normalize the intensities of various MR image modalities
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Updated
May 31, 2023 - Python
normalize the intensities of various MR image modalities
Comparing Long Term Short Memory (LSTM) & Gated Re-current Unit (GRU) during forecasting of oil price .Exploring multivariate relationships between West Texas Intermediate and S&P 500, Dow Jones Utility Avg, US Dollar Index Futures , US 10 Yr Treasury Bonds , Gold Futures.
BoolTest - polynomial randomness tester
This is pypi package for outlier detection
A robust framework to predict diabetes based different independent attributes. Outlier rejection, filling the missing values, data standardization, K-fold validation, and different Machine Learning (ML) classifiers were used to create optimal model.Finally, optimal model was deployed on a PaaS .
This application uses two types of TRNGs - True Random Number Generators (TrueRNG and Bitbbabler) for data collection and statistical analysis for several purposes, including mind-matter interaction research.
Automating the process of data entry from financial statements and predicting the solvency of the companies
Applied spatial statistics to spatio-temporal big data to identify statistically significant spatial hot spots on a 4-node cluster. (Java, Hadoop Distributed File System (HDFS), Apache Spark)
Practicum by Yandex Project 3: This Statistical Data Analysis project is prepared to analyze clients' behavior and determine which prepaid plan brings in more revenue.
Establishment of a Model to Define the Impact of Lombardy Region Citizens on PM2.5 Emissions During Their Daily Activities. The project aims to identify environmentally harmful actions and promote a more sustainable lifestyle through a ranking system of citizens. The model is based on the Z-Score Index.
Funções com algoritmos das fórmulas estatísticas.
Normalize a sample drawn from different populations and convert into a Z-score
Streaming statistics monitor for WildFly JVMs. Using RabbitMQ and Postgres and visualization in Grafana.
There are implemented some data mining and data processing algorithms over the NYC yellow taxies dataset, which have been provided in Kaggle.
Q1. The time required for servicing transmissions is normally distributed with mean = 45 minutes and SD = 8 minutes. The service manager plans to have work begin on the transmission of a customer’s car 10 minutes after the car is dropped off and the customer is told that the car will be ready within 1 hour from drop-off. What is the probability tha
A simple z-score calculator for UBC Vancouver campus. Runs on Android.
Predict breast cancer in women using KNN model
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