In the medical field, predicting a heart disease has become a very complicated and challenging task. So, in this contemporary lifestyle, there is an urgent need for a system that will help predict accurately the possibility of getting heart disease. This paper presents an observation-based comparison between four boosting algorithms namely Gradient boosting, XGBoost, ADAboost and CatBoost to predict heart failure efficiently. To do so, we have referred to the PLOS (Public Library of Science) Repository dataset. These algorithm’s performances have been evaluated using metrics like Accuracy, F1 score, Recall and many more. All values obtained ensured the superiority of these boosting algorithms based on several performance measures.
As Plato once rightfully said, ‘Music gives a soul to the universe, wings to the mind, flight to the imagination and life to everything.’ Music has always been an important art form, and more so in today’s science-driven world. Music genre classification paves the way for other applications such as music recommender models. Several approaches could be used to classify music genres. In this literature, we aimed to build a machine learning model to classify the genre of an input audio file using 8 machine learning algorithms and determine which algorithm is the best suitable for genre classification. We have obtained an accuracy of 91% using the XGBoost algorithm. Keywords: Machine Learning, Music Genre Classification, Decision Trees, K Nearest Neighbours, Logistic regression, Naïve Bayes, Neural Networks, Random Forest, Support Vector Machine, XGBoost
Sign Language is invaluable to hearing and speaking impaired people and is their only way of communicating among themselves. However, it has limitations with its reach as the rest of the people have no information regarding sign language interpretation. Sign language is communicated via hand gestures and visual modes and is therefore used by hearing and speaking impaired people to intercommunicate. These languages have alphabets and grammar of their own, which cannot be understood by people who have no knowledge about the specific symbols and rules. Thus, it has become essential for everyone to interpret, understand and communicate via sign language to overcome and alleviate the barriers of speech and communication. This can be tackled with the help of machine learning. This model is a Sign Language Interpreter that uses a dataset of images and interprets the sign language alphabets and sentences with 90.9% accuracy. For this paper, we have used an ASL (American Sign Language) Alphabet. We have used the CNN algorithm for this project. This paper ends with a summary of the model’s viability and its usefulness for interpretation of Sign Language. Keywords: Sign Language, Machine Learning, Interpretation model, Convoluted Neural Networks, American Sign Language
Modification of art may be viewed as enhancement or vandalization. Even though for a long time many were opposed to the idea of colorizing images, they now have finally viewed it for what it is - an enhancement of the art form. Grayscale image colorization has since been a long-standing artistic division. It has been used to revive or modify images taken prior to the invention of colour photography. This paper explores one method to reinvigorate grayscale images by colorizing them. We propose the use of deep learning, specifically the use of convolution neural networks. The obtained results show the ability of our model to realistically colorize grayscale images. Keywords: Deep Learning, Convolutional Neural Network, Image Colorization, Autoencoders.
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