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Item type:Publication, Predictive analytics to determine the Bitcoin price rise using machine learning techniques(Seventh Sense Research Group, 2022-03-01)As we know, today there have been several variations in the price of Bitcoin as it is not stable, that is to say, the price of Bitcoin rises and falls exaggeratedly as there are several factors that focus on the opening and closing price values of Bitcoin, also the highest and lowest Bitcoin price values reached in a day. Bitcoin price is determined by supply and demand. If user demand for this cryptocurrency is high, the price will increase and if it is very low, will go to. The price of Bitcoin has fluctuated considerably in recent years. This means that it is constantly changing and is in a stable range without major changes. In this paper, a machine learning method, also called Machine Learning, is proposed, which will allow us to automatically search and interpret relevant information from a large amount of data. Machine learning is the branch of artificial intelligence science that creates automated learning systems. In the case study, the Recurrent Neural Network (LSTM) algorithm will be used for the predictive analysis of Bitcoin value. Recurrent Neural Networks are network layers for analyzing time series and time-series data. An LSTM recurrent neural network aims to learn long-term dependencies; that is to say, learn the dependencies of future values of a sequence on previous values. The results of this research work showed that, by applying the Machine Learning technique and the LSTM algorithm, it was possible to predict the Bitcoin price increase. These results could benefit different companies or financial institutions in the investment of money with the help of Bitcoin. Companies such as Microsoft, Destinia, WordPress, among many others, already allow purchases with bitcoins, or other cryptocurrencies, on their websites. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine learning application for predicting heart attacks in patients from Europe(Science and Information Organization, 2022-01-01)Even today, there are still a large number of people suffering from heart attacks, which have already claimed numerous lives worldwide. To examine the main components of this problem in an objective and timely manner, we chose to work with a methodology that relies on taking and learning from real and existing data for use in training and testing predictive models. This was carried out to obtain useful data for the present research work. There are in parallel different methodologies that do not quite fit the model of this work. Data was collected from the "Center for Machine Learning and Intelligent Systems" which in turn contains data from patients who have ever suffered a cardiovascular attack and from patients who never suffered the disease, all of them being patients selected from different medical institutions. With the corresponding information, it was subjected to different processes such as cleaning, preparation, and training with the data, to obtain a logistic regression type automatic learning model ready to predict whether or not a person may suffer a cardiovascular attack. Finally, a result of 87% accuracy was obtained for people who suffered a heart attack and an accuracy of 81% for people who would not suffer from this disease. This can greatly reduce the mortality rate due to infarction, by knowing the condition of a person who is unaware of his or her health situation and thus being able to take appropriate measures.