Essays about: "Machine Learning"

Showing result 6 - 10 of 4233 essays containing the words Machine Learning.

1. 6. Predictive Modeling of Pipetting Dynamics. Multivariate Regression Analysis: PLS and ANN for Estimating Density and Volume from Pressure Recordings

   University essay from Lunds universitet/Avdelningen för Biomedicinsk teknik

   Author : Lisa Linard Pedersen; [2024]
   Keywords : Technology and Engineering;

   Abstract : Thermo Fisher Scientific manufacture automatic pipetting instruments for diagnostic tests. These tests are sensitive to abnormalities and changes in e.g. volume or density could potentially lead to less precision or other issues in the pipetting work flow. READ MORE

3. 7. Cross project Just-In-Time bug detection

   University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Author : Axel Pettersson; [2024]
   Keywords : JITLine; Bug detection; Software Development; JITLine; Bugg identifiering; Mjukvaruutveckling;

   Abstract : Software is present in almost all aspects of our lives, and with more parts of life beingdriven by code, the importance of limiting bugs is critical. Studies have shown that thelonger a bug is present in a system increases the complexity of finding and handlingthe bug. READ MORE

4. 8. Predicting True Sepsis and Culture-positive Sepsis in Intensive Care Unit with Machine Learning Techniques

   University essay from Lunds universitet/Matematisk statistik

   Author : Zeyuan Wu; [2024]
   Keywords : Machine Learning; Diagnosis of Sepsis; XGBoost; Logistic Regression; Mathematics and Statistics;

   Abstract : 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. READ MORE

5. 9. Station-level demand prediction in bike-sharing systems through machine learning and deep learning methods

   University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Author : Nikolaos Staikos; [2024]
   Keywords : Physical Geography; Ecosystem Analysis; Bike-sharing demand; Machine learning; Deep learning; Spatial regression; Graph Convolutional Neural Network; Multiple Linear Regression; Multilayer Perceptron Regressor; Support Vector Machine; Random Forest Regressor; Urban environment; Micro-mobility; Station planning; Geomatics; Earth and Environmental Sciences;

   Abstract : 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. READ MORE

6. 10. ML implementation for analyzing and estimating product prices

   University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Author : Abel Getachew Kenea; Gabriel Fagerslett; [2024]
   Keywords : Machine Learning; ML; Regression; Deep Learning; Artificial Neural Network; ANN; TensorFlow; ScikitLearn; CUDA; cuDNN; Estimation; Prediction; AI; Artificial Intelligence; Price Tracking; Price Logging; Price Estimation; Supervised Learning; Random Forest; Decision Trees; Batch Learning; Hyperparameter Tuning; Linear Regression; Multiple Linear Regression; Maskininlärning; Djup lärning; Artificiellt Neuralt Nätverk; Regression; TensorFlow; SciktLearn; ML; ANN; Estimation; Uppskattning; CUDA; cuDNN; AI; Artificiell Intelligens; pris loggning; pris estimation; prisspårning; Batchinlärning; Hyperparameterjustering; Linjär Regression; Multipel Linjär Regression; Supervised Learning; Random Forest; Decision Trees;

   Abstract : 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. READ MORE