Attendance marking of student's using face recognition in machine learning
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Updated
Mar 6, 2019 - PHP
Attendance marking of student's using face recognition in machine learning
COSC2500 2018 | Numerical analysis of diffusion equations.
TakenMind Global Internship Program is recognized under United Nations Sustainable Development and Growth (SDG) and is a highly recognized International Certification Program. - Reference Link to the United Nations SDG #26437 TakenMind Program. TakenMind (powered by United Nations SDG Program) is offering a Global Internship in Data Analytics an…
🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.
A set of python scripts for spatially explicit accuracy assessments of binary, gridded geospatial data, e.g., for human settlement data mapping built-up (1) and not built-up (0) areas.
A powerful Multi Page web application which can diagnose and predict diabetic symptoms and calculates pre risk diabetic features warning the user with an Interactive user friendly UI
Um sistema lúdico e gameficado para a avaliação da fluência e precisão de leitura. Para jogar, basta clicar no link abaixo:
Program an IR (information retrieval) system based on tagged datasets
This is a deep learning CNN image classifier written in Python in the JupyterLab environment.
Loan Application Data Analysis
This is implementation of customized bio-inspired algorithms for hyperparameter tuning of a custom-ANN, space and time complexity analysis of those bio inspired algos viz. ant-colony (contributed by me), swarm-bee and genetic algo and to compare their accuracies. ANN classifies if patient is prone to heart disease
This project aims to identify the inevitable trade-off between accuracy and safety when predicting poisonous mushrooms with ML.
Study and Implementation of various neural network pruning techniques. Extending the lottery ticket hypothesis to structured pruning for accelerated training while maintaining uncertainty and accuracy. The focus is on simplifying the model's complexity without sacrificing its overall performance or leading to overfitting.
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