Bengali-and-Hindi-Signature-Verification-using-Convolution-Siamese-Network
Verification of off-line signatures is one of the most challenging tasks in biometrics and document forensic science. In this thesis, we deal with Convolutional Siamese Network model which is capable of doing verification of Bengali and Hindi Signature. One particular advantage of Siamese Neural networks is the ability to generalize to new classes that it has not been trained on, and in fact, the number of classes that it is expected to support does not have to be known at training time. Also, the architecture commonly known as the Siamese network helped reduce the amount of training data needed for its implementation. The twin networks with shared weights were trained to learn feature space where similar observations are placed in proximity. Writer Independent verification model has been designed where an accuracy of 91.82% has been obtained for Bengali Dataset and 84% for Hindi DatasetLove Open Source and this site? Check out how you can help us