Sökning: "Prediction models"
Visar resultat 1 - 5 av 1380 uppsatser innehållade orden Prediction models.
1. Predicting True Sepsis and Culture-positive Sepsis in Intensive Care Unit with Machine Learning Techniques
Master-uppsats, Lunds universitet/Matematisk statistikSammanfattning : Sepsis, a serious medical condition often leading to patients requiring intensive care, has prompted numerous scientists to employ mathematical techniques to aid in its diagnosis. This thesis uses logistic regression and a machine learning technique, XGBoost, to predict true sepsis (as opposed to sepsis mimics) and culture-positive sepsis (among true sepsis) in critical care using blood test results, physiological measurements and other patient characteristics. LÄS MER
2. Station-level demand prediction in bike-sharing systems through machine learning and deep learning methods
Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskapSammanfattning : Public Bike-Sharing systems have been employed in many cities around the globe. Shared bikes are an efficient and convenient means of transportation in advanced societies. Nonetheless, station planning and local bike-sharing network effectiveness can be challenging. LÄS MER
3. ML implementation for analyzing and estimating product prices
Kandidat-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)Sammanfattning : Efficient price management is crucial for companies with many different products to keep track of, leading to the common practice of price logging. Today, these prices are often adjusted manually, but setting prices manually can be labor-intensive and prone to human error. LÄS MER
4. Assessing water balance and yields in Malawian cropping systems : maize soybean and maize Gliricidia systems resilience against climate change
Master-uppsats, SLU/Dept. of Soil and EnvironmentSammanfattning : In Malawi, maize monocultures are increasingly susceptible to extreme weather patterns, causing considerable yield reduction and heightened food insecurity for smallholder farmers dependent on rainfed subsistence agriculture. Diversifying cropping systems is crucial for ensuring yield resilience. LÄS MER
5. Predicting Electricity Consumption with ARIMA and Recurrent Neural Networks
Kandidat-uppsats, Uppsala universitet/Statistiska institutionenSammanfattning : Due to the growing share of renewable energy in countries' power systems, the need for precise forecasting of electricity consumption will increase. This paper considers two different approaches to time series forecasting, autoregressive moving average (ARMA) models and recurrent neural networks (RNNs). LÄS MER